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Please be aware that the following page relates to ERA5 on the current CDS system which will be decommissioned in September 2024. ERA5 data are now available from the new CDS-Beta. Please migrate to downloading ERA5 from CDS-Beta as soon as possible. Please read: CDS and ADS migrating to new infrastructure: Common Data Store (CDS) Engine . We also recommend users watch for announcements on our Forum. The page below will be updated as soon as possible. Thank you for your understanding. |
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Introduction
Here, we document the ERA5 dataset, which covers the period from January 1940 to the present and continues to be extended forward in near real time. For up to date information on ERA5, please consult the C3S Announcements on the Copernicus user forum.
ERA5 is produced using 4D-Var data assimilation and model forecasts in CY41R2 of the ECMWF Integrated Forecast System (IFS), with 137 hybrid sigma/pressure (model) levels in the vertical and the top level at 0.01 hPa. Atmospheric data are available on these levels and they are also interpolated to 37 pressure, 16 potential temperature and 1 potential vorticity level(s) by FULL-POS in the IFS. "Surface or single level" data are also available, containing 2D parameters such as precipitation, top of atmosphere radiation and vertical integrals over the entire depth of the atmosphere. The atmospheric model in the IFS is coupled to a land-surface model (HTESSEL), which produces parameters such as 2m temperature and soil temperatures, and an ocean wave model (WAM), the parameters of which are also designated as "Surface or single level" parameters.
The ERA5 dataset contains one (hourly, 31 km) high resolution realisation (referred to as "reanalysis" or "HRES") and a reduced resolution ten member ensemble (referred to as "ensemble" or "EDA"). The ensemble is required for the data assimilation procedure, but as a by-product also provides an estimate of the relative, random uncertainty. Generally, the data are available at a sub-daily and monthly frequency and consist of analyses and short (18 hour) forecasts, initialised twice daily from analyses at 06 and 18 UTC. Most analysed parameters are also available from the forecasts. However, there are a number of forecast parameters, e.g. mean rates/fluxes and accumulations, that are not available from the analyses.
The data are archived in the ECMWF data archive (MARS) and a pertinent sub-set of the data, interpolated to a regular latitude/longitude grid, has been copied to the C3S Climate Data Store (CDS) disks. On the CDS disks, where single level and pressure level data are available, analyses are provided rather than forecasts, unless the parameter is only available from the forecasts. The interpolation software (MIR) was updated when the ERA5 production was moved to the new ATOS HPC on 24 October 2022.
ERA5.1 is a re-run of ERA5, for the years 2000 to 2006 only, and was produced to improve upon the cold bias in the lower stratosphere seen in ERA5 during this period.
The original ERA5 release contained data from 1979 onwards. The final ERA5 back extension for 1940-1978 has been produced and is available alongside the original/main release.
An ERA5 back extension 1950-1978 (Preliminary version) was produced. Although in many other respects the quality was relatively good, this preliminary data did suffer from excessively intense tropical cyclones. This dataset is now deprecated.
Data update frequency
Initial release data, i.e. data no more than three months behind real time, is called ERA5T.
Both for the CDS and MARS, daily updates for ERA5T are available about 5 days behind real time and monthly mean updates are available about 5 days after the end of the month.
The daily updates for ERA5T data on the CDS occur at no fixed time during the day. However, although it is not guaranteed, the D-5 data are typically available by 12UTC. We are working on reducing the variability of the update time.
For the CDS, ERA5T data for a month is overwritten with the final ERA5 data about two months after the month in question.
For MARS, the final ERA5 data are available about two months after the month in question. In addition, the last few months of data are kept online and can be accessed much quicker than older data on tape.
In the event that serious flaws are detected in ERA5T, the latter could be different to the final ERA5 data. Based on experience with the production of ERA5 so far (and ERA-Interim in the past), our expectation is that such an event would not occur often. So far, it has only occurred once:
- from 1 September to 13 December 2021, the final ERA5 product is different to ERA5T due to the correction of the assimilation of incorrect snow observations in central Asia. Although the differences are mostly limited to that region and mainly to surface parameters, in particular snow depth and soil moisture and to a lesser extent 2m temperature and 2m dewpoint temperature, all the resulting reanalysis fields can differ over the whole globe but should be within their range of uncertainty (which is estimated by the ensemble spread and which can be large for some parameters). On the CDS disks, the initial, ERA5T, fields have been overwritten (with the usual 2-3 month delay), i.e., for these months, access to the original CDS disk, ERA5T product is not possible after it has been overwritten. Potentially incorrect snow observations have been assimilated in ERA5 up to this time, when the effects became noticeable. The quality control of snow observations has been improved in ERA5 from September 2021 and from 15 November 2021 in ERA5T.
For the hourly products on CDS disks for both single and pressure levels, some local differences exist between ERA5 and ERA5T for 1 to 24 October 2022 due to a change of the regridding software (MIR) when the ERA5 production was changed from the Cray to ATOS. Differences are not meteorologically significant. For October 2022, there is no difference for the data in native resolution (ERA5-complete).
The IFS and data assimilation
For ERA5, the IFS documentation for CY41R2 should be used.
The twice daily, short (18 hour) forecasts are run from the 06 and 18 UTC analyses.
The 4D-Var data assimilation uses 12 hour windows from 09 UTC to 21 UTC and 21 UTC to 09 UTC (the following day).
The model time step is 12 minutes for the HRES and 20 minutes for the EDA, though occasionally these numbers are adjusted to cope with instabilities.
Data assimilation is a process whereby a model forecast is blended with observations to obtain the best fit to both the forecast and the observations, given the known uncertainties of both. The result is called an analysis (of the state of the atmosphere). For the atmospheric parameters in ERA5, the 4D-Variational (4D-Var) data assimilation windows are 12 hours long, commencing after the first 3 hours of the short forecasts. All the available observations within each 12 hour window are considered by the system, though some might be discarded for various reasons, such as quality control. Some of the parameters under the category "Surface or single level" parameters, are produced by the Land-surface scheme, which uses 1D and 2D Optimal Interpolation and Extended Kalman Filter, data assimilation. The ERA5 MARS archive contains both the analyses and short forecasts. On the CDS disks, where single level and pressure level data are available, analyses are provided rather than forecasts, unless the parameter is only available from the forecasts.
The above data assimilation process, or something similar, is performed for Numerical Weather Prediction (NWP), which provides real time forecasts (and analyses) for many purposes and applications. It would be tempting to use the data produced therein, for climate purposes. However, NWP systems are being improved on a regular basis - typically twice per year at ECMWF. Therefore, the NWP data contain various abrupt changes, due to system improvements, which are mixed in with changes in the climate. Reanalysis avoids this problem by using a fixed NWP system to "re-analyse" the state of the atmosphere for long periods in the past. It should be remembered, however, that spurious changes will still be included in the reanalysis, due to changes in the observing system. The ERA5 data assimilation and forecasting system was used operationally for NWP in 2016. Once this fixed system becomes too old, the reanalysis should be re-done with a more modern, fixed system. Although "reanalysis" suggests that only analyses are provided, the short forecasts are also made available, as noted above.
Data format
Model level parameters are archived in GRIB2 format. All other parameters are in GRIB1 unless otherwise indicated, see Parameter listings.
In the CDS, there is the option of retrieving the data in netCDF format.
For GRIB, ERA5T data can be identified by the key expver=0005 in the GRIB header. ERA5 data is identified by the key expver=0001.
For netCDF data requests which return just ERA5 or just ERA5T data, there is no means of differentiating between ERA5 and ERA5T data in the resulting netCDF files.
For netCDF data requests which return a mixture of ERA5 and ERA5T data, the origin of the variables (1 or 5) will be identifiable in the resulting netCDF files. See this link for more details.
Data organisation and how to download ERA5
The full ERA5 and ERA5T datasets are held in the ECMWF data archive (MARS) and a pertinent sub-set of these data, interpolated to a regular latitude/longitude grid, has been copied to the C3S Climate Data Store (CDS) disks. ERA5.1 is not available from the CDS disks, but is available from MARS (for advice on using ERA5.1 in conjunction with ERA5, CDS data, see "ERA5: mixing CDS and MARS data" in Guidelines). On the CDS disks, where most single level and pressure level parameters are available, analyses are provided rather than forecasts, unless the parameter is only available from the forecasts.
ERA5 (and recent ERA5T) data on the CDS disks can be downloaded either from the relevant CDS download page or using the CDS API.
Getting data from the CDS disks provides the fastest access to ERA5.
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NOTE: MARS has stream and type, CDS only has product type. MARS has levtype but CDS puts that into the dataset. eg product_type=ensemble_spread is given by stream=enda or ewda, type=es, levtype=sfc or pl (not for ewda). |
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ERA5 data on the CDS disks can be downloaded either from the relevant CDS download page or, for larger data volumes in particular, using the CDS API. Subdivisions of the data are labelled using dataset and product_type. Datasets reanalysis-era5-single-levels and reanalysis-era5-pressure-levels contain the following (sub-daily) product types:
Datasets reanalysis-era5-single-levels-monthly-means and reanalysis-era5-pressure-levels-monthly-means contain the following (monthly) product types:
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ERA5 data in MARS can be accessed using the CDS API, but access is relatively slow.
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ERA5 data in MARS can be accessed with the CDS API by specifying dataset whereas member state users can access data in MARS by specifying class and expver, according to the following table:
1ERA5T data for a month is overwritten with the final ERA5 data about two months after the month in question. Subdivisions of the data are labelled using the keywords stream, type and levtype: Stream:
Type:
Levtype:
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Documentation is available on How to download ERA5.
Date and time specification
In MARS: the date and time of the data is specified with three MARS keywords: date, time and (forecast) step. For analyses, step=0 hours so that date and time specify the analysis date/time. For forecasts, date and time specify the forecast start time and step specifies the number of hours since that start time. The combination of date, time and step defines the validity date/time. For analyses, the validity date/time is equal to the analysis date/time.
In the CDS: analyses are provided rather than forecasts, unless the parameter is only available from the forecasts. The date and time of the data is specified using the validity date/time, so step does not need to be specified. For forecasts, steps between 1 and 12 hours have been used to provide data for all the validity times in each 24 hours, see Table 0 below.
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Temporal frequency
For sub-daily data for the HRES (stream=oper/wave) the analyses (type=an) are available hourly. The short forecasts, run twice daily from 06 and 18 UTC, provide hourly output forecast steps from 0 to 18 hours (only steps 1 to 12 hours are available on the CDS disks). For the EDA, the sub-daily non-wave data (stream=enda) are available every 3 hours but the sub-daily wave data (stream=ewda) are available hourly in MARS and 3 hourly on the CDS disks.
Spatial grid
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The ERA5 HRES atmospheric data has a resolution of 31km, 0.28125 degrees, and the EDA has a resolution of 63km, 0.5625 degrees. (Depending on the parameter, the data are archived either as spectral coefficients with a triangular truncation of T639 (HRES) and T319 (EDA) or on a reduced Gaussian grid with a resolution of N320 (HRES) and N160 (EDA). These grids are so called "linear grids", sometimes referred to as TL639 (HRES) and TL319 (EDA).)
The wave data are produced and archived on a different grid to that of the atmospheric model, namely a reduced latitude/longitude grid with a resolution of 0.36 degrees (HRES) and 1.0 degree (EDA).
ERA5 data available from the CDS disks has been pre-interpolated to a regular latitude/longitude grid appropriate for that data.
The interpolation method is based on the MIR software. For the production on the Cray HPC (1 January 1940 to 24 October 2022 inclusive) this was an early version of MIR, while for the production on ATOS (25 October 2022 onwards) this is based on the MIR version of the ECMWF MARS client. Differences between both versions are in general small, very localized and not meteorologically significant. For data on pressure levels, differences are mainly limited to the exact north and south pole (90N and 90S). For single-level data, for some fields there are differences at the poles as well, while for some other fields, there are additional sets of isolated points with differences. In both cases this represents an improvement of the interpolation software.
The article Model grid box and time step might be useful.
Surface elevation datasets used by ERA5
In order to define the surface geopotential in ERA5, the IFS uses surface elevation data interpolated from a combination of SRTM30 and other surface elevation datasets. For more details please see the IFS documentation, Cycle 41r2, Part IV. Physical processes, section 11.2.2 Surface elevation data at 30 arc seconds.
Spatial reference systems and Earth model
The IFS assumes that the underlying shape of the Earth is a perfect sphere, of radius 6371.229 km, with the surface elevation specified relative to that sphere. The geodetic latitude/longitude of the surface elevation datasets are used as if they were the spherical latitude/longitude of the IFS.
ERA5 data is referenced in the horizontal with respect to the WGS84 ellipse (which defines the major/minor axes) and in the vertical it is referenced to the EGM96 geoid over land but over ocean it is referenced to mean sea level, with the approximation that this is assumed to be coincident with the geoid. For more information on the relationship between mean sea level and the geoid, see for example Gregory et al. (2019).
For data in GRIB1 format the earth model is a sphere with radius = 6367.47 km (note, this is inconsistent with what is actually used in the IFS), as defined in the WMO GRIB Edition 1 specifications, Table 7, GDS Octet 17.
For data in GRIB2 format the earth model is a sphere with radius = 6371.2229 km (note, this is consistent with what is actually used in the IFS), as defined in the WMO GRIB2 specifications, section 2.2.1, Code Table 3.2, Code figure 6.
For data in NetCDF format (i.e. converted from the native GRIB format to NetCDF), the earth model is inherited from the GRIB data.
Production experiments
In order to speed up production, the historic ERA5 data was produced by running several parallel experiments which were then spliced together to form the final product.
A discontinuity can occur at the transition between the different experiments. Please see the Known issues for an example. The degree of discontinuity depends on how well the experiments were "spun-up". How well "spun-up" an experiment is, depends on the initial, chosen, state of the atmosphere and land surface at the beginning of the experiment, how long the experiment is run for, before being used for production, and the parameter(s) of interest - some parameters, such as those for the deeper soil and for the higher atmospheric levels, take longer to spin-up than others.
The information below gives the date ranges for the various production experiments (and hence the transition points) for the final version of ERA5 and also indicates when the computing system changed from the Cray to the ATOS.
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Note, that forecasts start from the relevant analysis at the forecast start date/time, so the provenance of the whole of each forecast is the same as that of the analysis at the forecast start date/time.
Accuracy and uncertainty
ERA5 is produced using 4D-Var data assimilation and model forecasts in CY41R2 of the IFS. The 4D-Var in ERA5 utilises 12 hour assimilation windows from 9-21 UTC and 21-9 UTC, where the background forecast and all the observations falling within a time window are used to specify all the analyses during that window. However, the accuracy of the analyses is not uniform throughout each window. If the model and observations are unbiased and their errors follow Gaussian distributions and if the observations are homogeneous in space and time, then the analysis error will be smallest in the middle of the assimilation window. However, because none of these assumptions are actually true in the IFS, the particular parameter and location of interest are important, too. Knowing that, a careful study should show at which points during the assimilation windows the analysis is most accurate.
The 10 member ensemble is required for the data assimilation procedure. However, as a useful by-product, this ensemble also provides an estimate of the relative, random uncertainty. The "spread" of the 10 member ensemble, encapsulated by the standard deviation, provides a measure of this uncertainty and is larger for time periods and spatial locations where the uncertainty is relatively large and is smaller when and where there is more certainty in the analysed/forecast values. The spread is a measure of the relative uncertainty, so the numbers do not provide the absolute uncertainty. On the whole, the uncertainty becomes larger as you go back in time, when the observing system was not as good as in the present day, and in data sparse locations such as the pre-satellite era, southern hemisphere. In general, apart from that for the sea surface temperature, the spread does not represent systematic uncertainty, only random, or "synoptic", uncertainty. For more information, see ERA5: uncertainty estimation.
Instantaneous parameters
All the analysed parameters and many of the forecast parameters are described as "instantaneous". For more information on what instantaneous means, see Parameters valid at the specified time. Such instantaneous parameters may, or may not, have been averaged in time, to produce monthly means.
Mean rates/fluxes and accumulations
Such parameters, which are only available from forecasts, have undergone particular types of statistical processing (temporal mean or accumulation, respectively) over a period of time called the processing period. In addition, these parameters may, or may not, have been averaged in time, to produce monthly means.
The accumulations (over the accumulation/processing period) in the short forecasts (from 06 and 18 UTC) of ERA5 are treated differently compared with those in ERA-Interim and operational data (where the accumulations are from the beginning of the forecast to the validity date/time). In the short forecasts of ERA5, the accumulations are since the previous post processing (archiving), so for:
- reanalysis: accumulations are over the hour (the accumulation/processing period) ending at the validity date/time
- ensemble: accumulations are over the 3 hours (the accumulation/processing period) ending at the validity date/time
- Monthly means (of daily means, stream=moda/edmo): accumulations have been scaled to have an "effective" processing period of one day, see section Monthly means
Mean rate/flux parameters in ERA5 (e.g. Table 4 for surface and single levels) provide similar information to accumulations (e.g. Table 3 for surface and single levels), except they are expressed as temporal means, over the same processing periods, and so have units of "per second".
- Mean rate/flux parameters are easier to deal with than accumulations because the units do not vary with the processing period.
- The mean rate hydrological parameters (e.g. the "Mean total precipitation rate") have units of "kg m-2 s-1", which are equivalent to "mm s-1". They can be multiplied by 86400 seconds (24 hours) to convert to kg m-2 day-1 or mm day-1.
Note that:
- For the CDS time, or validity time, of 00 UTC, the mean rates/fluxes and accumulations are over the hour (3 hours for the EDA) ending at 00 UTC i.e. the mean or accumulation is during part of the previous day.
- Mean rates/fluxes and accumulations are not available from the analyses.
- Mean rates/fluxes and accumulations at step=0 have values of zero because the length of the processing period is zero.
Minimum/maximum since the previous post processing
The short forecasts of ERA5 contain some surface and single level parameters that are the minimum or maximum value since the previous post processing (archiving), see Table 5 below. So, for:
- reanalysis: the minimum or maximum values are in the hour (the processing period) ending at the validity date/time
- ensemble: the minimum or maximum values are in the 3 hours (the processing period) ending at the validity date/time
Wave spectra
The ocean wave model used in ERA5 (WAM, which is included in the IFS) provides wave spectra with 24 directions and 30 frequencies (see "2D wave spectra (single)", Table 7).
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Download from ERA5 ERA5 wave spectra data is not available from the CDS disks. However, it is available in MARS and can be accessed through the CDS API. For more information see Data organisation and how to download ERA5 and How to download ERA5 (Option B: Download ERA5 family data that is NOT listed in the CDS online catalogue - SLOW ACCESS. Decoding 2D wave spectra in GRIB To decode wave spectra in GRIB format we recommend ecCodes. Wave spectra are encoded in a specific way that other tools might not decode correctly. In GRIB, the parameter is called 2d wave spectra (single) because in GRIB, the data are stored as a single global field per each spectral bin (a given frequency and direction), but in NetCDF, the fields are nicely recombined to produce a 2d matrix representing the discretized spectra at each grid point. The wave spectra are encoded in GRIB using a local table specific to ECMWF. Because of this, the conversion of the meta data containing the information about the frequencies and the directions are not properly converted from GRIB to NetCDF format. So rather than having the actual values of the frequencies and directions, values show index numbers (1,1) : first frequency, first direction, (1,2) first frequency, second direction, etc .... Also note that it is NOT the spectral density that is encoded but rather log10 of it, so to recover the spectral density, expressed in m^2 /(radian Hz), one has to take the power 10 (10^) of the NON missing decoded values. Missing data are for all land points, but also, as part of the GRIB compression, all small values below a certain threshold have been discarded and so those missing spectral values are essentially 0. m^2 /(gradient Hz). Decoding 2D wave spectra in NetCDF The NetCDF wave spectra file will have the dimensions longitude, latitude, direction, frequency and time. However, the direction and frequency bins are simply given as 1 to 24 and 1 to 30, respectively. The direction bins start at 7.5 degree and increase by 15 degrees until 352.5, with 90 degree being towards the east (Oceanographic convention). The frequency bins are non-linearly spaced. The first bin is 0.03453 Hz and the following bins are: f(n) = f(n-1)*1.1; n=2,30. The data provided is the log10 of spectra density. To obtain the spectral density one has to take to the power 10 (10 ** data). This will give the units 2D wave spectra as m**2 s radian**-1 . Very small values are discarded and set as missing values. These are essentially 0 m**2 s radian**-1. This recoding can be done with the Python xarray package, for example:
Units of 2D wave spectra Once decoded, the units of 2D wave spectra are m2 s radian-1 |
Monthly means
In addition to the sub-daily data, most analysed and forecast parameters are also available as monthly means. For the surface and single level parameters, there are some exceptions which are listed in Table 8.
Monthly means are available in two forms:
- Synoptic monthly means, for each particular time and forecast step (stream=mnth/wamo/edmm/ewmm) - in the CDS, referred to as "monthly averaged by hour of day".
- Monthly means (of daily means, stream=moda/wamd/edmo/ewmo) for the month as a whole - in the CDS, referred to as "monthly averaged". These monthly means are created from all the hourly (3 hourly for the ensemble) data in the month.
Monthly means for:
- forecast parameters are created using the first 12 hours of the twice daily short forecasts (beginning at 06 and 18 UTC).
- analysis and instantaneous forecast parameters are created from data with a validity time in the month, between 00 and 23 UTC, which excludes the time 00 UTC on the first day of the following month.
- accumulation and mean rate/flux forecast parameters are created from data with processing periods that fall within the month.
- monthly means of daily means, for accumulations and mean rates/fluxes are created from contiguous data with processing periods spanning from 00 UTC on the first day of the month to 00 UTC on the first day of the following month i.e. they are accumulations or mean rates/fluxes for the complete, whole month.
The accumulations in monthly means (of daily means, stream=moda/edmo) have been scaled to have an "effective" processing period of one day, so for accumulations in these streams:
- The hydrological parameters have effective units of "m of water per day" and so they should be multiplied by 1000 to convert to kgm-2day-1 or mmday-1.
- The energy (turbulent and radiative) and momentum fluxes should be divided by 86400 seconds (24 hours) to convert to the commonly used units of Wm-2 and Nm-2, respectively.
The monthly data in the CDS under 'ERA5 monthly averaged data' has been created by first creating monthly data on the native grid, then regridding this to the lat-lon grid used in the CDS. The hourly data in the CDS under 'ERA5 hourly data' has been created by regridding from the native to the lat-lon grid. Any calculation of monthly means using this hourly data takes place on the already regridded dataset.
In general, the monthly means calculated from the hourly data, or provided in the CDS should be identical, as regridding and averaging are both linear operations. However, when calculating wind speed, there is a nonlinear transformation sqrt(u*u+v*v) in between and then the order does matter. Therefore, small differences can be seen where the wind fields themselves vary quickly with location, like going from sea to the high volcanos over Hawaii.
Ensemble means and standard deviations
For the EDA sub-daily data (stream=enda/ewda), compared with HRES sub-daily data (stream=oper/wave), ensemble means and standard deviations (type=em/es) are also available. Both these quantities are calculated from all the 10-members (i.e., including the control).
Ensemble standard deviation is often referred to as ensemble spread and is calculated with respect to the ensemble mean. The ensemble standard deviation is not the sample stdv, so we divide by 10 rather than 9 (N-1).
Ensemble means and standard deviations contain analysed parameters when step=0, otherwise they contain forecast parameters. However, only surface and pressure level data (levtype=sfc/pl) contain forecast steps beyond 3 hours. There are no monthly means for ensemble means and standard deviations.
Level listings
Pressure levels (hPa): 1000/975/950/925/900/875/850/825/800/775/750/700/650/600/550/500/450/400/350/300/250/225/200/175/150/125/100/70/50/30/20/10/7/5/3/2/1
Potential temperature levels (K): 265/275/285/300/315/320/330/350/370/395/430/475/530/600/700/850
Potential vorticity level (10-9 K m2 kg-1 s-1 or 10-3 PVU): 2000 (which is representative of the dynamical tropopause)
Model levels: 1/to/137, which are described at L137 model level definitions and ERA5: compute pressure and geopotential on model levels, geopotential height and geometric height. The model levels are hybrid pressure/sigma. For more information, see the documentation of the underlying model, ECMWF's IFS, CY41R2, Part III. Dynamics and numerical procedures, Chapter 2 Basic equations and discretisation.
Parameter listings
Tables 1-6 below describe the surface and single level parameters (levtype=sfc), Table 7 describes wave parameters, Table 8 describes the monthly mean exceptions for surface and single level and wave parameters and Tables 9-13 describe upper air parameters on various levtypes.
Information on all ECMWF parameters (e.g. columns shortName and paramId) is available from the ECMWF parameter database
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Table 1: surface and single level parameters: invariants (in time)
(stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc)
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA))
count | name | units | Variable name in CDS | shortName | paramId | an | fc |
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1 | (0 - 1) | lake_cover | cl | 26 | x | x | |
2 | m | lake_depth | dl | 228007 | x | x | |
3 | (0 - 1) | low_vegetation_cover | cvl | 27 | x | ||
4 | (0 - 1) | high_vegetation_cover | cvh | 28 | x | ||
5 | ~ | type_of_low_vegetation | tvl | 29 | x | ||
6 | ~ | type_of_high_vegetation | tvh | 30 | x | ||
7 | ~ | soil_type | slt | 43 | x | ||
8 | m | standard_deviation_of_filtered_subgrid_orography | sdfor | 74 | x | ||
9 | m**2 s**-2 | geopotential | z | 129 | x | x | |
10 | ~ | standard_deviation_of_orography | sdor | 160 | x | ||
11 | ~ | anisotropy_of_sub_gridscale_orography | isor | 161 | x | ||
12 | radians | angle_of_sub_gridscale_orography | anor | 162 | x | ||
13 | ~ | slope_of_sub_gridscale_orography | slor | 163 | x | ||
14 | (0 - 1) | land_sea_mask | lsm | 172 | x | x |
1Soil type (texture) determines the saturation, field capacity and permanent wilting point at all the soil levels, see Table 8.9 in Chapter 8 Surface parametrization, Part IV Physical Processes of the IFS documentation (CY41R2 for ERA5).
Anchor | ||||
---|---|---|---|---|
|
(stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc)
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA))
count | name | units | Variable name in CDS | shortName | paramId | an | fc |
---|---|---|---|---|---|---|---|
1 | J kg**-1 | convective_inhibition | cin | 228001 | x | ||
2 | m s**-1 | friction_velocity | zust | 228003 | x | ||
3 | K | lake_mix_layer_temperature | lmlt | 228008 | x | x | |
4 | m | lake_mix_layer_depth | lmld | 228009 | x | x | |
5 | K | lake_bottom_temperature | lblt | 228010 | x | x | |
6 | K | lake_total_layer_temperature | ltlt | 228011 | x | x | |
7 | dimensionless | lake_shape_factor | lshf | 228012 | x | x | |
8 | K | lake_ice_temperature | lict | 228013 | x | x | |
9 | m | lake_ice_depth | licd | 228014 | x | x | |
10 | (0 - 1) | uv_visible_albedo_for_direct_radiation | aluvp | 15 | x | x | |
11 | Minimum vertical gradient of refractivity inside trapping layer | m**-1 | minimum_vertical_gradient_of_refractivity_inside_trapping_layer | dndzn | 228015 | x | |
12 | (0 - 1) | uv_visible_albedo_for_diffuse_radiation | aluvd | 16 | x | x | |
13 | Mean vertical gradient of refractivity inside trapping layer | m**-1 | mean_vertical_gradient_of_refractivity_inside_trapping_layer | dndza | 228016 | x | |
14 | (0 - 1) | near_ir_albedo_for_direct_radiation | alnip | 17 | x | x | |
15 | m | duct_base_height | dctb | 228017 | x | ||
16 | (0 - 1) | near_ir_albedo_for_diffuse_radiation | alnid | 18 | x | x | |
17 | m | trapping_layer_base_height | tplb | 228018 | x | ||
18 | m | trapping_layer_top_height | tplt | 228019 | x | ||
19 | m | cloud_base_height | cbh | 228023 | x | ||
20 | m | zero_degree_level | deg0l | 228024 | x | ||
21 | m s**-1 | instantaneous_10m_wind_gust | i10fg | 228029 | x | ||
22 | (0 - 1) | sea-ice_cover | ci | 31 | x | x | |
23 | (0 - 1) | snow_albedo | asn | 32 | x | x | |
24 | kg m**-3 | snow_density | rsn | 33 | x | x | |
25 | K | sea_surface_temperature | sst | 34 | x | x | |
26 | K | ice_temperature_layer_1 | istl1 | 35 | x | x | |
27 | K | ice_temperature_layer_2 | istl2 | 36 | x | x | |
28 | K | ice_temperature_layer_3 | istl3 | 37 | x | x | |
29 | K | ice_temperature_layer_4 | istl4 | 38 | x | x | |
30 | m**3 m**-3 | volumetric_soil_water_layer_1 | swvl1 | 39 | x | x | |
31 | m**3 m**-3 | volumetric_soil_water_layer_2 | swvl2 | 40 | x | x | |
32 | m**3 m**-3 | volumetric_soil_water_layer_3 | swvl3 | 41 | x | x | |
33 | m**3 m**-3 | volumetric_soil_water_layer_4 | swvl4 | 42 | x | x | |
34 | J kg**-1 | convective_available_potential_energy | cape | 59 | x | x | |
35 | m**2 m**-2 | leaf_area_index_low_vegetation | lai_lv | 66 | x | x | |
36 | m**2 m**-2 | leaf_area_index_high_vegetation | lai_hv | 67 | x | x | |
37 | m s**-1 | 10m_u-component_of_neutral_wind | u10n | 228131 | x | x | |
38 | m s**-1 | 10m_v-component_of_neutral_wind | v10n | 228132 | x | x | |
39 | Pa | surface_pressure | sp | 134 | x | x | |
40 | K | soil_temperature_level_1 | stl1 | 139 | x | x | |
41 | m of water equivalent | snow_depth | sd | 141 | x | x | |
42 | ~ | charnock | chnk | 148 | x | x | |
43 | Pa | mean_sea_level_pressure | msl | 151 | x | x | |
44 | m | boundary_layer_height | blh | 159 | x | x | |
45 | (0 - 1) | total_cloud_cover | tcc | 164 | x | x | |
46 | m s**-1 | 10m_u_component_of_wind | 10u | 165 | x | x | |
47 | m s**-1 | 10m_v_component_of_wind | 10v | 166 | x | x | |
48 | K | 2m_temperature | 2t | 167 | x | x | |
49 | K | 2m_dewpoint_temperature | 2d | 168 | x | x | |
50 | K | soil_temperature_level_2 | stl2 | 170 | x | x | |
51 | K | soil_temperature_level_3 | stl3 | 183 | x | x | |
52 | (0 - 1) | low_cloud_cover | lcc | 186 | x | x | |
53 | (0 - 1) | medium_cloud_cover | mcc | 187 | x | x | |
54 | (0 - 1) | high_cloud_cover | hcc | 188 | x | x | |
55 | m of water equivalent | skin_reservoir_content | src | 198 | x | x | |
56 | (0 - 1) | instantaneous_large_scale_surface_precipitation_fraction | ilspf | 228217 | x | ||
57 | kg m**-2 s**-1 | convective_rain_rate | crr | 228218 | x | ||
58 | kg m**-2 s**-1 | large_scale_rain_rate | lsrr | 228219 | x | ||
59 | kg m**-2 s**-1 | convective_snowfall_rate_water_equivalent | csfr | 228220 | x | ||
60 | kg m**-2 s**-1 | large_scale_snowfall_rate_water_equivalent | lssfr | 228221 | x | ||
61 | N m**-2 | instantaneous_eastward_turbulent_surface_stress | iews | 229 | x | x | |
62 | N m**-2 | instantaneous_northward_turbulent_surface_stress | inss | 230 | x | x | |
63 | W m**-2 | instantaneous_surface_sensible_heat_flux | ishf | 231 | x | x | |
64 | kg m**-2 s**-1 | instantaneous_moisture_flux | ie | 232 | x | x | |
65 | K | skin_temperature | skt | 235 | x | x | |
66 | K | soil_temperature_level_4 | stl4 | 236 | x | x | |
67 | K | temperature_of_snow_layer | tsn | 238 | x | x | |
68 | (0 - 1) | forecast_albedo | fal | 243 | x | x | |
69 | m | forecast_surface_roughness | fsr | 244 | x | x | |
70 | ~ | forecast_logarithm_of_surface_roughness_for_heat | flsr | 245 | x | x | |
71 | m s**-1 | 100m_u-component_of_wind | 100u | 228246 | x | x | |
72 | m s**-1 | 100m_v-component_of_wind | 100v | 228247 | x | x | |
73 | code table (4.201) | precipitation_type | ptype | 260015 | x | ||
74 | K | k_index | kx | 260121 | x | ||
75 | K | total_totals_index | totalx | 260123 | x |
Expand | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
| ||||||||||
Please note that in GRIB1, the largest value which can be stored in 1 octet is 255, so the layer 4 bottom value is set to "missing" (rather than 289). Some software can therefore give incorrect values for the lower boundary of this layer (e.g. CDO reports the value as 255). Please see https://confluence.ecmwf.int/x/uqOGC for more details. |
2GRIB2 format
3Leaf Area Index (LAI) parameters are based on a monthly climatology. Users will only see monthly variability, but not inter-annual variability.
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---|---|---|---|---|
|
(stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc)
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA))
count | name | units | Variable name in CDS | shortName | paramId | an | fc |
---|---|---|---|---|---|---|---|
1 | s | large_scale_precipitation_fraction | lspf | 50 | x | ||
2 | J m**-2 | downward_uv_radiation_at_the_surface | uvb | 57 | x | ||
3 | J m**-2 | boundary_layer_dissipation | bld | 145 | x | ||
4 | J m**-2 | surface_sensible_heat_flux | sshf | 146 | x | ||
5 | J m**-2 | surface_latent_heat_flux | slhf | 147 | x | ||
6 | J m**-2 | surface_solar_radiation_downwards | ssrd | 169 | x | ||
7 | J m**-2 | surface_thermal_radiation_downwards | strd | 175 | x | ||
8 | J m**-2 | surface_net_solar_radiation | ssr | 176 | x | ||
9 | J m**-2 | surface_net_thermal_radiation | str | 177 | x | ||
10 | J m**-2 | top_net_solar_radiation | tsr | 178 | x | ||
11 | J m**-2 | top_net_thermal_radiation | ttr | 179 | x | ||
12 | N m**-2 s | eastward_turbulent_surface_stress | ewss | 180 | x | ||
13 | N m**-2 s | northward_turbulent_surface_stress | nsss | 181 | x | ||
14 | N m**-2 s | eastward_gravity_wave_surface_stress | lgws | 195 | x | ||
15 | N m**-2 s | northward_gravity_wave_surface_stress | mgws | 196 | x | ||
16 | J m**-2 | gravity_wave_dissipation | gwd | 197 | x | ||
17 | J m**-2 | top_net_solar_radiation_clear_sky | tsrc | 208 | x | ||
18 | J m**-2 | top_net_thermal_radiation_clear_sky | ttrc | 209 | x | ||
19 | J m**-2 | surface_net_solar_radiation_clear_sky | ssrc | 210 | x | ||
20 | J m**-2 | surface_net_thermal_radiation_clear_sky | strc | 211 | x | ||
21 | J m**-2 | toa_incident_solar_radiation | tisr | 212 | x | ||
22 | kg m**-2 | vertically_integrated_moisture_divergence | vimd | 213 | x | ||
23 | J m**-2 | total_sky_direct_solar_radiation_at_surface | fdir | 228021 | x | ||
24 | J m**-2 | clear_sky_direct_solar_radiation_at_surface | cdir | 228022 | x | ||
25 | J m**-2 | surface_solar_radiation_downward_clear_sky | ssrdc | 228129 | x | ||
26 | J m**-2 | surface_thermal_radiation_downward_clear_sky | strdc | 228130 | x | ||
27 | m | surface_runoff | sro | 8 | x | ||
28 | m | sub_surface_runoff | ssro | 9 | x | ||
29 | m of water equivalent | snow_evaporation | es | 44 | x | ||
30 | m of water equivalent | snowmelt | smlt | 45 | x | ||
31 | m | large_scale_precipitation | lsp | 142 | x | ||
32 | m | convective_precipitation | cp | 143 | x | ||
33 | m of water equivalent | snowfall | sf | 144 | x | ||
34 | m of water equivalent | evaporation | e | 182 | x | ||
35 | m | runoff | ro | 205 | x | ||
36 | m | total_precipitation | tp | 228 | x | ||
37 | m of water equivalent | convective_snowfall | csf | 239 | x | ||
38 | m of water equivalent | large_scale_snowfall | lsf | 240 | x | ||
39 | m | potential_evaporation | pev | 228251 | x |
The accumulations in monthly means of daily means (stream=moda/edmo), see monthly means, have been scaled to have units that include "per day", so for accumulations in these streams:
- Most hydrological parameters are in units of "m of water per day", so these should be multiplied by 1000 to convert to kg m-2 day-1 or mm day-1.
- Energy (turbulent and radiative) and momentum fluxes should be divided by 86400 seconds (24 hours) to convert to the commonly used units of W m-2 and N m-2, respectively.
Anchor | ||||
---|---|---|---|---|
|
(stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc)
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA))
count | name | units | Variable name in CDS | shortName | paramId | an | fc |
---|---|---|---|---|---|---|---|
1 | kg m**-2 s**-1 | mean_surface_runoff_rate | msror | 235020 | x | ||
2 | kg m**-2 s**-1 | mean_sub_surface_runoff_rate | mssror | 235021 | x | ||
3 | kg m**-2 s**-1 | mean_snow_evaporation_rate | mser | 235023 | x | ||
4 | kg m**-2 s**-1 | mean_snowmelt_rate | msmr | 235024 | x | ||
5 | Proportion | mean_large_scale_precipitation_fraction | mlspf | 235026 | x | ||
6 | W m**-2 | mean_surface_downward_uv_radiation_flux | msdwuvrf | 235027 | x | ||
7 | kg m**-2 s**-1 | mean_large_scale_precipitation_rate | mlspr | 235029 | x | ||
8 | kg m**-2 s**-1 | mean_convective_precipitation_rate | mcpr | 235030 | x | ||
9 | kg m**-2 s**-1 | mean_snowfall_rate | msr | 235031 | x | ||
10 | W m**-2 | mean_boundary_layer_dissipation | mbld | 235032 | x | ||
11 | W m**-2 | mean_surface_sensible_heat_flux | msshf | 235033 | x | ||
12 | W m**-2 | mean_surface_latent_heat_flux | mslhf | 235034 | x | ||
13 | W m**-2 | mean_surface_downward_short_wave_radiation_flux | msdwswrf | 235035 | x | ||
14 | W m**-2 | mean_surface_downward_long_wave_radiation_flux | msdwlwrf | 235036 | x | ||
15 | W m**-2 | mean_surface_net_short_wave_radiation_flux | msnswrf | 235037 | x | ||
16 | W m**-2 | mean_surface_net_long_wave_radiation_flux | msnlwrf | 235038 | x | ||
17 | W m**-2 | mean_top_net_short_wave_radiation_flux | mtnswrf | 235039 | x | ||
18 | W m**-2 | mean_top_net_long_wave_radiation_flux | mtnlwrf | 235040 | x | ||
19 | N m**-2 | mean_eastward_turbulent_surface_stress | metss | 235041 | x | ||
20 | N m**-2 | mean_northward_turbulent_surface_stress | mntss | 235042 | x | ||
21 | kg m**-2 s**-1 | mean_evaporation_rate | mer | 235043 | x | ||
22 | N m**-2 | mean_eastward_gravity_wave_surface_stress | megwss | 235045 | x | ||
23 | N m**-2 | mean_northward_gravity_wave_surface_stress | mngwss | 235046 | x | ||
24 | W m**-2 | mean_gravity_wave_dissipation | mgwd | 235047 | x | ||
25 | kg m**-2 s**-1 | mean_runoff_rate | mror | 235048 | x | ||
26 | W m**-2 | mean_top_net_short_wave_radiation_flux_clear_sky | mtnswrfcs | 235049 | x | ||
27 | W m**-2 | mean_top_net_long_wave_radiation_flux_clear_sky | mtnlwrfcs | 235050 | x | ||
28 | W m**-2 | mean_surface_net_short_wave_radiation_flux_clear_sky | msnswrfcs | 235051 | x | ||
29 | W m**-2 | mean_surface_net_long_wave_radiation_flux_clear_sky | msnlwrfcs | 235052 | x | ||
30 | W m**-2 | mean_top_downward_short_wave_radiation_flux | mtdwswrf | 235053 | x | ||
31 | kg m**-2 s**-1 | mean_vertically_integrated_moisture_divergence | mvimd | 235054 | x | ||
32 | kg m**-2 s**-1 | mean_total_precipitation_rate | mtpr | 235055 | x | ||
33 | kg m**-2 s**-1 | mean_convective_snowfall_rate | mcsr | 235056 | x | ||
34 | kg m**-2 s**-1 | mean_large_scale_snowfall_rate | mlssr | 235057 | x | ||
35 | W m**-2 | mean_surface_direct_short_wave_radiation_flux | msdrswrf | 235058 | x | ||
36 | W m**-2 | mean_surface_direct_short_wave_radiation_flux_clear_sky | msdrswrfcs | 235059 | x | ||
37 | W m**-2 | mean_surface_downward_short_wave_radiation_flux_clear_sky | msdwswrfcs | 235068 | x | ||
38 | W m**-2 | mean_surface_downward_long_wave_radiation_flux_clear_sky | msdwlwrfcs | 235069 | x | ||
39 | kg m**-2 s**-1 | mean_potential_evaporation_rate | mper | 235070 | x |
The mean rates/fluxes in Table 4 provide similar information to the accumulations in Table 3, except they are expressed as temporal averages, and so have units of "per second". The mean rate hydrological parameters have units of "kg m-2 s-1" and so they can be multiplied by 86400 seconds (24 hours) to convert to kg m-2 day-1 or mm day-1.
Anchor | ||||
---|---|---|---|---|
|
(stream=oper/enda, levtype=sfc)
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA))
count | name | units | Variable name in CDS | shortName | paramId | an | fc |
---|---|---|---|---|---|---|---|
1 | m s**-1 | 10m_wind_gust_since_previous_post_processing | 10fg | 49 | x | ||
2 | Maximum temperature at 2 metres since previous post-processing | K | maximum_2m_temperature_since_previous_post_processing | mx2t | 201 | x | |
3 | Minimum temperature at 2 metres since previous post-processing | K | minimum_2m_temperature_since_previous_post_processing | mn2t | 202 | x | |
4 | Maximum total precipitation rate since previous post-processing | kg m**-2 s**-1 | maximum_total_precipitation_rate_since_previous_post_processing | mxtpr | 228226 | x | |
5 | Minimum total precipitation rate since previous post-processing | kg m**-2 s**-1 | minimum_total_precipitation_rate_since_previous_post_processing | mntpr | 228227 | x |
Anchor | ||||
---|---|---|---|---|
|
(stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc - vertical integrals not available for type=em/es
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA))
count | name | units | Variable name in CDS | shortName | paramId | an | fc |
---|---|---|---|---|---|---|---|
1 | kg m**-2 | vertical_integral_of_mass_of_atmosphere | vima | 162053 | x | x | |
2 | K kg m**-2 | vertical_integral_of_temperature | vit | 162054 | x | x | |
3 | J m**-2 | vertical_integral_of_kinetic_energy | vike | 162059 | x | x | |
4 | J m**-2 | vertical_integral_of_thermal_energy | vithe | 162060 | x | x | |
5 | J m**-2 | vertical_integral_of_potential_and_internal_energy | vipie | 162061 | x | x | |
6 | J m**-2 | vertical_integral_of_potential_internal_and_latent_energy | vipile | 162062 | x | x | |
7 | J m**-2 | vertical_integral_of_total_energy | vitoe | 162063 | x | x | |
8 | W m**-2 | vertical_integral_of_energy_conversion | viec | 162064 | x | x | |
9 | kg m**-1 s**-1 | vertical_integral_of_eastward_mass_flux | vimae | 162065 | x | x | |
10 | kg m**-1 s**-1 | vertical_integral_of_northward_mass_flux | viman | 162066 | x | x | |
11 | W m**-1 | vertical_integral_of_eastward_kinetic_energy_flux | vikee | 162067 | x | x | |
12 | W m**-1 | vertical_integral_of_northward_kinetic_energy_flux | viken | 162068 | x | x | |
13 | W m**-1 | vertical_integral_of_eastward_heat_flux | vithee | 162069 | x | x | |
14 | W m**-1 | vertical_integral_of_northward_heat_flux | vithen | 162070 | x | x | |
15 | kg m**-1 s**-1 | vertical_integral_of_eastward_water_vapour_flux | viwve | 162071 | x | x | |
16 | kg m**-1 s**-1 | vertical_integral_of_northward_water_vapour_flux | viwvn | 162072 | x | x | |
17 | W m**-1 | vertical_integral_of_eastward_geopotential_flux | vige | 162073 | x | x | |
18 | W m**-1 | vertical_integral_of_northward_geopotential_flux | vign | 162074 | x | x | |
19 | W m**-1 | vertical_integral_of_eastward_total_energy_flux | vitoee | 162075 | x | x | |
20 | W m**-1 | vertical_integral_of_northward_total_energy_flux | vitoen | 162076 | x | x | |
21 | kg m**-1 s**-1 | vertical_integral_of_eastward_ozone_flux | vioze | 162077 | x | x | |
22 | kg m**-1 s**-1 | vertical_integral_of_northward_ozone_flux | viozn | 162078 | x | x | |
23 | kg m**-2 s**-1 | vertical_integral_of_divergence_of_cloud_liquid_water_flux | vilwd | 162079 | x | x | |
24 | kg m**-2 s**-1 | vertical_integral_of_divergence_of_cloud_frozen_water_flux | viiwd | 162080 | x | x | |
25 | kg m**-2 s**-1 | vertical_integral_of_divergence_of_mass_flux | vimad | 162081 | x | x | |
26 | W m**-2 | vertical_integral_of_divergence_of_kinetic_energy_flux | viked | 162082 | x | x | |
27 | W m**-2 | vertical_integral_of_divergence_of_thermal_energy_flux | vithed | 162083 | x | x | |
28 | kg m**-2 s**-1 | vertical_integral_of_divergence_of_moisture_flux | viwvd | 162084 | x | x | |
29 | W m**-2 | vertical_integral_of_divergence_of_geopotential_flux | vigd | 162085 | x | x | |
30 | W m**-2 | vertical_integral_of_divergence_of_total_energy_flux | vitoed | 162086 | x | x | |
31 | kg m**-2 s**-1 | vertical_integral_of_divergence_of_ozone_flux | viozd | 162087 | x | x | |
32 | kg m**-1 s**-1 | vertical_integral_of_eastward_cloud_liquid_water_flux | vilwe | 162088 | x | x | |
33 | kg m**-1 s**-1 | vertical_integral_of_northward_cloud_liquid_water_flux | vilwn | 162089 | x | x | |
34 | kg m**-1 s**-1 | vertical_integral_of_eastward_cloud_frozen_water_flux | viiwe | 162090 | x | x | |
35 | kg m**-1 s**-1 | vertical_integral_of_northward_cloud_frozen_water_flux | viiwn | 162091 | x | x | |
36 | kg m**-2 s**-1 | vertical_integral_of_mass_tendency | vimat | 162092 | x | ||
37 | kg m**-2 | total_column_cloud_liquid_water | tclw | 78 | x | x | |
38 | kg m**-2 | total_column_cloud_ice_water | tciw | 79 | x | x | |
39 | kg m**-2 | total_column_supercooled_liquid_water | tcslw | 228088 | x | ||
40 | kg m**-2 | total_column_rain_water | tcrw | 228089 | x | x | |
41 | kg m**-2 | total_column_snow_water | tcsw | 228090 | x | x | |
42 | kg m**-2 | total_column_water | tcw | 136 | x | x | |
43 | kg m**-2 | total_column_water_vapour | tcwv | 137 | x | x | |
44 | kg m**-2 | total_column_ozone | tco3 | 206 | x | x |
Anchor | ||||
---|---|---|---|---|
|
(stream=wave/ewda/wamo/wamd/ewmm/ewmo)
(The native grid is the reduced latitude/longitude grid of 0.36 degrees (1.0 degree for the EDA))
count | name | units | Variable name in CDS | shortName | paramId | an | fc | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | m | significant_wave_height_of_first_swell_partition | swh1 | 140121 | x | x | ||||||
2 | degrees | mean_wave_direction_of_first_swell_partition | mwd1 | 140122 | x | x | ||||||
3 | s | mean_wave_period_of_first_swell_partition | mwp1 | 140123 | x | x | ||||||
4 | m | significant_wave_height_of_second_swell_partition | swh2 | 140124 | x | x | ||||||
5 | degrees | mean_wave_period_of_second_swell_partition | mwd2 | 140125 | x | x | ||||||
6 | s | mean_wave_period_of_second_swell_partition | mwp2 | 140126 | x | x | ||||||
7 | m | significant_wave_height_of_third_swell_partition | swh3 | 140127 | x | x | ||||||
8 | degrees | mean_wave_direction_of_third_swell_partition | mwd3 | 140128 | x | x | ||||||
9 | s | mean_wave_period_of_third_swell_partition | mwp3 | 140129 | x | x | ||||||
10 | dimensionless | wave_spectral_skewness | wss | 140207 | x | x | ||||||
11 | m s**-1 | free_convective_velocity_over_the_oceans | wstar | 140208 | x | x | ||||||
12 | kg m**-3 | air_density_over_the_oceans | rhoao | 140209 | x | x | ||||||
13 | dimensionless | normalized_energy_flux_into_waves | phiaw | 140211 | x | x | ||||||
14 | dimensionless | normalized_energy_flux_into_ocean | phioc | 140212 | x | x | ||||||
15 | dimensionless | normalized_stress_into_ocean | tauoc | 140214 | x | x | ||||||
16 | m s**-1 | u_component_stokes_drift | ust | 140215 | x | x | ||||||
17 | m s**-1 | v_component_stokes_drift | vst | 140216 | x | x | ||||||
18 | s | period_corresponding_to_maximum_individual_wave_height | tmax | 140217 | x | x | ||||||
19 | m | maximum_individual_wave_height | hmax | 140218 | x | x | ||||||
20 | m | model_bathymetry | wmb | 140219 | x | x | ||||||
21 | s | mean_wave_period_based_on_first_moment | mp1 | 140220 | x | x | ||||||
22 | s | mean_zero_crossing_wave_period | mp2 | 140221 | x | x | ||||||
23 | Radians | wave_spectral_directional_width | wdw | 140222 | x | x | ||||||
24 | s | mean_wave_period_based_on_first_moment_for_wind_waves | p1ww | 140223 | x | x | ||||||
25 | s | mean_wave_period_based_on_second_moment_for_wind_waves | p2ww | 140224 | x | x | ||||||
26 | Radians | wave_spectral_directional_width_for_wind_waves | dwww | 140225 | x | x | ||||||
27 | s | mean_wave_period_based_on_first_moment_for_swell | p1ps | 140226 | x | x | ||||||
28 | s | mean_wave_period_based_on_second_moment_for_wind_waves | p2ps | 140227 | x | x | ||||||
29 | Radians | wave_spectral_directional_width_for_swell | dwps | 140228 | x | x | ||||||
30 | m | significant_height_of_combined_wind_waves_and_swell | swh | 140229 | x | x | ||||||
31 | degrees | mean_wave_direction | mwd | 140230 | x | x | ||||||
32 | s | peak_wave_period | pp1d | 140231 | x | x | ||||||
33 | s | mean_wave_period | mwp | 140232 | x | x | ||||||
34 | dimensionless | coefficient_of_drag_with_waves | cdww | 140233 | x | x | ||||||
35 | m | significant_height_of_wind_waves | shww | 140234 | x | x | ||||||
36 | degrees | mean_direction_of_wind_waves | mdww | 140235 | x | x | ||||||
37 | s | mean_period_of_wind_waves | mpww | 140236 | x | x | ||||||
38 | m | significant_height_of_total_swell | shts | 140237 | x | x | ||||||
39 | degrees | mean_direction_of_total_swell | mdts | 140238 | x | x | ||||||
40 | s | mean_period_of_total_swell | mpts | 140239 | x | x | ||||||
41 | dimensionless | mean_square_slope_of_waves | msqs | 140244 | x | x | ||||||
42 |
| m s**-1 | ocean_surface_stress_equivalent_10m_neutral_wind_speed | wind | 140245 | x | x | |||||
43 | degrees | ocean_surface_stress_equivalent_10m_neutral_wind_direction | dwi | 140249 | x | x | ||||||
44 | dimensionless | wave_spectral_kurtosis | wsk | 140252 | x | x | ||||||
45 | dimensionless | benjamin_feir_index | bfi | 140253 | x | x | ||||||
46 | dimensionless | wave_spectral_peakedness | wsp | 140254 | x | x | ||||||
47 | m | Not available from the CDS disks | awh | 140246 | x | |||||||
48 | m | Not available from the CDS disks | acwh | 140247 | x | |||||||
49 | ~ | Not available from the CDS disks | arrc | 140248 | x | |||||||
50 | m**2 s radian**-1 | Not available from the CDS disks | 2dfd | 140251 | x |
1for 30 frequencies and 24 directions
Anchor | ||||
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|
(stream=mnth/moda/edmm/edmo, levtype=sfc or wamo/wamd/ewmm/ewmo)
count | name | units | Variable name in CDS | shortName | paramId | an | fc |
---|---|---|---|---|---|---|---|
1 | (0 - 1) | uv_visible_albedo_for_direct_radiation | aluvp | 15 | x | no mean | |
2 | (0 - 1) | uv_visible_albedo_for_diffuse_radiation | aluvd | 16 | x | no mean | |
3 | (0 - 1) | near_ir_albedo_for_direct_radiation | alnip | 17 | x | no mean | |
4 | (0 - 1) | near_ir_albedo_for_diffuse_radiation | alnid | 18 | x | no mean | |
5 | N m**-2 s | magnitude of turbulent surface stress | magss | 48 | x | ||
6 | Mean magnitude of turbulent surface stress2 | N m**-2 | mean magnitude of turbulent surface stress | mmtss | 235025 | x | |
7 | m s**-1 | 10m_wind_gust_since_previous_post_processing | 10fg | 49 | no mean | ||
8 | Maximum temperature at 2 metres since previous post-processing | K | maximum_2m_temperature_since_previous_post_processing | mx2t | 201 | no mean | |
9 | Minimum temperature at 2 metres since previous post-processing | K | minimum_2m_temperature_since_previous_post_processing | mn2t | 202 | no mean | |
10 | m s**-1 | 10m wind speed | 10si | 207 | x | x | |
11 | Maximum total precipitation rate since previous post-processing | kg m**-2 s**-1 | maximum_total_precipitation_rate_since_previous_post_processing | mxtpr | 228226 | no mean | |
12 | Minimum total precipitation rate since previous post-processing | kg m**-2 s**-1 | minimum_total_precipitation_rate_since_previous_post_processing | mntpr | 228227 | no mean | |
13 | m | Not available from the CDS disks | awh | 140246 | no mean | ||
14 | m | Not available from the CDS disks | acwh | 140247 | no mean | ||
15 | ~ | Not available from the CDS disks | arrc | 140248 | no mean | ||
16 | m**2 s radian**-1 | Not available from the CDS disks | 2dfd | 140251 | no mean |
1Accumulated parameter
2Mean rate/flux parameter
3Instantaneous parameter
Anchor | ||||
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(stream=oper/enda/mnth/moda/edmm/edmo, levtype=pl)
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA) or T639 spherical harmonics (T319 for the EDA), as indicated)
count | name | units | variable name in CDS | shortName | paramId | native grid | an | fc |
---|---|---|---|---|---|---|---|---|
1 | K m**2 kg**-1 s**-1 | potential_vorticity | pv | 60 | N320 (N160) | x | x | |
2 | kg kg**-1 | specific_rain_water_content | crwc | 75 | N320 (N160) | x | x | |
3 | kg kg**-1 | specific_snow_water_content | cswc | 76 | N320 (N160) | x | x | |
4 | m**2 s**-2 | geopotential | z | 129 | T639 (T319) | x | x | |
5 | K | temperature | t | 130 | T639 (T319) | x | x | |
6 | m s**-1 | u_component_of_wind | u | 131 | T639 (T319) | x | x | |
7 | m s**-1 | v_component_of_wind | v | 132 | T639 (T319) | x | x | |
8 | kg kg**-1 | specific_humidity | q | 133 | N320 (N160) | x | x | |
9 | Pa s**-1 | vertical_velocity | w | 135 | T639 (T319) | x | x | |
10 | s**-1 | vorticity | vo | 138 | T639 (T319) | x | x | |
11 | s**-1 | divergence | d | 155 | T639 (T319) | x | x | |
12 | % | relative_humidity | r | 157 | T639 (T319) | x | x | |
13 | kg kg**-1 | ozone_mass_mixing_ratio | o3 | 203 | N320 (N160) | x | x | |
14 | kg kg**-1 | specific_cloud_liquid_water_content | clwc | 246 | N320 (N160) | x | x | |
15 | kg kg**-1 | specific_cloud_ice_water_content | ciwc | 247 | N320 (N160) | x | x | |
16 | (0 - 1) | fraction_of_cloud_cover | cc | 248 | N320 (N160) | x | x |
Anchor | ||||
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(not available from the CDS disks)
(stream=oper/enda/mnth/moda/edmm/edmo, levtype=pt)
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA) or T639 spherical harmonics (T319 for the EDA), as indicated)
count | name | units | shortName | paramId | native grid | an | fc |
---|---|---|---|---|---|---|---|
1 | m**2 s**-2 | mont | 53 | T639 (T319) | x | ||
2 | Pa | pres | 54 | T639 (T319) | x | ||
3 | K m**2 kg**-1 s**-1 | pv | 60 | N320 (N160) | x | ||
4 | m s**-1 | u | 131 | T639 (T319) | x | ||
5 | m s**-1 | v | 132 | T639 (T319) | x | ||
6 | kg kg**-1 | q | 133 | N320 (N160) | x | ||
7 | s**-1 | vo | 138 | T639 (T319) | x | ||
8 | s**-1 | d | 155 | T639 (T319) | x | ||
9 | kg kg**-1 | o3 | 203 | N320 (N160) | x |
Anchor | ||||
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|
(not available from the CDS disks)
(stream=oper/enda/mnth/moda/edmm/edmo, levtype=pv)
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA) or T639 spherical harmonics (T319 for the EDA), as indicated)
count | name | units | shortName | paramId | native grid | an | fc |
---|---|---|---|---|---|---|---|
1 | K | pt | 3 | T639 (T319) | x | ||
2 | Pa | pres | 54 | T639 (T319) | x | ||
3 | m**2 s**-2 | z | 129 | T639 (T319) | x | ||
4 | m s**-1 | u | 131 | N320 (N160) | x | ||
5 | m s**-1 | v | 132 | N320 (N160) | x | ||
6 | kg kg**-1 | q | 133 | N320 (N160) | x | ||
7 | kg kg**-1 | o3 | 203 | N320 (N160) | x |
Anchor | ||||
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|
(GRIB2 format)
(not available from the CDS disks)
(stream=oper/enda/mnth/moda/edmm/edmo, levtype=ml)
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA) or T639 spherical harmonics (T319 for the EDA), as indicated)
count | name | units | shortName | paramId | native grid | an | fc |
---|---|---|---|---|---|---|---|
1 | kg kg**-1 | crwc | 75 | N320 (N160) | x | x | |
2 | kg kg**-1 | cswc | 76 | N320 (N160) | x | x | |
3 | s**-1 | etadot | 77 | T639 (T319) | x | x | |
4 | m**2 s**-2 | z | 129 | T639 (T319) | x | x | |
5 | K | t | 130 | T639 (T319) | x | x | |
6 | m s**-1 | u | 131 | T639 (T319) | x | x | |
7 | m s**-1 | v | 132 | T639 (T319) | x | x | |
8 | kg kg**-1 | q | 133 | N320 (N160) | x | x | |
9 | Pa s**-1 | w | 135 | T639 (T319) | x | x | |
10 | s**-1 | vo | 138 | T639 (T319) | x | x | |
11 | ~ | lnsp | 152 | T639 (T319) | x | x | |
12 | s**-1 | d | 155 | T639 (T319) | x | x | |
13 | kg kg**-1 | o3 | 203 | N320 (N160) | x | x | |
14 | kg kg**-1 | clwc | 246 | N320 (N160) | x | x | |
15 | kg kg**-1 | ciwc | 247 | N320 (N160) | x | x | |
16 | (0 - 1) | cc | 248 | N320 (N160) | x | x |
1Only archived on level=1.
Anchor | ||||
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|
(GRIB2 format)
(not available from the CDS disks)
(stream=oper/enda/mnth/moda/edmm/edmo, levtype=ml)
(The native grid is the reduced Gaussian grid N320 (N160 for the EDA))
count | name | units | shortName | paramId | an | fc |
---|---|---|---|---|---|---|
1 | Mean temperature tendency due to short-wave radiation | K s**-1 | mttswr | 235001 | x | |
2 | Mean temperature tendency due to long-wave radiation | K s**-1 | mttlwr | 235002 | x | |
3 | Mean temperature tendency due to short-wave radiation, clear sky | K s**-1 | mttswrcs | 235003 | x | |
4 | Mean temperature tendency due to long-wave radiation, clear sky | K s**-1 | mttlwrcs | 235004 | x | |
5 | Mean temperature tendency due to parametrisations | K s**-1 | mttpm | 235005 | x | |
6 | Mean specific humidity tendency due to parametrisations | kg kg**-1 s**-1 | mqtpm | 235006 | x | |
7 | Mean eastward wind tendency due to parametrisations | m s**-2 | mutpm | 235007 | x | |
8 | Mean northward wind tendency due to parametrisations | m s**-2 | mvtpm | 235008 | x | |
9 | Mean updraught mass flux1 | kg m**-2 s**-1 | mumf | 235009 | x | |
10 | Mean downdraught mass flux1 | kg m**-2 s**-1 | mdmf | 235010 | x | |
11 | Mean updraught detrainment rate | kg m**-3 s**-1 | mudr | 235011 | x | |
12 | Mean downdraught detrainment rate | kg m**-3 s**-1 | mddr | 235012 | x | |
13 | Mean total precipitation flux1 | kg m**-2 s**-1 | mtpf | 235013 | x | |
14 | Mean turbulent diffusion coefficient for heat1 | m**2 s**-1 | mtdch | 235014 | x |
1These parameters provide data for the model half levels - the interfaces of the model layers.
Observations
The observations (satellite and in-situ) used as input to ERA5 are listed below. For more information on the observational input to ERA5, including dates when particular sensors or observation types were used, please see Section 5 in the ERA5 journal article, The ERA5 global reanalysis.
Info | ||
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Sensor | Satellite | Satellite agency | Data provider+ | Measurement (sensitivities exploited in ERA5 / variables analysed) |
---|---|---|---|---|
Satellite radiances (infrared and microwave) | ||||
AIRS | AQUA | NASA | NOAA | BT (T, humidity and ozone) |
AMSR-2 | GCOM-W1* | JAXA | BT (column water vapour, cloud liquid water, precipitation and ocean surface wind speed) | |
AMSRE | AQUA* | JAXA | BT (column water vapour, cloud liquid water, precipitation and ocean surface wind speed) | |
AMSUA | NOAA-15/16/17/18/19, AQUA, METOP-A/B | NOAA,ESA,EUMETSAT | BT (T) | |
AMSUB | NOAA-15/16/17 | NOAA | BT (humidity) | |
ATMS | NPP | NOAA | BT (T and humidity) | |
CRIS | NPP | NOAA | BT (T, humidity and ozone) | |
HIRS | TIROS-N, NOAA-6 /7/8/9/11/14 | NOAA | BT (T, humidity and ozone) | |
IASI | METOP-A/B | EUMETSAT/ESA | EUMETSAT | BT (T, humidity and ozone) |
GMI | GPM | NASA/JAXA | BT (humidity, column water vapour, cloud liquid water, precipitation, ocean surface wind speed) | |
MHS | NOAA-18/19, METOP-A/B | NOAA, EUMETSAT/ESA | BT (humidity and precipitation) | |
MSU | TIROS-N, NOAA-6 to 12, NOAA-14 | BT (T) | ||
MWHS | FY-3-A/B | NRSCC | BT (humidity) | |
MWHS2 | FY-3-C | CMA | BT (T, humidity and precipitation) | |
MWTS | FY-3A/B | NRSCC | BT (T) | |
MWTS2 | FY-3C | CMA | BT (T) | |
SSM/I | DMSP-08*/10*/11*/13*/14*/15* | US Navy | NOAA,CMSAF* | BT (column water vapour, cloud liquid water, precipitation and ocean surface wind speed) |
SSMIS | DMSP-16/17/18 | US Navy | NOAA | BT (T, humidity, column water vapour, cloud liquid water, precipitation and ocean surface wind speed) |
SSU | TIROS-N, NOAA-6/7/8/9/11/14 | NOAA | BT (T) | |
TMI | TRMM | NASA/JAXA | BT (column water vapour, cloud liquid water, precipitation, ocean surface wind speed) | |
MVIRI | METEOSAT 5/7 | EUMETSAT/ESA | EUMETSAT | BT (water vapour, surface/cloud top T) |
SEVIRI | METEOSAT-8*/9*/10 | EUMETSAT/ESA | EUMETSAT | BT (water vapour, surface/cloud top T) |
GOES IMAGER | GOES-8/9/10/11/12/13/15 | NOAA | CIMMS,NESDIS | BT (water vapour, surface/cloud top T) |
MTSAT IMAGER | MTSAT-1R/MTSAT-2 | JMA | BT (water vapour, surface/cloud top T) | |
AHI | Himawari-8 | JMA | BT (water vapour, surface/cloud top T) | |
Satellite retrievals from radiance data | ||||
MVIRI | METEOSAT-2*/3*/4*/5*/7* | EUMETSAT/ESA | EUMETSAT | wind vector |
SEVIRI | METEOSAT-8*/9*/10 | EUMETSAT/ESA | EUMETSAT | wind vector |
GOES IMAGER | GOES-4-6/8*/9*/10*/11*/12*/13*/15* | NOAA | CIMMS*,NESDIS | wind vector |
GMS IMAGER | GMS-1*/2/3*/4*/5* | JMA | wind vector | |
MTSAT IMAGER | MTSAT-1R*/MTSAT2 | JMA | wind vector | |
AHI | Himawari-8 | JMA | JMA | wind vector |
AVHRR | NOAA-7 /9/10/11/12/14 to 18, METOP-A | NOAA | CIMMS,EUMETSAT | wind vector |
MODIS | AQUA/TERRA | NASA | NESDIS,CIMMS | wind vector |
GOME | ERS-2* | ESA | Ozone | |
GOME-2 | METOP*-A/B | ESA/EUMETSAT | Ozone | |
MIPAS | ENVISAT* | ESA | Ozone | |
MLS | EOS-AURA* | NASA | Ozone | |
OMI | EOS-AURA* | NASA | Ozone | |
SBUV,SBUV-2 | NIMBUS-7*,NOAA*9/11/14/16/17/18/19 | NOAA | NASA | Ozone |
SCIAMACHY | ENVISAT* | ESA | Ozone | |
TOMS | NIMBUS-7*,METEOR-3-5,ADEOS-1*,EARTH PROBE | NASA | Ozone | |
Satellite GPS-Radio Occultation data | ||||
BlackJack | CHAMP,GRACE*-A/B,SAC-C* | DLR,NASA/DLR,NASA/COMAE | GFZ,UCAR* | Bending angle |
GRAS | METOP-A/B | EUMETSAT/ESA | EUMETSAT | Bending angle |
IGOR | TerraSAR-X*, TanDEM-X, COSMIC*-1 to 6 | NSPO/NOAA | GFZ,UCAR* | Bending angle |
Satellite scatterometer data | ||||
AMI | ERS-1,ERS-2 | ESA | Backscatter sigma0, soil moisture | |
ASCAT | METOP-A/B* | EUMETSAT/ESA | EUMETSAT/TU Wien | Backscatter sigma0, soil moisture |
OSCAT | OCEANSAT-2 | ISRO | KNMI | Backscatter sigma0, vector wind |
SEAWINDS | QUIKSCAT | NASA | NASA | Backscatter sigma0 |
Satellite Altimeter data | ||||
RA | ERS-1*/2* | ESA | Wave Height | |
RA-2 | ENVISAT* | ESA | Wave Height | |
Poseidon-2 | JASON-1* | CNES/NASA | CNES | Wave Height |
Poseidon-3 | JASON-2 | CNES/NOAA/NASA/EUMETSAT | NOAA/EUMETSAT | Wave Height |
SIRAL | CRYOSAT-2 | ESA | Wave Height | |
AltiKa | SARAL | CNES/ISRO | EUMETSAT | Wave Height |
* reprocessed dataset
+ when different than the satellite agency
Anchor | ||||
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|
Dataset name | Observation type | Measurement |
---|---|---|
SYNOP | Land station | Surface Pressure, Temperature, humidity |
METAR | Land station | Surface Pressure, Temperature, humidity |
DRIBU/DRIBU-BATHY/DRIBU-TESAC/BUFR Drifting Buoy | Drifting buoys | 10m-wind, Surface Pressure |
BUFR Moored Buoy | Moored buoys | 10m-wind, Surface Pressure |
SHIP | ship station | Surface Pressure, Temperature, wind, humidity |
Land/ship PILOT | Radiosondes | wind profiles |
American Wind Profiler | Radar | wind profiles |
European Wind Profiler | Radar | wind profiles |
Japanese Wind Profiler | Radar | wind profiles |
TEMP SHIP | Radiosondes | Temperature, wind, humidity profiles |
DROP Sonde | Radiosondes | Temperature, wind, humidity profiles |
Land/Mobile TEMP | Radiosondes | Temperature, wind, humidity profiles |
AIREP | Aircraft data | Temperature, wind |
AMDAR | Aircraft data | Temperature, wind |
ACARS | Aircraft data | Temperature, wind, humidity |
WIGOS AMDAR | Aircraft data | Temperature, wind, humidity |
TAMDAR | Aircraft data | Temperature, wind |
ADS-C | Aircraft data | Temperature, wind |
Mode-S | Aircraft data | Wind |
Ground based radar | Radar precipitation composites | Rain rates |
Anchor | ||||
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Dataset name | Observation type | Measurement |
---|---|---|
SYNOP | Land station | Snow depth |
Additional national reports | Land station | Snow depth |
NOAA/NESDIS IMS | Merged satellite | Snow cover (NH only) |
Guidelines
The following advice is intended to help users understand particular features of the ERA5 data:
- In general, we recommend that the hourly (analysed) "2 metre temperature" be used to construct the minimum and maximum over longer periods, such as a day, rather than using the forecast parameters "Maximum temperature at 2 metres since previous post-processing" and "Minimum temperature at 2 metres since previous post-processing".
- ERA5: compute pressure and geopotential on model levels, geopotential height and geometric height
- ERA5: How to calculate wind speed and wind direction from u and v components of the wind?
- Sea surface temperature and sea-ice cover (sea ice area fraction), see Table 2 above, are available at the usual times, eg hourly for the HRES, but their content is only updated once daily. However, for inland water bodies (lakes, reservoirs, rivers and coastal waters) the FLake model calculates the surface temperature (ie the lake mixed-layer temperature or lake ice temperature) and does include diurnal variations.
- Mean rates/fluxes and accumulations at step=0 have values of zero because the length of the processing period is zero.
- Convective Inhibition (CIN). A missing value is assigned to CIN for values of CIN > 1000 or where there is no cloud base. This can occur where convective available potential energy (CAPE) is low.
Expand title ERA5: mixing CDS and MARS data In the ECMWF data archive (MARS), ERA5 data is archived on various native grids. For the CDS disks, ERA5 data have been interpolated and are stored on regular latitude/longitude grids. For more information, see Spatialgrid.
Storing the data on these different grids can cause incompatibilities, particularly when comparing native spherical harmonic, pressure level, MARS data with CDS disk data on a third, coarse grid.
Native spherical harmonic, pressure level parameters are comprised of: Geopotential, Temperature, U component of wind, V component of wind, Vertical velocity, Vorticity, Divergence and Relative humidity. When these parameters are retrieved from MARS and a coarse output grid is specified, the default behaviour is that the spherical harmonics are truncated to prevent aliasing on the output grid. The coarser the output grid, the more severe the truncation. This truncation removes the higher wavenumbers, making the data smoother. However, the CDS disk data has been simply interpolated to the third grid, without smoothing.
This incompatibility is particularly relevant when comparing ERA5.1 data (which are only available from MARS - see DataorganisationandhowtodownloadERA5 - and only for 2000-2006) with ERA5 data on the CDS disks.
The simplest means of minimising such incompatibilities is to retrieve the MARS data on the same grid as that used to store the ERA5 CDS disk data.
Expand title ERA5: Land-sea mask for wave variables The land-sea mask in ERA5 is an invariant field.
This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box.
This parameter has values ranging between zero and one and is dimensionless.
In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.The ERA5 land-sea mask provided is not suitable for direct use with wave parameters, as the time variability of the sea-ice cover needs to be taken into account and wave parameters are undefined for non-sea points.
In order to produce a land-sea mask for use with wave parameters, users need to download the following ERA5 data (for the required period):
- the model bathymetry (Model bathymetry. Fig 1)
- the sea-ice cover (Sea ice area fraction, Fig 2)
and combine these data to produce the land-sea mask (Fig 3). See attached pictures:
Fig 1: Model bathymetry Fig 2: Sea-ice cover Fig 3: Combined mask
Note Please note that sea-ice cover is only updated once daily.
Please see the Toolbox workflow below to see a possible way to proceed. The results is a carousel of land-sea mask for each time step requested:
Code Block title Toolbox workflow collapse true import cdstoolbox as ct @ct.application(title='Download data') @ct.output.download() @ct.output.carousel() def download_application(): count = 0 years=['1980'] months = [ '01', #'02', '03', # '04', '05', '06', # '07', '08', '09', # '10', '11', '12' ] # For hourly data hourly=True # For monthly data monthly=True hourly = True monthly = False for yr in years: for mn in months: if hourly == True: mb,si = get_hourly_data(yr, mn) elif monthly == True: mb,si = get_monthly_data(yr, mn) print(mb) # Check values are >= 0.0 in the model bathymetry mask compare_ge_mb = ct.operator.ge(mb, 0.0) print(si) # Check values are > 0.5 in the sea ice mask compare_ge_si = ct.operator.gt(si, 0.500) # Invert model bathymetry mask new = ct.operator.add(compare_ge_mb, -1.0) new1 = ct.operator.mul(new, -1.0) # Add the Bathymetry Mask to the Sea Ice Mask new_all = ct.operator.add(compare_ge_si,new1) # Reset scale to land=1, ocean=0 new_all_final = ct.operator.ge(new_all, 1.0) print(new_all_final) if count == 0: combined_mask = new_all_final else: combined_mask = ct.cube.concat([combined_mask, new_all_final], dim = 'time') count = count + 1 renamed_data = ct.cdm.rename(combined_mask, "wavemask") new_data = ct.cdm.update_attributes(renamed_data, attrs={'long_name': 'Wave Land Sea Mask'}) combined_mask = new_data print("combined_mask") print(combined_mask) # Plot mask for first timestep fig_list = ct.cdsplot.geoseries(combined_mask) return combined_mask, fig_list def get_monthly_data(y,m): m,s = ct.catalogue.retrieve( 'reanalysis-era5-single-levels-monthly-means', { 'product_type': 'monthly_averaged_reanalysis', 'variable': [ 'model_bathymetry', 'sea_ice_cover', ], 'year': y, 'month': m, 'time': '00:00', } ) return m, s def get_hourly_data(y,m): m,s = ct.catalogue.retrieve( 'reanalysis-era5-single-levels', { 'product_type': 'reanalysis', 'variable': [ 'model_bathymetry', 'sea_ice_cover', ], 'year': y, 'month': m, 'day': [ '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', ], 'time': [ '00:00', '01:00', '02:00', '03:00', '04:00', '05:00', '06:00', '07:00', '08:00', '09:00', '10:00', '11:00', '12:00', '13:00', '14:00', '15:00', '16:00', '17:00', '18:00', '19:00', '20:00', '21:00', '22:00', '23:00', ], } ) return m, s
Expand title Altimeter wave parameters The following wave parameters are sparse observations, or quantities derived from the observations, that have been interpolated to the wave model grid and contain many missing values:
- altimeter_wave_height (140246)
- altimeter_corrected_wave_height (140247)
- altimeter_range_relative_correction (140248)
These parameters are not available from the CDS disks but can be retrieved from MARS using the CDS API.
Expand title Computation of near-surface humidity Near-surface humidity is not archived directly in ERA datasets, but the archive contains near-surface (2m from the surface) temperature (T) and dew point temperature (Td), and also surface pressure (sp), from which you can calculate specific and relative humidity at 2m.
- Specific humidity can be calculated using equations 7.4 and 7.5 from Part IV, Physical processes section (Chapter 7, section 7.2.1b) in the documentation of the IFS for CY41R2. Use the 2m dew point temperature and surface pressure (which is approximately equal to the pressure at 2m) in these equations. The constants in 7.4 are to be found in Chapter 12 (of Part IV: Physical processes) and the parameters in 7.5 should be set for saturation over water because the dew point temperature is being used.
- Relative humidity should be calculated from: RH = 100 * es(Td)/es(T)
Relative humidity can be calculated with respect to saturation over water, ice or mixed phase by defining es(T) with respect to saturation over water, ice or mixed phase (water and ice). The usual practice is to define near-surface relative humidity with respect to saturation over water. Note that in ERA5, the relative humidity on pressure levels has been calculated with respect to saturation over mixed phase.
Expand title Computation of snow cover In the ECMWF model (IFS), snow is represented by an additional layer on top of the uppermost soil level. The whole grid box may not be covered in snow. The snow cover gives the fraction of the grid box that is covered in snow.
For ERA5, the snow cover (SC) is computed using snow water equivalent (ie parameter SD (141.128)) as follows:
Panel title ERA5 Snow cover formula snow_cover (SC) = min(1, (RW*SD/RSN) / 0.1 )
where RW is density of water equal to 1000 and RSN is density of snow (parameter 33.128).
ERA5 physical depth of snow where there is snow cover is equal to RW*SD/(RSN*SC).Expand title "Forecast albedo" is only for diffuse radiation The parameter "Forecast albedo" is only for diffuse radiation and assuming a fixed spectrum of downward short-wave radiation at the surface. The true broadband, all-sky, surface albedo can be calculated from accumulated parameters:
(SSRD-SSR)/SSRD
where SSRD is parameter 169.128 and SSR is 176.128. This true surface albedo cannot be calculated at night when SSRD is zero. For more information, see Radiation quantities in the ECMWF model and MARS.
Expand title Actual and potential evapotranspiration Actual evapotranspiration in the ERA5 single levels datasets is called "Evaporation" (param ID 182) and is the sum of the following four evaporation components (which are not available separately in ERA5 but only for ERA5-Land):
- Evaporation from bare soil
- Evaporation from open water surfaces excluding oceans
- Evaporation from the top of canopy
- Evaporation from vegetation transpiration
For the ERA5 single levels datasets, actual evapotranspiration can be downloaded from the C3S Climate Data Store (CDS) under the category heading "Evaporation and Runoff", in the "Download data" tab.
For details about the computation of actual evapotranspiration, please see Chapter 8 of Part IV : Physical processes, of the IFS documentation:
The potential evapotranspiration in the ERA5 single levels CDS dataset is given by the parameter potential evaporation (pev).
Pev data can be downloaded from the CDS under the category heading "Evaporation and Runoff", in the "Download data" tab for the ERA5 single levels datasets.
Note The definitions of potential and reference evapotranspiration may vary according to the scientific application and can have the same definition in some cases. Users should therefore ensure that the definition of this parameter is suitable for their application.
Note Please note that based on ERA5 atmospheric forcing, other independent (offline) methods such us "Priesley-Taylor1 (1972) , Schmidt2 (1915) or de Bruin3 (2000)" can also be used to estimate Potential evapotranspiration.
1PRIESTLEY, C. H. B., & TAYLOR, R. J. (1972). On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Monthly Weather Review, 100(2), 81-92. Retrieved Aug 27, 2021, from https://journals.ametsoc.org/view/journals/mwre/100/2/1520-0493_1972_100_0081_otaosh_2_3_co_2.xml
2Schmidt, W., 1915: Strahlung und Verdunstung an freien Wasserflächen; ein Beitrag zum Wärmehaushalt des Weltmeers und zum Wasserhaushalt der Erde (Radiation and evaporation over open water surfaces; a contribution to the heat budget of the world ocean and to the water budget of the earth). Ann. Hydro. Maritimen Meteor., 43, 111–124, 169–178.
3de Bruin, H. A. R., , and Stricker J. N. M. , 2000: Evaporation of grass under non-restricted soil moisture conditions. Hydrol. Sci. J., 45, 391–406, doi:10.1080/02626660009492337.
- Values below the surface are there for convenience and simplicity: the fields would be more difficult to deal with if they contained voids. This can be problematic for many applications, such as time averages where the voids move around with time. However, extrapolating into the data voids can contaminate the "real" atmosphere. This happens if the parameter is archived in spectral space or if it's horizontally interpolated. The method for extrapolation below the surface is parameter dependent, but wind components and humidity are kept constant from the lowest model level, whereas temperature is linearly interpolated from the lowest model level to a surface temperature and then a cubic polynomial extrapolation is used thereafter.
Expand title "Evaporation" and "Instantaneous moisture flux" The "Instantaneous moisture flux" (units: kg m-2 s-1; paramId=232) incorporates the same processes as "Evaporation" (units: m of water equivalent; paramId=182), but the latter is accumulated over a particular time period (during the hour preceeding the validity date/time, in the ERA5 HRES), whereas the former is an instantaneous parameter. Note, the different units of these two parameters.
For the atmosphere, these two parameters only involve water vapour. Cloud liquid does not sediment and the cloud ice sedimentation flux is included in the snowfall flux.
Here are some further details about the processes in the "Instantaneous moisture flux" and "Evaporation":
Surface characteristics Process from surface to atmosphere
(defined to be negative)
Process from atmosphere to surface
(defined to be positive)
Warm surface Evaporation from liquid water to water vapour Dew deposition from water vapour Cold vegetation surface Evaporation from liquid water to water vapour Dew deposition from water vapour Ice surface Sublimation from ice to water vapour Ice deposition from water vapour Snow surface Sublimation from snow to water vapour Snow deposition from water vapour
Known issues
Currently, we are aware of these issues with ERA5:
- ERA5T: from 1 September to 13 December 2021, the final ERA5 product is different to ERA5T due to the correction of the assimilation of incorrect snow observations in central Asia. Although the differences are mostly limited to that region and mainly to surface parameters, in particular snow depth and soil moisture and to a lesser extent 2m temperature and 2m dewpoint temperature, all the resulting reanalysis fields can differ over the whole globe but should be within their range of uncertainty (which is estimated by the ensemble spread and which can be large for some parameters). On the CDS disks, the initial, ERA5T, fields have been overwritten (with the usual 2-3 month delay), i.e., for these months, access to the original CDS disk, ERA5T product is not possible after it has been overwritten. Potentially incorrect snow observations have been assimilated in ERA5 up to this time, when the effects became noticeable. The quality control of snow observations has been improved in ERA5 from September 2021 and from 15 November 2021 in ERA5T.
- ERA5 uncertainty: although small values of ensemble spread correctly mark more confident estimates than large values, numerical values are over confident. The spread does give an indication of the relative, random uncertainty in space and time.
- ERA5 suffers from an overly strong equatorial mesospheric jet, particularly in the transition seasons.
- From 2000 to 2006, ERA5 has a poor fit to radiosonde temperatures in the stratosphere, with a cold bias in the lower stratosphere. In addition, a warm bias higher up persists for much of the ERA5 period. The lower stratospheric cold bias was rectified in a re-run for the years 2000 to 2006, called ERA5.1, see "Resolved issues" below.
- Discontinuities in ERA5: The historic ERA5 data was produced by running several parallel experiments, each for a different period, which were then spliced together to create the final product. This can create discontinuities at the transition points.
- The analysed "2 metre temperature" can be larger than the forecast "Maximum temperature at 2 metres since previous post-processing".
- The analysed 10 metre wind speed (derived from the 10 metre wind components) can be larger than the forecast "10 metre wind gust since previous post-processing".
- ERA5 diurnal cycle for near surface winds: the hourly data reveals a mismatch in the analysed near surface wind speed between the end of one assimilation cycle and the beginning of the next (which occurs at 9:00 - 10:00 and 21:00 - 22:00 UTC). This problem mostly occurs in low latitude oceanic regions, though it can also be seen over Europe and the USA. We cannot rectify this problem in the analyses. The forecast near surface winds show much better agreement between the assimilation cycles, at least on average, so if this mismatch is problematic for a particular application, our advice would be to use the forecast winds. The forecast near surface winds are available from MARS, see the section, Data organisation and how to download ERA5.
- ERA5 diurnal cycle for near surface temperature and humidity: some locations do suffer from a mismatch in the analysed values between the end of one assimilation cycle and the beginning of the next, in a similar fashion to that for the near surface winds (see above), but this problem is thought not to be so widespread as that for the near surface winds. The forecast values for near surface temperature and humidity are usually smoother than the analyses, but the forecast low level temperatures suffer from a cold bias over most parts of the globe. The forecast near surface temperature and humidity are available from MARS, see the section Data organisation and how to download ERA5.
- ERA5: large 10m winds: up to a few times per year, the analysed low level winds, eg 10m winds, become very large in a particular location, which varies amongst a few apparently preferred locations. The largest values seen so far are about 300 ms-1.
- ERA5 rain bombs: up to a few times per year, the rainfall (precipitation) can become extremely large in small areas. This problem occurs mostly over Africa, in regions of high orography.
- Large values of CAPE: occasionally, the Convective available potential energy in ERA5 is unrealistically large.
- Ship tracks in the SST: prior to September 2007, in the period when HadISST2 was used, ship tracks can be visible in the SST.
- Prior to 2014, the SST was not used over the Great Lakes to nudge the lake model. Consequently, the 2 metre temperature has an annual cycle that is too strong, with temperatures being too cold in winter and too warm in summer.
- The Potential Evaporation field (pev, parameter Id 228251) is largely underestimated over deserts and high-forested areas. This is due to a bug in the code that does not allow transpiration to occur in the situation where there is no low vegetation.
- Wave parameters (Table 7 above) for the three swell partitions: these parameters have been calculated incorrectly. The problem is most evident in the swell partition parameters involving the mean wave period: Mean wave period of first swell partition, Mean wave period of second swell partition and Mean wave period of third swell partition, where the periods are far too long.
- Surface photosynthetically available radiation (PAR) is too low in the version (CY41R2) of the ECMWF Integrated Forecasting System (IFS) used to produce ERA5, so PAR and clear sky PAR have not been published in ERA5. There is a bug in the calculation of PAR, with it being taken from the wrong parts of the spectrum. The shortwave bands include 0.442-0.625 micron, 0.625-0.778 micron and 0.778-1.24 micron. PAR should be coded to be the sum of the radiation in the first of these bands and 0.42 of the second (to account for the fact that PAR is normally defined to stop at 0.7 microns). However, in CY41R2, PAR is in fact calculated from the sum of the second band plus 0.42 of the third. We will try to fix this in a future cycle.
Expand title The instantaneous turbulent surface stress components (eastward and northward) and friction velocity tend to be too small The ERA5 analysed and forecast step=0, instantaneous surface stress components and surface roughness and the forecast step=0, friction velocity (friction velocity is not available from the analyses in ERA5) tend to suffer from values that are too low over the oceans.
The analysis for such parameters is obtained by running the surface module to connect the surface with the model level analysed variables.
However, at that stage, the surface aero-dynamical roughness length scale (z0) over the oceans is not initialised from its actual value but a constant value of 0.0001 is used instead.
This initial value of z0 is needed to determine the initial value of u* and the surface stress based on solving for a simple logarithmic wind profile between the surface and the lowest model level. This initial u* is in turn used to determine an updated value of z0 based on the input Charnock parameter and then the value of the exchange coefficients needed to determine the output 10m winds (normal and neutral) and u* (see (3.91) to (3.94) with (3.26) in the IFS documentation). The surface stress is output as initialised.
This initial value for z0 is generally too low ( by one order of magnitude or more):
Over the oceans, for winds above few m/s, z0 is modelled using the Charnock relation:
z0 ~ (alpha/g) u*2
where alpha is the Charnock parameter, g is gravity, and u* is the friction velocity
with typical values of
alpha ~ 0.018
g=9.81
u*2 = Cd U102
where Cd is the drag coefficient
Cd ~ 0.008 + 0.0008 U10
for U10=10m/s => z0 ~ 0.003
As a consequence, the analysed instantaneous surface stress components will tend to be too low and even the updated value of z0 (surface roughness) will also tend to be too low.
For forecast, instantaneous surface stress components, surface roughness and friction velocity, the same problem affects step 0. However, this problem will not affect the accumulated surface stress parameters (recall the accumulated parameters are produced by running short range forecasts), because the accumulation starts from the first time step (i.e. at time step 0 all accumulated variables are initialised to 0).
This problem can easily be fixed, by using the initial value of Charnock that is available at the initial time.
Note, in ERA5 the parameter for surface roughness is called "forecast surface roughness", even when it's analysed.
ERA5 forecast parameters are missing for the validity times of 1st January 1940 from 00 UTC to 06 UTC (except for forecast step=0). This problem occurs because the first forecast in ERA5 was initiated from 1st January 1940 at 06 UTC.
Maximum temperature at 2 metres since previous post-processing: in a small region over Peru, at 19 UTC, 2 August 2013, this forecast parameter exhibited erroneous values, which were greater than 50C. This occurrence is under investigation. Note, in general, we recommend that the hourly (analysed) "2 metre temperature" be used to construct the minimum and maximum over longer periods, such as a day.
Expand title Four reasons why hourly data might not be consistent with their monthly mean The ERA5 monthly means are calculated from the hourly (3 hourly for the EDA) data, on the native grid (including spherical harmonics) from the GRIB data, in each production "stream" or experiment. This can give rise to inconsistencies between the sub-daily data and their monthly mean, particularly in the CDS. In general, the inconsistencies will be small.
- In the CDS, the ERA5 data (sub-daily and monthly mean) has been interpolated to a regular latitude/longitude grid. This interpolated sub-daily data will be slightly different to the native sub-daily data used in the production of the ERA5 monthly means.
- The netCDF data available in the CDS has been packed, see What are NetCDF files and how can I read them, which states "unpacked_data_value = (packed_data_value * scale_factor) + add_offset" and "packed_data_value = nint((unpacked_data_value - add_offset) / scale_factor)". This netCDF packing will change the sub-daily values slightly, compared with the native sub-daily data used in the production of the ERA5 monthly means.
- The GRIB data in the ERA5 monthly means (and sub-daily data) has been packed using a binning algorithm (which is different to the netCDF packing algorithm). Monthly means produced in other formats, such as netCDF, will differ from the ERA5 monthly means because of this packing.
- Finally, there is a further reason why monthly mean values might be different to the mean of the sub-daily values, which even occurs in MARS. This cause only affects forecast parameters (the CDS provides analysed parameters unless the parameter is only available from the forecasts), such as the Total precipitation, and only occurs sporadically. In order to speed up production, ERA5 is produced in several parallel "streams" or experiments, which are then spliced together to produce the final product. Consider, the "stream" change at the beginning of 2015. The ERA5 forecast monthly means for January 2015 have been produced from the sub-daily data from that "stream", the first few hours of which (up until 06 UTC on 1st January 2015) come from the 18 UTC forecast on 31 December 2014. However, the sub-daily forecast data published in ERA5, is based on the date of the start of the forecast, so these first few hours of 2015 originate from the "stream" that produced December 2014. These two "streams" are different experiments, with different data values. The resulting inconsistencies might be larger than for the other three causes, above, depending on how consistent the two streams are.
- ERA5 sea-ice cover and 2 metre temperature: in the period 1979-1989, in a region just to the north of Greenland, the sea-ice cover outside of the melt season is too low and hence the 2 metre temperature is too high. For more information, see Section 3.5.4 of Low frequency variability and trends in surface air temperature and humidity from ERA5 and other datasets
- ERA5 sea-ice cover is missing in the Caspian Sea from late 2007 to 2013, inclusive.
- ERA5 sea-ice surface temperature (skin temperature) in the Arctic, during winter, can have a warm bias of 5K or more. This issue is most pronounced over thick snow-covered sea ice under cold clear-sky conditions, when the modelled conductive heat flux from the warm ocean underneath the ice and snow layer is too high. More information can be found in Batrak and Müller (2019) and Zampieri et al., (2023), the latter of which, also describes a method to improve on this bias.
- Altimeter wave height observations have not been available for ERA5 in the following periods (since coverage began in mid-1991): early February 2021 to mid-January 2022; mid-October 2023 onwards.
- ERA5 CDS: wind values are far too low on pressure levels at the poles in the CDS
- Snow present in Iberia throughout 1978 due to assimilation of erroneous in situ snow data. This has an effect on 2m temperature, which shows negative anomalies of several degrees Celsius. There is no snow present in ERA5-Land, as this snow data is not assimilated. However, the 2m temperature anomaly is present, as the forcing comes from the erroneous ERA5 data. These figures show ERA monthly averaged 2m temperature (t2m) and snow depth (sd) (38 to 43N, -8 to -6W), from 1940-2023, with 1978 highlighted in red. The same snow depth plot, limited to 1977-07 to 1979-04 shows more detail, with the period of erroneous snow depth in ERA5 extending from 1977-12 to 1979-03 in the monthly mean dataset.
Expand title Constant SST values from 24th to 29th June 2024 From 24th to 29th June 2024 global SSTs in ERA5 remain constant, due to a technical error with delivery of the OSTIA SST product that is used. The data from 24th June 22:00 is persisted until new SSTs on 29th June 22:00. Changes in SST over the lakes can be seen during this period, as these are products of the lake model in ERA5. The plot shows mean SSTs 60S to 35N during this period, with this domain chosen to reduce the impact of changing SSTs in lakes.
Figure: Mean SST values 60S to 35N
Resolved issues
ERA5.1 is a re-run of ERA5, for the years 2000 to 2006 only, and was produced to improve upon the cold bias in the lower stratosphere seen in ERA5.
Expand title More information and details for downloading ERA5.1 ERA5.1 is a re-run of ERA5 for the years 2000 to 2006 only. ERA5.1 was produced to improve upon the cold bias in the lower stratosphere exhibited by ERA5 during this period. Moreover, ERA5.1 analyses have a better representation of the following features:
- upper stratospheric temperature
- stratospheric humidity
The lower and middle troposphere in ERA5.1 are similar to those in ERA5, as is the synoptic evolution in the extratropical stratosphere.
For access to ERA5.1 data read Data organisation and how to download ERA5. The dataset is 'reanalysis-era5.1-complete' in the CDS API.
- ERA5.1 CDS: If you retrieved ERA5.1 using the CDS API anytime before 20/05/2020 08:00 UTC, for any stream other than oper (i.e. streams: wave, enda, edmo, ewmo, edmm, ewmm, ewda, moda, wamd, mnth, wamo), you will need to request the data again. Prior to this date, stream oper would be delivered regardless of which stream was requested.
- ERA5 CDS: incorrect values of U/V on pressure levels in the CDS
- ERA5 CDS: Data corruption
User support
There is a range of user support available for ERA5, including a Knowledge Base (where this article resides), a Forum and a ticketed system for questions - for more information see the C3S Help and Support Page.
How to acknowledge and cite ERA5
If you have downloaded ERA5 data on the "CDS disks" and/or downloaded ERA5 data in MARS, using either the CDS API ('reanalysis-era5-complete'
or'reanalysis-era5.
1
-complete'
) or via authorised direct access to MARS, please follow the instructions below:
In addition to the terms and conditions of the license(s), users must:
- cite the CDS catalogue entry;
- provide clear and visible attribution to the Copernicus programme and attribute each data product used;
Step 1: Check the licence to use Copernicus Products for attribution/reference clause
Step 2: Cite the CDS catalogue entry (as traceable source of data). Note that a catalogue entry for ERA5-complete and ERA5.1 is now also available in the CDS.
Step 3: Provide clear and visible attribution to the Copernicus programme and attribute each data product used (to accredit the creators of the data). Throughout the content of your publication, the dataset used is referred to as Author (YYYY)
The 3-steps procedure above is illustrated with this example: Use Case 2: ERA5 hourly data on single levels from 1940 to present
For complete details, please refer to How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it.
References
The ERA5 global reanalysis: Preliminary extension to 1950
Global stratospheric temperature bias and other stratospheric aspects of ERA5 and ERA5.1
Further ERA5 references are available from the ECMWF website.
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This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view |
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Introduction
Here we document the ERA5 dataset, which, eventually, will cover the period January 1950 to near real time (NRT). ERA5 data so far released covers the period 2008-present.
ERA5 was produced using 4DVar data assimilation in CY41R2 of ECMWF’s Integrated Forecast System (IFS), with 137 hybrid sigma/pressure (model) levels in the vertical, with the top level at 0.01 hPa. Atmospheric data are available on these levels and they are also interpolated to 37 pressure, 16 potential temperature and 1 potential vorticity level(s). "Surface or single level" data are also available, containing 2D parameters such as precipitation, 2m temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model.
The ERA5 dataset contains one (31 km) high resolution realisation (HRES) and a reduced resolution ten member ensemble (EDA). Generally, the data are available at a sub-daily and monthly frequency and consist of analyses and short (18 hour) forecasts, initialised twice daily from analyses at 06 and 18 UTC. Most analysed parameters are also available from the forecasts. There are a number of forecast parameters, e.g. mean rates and accumulations, that are not available from the analyses.
The data are archived in the ECMWF data archive (MARS) and can be retrieved using the ECMWF Public Dataset service via the WebAPI (Member State users can access the data using MARS directly, in the usual manner). In the future, the data will be available from the Climate Data Store (CDS).
The IFS and data assimilation
The model documentation for CY41R2 is at https://www.ecmwf.int/search/elibrary/part?solrsort=sort_label%20asc&title=part&secondary_title=CY41r2&f[0]=ts_biblio_year%3A2016.
The 4DVar data assimilation uses 12 hour windows from 09 UTC to 21 UTC and 21 UTC to 09 UTC (the following day).
Data organisation
The data can be accessed using the MARS keywords class=ea and expver=0001 (or ‘dataset’ : “era5” for the ECMWF Public Dataset service via the WebAPI). Subdivisions of the data are labelled using stream, type and levtype.
Stream:
- oper: HRES sub-daily
- wave: HRES waves sub-daily
- mnth: HRES synoptic monthly means
- moda: HRES monthly means of daily means
- wamo: HRES waves synoptic monthly means
- wamd: HRES waves monthly means of daily means
- enda: EDA sub-daily
- ewda: EDA waves sub-daily
- edmm: EDA synoptic monthly means
- edmo: EDA monthly means of daily means
- ewmm: EDA waves synoptic monthly means
- ewmo: EDA waves monthly means of daily means
Type:
- an: analyses
- fc: forecasts
- em: ensemble mean
- es: ensemble standard deviation
Levtype:
- sfc: surface or single level
- pl: pressure levels
- pt: potential temperature levels
- pv: potential vorticity level
- ml: model levels
Spatial grid
The ERA5 HRES atmospheric data has a resolution of 31km, 0.28125 degrees, and the EDA has a resolution of 62km, 0.5625 degrees. The data are available either as spectral coefficients with a triangular truncation of T639 (HRES) and T319 (EDA) or on a reduced Gaussian grid with a resolution of N320 (HRES) and N160 (EDA). These grids are so called "linear grids", sometimes referred to as TL639 (HRES) and TL319 (EDA).
The wave data are produced and archived on a different grid to that of the atmospheric model, namely a reduced latitude/longitude grid with a resolution of 0.36 degrees (HRES) and 1.0 degrees (EDA).
Temporal frequency
For sub-daily data for the HRES (stream=oper/wave) the analyses (type=an) are available hourly. The short forecasts, run from 06 and 18 UTC, have hourly steps from 0 to 18 hours. For the EDA, the sub-daily non-wave data (stream=enda) are available every 3 hours but the sub-daily wave data (stream=ewda) are available hourly.
Mean rates and accumulations
The accumulations in the short forecasts (from 06 and 18 UTC) of ERA5 are treated differently compared with those in ERA-Interim (where they are from the beginning of the forecast to the forecast step). In the short forecasts of ERA5 the accumulations are since the previous post processing (archiving), so for:
- HRES: accumulations are in the hour ending at the forecast step
- EDA: accumulations are in the 3 hours ending at the forecast step
Mean rate parameters in ERA5 are similar to accumulations except that the quantities are averaged, instead of accumulated, over the processing period, so the units include "per second".
Mean rates and accumulations are not available from the analyses.
Mean rates and accumulations at step=0 have values of zero because the length of the processing period is zero.
Minimum/maximum since the previous post processing
In ERA5 there are some surface and single level parameters that are the minimum or maximum value since the previous post processing (archiving), see Table 4 below. So, for:
- HRES: the minimum or maximum values are in the hour ending at the forecast step
- EDA: the minimum or maximum values are in the 3 hours ending at the forecast step
Monthly means
Most parameters are also available as synoptic monthly means, for each particular time and forecast step, (stream=mnth/wamo/edmm/ewmm) and monthly means of daily means, for the month as a whole (stream=moda/wamd/edmo/ewmo). For the surface and single level parameters, there are some exceptions which are listed in Table 7.
Monthly means for analyses and instantaneous forecasts are created from data with a valid time in the month, between 00 and 23 UTC, which excludes the time 00 UTC on the first day of the following month. Monthly means for accumulations and mean rates are created from data with a forecast period falling within the month. For example, monthly means of daily means for accumulations and mean rates are created from contiguous data with forecast periods spanning from 00 UTC on the first day of the month to 00 UTC on the first day of the following month.
The data values for accumulations in stream=moda/edmo (monthly means of daily means) have been scaled to give units "per day". Thus, the hydrological parameters are in units of "m of water per day" and so they should be multiplied by 1000 to convert to kgm-2day-1 or mmday-1. Energy (turbulent and radiative) and momentum fluxes should be divided by 86400 seconds (24 hours) to convert to the commonly used units of Wm-2 and Nm-2, respectively.
Ensemble means and standard deviations
For the EDA sub-daily data (stream=enda/ewda), compared with HRES sub-daily data (stream=oper/wave), there are also ensemble means and standard deviations (type=em/es).
Ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample stdv, so we divide by 10 rather than 9 (N-1).
Ensemble means and standard deviations contain analysed parameters when step=0, otherwise they contain forecast parameters. However, only surface and pressure level data (levtype=sfc/pl) contain forecast steps beyond 3 hours. There are no monthly means for ensemble means and standard deviations.
Data format
Model level fields are in GRIB2 format. All other fields are in GRIB1, unless otherwise indicated.
Level listings
Pressure levels: 1000/975/950/925/900/875/850/825/800/775/750/700/650/600/550/500/450/400/350/300/250/225/200/175/150/125/100/70/50/30/20/10/7/5/3/2/1
Potential temperature levels: 265/275/285/300/315/320/330/350/370/395/430/475/530/600/700/850
Potential vorticity level: 2000
Model levels: 1/to/137, which are described at https://www.ecmwf.int/en/forecasts/documentation-and-support/137-model-levels.
Parameter listings
Tables 1-5 below describe the surface and single level parameters (levtype=sfc), Table 6 describes wave parameters, Table 7 describes the monthly mean exceptions for surface and single level and wave parameters and Tables 8-12 describe upper air parameters on various levtypes.
Table 1: stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc: surface and single level parameters: invariants
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
Lake cover
...
(0 - 1)
...
cl
...
26
...
x
...
x
...
2
...
Lake depth
...
m
...
dl
...
228007
...
x
...
x
...
3
...
Low vegetation cover
...
(0 - 1)
...
cvl
...
27
...
x
...
...
4
...
High vegetation cover
...
(0 - 1)
...
cvh
...
28
...
x
...
...
5
...
Type of low vegetation
...
~
...
tvl
...
29
...
x
...
...
6
...
Type of high vegetation
...
~
...
tvh
...
30
...
x
...
...
7
...
Soil type
...
~
...
slt
...
43
...
x
...
...
8
...
Standard deviation of filtered subgrid orography
...
m
...
sdfor
...
74
...
x
...
...
9
...
Geopotential
...
m**2 s**-2
...
z
...
129
...
x
...
x
...
10
...
Standard deviation of orography
...
~
...
sdor
...
160
...
x
...
...
11
...
Anisotropy of sub-gridscale orography
...
~
...
isor
...
161
...
x
...
...
12
...
Angle of sub-gridscale orography
...
radians
...
anor
...
162
...
x
...
...
13
...
Slope of sub-gridscale orography
...
~
...
slor
...
163
...
x
...
...
14
...
Land-sea mask
...
(0 - 1)
...
lsm
...
172
...
x
...
x
Table 2: stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc: surface and single level parameters: instantaneous
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
Convective inhibition
...
J kg**-1
...
cin
...
228001
...
x
...
2
...
Friction velocity
...
m s**-1
...
zust
...
228003
...
x
...
3
...
Lake mix-layer temperature
...
K
...
lmlt
...
228008
...
x
...
x
...
4
...
Lake mix-layer depth
...
m
...
lmld
...
228009
...
x
...
x
...
5
...
Lake bottom temperature
...
K
...
lblt
...
228010
...
x
...
x
...
6
...
Lake total layer temperature
...
K
...
ltlt
...
228011
...
x
...
x
...
7
...
Lake shape factor
...
dimensionless
...
lshf
...
228012
...
x
...
x
...
8
...
Lake ice temperature
...
K
...
lict
...
228013
...
x
...
x
...
9
...
Lake ice depth
...
m
...
licd
...
228014
...
x
...
x
...
10
...
UV visible albedo for direct radiation
...
(0 - 1)
...
aluvp
...
15
...
x
...
x
...
11
...
Minimum vertical gradient of refractivity inside trapping layer
...
m**-1
...
dndzn
...
228015
...
x
...
12
...
UV visible albedo for diffuse radiation
...
(0 - 1)
...
aluvd
...
16
...
x
...
x
...
13
...
Mean vertical gradient of refractivity inside trapping layer
...
m**-1
...
dndza
...
228016
...
x
...
14
...
Near IR albedo for direct radiation
...
(0 - 1)
...
alnip
...
17
...
x
...
x
...
15
...
Duct base height
...
m
...
dctb
...
228017
...
x
...
16
...
Near IR albedo for diffuse radiation
...
(0 - 1)
...
alnid
...
18
...
x
...
x
...
17
...
Trapping layer base height
...
m
...
tplb
...
228018
...
x
...
18
...
Trapping layer top height
...
m
...
tplt
...
228019
...
x
...
19
...
Cloud base height
...
m
...
cbh
...
228023
...
x
...
20
...
Zero degree level
...
m
...
deg0l
...
228024
...
x
...
21
...
Instantaneous 10 metre wind gust
...
m s**-1
...
i10fg
...
228029
...
x
...
22
...
Sea-ice cover
...
(0 - 1)
...
ci
...
31
...
x
...
x
...
23
...
Snow albedo
...
(0 - 1)
...
asn
...
32
...
x
...
x
...
24
...
Snow density
...
kg m**-3
...
rsn
...
33
...
x
...
x
...
25
...
Sea surface temperature
...
K
...
sst
...
34
...
x
...
x
...
26
...
Ice temperature layer 1
...
K
...
istl1
...
35
...
x
...
x
...
27
...
Ice temperature layer 2
...
K
...
istl2
...
36
...
x
...
x
...
28
...
Ice temperature layer 3
...
K
...
istl3
...
37
...
x
...
x
...
29
...
Ice temperature layer 4
...
K
...
istl4
...
38
...
x
...
x
...
30
...
Volumetric soil water layer 1
...
m**3 m**-3
...
swvl1
...
39
...
x
...
x
...
31
...
Volumetric soil water layer 2
...
m**3 m**-3
...
swvl2
...
40
...
x
...
x
...
32
...
Volumetric soil water layer 3
...
m**3 m**-3
...
swvl3
...
41
...
x
...
x
...
33
...
Volumetric soil water layer 4
...
m**3 m**-3
...
swvl4
...
42
...
x
...
x
...
34
...
Convective available potential energy
...
J kg**-1
...
cape
...
59
...
x
...
x
...
35
...
Leaf area index, low vegetation
...
m**2 m**-2
...
lai_lv
...
66
...
x
...
x
...
36
...
Leaf area index, high vegetation
...
m**2 m**-2
...
lai_hv
...
67
...
x
...
x
...
37
...
Total column cloud liquid water
...
kg m**-2
...
tclw
...
78
...
x
...
x
...
38
...
Total column cloud ice water
...
kg m**-2
...
tciw
...
79
...
x
...
x
...
39
...
Total column supercooled liquid water
...
kg m**-2
...
tcslw
...
228088
...
x
...
40
...
Total column rain water
...
kg m**-2
...
tcrw
...
228089
...
x
...
x
...
41
...
Total column snow water
...
kg m**-2
...
tcsw
...
228090
...
x
...
x
...
42
...
Neutral wind at 10 m u-component
...
m s**-1
...
u10n
...
228131
...
x
...
x
...
43
...
Neutral wind at 10 m v-component
...
m s**-1
...
v10n
...
228132
...
x
...
x
...
44
...
Surface pressure
...
Pa
...
sp
...
134
...
x
...
x
...
45
...
Total column water
...
kg m**-2
...
tcw
...
136
...
x
...
x
...
46
...
Total column water vapour
...
kg m**-2
...
tcwv
...
137
...
x
...
x
...
47
...
Soil temperature level 1
...
K
...
stl1
...
139
...
x
...
x
...
48
...
Snow depth
...
m of water equivalent
...
sd
...
141
...
x
...
x
...
49
...
Charnock
...
~
...
chnk
...
148
...
x
...
x
...
50
...
Mean sea level pressure
...
Pa
...
msl
...
151
...
x
...
x
...
51
...
Boundary layer height
...
m
...
blh
...
159
...
x
...
x
...
52
...
Total cloud cover
...
(0 - 1)
...
tcc
...
164
...
x
...
x
...
53
...
10 metre U wind component
...
m s**-1
...
10u
...
165
...
x
...
x
...
54
...
10 metre V wind component
...
m s**-1
...
10v
...
166
...
x
...
x
...
55
...
2 metre temperature
...
K
...
2t
...
167
...
x
...
x
...
56
...
2 metre dewpoint temperature
...
K
...
2d
...
168
...
x
...
x
...
57
...
Soil temperature level 2
...
K
...
stl2
...
170
...
x
...
x
...
58
...
Soil temperature level 3
...
K
...
stl3
...
183
...
x
...
x
...
59
...
Low cloud cover
...
(0 - 1)
...
lcc
...
186
...
x
...
x
...
60
...
Medium cloud cover
...
(0 - 1)
...
mcc
...
187
...
x
...
x
...
61
...
High cloud cover
...
(0 - 1)
...
hcc
...
188
...
x
...
x
...
62
...
Skin reservoir content
...
m of water equivalent
...
src
...
198
...
x
...
x
...
63
...
Total column ozone
...
kg m**-2
...
tco3
...
206
...
x
...
x
...
64
...
Instantaneous large-scale surface precipitation fraction
...
(0 - 1)
...
ilspf
...
228217
...
x
...
65
...
Convective rain rate
...
kg m**-2 s**-1
...
crr
...
228218
...
x
...
66
...
Large scale rain rate
...
kg m**-2 s**-1
...
lsrr
...
228219
...
x
...
67
...
Convective snowfall rate water equivalent
...
kg m**-2 s**-1
...
csfr
...
228220
...
x
...
68
...
Large scale snowfall rate water equivalent
...
kg m**-2 s**-1
...
lssfr
...
228221
...
x
...
69
...
Instantaneous eastward turbulent surface stress
...
N m**-2
...
iews
...
229
...
x
...
x
...
70
...
Instantaneous northward turbulent surface stress
...
N m**-2
...
inss
...
230
...
x
...
x
...
71
...
Instantaneous surface sensible heat flux
...
W m**-2
...
ishf
...
231
...
x
...
x
...
72
...
Instantaneous moisture flux
...
kg m**-2 s**-1
...
ie
...
232
...
x
...
x
...
73
...
Skin temperature
...
K
...
skt
...
235
...
x
...
x
...
74
...
Soil temperature level 4
...
K
...
stl4
...
236
...
x
...
x
...
75
...
Temperature of snow layer
...
K
...
tsn
...
238
...
x
...
x
...
76
...
Forecast albedo
...
(0 - 1)
...
fal
...
243
...
x
...
x
...
77
...
Forecast surface roughness
...
m
...
fsr
...
244
...
x
...
x
...
78
...
Forecast logarithm of surface roughness for heat
...
~
...
flsr
...
245
...
x
...
x
...
79
...
100 metre U wind component
...
m s**-1
...
100u
...
228246
...
x
...
x
...
80
...
100 metre V wind component
...
m s**-1
...
100v
...
228247
...
x
...
x
...
81
...
Precipitation type
...
code table (4.201)
...
ptype
...
260015*
...
x
...
82
...
K index
...
K
...
kx
...
260121*
...
x
...
83
...
Total totals index
...
K
...
totalx
...
260123*
...
x
*GRIB2 format
Table 3: stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc: surface and single level parameters: accumulations
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
Large-scale precipitation fraction
...
s
...
lspf
...
50
...
x
...
2
...
Downward UV radiation at the surface
...
J m**-2
...
uvb
...
57
...
x
...
3
...
Boundary layer dissipation
...
J m**-2
...
bld
...
145
...
x
...
4
...
Surface sensible heat flux
...
J m**-2
...
sshf
...
146
...
x
...
5
...
Surface latent heat flux
...
J m**-2
...
slhf
...
147
...
x
...
6
...
Surface solar radiation downwards
...
J m**-2
...
ssrd
...
169
...
x
...
7
...
Surface thermal radiation downwards
...
J m**-2
...
strd
...
175
...
x
...
8
...
Surface net solar radiation
...
J m**-2
...
ssr
...
176
...
x
...
9
...
Surface net thermal radiation
...
J m**-2
...
str
...
177
...
x
...
10
...
Top net solar radiation
...
J m**-2
...
tsr
...
178
...
x
...
11
...
Top net thermal radiation
...
J m**-2
...
ttr
...
179
...
x
...
12
...
Eastward turbulent surface stress
...
N m**-2 s
...
ewss
...
180
...
x
...
13
...
Northward turbulent surface stress
...
N m**-2 s
...
nsss
...
181
...
x
...
14
...
Eastward gravity wave surface stress
...
N m**-2 s
...
lgws
...
195
...
x
...
15
...
Northward gravity wave surface stress
...
N m**-2 s
...
mgws
...
196
...
x
...
16
...
Gravity wave dissipation
...
J m**-2
...
gwd
...
197
...
x
...
17
...
Top net solar radiation, clear sky
...
J m**-2
...
tsrc
...
208
...
x
...
18
...
Top net thermal radiation, clear sky
...
J m**-2
...
ttrc
...
209
...
x
...
19
...
Surface net solar radiation, clear sky
...
J m**-2
...
ssrc
...
210
...
x
...
20
...
Surface net thermal radiation, clear sky
...
J m**-2
...
strc
...
211
...
x
...
21
...
TOA incident solar radiation
...
J m**-2
...
tisr
...
212
...
x
...
22
...
Vertically integrated moisture divergence
...
kg m**-2
...
vimd
...
213
...
x
...
23
...
Total sky direct solar radiation at surface
...
J m**-2
...
fdir
...
228021
...
x
...
24
...
Clear-sky direct solar radiation at surface
...
J m**-2
...
cdir
...
228022
...
x
...
25
...
Surface solar radiation downward clear-sky
...
J m**-2
...
ssrdc
...
228129
...
x
...
26
...
Surface thermal radiation downward clear-sky
...
J m**-2
...
strdc
...
228130
...
x
...
27
...
Surface runoff
...
m
...
sro
...
8
...
x
...
28
...
Sub-surface runoff
...
m
...
ssro
...
9
...
x
...
29
...
Snow evaporation
...
m of water equivalent
...
es
...
44
...
x
...
30
...
Snowmelt
...
m of water equivalent
...
smlt
...
45
...
x
...
31
...
Large-scale precipitation
...
m
...
lsp
...
142
...
x
...
32
...
Convective precipitation
...
m
...
cp
...
143
...
x
...
33
...
Snowfall
...
m of water equivalent
...
sf
...
144
...
x
...
34
...
Evaporation
...
m of water equivalent
...
e
...
182
...
x
...
35
...
Runoff
...
m
...
ro
...
205
...
x
...
36
...
Total precipitation
...
m
...
tp
...
228
...
x
...
37
...
Convective snowfall
...
m of water equivalent
...
csf
...
239
...
x
...
38
...
Large-scale snowfall
...
m of water equivalent
...
lsf
...
240
...
x
...
39
...
Potential evaporation
...
m
...
pev
...
228251
...
x
The data values for accumulations in stream=moda/edmo (monthly means of daily means) have been scaled to give units "per day". Thus, the hydrological parameters are in units of "m of water per day" and so they should be multiplied by 1000 to convert to kg m-2 day-1 or mm day-1. Energy (turbulent and radiative) and momentum fluxes should be divided by 86400 seconds (24 hours) to convert to the commonly used units of W m-2 and N m-2, respectively.
Table 4: stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc: surface and single level parameters: mean rates
...
Mean surface runoff rate
...
kg m**-2 s**-1
...
msror
...
235020
...
Mean sub-surface runoff rate
...
kg m**-2 s**-1
...
mssror
...
235021
...
Mean snow evaporation rate
...
kg m**-2 s**-1
...
mser
...
235023
...
Mean snowmelt rate
...
kg m**-2 s**-1
...
msmr
...
235024
...
Mean large-scale precipitation fraction
...
Proportion
...
mlspf
...
235026
...
Mean surface downward UV radiation flux
...
W m**-2
...
msdwuvrf
...
235027
...
Mean large-scale precipitation rate
...
kg m**-2 s**-1
...
mlspr
...
235029
...
Mean convective precipitation rate
...
kg m**-2 s**-1
...
mcpr
...
235030
...
Mean snowfall rate
...
kg m**-2 s**-1
...
msr
...
235031
...
Mean boundary layer dissipation
...
W m**-2
...
mbld
...
235032
...
Mean surface sensible heat flux
...
W m**-2
...
msshf
...
235033
...
Mean surface latent heat flux
...
W m**-2
...
mslhf
...
235034
...
Mean surface downward short-wave radiation flux
...
W m**-2
...
msdwswrf
...
235035
...
Mean surface downward long-wave radiation flux
...
W m**-2
...
msdwlwrf
...
235036
...
Mean surface net short-wave radiation flux
...
W m**-2
...
msnswrf
...
235037
...
Mean surface net long-wave radiation flux
...
W m**-2
...
msnlwrf
...
235038
...
Mean top net short-wave radiation flux
...
W m**-2
...
mtnswrf
...
235039
...
Mean top net long-wave radiation flux
...
W m**-2
...
mtnlwrf
...
235040
...
Mean eastward turbulent surface stress
...
N m**-2
...
metss
...
235041
...
Mean northward turbulent surface stress
...
N m**-2
...
mntss
...
235042
...
Mean evaporation rate
...
kg m**-2 s**-1
...
mer
...
235043
...
Mean eastward gravity wave surface stress
...
N m**-2
...
megwss
...
235045
...
Mean northward gravity wave surface stress
...
N m**-2
...
mngwss
...
235046
...
Mean gravity wave dissipation
...
W m**-2
...
mgwd
...
235047
...
Mean runoff rate
...
kg m**-2 s**-1
...
mror
...
235048
...
Mean top net short-wave radiation flux, clear sky
...
W m**-2
...
mtnswrfcs
...
235049
...
Mean top net long-wave radiation flux, clear sky
...
W m**-2
...
mtnlwrfcs
...
235050
...
Mean surface net short-wave radiation flux, clear sky
...
W m**-2
...
msnswrfcs
...
235051
...
Mean surface net long-wave radiation flux, clear sky
...
W m**-2
...
msnlwrfcs
...
235052
...
Mean top downward short-wave radiation flux
...
W m**-2
...
mtdwswrf
...
235053
...
Mean vertically integrated moisture divergence
...
kg m**-2 s**-1
...
mvimd
...
235054
...
Mean total precipitation rate
...
kg m**-2 s**-1
...
mtpr
...
235055
...
Mean convective snowfall rate
...
kg m**-2 s**-1
...
mcsr
...
235056
...
Mean large-scale snowfall rate
...
kg m**-2 s**-1
...
mlssr
...
235057
...
Mean surface direct short-wave radiation flux
...
W m**-2
...
msdrswrf
...
235058
...
Mean surface direct short-wave radiation flux, clear sky
...
W m**-2
...
msdrswrfcs
...
235059
...
Mean surface downward short-wave radiation flux, clear sky
...
W m**-2
...
msdwswrfcs
...
235068
...
Mean surface downward long-wave radiation flux, clear sky
...
W m**-2
...
msdwlwrfcs
...
235069
...
Mean potential evaporation rate
...
kg m**-2 s**-1
...
mper
...
235070
...
The mean rates in Table 4 provide similar information to the accumulations in Table 3, except that they are temporal averages instead of accumulations, and so have units of "per second". The hydrological parameters are in units of "kg m-2 s-1" and so they can be multiplied by 86400 to convert to kg m-2 day-1 or mm day-1.
Table 5: stream=oper/enda, levtype=sfc: surface and single level parameters: minimum/maximum
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
10 metre wind gust since previous post-processing
...
m s**-1
...
10fg
...
49
...
x
...
2
...
Maximum temperature at 2 metres since previous post-processing
...
K
...
mx2t
...
201
...
x
...
3
...
Minimum temperature at 2 metres since previous post-processing
...
K
...
mn2t
...
202
...
x
...
4
...
Maximum total precipitation rate since previous post-processing
...
kg m**-2 s**-1
...
mxtpr
...
228226
...
x
...
5
...
Minimum total precipitation rate since previous post-processing
...
kg m**-2 s**-1
...
mntpr
...
228227
...
x
Table 6: stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc: surface and single level parameters: vertical integrals (not available for type=em/es)
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
Vertical integral of mass of atmosphere
...
kg m**-2
...
vima
...
162053
...
x
...
x
...
2
...
Vertical integral of temperature
...
K kg m**-2
...
vit
...
162054
...
x
...
x
...
3
...
Vertical integral of kinetic energy
...
J m**-2
...
vike
...
162059
...
x
...
x
...
4
...
Vertical integral of thermal energy
...
J m**-2
...
vithe
...
162060
...
x
...
x
...
5
...
Vertical integral of potential+internal energy
...
J m**-2
...
vipie
...
162061
...
x
...
x
...
6
...
Vertical integral of potential+internal+latent energy
...
J m**-2
...
vipile
...
162062
...
x
...
x
...
7
...
Vertical integral of total energy
...
J m**-2
...
vitoe
...
162063
...
x
...
x
...
8
...
Vertical integral of energy conversion
...
W m**-2
...
viec
...
162064
...
x
...
x
...
9
...
Vertical integral of eastward mass flux
...
kg m**-1 s**-1
...
vimae
...
162065
...
x
...
x
...
10
...
Vertical integral of northward mass flux
...
kg m**-1 s**-1
...
viman
...
162066
...
x
...
x
...
11
...
Vertical integral of eastward kinetic energy flux
...
W m**-1
...
vikee
...
162067
...
x
...
x
...
12
...
Vertical integral of northward kinetic energy flux
...
W m**-1
...
viken
...
162068
...
x
...
x
...
13
...
Vertical integral of eastward heat flux
...
W m**-1
...
vithee
...
162069
...
x
...
x
...
14
...
Vertical integral of northward heat flux
...
W m**-1
...
vithen
...
162070
...
x
...
x
...
15
...
Vertical integral of eastward water vapour flux
...
kg m**-1 s**-1
...
viwve
...
162071
...
x
...
x
...
16
...
Vertical integral of northward water vapour flux
...
kg m**-1 s**-1
...
viwvn
...
162072
...
x
...
x
...
17
...
Vertical integral of eastward geopotential flux
...
W m**-1
...
vige
...
162073
...
x
...
x
...
18
...
Vertical integral of northward geopotential flux
...
W m**-1
...
vign
...
162074
...
x
...
x
...
19
...
Vertical integral of eastward total energy flux
...
W m**-1
...
vitoee
...
162075
...
x
...
x
...
20
...
Vertical integral of northward total energy flux
...
W m**-1
...
vitoen
...
162076
...
x
...
x
...
21
...
Vertical integral of eastward ozone flux
...
kg m**-1 s**-1
...
vioze
...
162077
...
x
...
x
...
22
...
Vertical integral of northward ozone flux
...
kg m**-1 s**-1
...
viozn
...
162078
...
x
...
x
...
23
...
Vertical integral of divergence of cloud liquid water flux
...
kg m**-2 s**-1
...
vilwd
...
162079
...
x
...
x
...
24
...
Vertical integral of divergence of cloud frozen water flux
...
kg m**-2 s**-1
...
viiwd
...
162080
...
x
...
x
...
25
...
Vertical integral of divergence of mass flux
...
kg m**-2 s**-1
...
vimad
...
162081
...
x
...
x
...
26
...
Vertical integral of divergence of kinetic energy flux
...
W m**-2
...
viked
...
162082
...
x
...
x
...
27
...
Vertical integral of divergence of thermal energy flux
...
W m**-2
...
vithed
...
162083
...
x
...
x
...
28
...
Vertical integral of divergence of moisture flux
...
kg m**-2 s**-1
...
viwvd
...
162084
...
x
...
x
...
29
...
Vertical integral of divergence of geopotential flux
...
W m**-2
...
vigd
...
162085
...
x
...
x
...
30
...
Vertical integral of divergence of total energy flux
...
W m**-2
...
vitoed
...
162086
...
x
...
x
...
31
...
Vertical integral of divergence of ozone flux
...
kg m**-2 s**-1
...
viozd
...
162087
...
x
...
x
...
32
...
Vertical integral of eastward cloud liquid water flux
...
kg m**-1 s**-1
...
vilwe
...
162088
...
x
...
x
...
33
...
Vertical integral of northward cloud liquid water flux
...
kg m**-1 s**-1
...
vilwn
...
162089
...
x
...
x
...
34
...
Vertical integral of eastward cloud frozen water flux
...
kg m**-1 s**-1
...
viiwe
...
162090
...
x
...
x
...
35
...
Vertical integral of northward cloud frozen water flux
...
kg m**-1 s**-1
...
viiwn
...
162091
...
x
...
x
...
36
...
Vertical integral of mass tendency
...
kg m**-2 s**-1
...
vimat
...
162092
...
x
...
Table 7: stream=wave/ewda/wamo/wamd/ewmm/ewmo: wave parameters
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
Significant wave height of first swell partition
...
m
...
swh1
...
140121
...
x
...
x
...
2
...
Mean wave direction of first swell partition
...
degrees
...
mwd1
...
140122
...
x
...
x
...
3
...
Mean wave period of first swell partition
...
s
...
mwp1
...
140123
...
x
...
x
...
4
...
Significant wave height of second swell partition
...
m
...
swh2
...
140124
...
x
...
x
...
5
...
Mean wave direction of second swell partition
...
degrees
...
mwd2
...
140125
...
x
...
x
...
6
...
Mean wave period of second swell partition
...
s
...
mwp2
...
140126
...
x
...
x
...
7
...
Significant wave height of third swell partition
...
m
...
swh3
...
140127
...
x
...
x
...
8
...
Mean wave direction of third swell partition
...
degrees
...
mwd3
...
140128
...
x
...
x
...
9
...
Mean wave period of third swell partition
...
s
...
mwp3
...
140129
...
x
...
x
...
10
...
Wave Spectral Skewness
...
dimensionless
...
wss
...
140207
...
x
...
x
...
11
...
Free convective velocity over the oceans
...
m s**-1
...
wstar
...
140208
...
x
...
x
...
12
...
Air density over the oceans
...
kg m**-3
...
rhoao
...
140209
...
x
...
x
...
13
...
Normalized energy flux into waves
...
dimensionless
...
phiaw
...
140211
...
x
...
x
...
14
...
Normalized energy flux into ocean
...
dimensionless
...
phioc
...
140212
...
x
...
x
...
15
...
Normalized stress into ocean
...
dimensionless
...
tauoc
...
140214
...
x
...
x
...
16
...
U-component stokes drift
...
m s**-1
...
ust
...
140215
...
x
...
x
...
17
...
V-component stokes drift
...
m s**-1
...
vst
...
140216
...
x
...
x
...
18
...
Period corresponding to maximum individual wave height
...
s
...
tmax
...
140217
...
x
...
x
...
19
...
Maximum individual wave height
...
m
...
hmax
...
140218
...
x
...
x
...
20
...
Model bathymetry
...
m
...
wmb
...
140219
...
x
...
x
...
21
...
Mean wave period based on first moment
...
s
...
mp1
...
140220
...
x
...
x
...
22
...
Mean wave period based on second moment
...
s
...
mp2
...
140221
...
x
...
x
...
23
...
Wave spectral directional width
...
dimensionless
...
wdw
...
140222
...
x
...
x
...
24
...
Mean wave period based on first moment for wind waves
...
s
...
p1ww
...
140223
...
x
...
x
...
25
...
Mean wave period based on second moment for wind waves
...
s
...
p2ww
...
140224
...
x
...
x
...
26
...
Wave spectral directional width for wind waves
...
dimensionless
...
dwww
...
140225
...
x
...
x
...
27
...
Mean wave period based on first moment for swell
...
s
...
p1ps
...
140226
...
x
...
x
...
28
...
Mean wave period based on second moment for swell
...
s
...
p2ps
...
140227
...
x
...
x
...
29
...
Wave spectral directional width for swell
...
dimensionless
...
dwps
...
140228
...
x
...
x
...
30
...
Significant height of combined wind waves and swell
...
m
...
swh
...
140229
...
x
...
x
...
31
...
Mean wave direction
...
degrees
...
mwd
...
140230
...
x
...
x
...
32
...
Peak period of 1D spectra
...
s
...
pp1d
...
140231
...
x
...
x
...
33
...
Mean wave period
...
s
...
mwp
...
140232
...
x
...
x
...
34
...
Coefficient of drag with waves
...
dimensionless
...
cdww
...
140233
...
x
...
x
...
35
...
Significant height of wind waves
...
m
...
shww
...
140234
...
x
...
x
...
36
...
Mean direction of wind waves
...
degrees
...
mdww
...
140235
...
x
...
x
...
37
...
Mean period of wind waves
...
s
...
mpww
...
140236
...
x
...
x
...
38
...
Significant height of total swell
...
m
...
shts
...
140237
...
x
...
x
...
39
...
Mean direction of total swell
...
degrees
...
mdts
...
140238
...
x
...
x
...
40
...
Mean period of total swell
...
s
...
mpts
...
140239
...
x
...
x
...
41
...
Mean square slope of waves
...
dimensionless
...
msqs
...
140244
...
x
...
x
...
42
...
10 metre wind speed
...
m s**-1
...
wind
...
140245
...
x
...
x
...
43
...
Altimeter wave height
...
m
...
awh
...
140246
...
x
...
44
...
Altimeter corrected wave height
...
m
...
acwh
...
140247
...
x
...
45
...
Altimeter range relative correction
...
~
...
arrc
...
140248
...
x
...
46
...
10 metre wind direction
...
degrees
...
dwi
...
140249
...
x
...
x
...
47
...
Wave spectral kurtosis
...
dimensionless
...
wsk
...
140252
...
x
...
x
...
48
...
Benjamin-Feir index
...
dimensionless
...
bfi
...
140253
...
x
...
x
...
49
...
Wave spectral peakedness
...
dimensionless
...
wsp
...
140254
...
x
...
x
...
50
...
2D wave spectra (single)
...
m**2 s radian**-1
...
2dfd
...
140251*
...
x
...
*for 30 frequencies and 24 directions
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
UV visible albedo for direct radiation
...
(0 - 1)
...
aluvp
...
15
...
x
...
no mean
...
2
...
UV visible albedo for diffuse radiation
...
(0 - 1)
...
aluvd
...
16
...
x
...
no mean
...
3
...
Near IR albedo for direct radiation
...
(0 - 1)
...
alnip
...
17
...
x
...
no mean
...
4
...
Near IR albedo for diffuse radiation
...
(0 - 1)
...
alnid
...
18
...
x
...
no mean
...
5
...
Magnitude of turbulent surface stress
...
N m**-2 s
...
magss
...
48
...
...
x
...
7
...
10 metre wind gust since previous post-processing
...
m s**-1
...
10fg
...
49
...
no mean
...
8
...
Maximum temperature at 2 metres since previous post-processing
...
K
...
mx2t
...
201
...
no mean
...
9
...
Minimum temperature at 2 metres since previous post-processing
...
K
...
mn2t
...
202
...
no mean
...
10
...
10 metre wind speed
...
m s**-1
...
10si
...
207
...
x
...
x
...
11
...
Maximum total precipitation rate since previous post-processing
...
kg m**-2 s**-1
...
mxtpr
...
228226
...
no mean
...
12
...
Minimum total precipitation rate since previous post-processing
...
kg m**-2 s**-1
...
mntpr
...
228227
...
no mean
...
13
...
Altimeter wave height
...
m
...
awh
...
140246
...
no mean
...
14
...
Altimeter corrected wave height
...
m
...
acwh
...
140247
...
no mean
...
15
...
Altimeter range relative correction
...
~
...
arrc
...
140248
...
no mean
...
16
...
2D wave spectra (single)
...
m**2 s radian**-1
...
2dfd
...
140251
...
no mean
...
Table 9: stream=oper/enda/mnth/moda/edmm/edmo, levtype=pl: pressure level parameters: instantaneous
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
Potential vorticity
...
K m**2 kg**-1 s**-1
...
pv
...
60
...
x
...
x
...
2
...
Specific rain water content
...
kg kg**-1
...
crwc
...
75
...
x
...
x
...
3
...
Specific snow water content
...
kg kg**-1
...
cswc
...
76
...
x
...
x
...
4
...
Geopotential
...
m**2 s**-2
...
z
...
129
...
x
...
x
...
5
...
Temperature
...
K
...
t
...
130
...
x
...
x
...
6
...
U component of wind
...
m s**-1
...
u
...
131
...
x
...
x
...
7
...
V component of wind
...
m s**-1
...
v
...
132
...
x
...
x
...
8
...
Specific humidity
...
kg kg**-1
...
q
...
133
...
x
...
x
...
9
...
Vertical velocity
...
Pa s**-1
...
w
...
135
...
x
...
x
...
10
...
Vorticity (relative)
...
s**-1
...
vo
...
138
...
x
...
x
...
11
...
Divergence
...
s**-1
...
d
...
155
...
x
...
x
...
12
...
Relative humidity
...
%
...
r
...
157
...
x
...
x
...
13
...
Ozone mass mixing ratio
...
kg kg**-1
...
o3
...
203
...
x
...
x
...
14
...
Specific cloud liquid water content
...
kg kg**-1
...
clwc
...
246
...
x
...
x
...
15
...
Specific cloud ice water content
...
kg kg**-1
...
ciwc
...
247
...
x
...
x
...
16
...
Fraction of cloud cover
...
(0 - 1)
...
cc
...
248
...
x
...
x
Table 10: stream=oper/enda/mnth/moda/edmm/edmo, levtype=pt: potential temperature level parameters: instantaneous
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
Montgomery potential
...
m**2 s**-2
...
mont
...
53
...
x
...
2
...
Pressure
...
Pa
...
pres
...
54
...
x
...
3
...
Potential vorticity
...
K m**2 kg**-1 s**-1
...
pv
...
60
...
x
...
4
...
U component of wind
...
m s**-1
...
u
...
131
...
x
...
5
...
V component of wind
...
m s**-1
...
v
...
132
...
x
...
6
...
Specific humidity
...
kg kg**-1
...
q
...
133
...
x
...
7
...
Vorticity (relative)
...
s**-1
...
vo
...
138
...
x
...
8
...
Divergence
...
s**-1
...
d
...
155
...
x
...
9
...
Ozone mass mixing ratio
...
kg kg**-1
...
o3
...
203
...
x
...
Table 11: stream=oper/enda/mnth/moda/edmm/edmo, levtype=pv: potential vorticity level parameters: instantaneous
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
Potential temperature
...
K
...
pt
...
3
...
x
...
2
...
Pressure
...
Pa
...
pres
...
54
...
x
...
3
...
Geopotential
...
m**2 s**-2
...
z
...
129
...
x
...
4
...
U component of wind
...
m s**-1
...
u
...
131
...
x
...
5
...
V component of wind
...
m s**-1
...
v
...
132
...
x
...
6
...
Specific humidity
...
kg kg**-1
...
q
...
133
...
x
...
7
...
Ozone mass mixing ratio
...
kg kg**-1
...
o3
...
203
...
x
...
Table 12: stream=oper/enda/mnth/moda/edmm/edmo, levtype=ml: model level parameters: instantaneous
...
count
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
1
...
Specific rain water content
...
kg kg**-1
...
crwc
...
75
...
x
...
x
...
2
...
Specific snow water content
...
kg kg**-1
...
cswc
...
76
...
x
...
x
...
3
...
Eta-coordinate vertical velocity
...
s**-1
...
etadot
...
77
...
x
...
x
...
4
...
Geopotential*
...
m**2 s**-2
...
z
...
129
...
x
...
x
...
5
...
Temperature
...
K
...
t
...
130
...
x
...
x
...
6
...
U component of wind
...
m s**-1
...
u
...
131
...
x
...
x
...
7
...
V component of wind
...
m s**-1
...
v
...
132
...
x
...
x
...
8
...
Specific humidity
...
kg kg**-1
...
q
...
133
...
x
...
x
...
9
...
Vertical velocity
...
Pa s**-1
...
w
...
135
...
x
...
x
...
10
...
Vorticity (relative)
...
s**-1
...
vo
...
138
...
x
...
x
...
11
...
Logarithm of surface pressure*
...
~
...
lnsp
...
152
...
x
...
x
...
12
...
Divergence
...
s**-1
...
d
...
155
...
x
...
x
...
13
...
Ozone mass mixing ratio
...
kg kg**-1
...
o3
...
203
...
x
...
x
...
14
...
Specific cloud liquid water content
...
kg kg**-1
...
clwc
...
246
...
x
...
x
...
15
...
Specific cloud ice water content
...
kg kg**-1
...
ciwc
...
247
...
x
...
x
...
16
...
Fraction of cloud cover
...
(0 - 1)
...
cc
...
248
...
x
...
x
*Only archived on level=1.
Table 13: stream=oper/enda/mnth/moda/edmm/edmo, levtype=ml: model level parameters: mean rates
...
Observations
The observations (satellite and in-situ) used as input into ERA5 are listed below.
Satellite Data
...
* reprocessed dataset
+ when different than the satellite agency
In-situ data, provided by WMO WIS
...
Guidelines
The following advice is intended to help users understand particular features of the ERA5 data:
- Sea surface temperature and sea-ice cover (see Table 2 above) are available at the usual times, eg hourly for the HRES, but their content is only updated once daily.
- Mean rates and accumulations at step=0 have values of zero because the length of the processing period is zero.
Known issues
At the time of writing (2017-07) we are aware of these issues with ERA5:
ERA5 shows too strong tropical jet in the mesosphere
ERA5 has poor fit to radiosonde temperatures in the lower stratosphere indicating some cold bias
Although small values of ensemble spread correctly mark more confident estimates than large values, numerical values are over confident
- The Potential Evaporation field (pev, parameter Id 228251) is largely underestimated over deserts and high-forested areas. This is due to a bug in the code that does not allow transpiration in case no low vegetation type is present.
This list will be updated as we become aware of further issues in ERA5.
How to cite ERA5
Please acknowledge the use of ERA5 as stated in the Copernicus C3S/CAMS License agreement:
...
...
5.1.3 Any such publication or distribution covered by clauses 5.1.1 and 5.1.2 shall state that neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus Information or Data it contains."
References
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