| Page info | ||||||
|---|---|---|---|---|---|---|
|
| Info | ||||
|---|---|---|---|---|
| ||||
|
| Easy Heading Macro | ||
|---|---|---|
|
Introduction
Here, we document the ERA5 dataset, which , eventually, will cover covers the period from January 1950 onwards. Complete ERA5 data released so far covers the period from 1979 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 "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. In order to speed up production, ERA5 is produced by running several parallel "streams" or experiments, which are then spliced together to form the published versionThe 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 An ERA5 back extension 1950-1978 (Preliminary version) has been was produced. Although Although in many other respects the quality is was relatively good, this preliminary data does did suffer from excessively intense tropical cyclones. This dataset is available as a separate entry in the CDS catalogue (and in MARS) for a short period of time, after which it will be deprecated and replaced by a new updated version which will be accessible through the main ERA5 entry. The main entry currently contains data from 1979 onwards.
Data update frequency
now deprecated.
Data update frequency
Initial release dataInitial release data, i.e. data no more than three months behind real time, is called ERA5T.
For 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.
...
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 , monthly updates 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:
- for the months September, October, November and at least a part of from 1 September to 13 December 2021, the final ERA5 product is and will be different from 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 do can differ slightly 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 and will be overwrittenoverwritten (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
...
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)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 most single level and pressure level parameters data 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.
| Hide content |
|---|
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). |
| Expand | ||
|---|---|---|
| ||
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:
Datasets reanalysis-era5-single-levels-preliminary-back-extension and reanalysis-era5-pressure-levels-preliminary-back-extension contain the following (sub-daily) product types:
Datasets reanalysis-era5-single-levels-monthly-means-preliminary-back-extension and reanalysis-era5-pressure-levels-monthly-means-preliminary-back-extension contain the following (monthly) product types:
|
ERA5 data in MARS can be accessed using the CDS API, but access is relatively slow.
...
| title | Data organisation in MARS |
|---|
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.
| Hide content |
|---|
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). |
| Expand | ||
|---|---|---|
| ||
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:
|
ERA5 data in MARS can be accessed using the CDS API, but access is relatively slow.
| Expand | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ||||||||||||
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:
|
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.
| Expand | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
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:
...
CDS API access to MARS
(specify the dataset)
...
Member state access to MARS
(specify class and expver)
...
reanalysis-era5.1-complete
...
ERA5 back extension 1950-1978
(Preliminary version)
...
Subdivisions of the data are labelled using the keywords stream, type and levtype:
Stream:
- oper (HRES sub-daily)
- wave (HRES sub-daily, for waves)
- mnth (HRES synoptic monthly means, ie by hour of day)
- moda (HRES monthly means of daily means)
- wamo (HRES synoptic monthly means, ie by hour of day, for waves)
- wamd (HRES monthly means of daily means, for waves)
- enda (EDA sub-daily)
- ewda (EDA sub-daily, for waves)
- edmm (EDA synoptic monthly means, ie by hour of day)
- edmo (EDA monthly means of daily means)
- ewmm (EDA synoptic monthly means, ie by hour of day, for waves)
- ewmo (EDA monthly means of daily means, for waves)
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
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.
| Expand | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
...
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
| Anchor | ||||
|---|---|---|---|---|
|
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).)
...
ERA5 data available from the CDS disks has been pre-interpolated to a regular latitude/longitude grid appropriate for that data.
The article Model grid box and time step might be useful.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 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, but the geodetic latitude/longitude of the surface elevation datasets 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.
ECMWF ERA5 data is referenced in the horizontal with respect to the WGS84 ellipse (which defines the major/minor axes) but and in the vertical it is referenced to the Geoid (EGM96).
Earth model
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 the WMO GRIB Edition 1 specifications, Table 7, GDS Octet 17.
For data in GRIB2 format the earth model is a sphere with radius = 6371.2290 2229 km (note, this is consistent with what is actually used in the IFS), as defined in the 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.
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
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.
| Expand | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
| Expand | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
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 parametersSuch 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.
...
- 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.
...
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.
...
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 https://www.ecmwf.int/en/forecasts/documentation-and-support/137-model-levels 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.
...
count | name | units | Variable name in CDS | shortName | paramId | an | fc |
|---|---|---|---|---|---|---|---|
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 |
...
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.
...
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 |
...
| 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 |
...
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 |
...
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 |
...
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 | dimensionlessRadians | 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 | dimensionlessRadians | 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 | dimensionlessRadians | 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 |
...
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 |
...
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 |
...
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 |
...
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 |
...
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 |
...
| 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.
...
| 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 | Aircraft-sondesRadiosondes | Temperature, wind, humidity profiles |
| Land/Mobile TEMP | Radiosondes | Temperature, wind, humidity profiles |
| AIREP | Aircraft data | Temperature, wind profiles |
| AMDAR | Aircraft data | Temperature, wind profiles |
| ACARS | Aircraft data | Temperature, wind profiles, humidity |
| WIGOS AMDAR | Aircraft data | Temperature, wind profiles, 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 | ||||
|---|---|---|---|---|
|
...
- 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 dailyhourly 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, sExpand 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. For further guidelines, please see: Altimeter wave height in the Climate Data Store (CDS)
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 model and 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:
ERA5 physical depth of snow where there is snow cover is equal to RW*SD/(RSN*SC).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).
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.
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 Expand title Parameter "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.
Known issues
Currently, we are aware of these issues with ERA5:
- ERA5T: for the months September, October, November and at least a part of from 1 September to 13 December 2021, the final ERA5 product is and will be different from 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 spreadthe ensemble spread and which can be large for some parameters). On the CDS disks, the initial, ERA5T, fields have been and will be overwrittenoverwritten (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 from 1979. 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 is data was produced by running several parallel experiments, each for a different period, which are were then appended 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.
Expand title ERA5 forecast parameters are missing on 1st January 1979 from 00 UTC to 06 UTC ERA5 forecast parameters are missing for the validity times of 1st January 1979 from 00 UTC to 06 UTC. This problem has occurred because the forecast producing these data started from 18 UTC on the last day of 1978. This gap can be filled by using forecast data from the ERA5 back extension (preliminary version), with date=19781231, time=18 and step=6/to/12:
Eventually, the data gap will be filled by the re-run of the ERA5 back extensionCode Block language py title Request for total precipitation forecast hourly data for 1st January 00UTC-06UTC #!/usr/bin/env python3 import cdsapi c = cdsapi.Client() c.retrieve('reanalysis-era5-complete-preliminary-back-extension', { 'date': '1978-12-31', 'levtype': 'sfc', 'param': '228.128', 'time':'18:00:00', 'step':'6/7/8/9/10/11/12', 'stream': 'oper', 'type': 'fc', 'grid': '0.25/0.25', 'format': 'netcdf', }, 'era5.preliminary-back-extension-temperature-tp.nc'), 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 datasetsand 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
- ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense
- ERA5 back extension 1950-1978 (Preliminary version): large bias in surface analysis over Australia prior to 1970
- ERA5 back extension 1950-1978 (Preliminary version): the deep soil moisture tends to be too drySnow 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.
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
...
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
For If you have downloaded ERA5 data on the "CDS disks" All users of 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 data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:
Acknowledge according to the licence to use Copernicus Products.
Cite each dataset used as indicated on the relevant CDS entries (see link to "Citation" under References on the Overview page of the dataset entry) .
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 1979 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.
For ERA5 data in MARS,
If you have downloaded ERA5 data in MARS, using either the CDS API ('reanalysis-era5-complete' or 'reanalysis-era5.1-complete' or ') or via authorised direct access to MARS, please contact us.reanalysis-era5-complete-preliminary-back-extension'
References
...
Further ERA5 references are available from the ECMWF e-Librarywebsite.
| Info | ||
|---|---|---|
| ||
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 agreementAgreement 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. |
Related articles
| Content by Label | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|