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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. The interpolation software (MIR) was updated when the ERA5 production was moved to the new ATOS HPC on 24 October 2022.

ERA5.1 is a re-run of ERA5, for the years 2000 to 2006 only, and was produced to improve upon the cold bias in the lower stratosphere seen in ERA5 during this period.

The original ERA5 release contained data from 1979 onwards. The final ERA5 back extension for 1940-1978 has been produced and is available alongside the original/main release. 

 An 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 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.

Data update frequency

is now deprecated.

Data update frequency

Initial release data, i.e. data no more than three months behind real time, is called ERA5T.

Both for the CDS and MARS, daily updates for ERA5T are available about 5 days behind real time and monthly mean updates are available about 5 days after the end of the month.

The daily updates for ERA5T data on the CDS occur at no fixed time during the day. However, although it is not guaranteed, the D-5 data are typically available by 12UTC. We are working on reducing the variability of the update time.

For the CDS, ERA5T data for a month is overwritten with the final ERA5 data about two months after the month in question.

For MARS, the final ERA5 data are available about two months after the month in question. In addition, the last few months of data are kept online and can be accessed much quicker than older data on tape.

Initial release data, i.e. data no more than three months behind real time, is called ERA5T.  In the event that serious flaws are detected in ERA5T, this data the latter could be different to the final ERA5 data. In practice, though, this will be very unlikely to occur. 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 more than once every few years, if at all. In the unlikely event that such a correction is required, users will be notified as soon as possible.

For the CDS, daily updates are available about 5 days behind real time and monthly mean updates are available about 5 days after the end of the month.

Note: At the moment the timing of the availability of ERA5T data on the CDS on a daily basis can vary. We do not work to a specific target schedule. However, the D-5 data are typically available by 12UTC, but not guaranteed. We are working on reducing the variability of the time of availability, but this may take several months to achieve.

For MARS ERA5 data, monthly updates are available about two months after the month in question.

For GRIB data, ERA5T can be identified by the key expver=0005 in the GRIB header. ERA5 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.

often. So far, it has only occurred once:

  • from 1 September to 13 December 2021, the final ERA5 product is different to ERA5T due to the correction of the assimilation of incorrect snow observations in central Asia. Although the differences are mostly limited to that region and mainly to surface parameters, in particular snow depth and soil moisture and to a lesser extent 2m temperature and 2m dewpoint temperature, all the resulting reanalysis fields can differ over the whole globe but should be within their range of uncertainty (which is estimated by the ensemble spread and which can be large for some parameters). On the CDS disks, the initial, ERA5T, fields have been overwritten (with the usual 2-3 month delay), i.e., for these months, access to the original CDS disk, ERA5T product is not possible after it has been overwritten. Potentially incorrect snow observations have been assimilated in ERA5 up to this time, when the effects became noticeable. The quality control of snow observations has been improved in ERA5 from September 2021 and from 15 November 2021 in ERA5T.

For the hourly products on CDS disks for both single and pressure levels, some local differences exist between ERA5 and ERA5T for 1 to 24 October 2022 due to a change of the regridding software (MIR) when the ERA5 production was changed from the Cray to ATOS. Differences are not meteorologically significant. For October 2022, there is no difference for the data in native resolution (ERA5-complete)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 the link for more details.

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

...

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 version.

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.

ERA5 data in MARS can be accessed using the CDS API, but access is relatively slow.

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).

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
titleData organisation on the CDS disks

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.

Expand
titleData organisation on the CDS disks

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:

  • reanalysis
  • ensemble_mean
  • ensemble_spread
  • ensemble_members

Datasets reanalysis-era5-single-levels-monthly-means and reanalysis-era5-pressure-levels-monthly-means contain the following (monthly) product types:

  • monthly_averaged_reanalysis
  • monthly_averaged_reanalysis_by_hour_of_day
  • monthly_averaged_ensemble_members
  • monthly_averaged_ensemble_members_by_hour_of_day

Datasets reanalysis-era5-single-levels-preliminary-back-extension and reanalysis-era5-pressure-levels-preliminary-back-extension contain the following (sub-daily) product types:

  • reanalysis
  • ensemble_mean
  • ensemble_spread
  • ensemble_members

    Datasets reanalysis-era5-single-levels-monthly-means-preliminary-back-extension and reanalysis-era5-pressure-levels-monthly-means-preliminary-back-extension contain the following (sub-daily) product types:

    • reanalysis
    • ensemble_mean
    • ensemble_spread
    • ensemble_members

    Datasets reanalysis-era5-single-levels-monthly-means and reanalysis-era5-pressure-levels-monthly-means contain the following (monthly) product types:

    • monthly_averaged_reanalysis
    • monthly_averaged_reanalysis_by_hour_of_day
    • monthly_averaged_ensemble_members
    • monthly_averaged_ensemble_members_by_hour_of_day


    ERA5 data in MARS can be accessed using the CDS API, but access is relatively slow.

    Expand
    titleData organisation in MARS

    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:

    ERA5 back extension 1950-1978

    (Preliminary version)-preliminary-back-extension0098

    CDS API access to MARS

    (specify the dataset)

    Member state access to MARS

    (specify class and expver)

    ERA5reanalysis-era5-completeclass=ea, expver=0001
    ERA5.1

    reanalysis-era5.1-complete

    class=ea, expver=0051
    ERA5TNOT available at the momentclass=ea, expver=0005reanalysis-era5-complete1class=ea, expver=0005

    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:

    • 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

    ...

    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
    SpatialGrid
    SpatialGrid

    The ERA5 HRES atmospheric data has a resolution of 31km, 0.28125 degrees, and the EDA has a resolution of 63km, 0.5625 degrees. (Depending on the parameter, the data are archived either as spectral coefficients with a triangular truncation of T639 (HRES) and T319 (EDA) or on a reduced Gaussian grid with a resolution of N320 (HRES) and N160 (EDA). These grids are so called "linear grids", sometimes referred to as TL639 (HRES) and TL319 (EDA).)

    The wave data are produced and archived on a different grid to that of the atmospheric model, namely a reduced latitude/longitude grid with a resolution of 0.36 degrees (HRES) and 1.0 degrees degree (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.

    Surface elevation datasets used by ERA5

    interpolation method is based on the MIR software. For the production on the Cray HPC (1 January 1940 to 24 October 2022 inclusive) this was an early version of MIR, while for the production on ATOS (25 October 2022 onwards) this is based on the MIR version of the ECMWF MARS client. Differences between both versions are in general small, very localized and not meteorologically significant.  For data on pressure levels, differences are mainly limited to the exact north and south pole (90N and 90S). For single-level data, for some fields there are differences at the poles as well, while for some other fields, there are additional sets of isolated points with differences. In both cases this represents an improvement of the interpolation software.

    The article Model grid box and time step might be useful.

    Surface elevation datasets used by ERA5

    In order to define the surface geopotential in ERA5, the IFS uses surface 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 Earth underlying shape of the Earth is a perfect sphere, but the 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 EGM96 geoid over land but over ocean it is referenced to mean sea level, with the approximation that this is assumed to the Geoid (EGM96).

    ...

    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

    Such parameters, which are only available from forecasts, have undergone particular types of statistical processing (temporal mean or accumulation, respectively) over a period of time called the processing period. In addition, these parameters may, or may not, have been averaged in time, to produce monthly means.

    The accumulations (over the accumulation/processing period) in the short forecasts (from 06 and 18 UTC) of ERA5 are treated differently compared with those in ERA-Interim and operational data (where the accumulations are from the beginning of the forecast to the validity date/time). In the short forecasts of ERA5, the accumulations are since the previous post processing (archiving), so for:

    • reanalysis: accumulations are over the hour (the accumulation/processing period) ending at the validity date/time
    • ensemble: accumulations are over the 3 hours (the accumulation/processing period) ending at the validity date/time
    • Monthly means (of daily means, stream=moda/edmo): accumulations have been scaled to have an "effective" processing period of one day, see section Monthly means

    Mean rate/flux parameters in ERA5 (e.g. Table 4 for surface and single levels) provide similar information to accumulations (e.g. Table 3 for surface and single levels), except they are expressed as temporal means, over the same processing periods, and so have units of "per second".

    • Mean rate/flux parameters are easier to deal with than accumulations because the units do not vary with the processing period.
    • The mean rate hydrological parameters (e.g. the "Mean total precipitation rate") have units of "kg m-2 s-1", which are equivalent to "mm s-1". They can be multiplied by 86400 seconds (24 hours) to convert to kg m-2 day-1 or mm day-1.

    Note that:

    • For the CDS time, or validity time, of 00 UTC, the mean rates/fluxes and accumulations are over the hour (3 hours for the EDA) ending at 00 UTC i.e. the mean or accumulation is during the previous day.
    • Mean rates/fluxes and accumulations are not available from the analyses.
    • Mean rates/fluxes and accumulations at step=0 have values of zero because the length of the processing period is zero.

    Minimum/maximum since the previous post processing

    The short forecasts of ERA5 contain some surface and single level parameters that are the minimum or maximum value since the previous post processing (archiving), see Table 5 below. So, for:

    • reanalysis: the minimum or maximum values are in the hour (the processing period) ending at the validity date/time
    • ensemble: the minimum or maximum values are in the 3 hours (the processing period) ending at the validity date/time

    Wave spectra

    The ocean wave model used in ERA5 (WAM, which is included in the IFS) provides wave spectra with 24 directions and 30 frequencies (see "2D wave spectra (single)", Table 7).

    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
    titleAnalysis date ranges for the HRES production experiments



    Start date (YYYYMMDD)Start time (UTC)End date (YYYYMMDD)End time (UTC)Computing system

    19400101

    00

    19431231

    21Cray

    19431231

    22

    19481231

    21Cray

    19481231

    22

    19531231

    21Cray

    19531231

    22

    19581231

    21Cray

    19581231

    22

    19631231

    21Cray

    19631231

    22

    19681231

    21Cray

    19681231

    22

    19731231

    21Cray

    19731231

    22

    19781231

    23Cray

    19790101

    00

    19810630

    23Cray

    19810701

    00

    19860331

    23Cray

    19860401

    00

    19880930

    23Cray

    19881001

    00

    19930731

    23Cray

    19930801

    00

    19950831

    23Cray

    19950901

    00

    19991231

    23Cray

    20000101

    00

    20000930

    23Cray

    20001001

    00

    20010930

    23Cray

    20011001

    00

    20020930

    23Cray

    20021001

    00

    20030930

    23Cray

    20031001

    00

    20040930

    23Cray

    20041001

    00

    20050930

    23Cray

    20051001

    00

    20060930

    23Cray

    20061001

    00

    20071231

    23Cray

    20080101

    00

    20091231

    23Cray

    20100101

    00

    20141231

    23Cray

    20150101

    00

    20190228

    23Cray

    20190301

    00

    20210831

    23Cray

    20210901

    00

    20211231

    23Cray

    20220101

    00

    20221023

    21Cray
    2022102322ongoingongoingATOS



    Expand
    titleAnalysis date ranges for the EDA production experiments


    Start date (YYYYMMDD)Start time (UTC)End date (YYYYMMDD)End time (UTC)Computing system

    19400101

    00

    19431231

    21Cray

    19440101

    00

    19481231

    21Cray

    19490101

    00

    19531231

    21Cray

    19540101

    00

    19581231

    21Cray

    19590101

    00

    19631231

    21Cray

    19640101

    00

    19681231

    21Cray

    19690101

    00

    19731231

    21Cray

    19740101

    00

    19781231

    21Cray

    19790101

    00

    19860331

    21Cray

    19860401

    00

    19930731

    21Cray

    19930801

    00

    19991231

    21Cray

    20000101

    00

    20091231

    21Cray

    20100101

    00

    20141231

    21Cray

    20150101

    00

    20190228

    21Cray

    20190301

    00

    20210831

    21Cray

    20210901

    00

    20211231

    21Cray

    20220101

    00

    20221023

    21Cray
    2022102400ongoingongoingATOS


    Note, that forecasts start from the relevant analysis at the forecast start date/time, so the provenance of the whole of each forecast is the same as that of the analysis at the forecast start date/time.

    Accuracy and uncertainty

    ERA5 is produced using 4D-Var data assimilation and model forecasts in CY41R2 of the IFS. The 4D-Var in ERA5 utilises 12 hour assimilation windows from 9-21 UTC and 21-9 UTC, where the background forecast and all the observations falling within a time window are used to specify all the analyses during that window. However, the accuracy of the analyses is not uniform throughout each window. If the model and observations are unbiased and their errors follow Gaussian distributions and if the observations are homogeneous in space and time, then the analysis error will be smallest in the middle of the assimilation window. However, because none of these assumptions are actually true in the IFS, the particular parameter and location of interest are important, too. Knowing that, a careful study should show at which points during the assimilation windows the analysis is most accurate.

    The 10 member ensemble is required for the data assimilation procedure. However, as a useful by-product, this ensemble also provides an estimate of the relative, random uncertainty. The "spread" of the 10 member ensemble, encapsulated by the standard deviation, provides a measure of this uncertainty and is larger for time periods and spatial locations where the uncertainty is relatively large and is smaller when and where there is more certainty in the analysed/forecast values. The spread is a measure of the relative uncertainty, so the numbers do not provide the absolute uncertainty. On the whole, the uncertainty becomes larger as you go back in time, when the observing system was not as good as in the present day, and in data sparse locations such as the pre-satellite era, southern hemisphere. In general, apart from that for the sea surface temperature, the spread does not represent systematic uncertainty, only random, or "synoptic", uncertainty. For more information, see ERA5: uncertainty estimation.

    Instantaneous parameters

    All the analysed parameters and many of the forecast parameters are described as "instantaneous". For more information on what instantaneous means, see Parameters valid at the specified time. Such instantaneous parameters may, or may not, have been averaged in time, to produce monthly means.

    Mean rates/fluxes and accumulations

    Such parameters, which are only available from forecasts, have undergone particular types of statistical processing (temporal mean or accumulation, respectively) over a period of time called the processing period. In addition, these parameters may, or may not, have been averaged in time, to produce monthly means.

    The accumulations (over the accumulation/processing period) in the short forecasts (from 06 and 18 UTC) of ERA5 are treated differently compared with those in ERA-Interim and operational data (where the accumulations are from the beginning of the forecast to the validity date/time). In the short forecasts of ERA5, the accumulations are since the previous post processing (archiving), so for:

    • reanalysis: accumulations are over the hour (the accumulation/processing period) ending at the validity date/time
    • ensemble: accumulations are over the 3 hours (the accumulation/processing period) ending at the validity date/time
    • Monthly means (of daily means, stream=moda/edmo): accumulations have been scaled to have an "effective" processing period of one day, see section Monthly means

    Mean rate/flux parameters in ERA5 (e.g. Table 4 for surface and single levels) provide similar information to accumulations (e.g. Table 3 for surface and single levels), except they are expressed as temporal means, over the same processing periods, and so have units of "per second".

    • Mean rate/flux parameters are easier to deal with than accumulations because the units do not vary with the processing period.
    • The mean rate hydrological parameters (e.g. the "Mean total precipitation rate") have units of "kg m-2 s-1", which are equivalent to "mm s-1". They can be multiplied by 86400 seconds (24 hours) to convert to kg m-2 day-1 or mm day-1.

    Note that:

    • For the CDS time, or validity time, of 00 UTC, the mean rates/fluxes and accumulations are over the hour (3 hours for the EDA) ending at 00 UTC i.e. the mean or accumulation is during part of the previous day.
    • Mean rates/fluxes and accumulations are not available from the analyses.
    • Mean rates/fluxes and accumulations at step=0 have values of zero because the length of the processing period is zero.

    Minimum/maximum since the previous post processing

    The short forecasts of ERA5 contain some surface and single level parameters that are the minimum or maximum value since the previous post processing (archiving), see Table 5 below. So, for:

    • reanalysis: the minimum or maximum values are in the hour (the processing period) ending at the validity date/time
    • ensemble: the minimum or maximum values are in the 3 hours (the processing period) ending at the validity date/time

    Wave spectra

    The ocean wave model used in ERA5 (WAM, which is included in the IFS) provides wave spectra with 24 directions and 30 frequencies (see "2D wave spectra (single)", Table 7).

    Expand
    titleDecoding 2D wave spectra

    Download from ERA5

    ERA5 wave spectra data is not available from the CDS disks. However, it is available in MARS and can be accessed through the CDS API. For more information see Data organisation and how to download ERA5 and How to download ERA5 (Option B: Download ERA5 family data that is NOT listed in the CDS online catalogue - SLOW ACCESS.

    Decoding 2D wave spectra in GRIB

    To decode wave spectra in GRIB format we recommend ecCodes. Wave spectra are encoded in a specific way that other tools might not decode correctly.

    In GRIB, the parameter is called 2d wave spectra (single) because in GRIB, the data are stored as a single global field per each spectral bin (a given frequency and direction), but in NetCDF, the fields are nicely recombined to produce a 2d matrix representing the discretized spectra at each grid point.

    The wave spectra are encoded in GRIB using a

    Expand
    titleDecoding 2D wave spectra

    Download from ERA5

    ERA5 wave spectra data is not available from the CDS disks. However, it is available in MARS and can be accessed through the CDS API. For more information see Data organisation and how to download ERA5 and How to download ERA5 (Option B: Download ERA5 family data that is NOT listed in the CDS online catalogue - SLOW ACCESS.

    Decoding 2D wave spectra in GRIB

    To decode wave spectra in GRIB format we recommend ecCodes. Wave spectra are encoded in a specific way that other tools might not decode correctly.

    In GRIB, the parameter is called 2d wave spectra (single) because in GRIB, the data are stored as a single global field per each spectral bin (a given frequency and direction), but in NetCDF, the fields are nicely recombined to produce a 2d matrix representing the discretized spectra at each grid point.

    The wave spectra are encoded in GRIB using a local table specific to ECMWF. Because of this, the conversion of the meta data containing the information about the frequencies and the directions are not properly converted from GRIB to NetCDF format. So rather than having the actual values of the frequencies and directions, values show index numbers (1,1) : first frequency, first direction, (1,2) first frequency, second direction, etc ....

    For ERA, because there are a total of 24 directions, the direction increment is 15 degrees with the first direction given by half the increment, namely 7.5 degree, where direction 0. means going towards the north and 90 towards the east (Oceanographic convention), or more precisely, this should be expressed in gradient since the spectra are in m^2 /(Hz radian)
    The first frequency is 0.03453 Hz and the following ones are : f(n) = f(n-1)*1.1, n=2,30

    Also note that it is NOT the spectral density that is encoded but rather log10 of it, so to recover the spectral density, expressed in m^2 /(radian Hz), one has to take the power 10 (10^) of the NON missing decoded values. Missing data are for all land points, but also, as part of the GRIB compression, all small values below a certain threshold have been discarded and so those missing spectral values are essentially 0. m^2 /(gradient Hz).

    Decoding 2D wave spectra in NetCDF

    The NetCDF wave spectra file will have the dimensions longitude, latitude, direction, frequency and time.

    However, the direction and frequency bins are simply given as 1 to 24 and 1 to 30, respectively.

    The direction bins start at 7.5 degree and increase by 15 degrees until 352.5, with 90 degree being towards the east (Oceanographic convention).

    The frequency bins are non-linearly spaced. The first bin is 0.03453 Hz and the following bins are: f(n) = f(n-1)*1.1; n=2,30. The data provided is the log10 of spectra density. To obtain the spectral density one has to take to the power 10 (10 ** data). This will give the units 2D wave spectra as m**2 s radian**-1 . Very small values are discarded and set as missing values. These are essentially 0 m**2 s radian**-1.

    This recoding can be done with the Python xarray package, for example:

    Code Block
    languagepy
    import xarray as xr
    import numpy as np
    da = xr.open_dataarray('2d_spectra_201601.nc')
    da = da.assign_coords(direction=np.arange(7.5, 352.5 + 15, 15))
    da = da.assign_coords(frequency=np.full(30, 0.03453) * (1.1 ** np.arange(0, 30)))
    da = 10 ** da
    da = da.fillna(0)
    da.to_netcdf(path='2d_spectra_201601_recoded.nc')

    Units of 2D wave spectra

    Once decoded, the units of 2D wave spectra are m2 s radian-1

    ...

    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: 2000vorticity 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 heightThe 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.

    ...

    (stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc)
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA))

    count

    name

    units

    Variable name in CDS

    shortName

    paramId

    an

    fc

    1

    Lake cover

    (0 - 1)

    lake_cover

    cl

    26

    x

    x

    2

    Lake depth

    m

    lake_depth

    dl

    228007

    x

    x

    3

    Low vegetation cover

    (0 - 1)

    low_vegetation_cover

    cvl

    27

    x


    4

    High vegetation cover

    (0 - 1)

    high_vegetation_cover

    cvh

    28

    x


    5

    Type of low vegetation

    ~

    type_of_low_vegetation

    tvl

    29

    x


    6

    Type of high vegetation

    ~

    type_of_high_vegetation

    tvh

    30

    x


    7

    Soil type1

    ~

    soil_type

    slt

    43

    x


    8

    Standard deviation of filtered subgrid orography

    m

    standard_deviation_of_filtered_subgrid_orography

    sdfor

    74

    x


    9

    Geopotential

    m**2 s**-2

    geopotential

    z

    129

    x

    x

    10

    Standard deviation of sub-gridscale orography

    ~

    standard_deviation_of_orography

    sdor

    160

    x


    11

    Anisotropy of sub-gridscale orography

    ~

    anisotropy_of_sub_gridscale_orography

    isor

    161

    x


    12

    Angle of sub-gridscale orography

    radians

    angle_of_sub_gridscale_orography

    anor

    162

    x


    13

    Slope of sub-gridscale orography

    ~

    slope_of_sub_gridscale_orography

    slor

    163

    x


    14

    Land-sea mask

    (0 - 1)

    land_sea_mask

    lsm

    172

    x

    x

    ...

    (stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc)
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA))

    count

    name

    units

    Variable name in CDS

    shortName

    paramId

    an

    fc

    1

    Convective inhibition

    J kg**-1

    convective_inhibition

    cin

    228001


    x

    2

    Friction velocity

    m s**-1

    friction_velocity

    zust

    228003


    x

    3

    Lake mix-layer temperature

    K

    lake_mix_layer_temperature

    lmlt

    228008

    x

    x

    4

    Lake mix-layer depth

    m

    lake_mix_layer_depth

    lmld

    228009

    x

    x

    5

    Lake bottom temperature

    K

    lake_bottom_temperature

    lblt

    228010

    x

    x

    6

    Lake total layer temperature

    K

    lake_total_layer_temperature

    ltlt

    228011

    x

    x

    7

    Lake shape factor

    dimensionless

    lake_shape_factor

    lshf

    228012

    x

    x

    8

    Lake ice temperature

    K

    lake_ice_temperature

    lict

    228013

    x

    x

    9

    Lake ice depth

    m

    lake_ice_depth

    licd

    228014

    x

    x

    10

    UV visible albedo for direct radiation

    (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

    UV visible albedo for diffuse radiation

    (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

    Near IR albedo for direct radiation

    (0 - 1)

    near_ir_albedo_for_direct_radiation

    alnip

    17

    x

    x

    15

    Duct base height

    m

    duct_base_height

    dctb

    228017


    x

    16

    Near IR albedo for diffuse radiation

    (0 - 1)

    near_ir_albedo_for_diffuse_radiation

    alnid

    18

    x

    x

    17

    Trapping layer base height

    m

    trapping_layer_base_height

    tplb

    228018


    x

    18

    Trapping layer top height

    m

    trapping_layer_top_height

    tplt

    228019


    x

    19

    Cloud base height

    m

    cloud_base_height

    cbh

    228023


    x

    20

    Zero degree level

    m

    zero_degree_level

    deg0l

    228024


    x

    21

    Instantaneous 10 metre wind gust

    m s**-1

    instantaneous_10m_wind_gust

    i10fg

    228029


    x

    22

    Sea ice area fraction

    (0 - 1)

    sea-ice_cover

    ci

    31

    x

    x

    23

    Snow albedo

    (0 - 1)

    snow_albedo

    asn

    32

    x

    x

    24

    Snow density

    kg m**-3

    snow_density

    rsn

    33

    x

    x

    25

    Sea surface temperature

    K

    sea_surface_temperature

    sst

    34

    x

    x

    26

    Ice temperature layer 1

    K

    ice_temperature_layer_1

    istl1

    35

    x

    x

    27

    Ice temperature layer 2

    K

    ice_temperature_layer_2

    istl2

    36

    x

    x

    28

    Ice temperature layer 3

    K

    ice_temperature_layer_3

    istl3

    37

    x

    x

    29

    Ice temperature layer 4

    K

    ice_temperature_layer_4

    istl4

    38

    x

    x

    30

    Volumetric soil water layer 11

    m**3 m**-3

    volumetric_soil_water_layer_1

    swvl1

    39

    x

    x

    31

    Volumetric soil water layer 21

    m**3 m**-3

    volumetric_soil_water_layer_2

    swvl2

    40

    x

    x

    32

    Volumetric soil water layer 31

    m**3 m**-3

    volumetric_soil_water_layer_3

    swvl3

    41

    x

    x

    33

    Volumetric soil water layer 41

    m**3 m**-3

    volumetric_soil_water_layer_4

    swvl4

    42

    x

    x

    34

    Convective available potential energy

    J kg**-1

    convective_available_potential_energy

    cape

    59

    x

    x

    35

    Leaf area index, low vegetation3

    m**2 m**-2

    leaf_area_index_low_vegetation

    lai_lv

    66

    x

    x

    36

    Leaf area index, high vegetation3

    m**2 m**-2

    leaf_area_index_high_vegetation

    lai_hv

    67

    x

    x

    37

    Neutral wind at 10 m u-component

    m s**-1

    10m_u-component_of_neutral_wind

    u10n

    228131

    x

    x

    38

    Neutral wind at 10 m v-component

    m s**-1

    10m_v-component_of_neutral_wind

    v10n

    228132

    x

    x

    39

    Surface pressure

    Pa

    surface_pressure

    sp

    134

    x

    x

    40

    Soil temperature level 11

    K

    soil_temperature_level_1

    stl1

    139

    x

    x

    41

    Snow depth

    m of water equivalent

    snow_depth

    sd

    141

    x

    x

    42

    Charnock

    ~

    charnock

    chnk

    148

    x

    x

    43

    Mean sea level pressure

    Pa

    mean_sea_level_pressure

    msl

    151

    x

    x

    44

    Boundary layer height

    m

    boundary_layer_height

    blh

    159

    x

    x

    45

    Total cloud cover

    (0 - 1)

    total_cloud_cover

    tcc

    164

    x

    x

    46

    10 metre U wind component

    m s**-1

    10m_u-_component_of_wind

    10u

    165

    x

    x

    47

    10 metre V wind component

    m s**-1

    10m_v-_component_of_wind

    10v

    166

    x

    x

    48

    2 metre temperature

    K

    2m_temperature

    2t

    167

    x

    x

    49

    2 metre dewpoint temperature

    K

    2m_dewpoint_temperature

    2d

    168

    x

    x

    50

    Soil temperature level 21

    K

    soil_temperature_level_2

    stl2

    170

    x

    x

    51

    Soil temperature level 31

    K

    soil_temperature_level_3

    stl3

    183

    x

    x

    52

    Low cloud cover

    (0 - 1)

    low_cloud_cover

    lcc

    186

    x

    x

    53

    Medium cloud cover

    (0 - 1)

    medium_cloud_cover

    mcc

    187

    x

    x

    54

    High cloud cover

    (0 - 1)

    high_cloud_cover

    hcc

    188

    x

    x

    55

    Skin reservoir content

    m of water equivalent

    skin_reservoir_content

    src

    198

    x

    x

    56

    Instantaneous large-scale surface precipitation fraction

    (0 - 1)

    instantaneous_large_scale_surface_precipitation_fraction

    ilspf

    228217


    x

    57

    Convective rain rate

    kg m**-2 s**-1

    convective_rain_rate

    crr

    228218


    x

    58

    Large scale rain rate

    kg m**-2 s**-1

    large_scale_rain_rate

    lsrr

    228219


    x

    59

    Convective snowfall rate water equivalent

    kg m**-2 s**-1

    convective_snowfall_rate_water_equivalent

    csfr

    228220


    x

    60

    Large scale snowfall rate water equivalent

    kg m**-2 s**-1

    large_scale_snowfall_rate_water_equivalent

    lssfr

    228221


    x

    61

    Instantaneous eastward turbulent surface stress

    N m**-2

    instantaneous_eastward_turbulent_surface_stress

    iews

    229

    x

    x

    62

    Instantaneous northward turbulent surface stress

    N m**-2

    instantaneous_northward_turbulent_surface_stress

    inss

    230

    x

    x

    63

    Instantaneous surface sensible heat flux

    W m**-2

    instantaneous_surface_sensible_heat_flux

    ishf

    231

    x

    x

    64

    Instantaneous moisture flux

    kg m**-2 s**-1

    instantaneous_moisture_flux

    ie

    232

    x

    x

    65

    Skin temperature

    K

    skin_temperature

    skt

    235

    x

    x

    66

    Soil temperature level 41

    K

    soil_temperature_level_4

    stl4

    236

    x

    x

    67

    Temperature of snow layer

    K

    temperature_of_snow_layer

    tsn

    238

    x

    x

    68

    Forecast albedo

    (0 - 1)

    forecast_albedo

    fal

    243

    x

    x

    69

    Forecast surface roughness

    m

    forecast_surface_roughness

    fsr

    244

    x

    x

    70

    Forecast logarithm of surface roughness for heat

    ~

    forecast_logarithm_of_surface_roughness_for_heat

    flsr

    245

    x

    x

    71

    100 metre U wind component

    m s**-1

    100m_u-component_of_wind

    100u

    228246

    x

    x

    72

    100 metre V wind component

    m s**-1

    100m_v-component_of_wind

    100v

    228247

    x

    x

    73

    Precipitation type2

    code table (4.201)

    precipitation_type

    ptype

    260015


    x

    74

    K index2

    K

    k_index

    kx

    260121


    x

    75

    Total totals index2

    K

    total_totals_index

    totalx

    260123


    x

    ...

    Expand
    title1 Soil layers


    LayerRange
    Layer 10 - 7 cm
    Layer 27 - 28 cm
    Layer 328 - 100 cm
    Layer 4100 - 289 cm
    Layer 4100 - 289 cm

    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.

    ...

    (stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc)
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA))

    count

    name

    units

    Variable name in CDS

    shortName

    paramId

    an

    fc

    1

    Large-scale precipitation fraction

    s

    large_scale_precipitation_fraction

    lspf

    50


    x

    2

    Downward UV radiation at the surface

    J m**-2

    downward_uv_radiation_at_the_surface

    uvb

    57


    x

    3

    Boundary layer dissipation

    J m**-2

    boundary_layer_dissipation

    bld

    145


    x

    4

    Surface sensible heat flux

    J m**-2

    surface_sensible_heat_flux

    sshf

    146


    x

    5

    Surface latent heat flux

    J m**-2

    surface_latent_heat_flux

    slhf

    147


    x

    6

    Surface solar radiation downwards

    J m**-2

    surface_solar_radiation_downwards

    ssrd

    169


    x

    7

    Surface thermal radiation downwards

    J m**-2

    surface_thermal_radiation_downwards

    strd

    175


    x

    8

    Surface net solar radiation

    J m**-2

    surface_net_solar_radiation

    ssr

    176


    x

    9

    Surface net thermal radiation

    J m**-2

    surface_net_thermal_radiation

    str

    177


    x

    10

    Top net solar radiation

    J m**-2

    top_net_solar_radiation

    tsr

    178


    x

    11

    Top net thermal radiation

    J m**-2

    top_net_thermal_radiation

    ttr

    179


    x

    12

    Eastward turbulent surface stress

    N m**-2 s

    eastward_turbulent_surface_stress

    ewss

    180


    x

    13

    Northward turbulent surface stress

    N m**-2 s

    northward_turbulent_surface_stress

    nsss

    181


    x

    14

    Eastward gravity wave surface stress

    N m**-2 s

    eastward_gravity_wave_surface_stress

    lgws

    195


    x

    15

    Northward gravity wave surface stress

    N m**-2 s

    northward_gravity_wave_surface_stress

    mgws

    196


    x

    16

    Gravity wave dissipation

    J m**-2

    gravity_wave_dissipation

    gwd

    197


    x

    17

    Top net solar radiation, clear sky

    J m**-2

    top_net_solar_radiation_clear_sky

    tsrc

    208


    x

    18

    Top net thermal radiation, clear sky

    J m**-2

    top_net_thermal_radiation_clear_sky

    ttrc

    209


    x

    19

    Surface net solar radiation, clear sky

    J m**-2

    surface_net_solar_radiation_clear_sky

    ssrc

    210


    x

    20

    Surface net thermal radiation, clear sky

    J m**-2

    surface_net_thermal_radiation_clear_sky

    strc

    211


    x

    21

    TOA incident solar radiation

    J m**-2

    toa_incident_solar_radiation

    tisr

    212


    x

    22

    Vertically integrated moisture divergence

    kg m**-2

    vertically_integrated_moisture_divergence

    vimd

    213


    x

    23

    Total sky direct solar radiation at surface

    J m**-2

    total_sky_direct_solar_radiation_at_surface

    fdir

    228021


    x

    24

    Clear-sky direct solar radiation at surface

    J m**-2

    clear_sky_direct_solar_radiation_at_surface

    cdir

    228022


    x

    25

    Surface solar radiation downward clear-sky

    J m**-2

    surface_solar_radiation_downward_clear_sky

    ssrdc

    228129


    x

    26

    Surface thermal radiation downward clear-sky

    J m**-2

    surface_thermal_radiation_downward_clear_sky

    strdc

    228130


    x

    27

    Surface runoff

    m

    surface_runoff

    sro

    8


    x

    28

    Sub-surface runoff

    m

    sub_surface_runoff

    ssro

    9


    x

    29

    Snow evaporation

    m of water equivalent

    snow_evaporation

    es

    44


    x

    30

    Snowmelt

    m of water equivalent

    snowmelt

    smlt

    45


    x

    31

    Large-scale precipitation

    m

    large_scale_precipitation

    lsp

    142


    x

    32

    Convective precipitation

    m

    convective_precipitation

    cp

    143


    x

    33

    Snowfall

    m of water equivalent

    snowfall

    sf

    144


    x

    34

    Evaporation

    m of water equivalent

    evaporation

    e

    182


    x

    35

    Runoff

    m

    runoff

    ro

    205


    x

    36

    Total precipitation

    m

    total_precipitation

    tp

    228


    x

    37

    Convective snowfall

    m of water equivalent

    convective_snowfall

    csf

    239


    x

    38

    Large-scale snowfall

    m of water equivalent

    large_scale_snowfall

    lsf

    240


    x

    39

    Potential evaporation

    m

    potential_evaporation

    pev

    228251


    x

    ...

    (stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc)
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA))

    countnameunitsVariable name in CDSshortNameparamIdanfc
    1

    Mean surface runoff rate

    kg m**-2 s**-1

    mean_surface_runoff_rate

    msror

    235020


    x
    2

    Mean sub-surface runoff rate

    kg m**-2 s**-1

    mean_sub_surface_runoff_rate

    mssror

    235021


    x
    3

    Mean snow evaporation rate

    kg m**-2 s**-1

    mean_snow_evaporation_rate

    mser

    235023


    x
    4

    Mean snowmelt rate

    kg m**-2 s**-1

    mean_snowmelt_rate

    msmr

    235024


    x
    5

    Mean large-scale precipitation fraction

    Proportion

    mean_large_scale_precipitation_fraction

    mlspf

    235026


    x
    6

    Mean surface downward UV radiation flux

    W m**-2

    mean_surface_downward_uv_radiation_flux

    msdwuvrf

    235027


    x
    7

    Mean large-scale precipitation rate

    kg m**-2 s**-1

    mean_large_scale_precipitation_rate

    mlspr

    235029


    x
    8

    Mean convective precipitation rate

    kg m**-2 s**-1

    mean_convective_precipitation_rate

    mcpr

    235030


    x
    9

    Mean snowfall rate

    kg m**-2 s**-1

    mean_snowfall_rate

    msr

    235031


    x
    10

    Mean boundary layer dissipation

    W m**-2

    mean_boundary_layer_dissipation

    mbld

    235032


    x
    11

    Mean surface sensible heat flux

    W m**-2

    mean_surface_sensible_heat_flux

    msshf

    235033


    x
    12

    Mean surface latent heat flux

    W m**-2

    mean_surface_latent_heat_flux

    mslhf

    235034


    x
    13

    Mean surface downward short-wave radiation flux

    W m**-2

    mean_surface_downward_short_wave_radiation_flux

    msdwswrf

    235035


    x
    14

    Mean surface downward long-wave radiation flux

    W m**-2

    mean_surface_downward_long_wave_radiation_flux

    msdwlwrf

    235036


    x
    15

    Mean surface net short-wave radiation flux

    W m**-2

    mean_surface_net_short_wave_radiation_flux

    msnswrf

    235037


    x
    16

    Mean surface net long-wave radiation flux

    W m**-2

    mean_surface_net_long_wave_radiation_flux

    msnlwrf

    235038


    x
    17

    Mean top net short-wave radiation flux

    W m**-2

    mean_top_net_short_wave_radiation_flux

    mtnswrf

    235039


    x
    18

    Mean top net long-wave radiation flux

    W m**-2

    mean_top_net_long_wave_radiation_flux

    mtnlwrf

    235040


    x
    19

    Mean eastward turbulent surface stress

    N m**-2

    mean_eastward_turbulent_surface_stress

    metss

    235041


    x
    20

    Mean northward turbulent surface stress

    N m**-2

    mean_northward_turbulent_surface_stress

    mntss

    235042


    x
    21

    Mean evaporation rate

    kg m**-2 s**-1

    mean_evaporation_rate

    mer

    235043


    x
    22

    Mean eastward gravity wave surface stress

    N m**-2

    mean_eastward_gravity_wave_surface_stress

    megwss

    235045


    x
    23

    Mean northward gravity wave surface stress

    N m**-2

    mean_northward_gravity_wave_surface_stress

    mngwss

    235046


    x
    24

    Mean gravity wave dissipation

    W m**-2

    mean_gravity_wave_dissipation

    mgwd

    235047


    x
    25

    Mean runoff rate

    kg m**-2 s**-1

    mean_runoff_rate

    mror

    235048


    x
    26

    Mean top net short-wave radiation flux, clear sky

    W m**-2

    mean_top_net_short_wave_radiation_flux_clear_sky

    mtnswrfcs

    235049


    x
    27

    Mean top net long-wave radiation flux, clear sky

    W m**-2

    mean_top_net_long_wave_radiation_flux_clear_sky

    mtnlwrfcs

    235050


    x
    28

    Mean surface net short-wave radiation flux, clear sky

    W m**-2

    mean_surface_net_short_wave_radiation_flux_clear_sky

    msnswrfcs

    235051


    x
    29

    Mean surface net long-wave radiation flux, clear sky

    W m**-2

    mean_surface_net_long_wave_radiation_flux_clear_sky

    msnlwrfcs

    235052


    x
    30

    Mean top downward short-wave radiation flux

    W m**-2

    mean_top_downward_short_wave_radiation_flux

    mtdwswrf

    235053


    x
    31

    Mean vertically integrated moisture divergence

    kg m**-2 s**-1

    mean_vertically_integrated_moisture_divergence

    mvimd

    235054


    x
    32

    Mean total precipitation rate

    kg m**-2 s**-1

    mean_total_precipitation_rate

    mtpr

    235055


    x
    33

    Mean convective snowfall rate

    kg m**-2 s**-1

    mean_convective_snowfall_rate

    mcsr

    235056


    x
    34

    Mean large-scale snowfall rate

    kg m**-2 s**-1

    mean_large_scale_snowfall_rate

    mlssr

    235057


    x
    35

    Mean surface direct short-wave radiation flux

    W m**-2

    mean_surface_direct_short_wave_radiation_flux

    msdrswrf

    235058


    x
    36

    Mean surface direct short-wave radiation flux, clear sky

    W m**-2

    mean_surface_direct_short_wave_radiation_flux_clear_sky

    msdrswrfcs

    235059


    x
    37

    Mean surface downward short-wave radiation flux, clear sky

    W m**-2

    mean_surface_downward_short_wave_radiation_flux_clear_sky

    msdwswrfcs

    235068


    x
    38

    Mean surface downward long-wave radiation flux, clear sky

    W m**-2

    mean_surface_downward_long_wave_radiation_flux_clear_sky

    msdwlwrfcs

    235069


    x
    39

    Mean potential evaporation rate

    kg m**-2 s**-1

    mean_potential_evaporation_rate

    mper

    235070


    x

    ...

    (stream=oper/enda, levtype=sfc)
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA))

    count

    name

    units

    Variable name in CDS

    shortName

    paramId

    an

    fc

    1

    10 metre wind gust since previous post-processing

    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

    ...

    (stream=oper/enda/mnth/moda/edmm/edmo, levtype=sfc - vertical integrals not available for type=em/es, levtype=sfc
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA))

    count

    name

    units

    Variable name in CDS

    shortName

    paramId

    an

    fc

    1

    Vertical integral of mass of atmosphere

    kg m**-2

    vertical_integral_of_mass_of_atmosphere

    vima

    162053

    x

    x

    2

    Vertical integral of temperature

    K kg m**-2

    vertical_integral_of_temperature

    vit

    162054

    x

    x

    3

    Vertical integral of kinetic energy

    J m**-2

    vertical_integral_of_kinetic_energy

    vike

    162059

    x

    x

    4

    Vertical integral of thermal energy

    J m**-2

    vertical_integral_of_thermal_energy

    vithe

    162060

    x

    x

    5

    Vertical integral of potential+internal energy

    J m**-2

    vertical_integral_of_potential_and_internal_energy

    vipie

    162061

    x

    x

    6

    Vertical integral of potential+internal+latent energy

    J m**-2

    vertical_integral_of_potential_internal_and_latent_energy

    vipile

    162062

    x

    x

    7

    Vertical integral of total energy

    J m**-2

    vertical_integral_of_total_energy

    vitoe

    162063

    x

    x

    8

    Vertical integral of energy conversion

    W m**-2

    vertical_integral_of_energy_conversion

    viec

    162064

    x

    x

    9

    Vertical integral of eastward mass flux

    kg m**-1 s**-1

    vertical_integral_of_eastward_mass_flux

    vimae

    162065

    x

    x

    10

    Vertical integral of northward mass flux

    kg m**-1 s**-1

    vertical_integral_of_northward_mass_flux

    viman

    162066

    x

    x

    11

    Vertical integral of eastward kinetic energy flux

    W m**-1

    vertical_integral_of_eastward_kinetic_energy_flux

    vikee

    162067

    x

    x

    12

    Vertical integral of northward kinetic energy flux

    W m**-1

    vertical_integral_of_northward_kinetic_energy_flux

    viken

    162068

    x

    x

    13

    Vertical integral of eastward heat flux

    W m**-1

    vertical_integral_of_eastward_heat_flux

    vithee

    162069

    x

    x

    14

    Vertical integral of northward heat flux

    W m**-1

    vertical_integral_of_northward_heat_flux

    vithen

    162070

    x

    x

    15

    Vertical integral of eastward water vapour flux

    kg m**-1 s**-1

    vertical_integral_of_eastward_water_vapour_flux

    viwve

    162071

    x

    x

    16

    Vertical integral of northward water vapour flux

    kg m**-1 s**-1

    vertical_integral_of_northward_water_vapour_flux

    viwvn

    162072

    x

    x

    17

    Vertical integral of eastward geopotential flux

    W m**-1

    vertical_integral_of_eastward_geopotential_flux

    vige

    162073

    x

    x

    18

    Vertical integral of northward geopotential flux

    W m**-1

    vertical_integral_of_northward_geopotential_flux

    vign

    162074

    x

    x

    19

    Vertical integral of eastward total energy flux

    W m**-1

    vertical_integral_of_eastward_total_energy_flux

    vitoee

    162075

    x

    x

    20

    Vertical integral of northward total energy flux

    W m**-1

    vertical_integral_of_northward_total_energy_flux

    vitoen

    162076

    x

    x

    21

    Vertical integral of eastward ozone flux

    kg m**-1 s**-1

    vertical_integral_of_eastward_ozone_flux

    vioze

    162077

    x

    x

    22

    Vertical integral of northward ozone flux

    kg m**-1 s**-1

    vertical_integral_of_northward_ozone_flux

    viozn

    162078

    x

    x

    23

    Vertical integral of divergence of cloud liquid water flux

    kg m**-2 s**-1

    vertical_integral_of_divergence_of_cloud_liquid_water_flux

    vilwd

    162079

    x

    x

    24

    Vertical integral of divergence of cloud frozen water flux

    kg m**-2 s**-1

    vertical_integral_of_divergence_of_cloud_frozen_water_flux

    viiwd

    162080

    x

    x

    25

    Vertical integral of divergence of mass flux

    kg m**-2 s**-1

    vertical_integral_of_divergence_of_mass_flux

    vimad

    162081

    x

    x

    26

    Vertical integral of divergence of kinetic energy flux

    W m**-2

    vertical_integral_of_divergence_of_kinetic_energy_flux

    viked

    162082

    x

    x

    27

    Vertical integral of divergence of thermal energy flux

    W m**-2

    vertical_integral_of_divergence_of_thermal_energy_flux

    vithed

    162083

    x

    x

    28

    Vertical integral of divergence of moisture flux

    kg m**-2 s**-1

    vertical_integral_of_divergence_of_moisture_flux

    viwvd

    162084

    x

    x

    29

    Vertical integral of divergence of geopotential flux

    W m**-2

    vertical_integral_of_divergence_of_geopotential_flux

    vigd

    162085

    x

    x

    30

    Vertical integral of divergence of total energy flux

    W m**-2

    vertical_integral_of_divergence_of_total_energy_flux

    vitoed

    162086

    x

    x

    31

    Vertical integral of divergence of ozone flux

    kg m**-2 s**-1

    vertical_integral_of_divergence_of_ozone_flux

    viozd

    162087

    x

    x

    32

    Vertical integral of eastward cloud liquid water flux

    kg m**-1 s**-1

    vertical_integral_of_eastward_cloud_liquid_water_flux

    vilwe

    162088

    x

    x

    33

    Vertical integral of northward cloud liquid water flux

    kg m**-1 s**-1

    vertical_integral_of_northward_cloud_liquid_water_flux

    vilwn

    162089

    x

    x

    34

    Vertical integral of eastward cloud frozen water flux

    kg m**-1 s**-1

    vertical_integral_of_eastward_cloud_frozen_water_flux

    viiwe

    162090

    x

    x

    35

    Vertical integral of northward cloud frozen water flux

    kg m**-1 s**-1

    vertical_integral_of_northward_cloud_frozen_water_flux

    viiwn

    162091

    x

    x

    36

    Vertical integral of mass tendency

    kg m**-2 s**-1

    vertical_integral_of_mass_tendency

    vimat

    162092

    x


    37

    Total column cloud liquid water

    kg m**-2

    total_column_cloud_liquid_water

    tclw

    78

    x

    x

    38

    Total column cloud ice water

    kg m**-2

    total_column_cloud_ice_water

    tciw

    79

    x

    x

    39

    Total column supercooled liquid water

    kg m**-2

    total_column_supercooled_liquid_water

    tcslw

    228088


    x

    40

    Total column rain water

    kg m**-2

    total_column_rain_water

    tcrw

    228089

    x

    x

    41

    Total column snow water

    kg m**-2

    total_column_snow_water

    tcsw

    228090

    x

    x

    42

    Total column water

    kg m**-2

    total_column_water

    tcw

    136

    x

    x

    43

    Total column water vapour

    kg m**-2

    total_column_water_vapour

    tcwv

    137

    x

    x

    44

    Total column ozone

    kg m**-2

    total_column_ozone

    tco3

    206

    x

    x

    ...

    (stream=wave/ewda/wamo/wamd/ewmm/ewmo)
    (The native grid is the reduced latitude/longitude grid of 0.36 degrees (1.0 degree for the EDA))

    count

    name

    units

    Variable name in CDS

    shortName

    paramId

    an

    fc

    1

    Significant wave height of first swell partition

    m

    significant_wave_height_of_first_swell_partition

    swh1

    140121

    x

    x

    2

    Mean wave direction of first swell partition

    degrees

    mean_wave_direction_of_first_swell_partition

    mwd1

    140122

    x

    x

    3

    Mean wave period of first swell partition

    s

    mean_wave_period_of_first_swell_partition

    mwp1

    140123

    x

    x

    4

    Significant wave height of second swell partition

    m

    significant_wave_height_of_second_swell_partition

    swh2

    140124

    x

    x

    5

    Mean wave direction of second swell partition

    degrees

    mean_wave_period_of_second_swell_partition

    mwd2

    140125

    x

    x

    6

    Mean wave period of second swell partition

    s

    mean_wave_period_of_second_swell_partition

    mwp2

    140126

    x

    x

    7

    Significant wave height of third swell partition

    m

    significant_wave_height_of_third_swell_partition

    swh3

    140127

    x

    x

    8

    Mean wave direction of third swell partition

    degrees

    mean_wave_direction_of_third_swell_partition

    mwd3

    140128

    x

    x

    9

    Mean wave period of third swell partition

    s

    mean_wave_period_of_third_swell_partition

    mwp3

    140129

    x

    x

    10

    Wave Spectral Skewness

    dimensionless

    wave_spectral_skewness

    wss

    140207

    x

    x

    11

    Free convective velocity over the oceans

    m s**-1

    free_convective_velocity_over_the_oceans

    wstar

    140208

    x

    x

    12

    Air density over the oceans

    kg m**-3

    air_density_over_the_oceans

    rhoao

    140209

    x

    x

    13

    Normalized energy flux into waves

    dimensionless

    normalized_energy_flux_into_waves

    phiaw

    140211

    x

    x

    14

    Normalized energy flux into ocean

    dimensionless

    normalized_energy_flux_into_ocean

    phioc

    140212

    x

    x

    15

    Normalized stress into ocean

    dimensionless

    normalized_stress_into_ocean

    tauoc

    140214

    x

    x

    16

    U-component stokes drift

    m s**-1

    u_component_stokes_drift

    ust

    140215

    x

    x

    17

    V-component stokes drift

    m s**-1

    v_component_stokes_drift

    vst

    140216

    x

    x

    18

    Period corresponding to maximum individual wave height

    s

    period_corresponding_to_maximum_individual_wave_height

    tmax

    140217

    x

    x

    19

    Maximum individual wave height

    m

    maximum_individual_wave_height

    hmax

    140218

    x

    x

    20

    Model bathymetry

    m

    model_bathymetry

    wmb

    140219

    x

    x

    21

    Mean wave period based on first moment

    s

    mean_wave_period_based_on_first_moment

    mp1

    140220

    x

    x

    22

    Mean zero-crossing wave period

    s

    mean_zero_crossing_wave_period

    mp2

    140221

    x

    x

    23

    Wave spectral directional width

    dimensionlessRadians

    wave_spectral_directional_width

    wdw

    140222

    x

    x

    24

    Mean wave period based on first moment for wind waves

    s

    mean_wave_period_based_on_first_moment_for_wind_waves

    p1ww

    140223

    x

    x

    25

    Mean wave period based on second moment for wind waves

    s

    mean_wave_period_based_on_second_moment_for_wind_waves

    p2ww

    140224

    x

    x

    26

    Wave spectral directional width for wind waves

    dimensionlessRadians

    wave_spectral_directional_width_for_wind_waves

    dwww

    140225

    x

    x

    27

    Mean wave period based on first moment for swell

    s

    mean_wave_period_based_on_first_moment_for_swell

    p1ps

    140226

    x

    x

    28

    Mean wave period based on second moment for swell

    s

    mean_wave_period_based_on_second_moment_for_wind_waves

    p2ps

    140227

    x

    x

    29

    Wave spectral directional width for swell

    dimensionlessRadians

    wave_spectral_directional_width_for_swell

    dwps

    140228

    x

    x

    30

    Significant height of combined wind waves and swell

    m

    significant_height_of_combined_wind_waves_and_swell

    swh

    140229

    x

    x

    31

    Mean wave direction

    degrees

    mean_wave_direction

    mwd

    140230

    x

    x

    32

    Peak wave period

    s

    peak_wave_period

    pp1d

    140231

    x

    x

    33

    Mean wave period

    s

    mean_wave_period

    mwp

    140232

    x

    x

    34

    Coefficient of drag with waves

    dimensionless

    coefficient_of_drag_with_waves

    cdww

    140233

    x

    x

    35

    Significant height of wind waves

    m

    significant_height_of_wind_waves

    shww

    140234

    x

    x

    36

    Mean direction of wind waves

    degrees

    mean_direction_of_wind_waves

    mdww

    140235

    x

    x

    37

    Mean period of wind waves

    s

    mean_period_of_wind_waves

    mpww

    140236

    x

    x

    38

    Significant height of total swell

    m

    significant_height_of_total_swell

    shts

    140237

    x

    x

    39

    Mean direction of total swell

    degrees

    mean_direction_of_total_swell

    mdts

    140238

    x

    x

    40

    Mean period of total swell

    s

    mean_period_of_total_swell

    mpts

    140239

    x

    x

    41

    Mean square slope of waves

    dimensionless

    mean_square_slope_of_waves

    msqs

    140244

    x

    x

    42


    Expand
    title10 metre wind speed

    This 10m wind parameter is the wind speed that has been used by the wave model, which is coupled to the atmospheric model.

    For this reason:

    •  it is archived on the wave model's native grid, with the same land-sea mask as that model. Therefore, this parameter is not defined over land and wherever else the wave model is not defined, where it is encoded as missing data. Improper decoding of the missing value usually results in very large values being given for these land points.
    • the wave model resets all values below 2 m/s to 2m/s. The reason for this is that as the winds become weak, the long waves (swell) try to drive the wind from below but this is not modelled in the IFS, as it assumes that the wind profile should be logarithmic (+- stability correction). To account for this effect, the whole of the boundary layer scheme would need to be revised. A simple trick to avoid the problem is to boost the weak winds to 2m/s, which is outside the range where the waves can potentially drive the wind.
    • this parameter is actually the 10m neutral wind speed as determined from the atmospheric surface stress (see documentation on Ocean Wave model output parameters). 
    • If wave altimeter data were assimilated, the analysis of this parameter also contains wind speed updates that come directly out of the wave height updates.

    This parameter should not be used for looking at the quality of reanalysis surface wind - the u and v components of the 10m wind (atmospheric parameters 165 and 166) should be used instead.


    m s**-1

    ocean_surface_stress_equivalent_10m_neutral_wind_speed

    wind

    140245

    x

    x

    43

    10 metre wind direction

    degrees

    ocean_surface_stress_equivalent_10m_neutral_wind_direction

    dwi

    140249

    x

    x

    44

    Wave spectral kurtosis

    dimensionless

    wave_spectral_kurtosis

    wsk

    140252

    x

    x

    45

    Benjamin-Feir index

    dimensionless

    benjamin_feir_index

    bfi

    140253

    x

    x

    46

    Wave spectral peakedness

    dimensionless

    wave_spectral_peakedness

    wsp

    140254

    x

    x

    47

    Altimeter wave height

    m

    Not available from the CDS disks

    awh

    140246

    x


    48

    Altimeter corrected wave height

    m

    Not available from the CDS disks

    acwh

    140247

    x


    49

    Altimeter range relative correction

    ~

    Not available from the CDS disks

    arrc

    140248

    x


    50

    2D wave spectra (single)1

    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

    UV visible albedo for direct radiation

    (0 - 1)

    uv_visible_albedo_for_direct_radiation

    aluvp

    15

    x

    no mean

    2

    UV visible albedo for diffuse radiation

    (0 - 1)

    uv_visible_albedo_for_diffuse_radiation

    aluvd

    16

    x

    no mean

    3

    Near IR albedo for direct radiation

    (0 - 1)

    near_ir_albedo_for_direct_radiation

    alnip

    17

    x

    no mean

    4

    Near IR albedo for diffuse radiation

    (0 - 1)

    near_ir_albedo_for_diffuse_radiation

    alnid

    18

    x

    no mean

    5

    Magnitude of turbulent surface stress1

    N m**-2 s

    magnitude of turbulent surface stress

    magss

    48


    x

    6Mean magnitude of turbulent surface stress2N m**-2mean magnitude of turbulent surface stressmmtss235025
    x

    7

    10 metre wind gust since previous post-processing

    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

    10 metre wind speed3

    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

    Altimeter wave height

    m

    Not available from the CDS disks

    awh

    140246

    no mean


    14

    Altimeter corrected wave height

    m

    Not available from the CDS disks

    acwh

    140247

    no mean


    15

    Altimeter range relative correction

    ~

    Not available from the CDS disks

    arrc

    140248

    no mean


    16

    2D wave spectra (single)

    m**2 s radian**-1

    Not available from the CDS disks

    2dfd

    140251

    no mean


    ...

    Anchor
    Table9
    Table9
    Table 9: pressure level parameters: instantaneous: instantaneous

    (stream=oper/enda/mnth/moda/edmm/edmo, levtype=pl)
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA) or T639 spherical harmonics (T319 for the EDA), as indicated(stream=oper/enda/mnth/moda/edmm/edmo, levtype=pl)

    count

    name

    units

    Variable variable name in CDS

    shortName

    paramId

    native grid

    an

    fc

    1

    Potential vorticity

    K m**2 kg**-1 s**-1

    potential_vorticity

    pv

    60

    N320 (N160)

    x

    x

    2

    Specific rain water content

    kg kg**-1

    specific_rain_water_content

    crwc

    75

    N320 (N160)

    x

    x

    3

    Specific snow water content

    kg kg**-1

    specific_snow_water_content

    cswc

    76

    N320 (N160)

    x

    x

    4

    Geopotential

    m**2 s**-2

    geopotential

    z

    129

    T639 (T319)

    x

    x

    5

    Temperature

    K

    temperature

    t

    130

    T639 (T319)

    x

    x

    6

    U component of wind

    m s**-1

    u_component_of_wind

    u

    131

    T639 (T319)

    x

    x

    7

    V component of wind

    m s**-1

    v_component_of_wind

    v

    132

    T639 (T319)

    x

    x

    8

    Specific humidity

    kg kg**-1

    specific_humidity

    q

    133

    N320 (N160)

    x

    x

    9

    Vertical velocity

    Pa s**-1

    vertical_velocity

    w

    135

    T639 (T319)

    x

    x

    10

    Vorticity (relative)

    s**-1

    vorticity

    vo

    138

    T639 (T319)

    x

    x

    11

    Divergence

    s**-1

    divergence

    d

    155

    T639 (T319)

    x

    x

    12

    Relative humidity

    %

    relative_humidity

    r

    157

    T639 (T319)

    x

    x

    13

    Ozone mass mixing ratio

    kg kg**-1

    ozone_mass_mixing_ratio

    o3

    203

    N320 (N160)

    x

    x

    14

    Specific cloud liquid water content

    kg kg**-1

    specific_cloud_liquid_water_content

    clwc

    246

    N320 (N160)

    x

    x

    15

    Specific cloud ice water content

    kg kg**-1

    specific_cloud_ice_water_content

    ciwc

    247

    N320 (N160)

    x

    x

    16

    Fraction of cloud cover

    (0 - 1)

    fraction_of_cloud_cover

    cc

    248

    N320 (N160)

    x

    x


    Anchor
    Table10
    Table10
    Table 10: potential temperature level parameters: instantaneous

    (not available from the CDS disks)
    (stream=oper/enda/mnth/moda/edmm/edmo, levtype=pt)
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA) or T639 spherical harmonics (T319 for the EDA), as indicated)

    count

    name

    units

    shortName

    paramId

    native grid

    an

    fc

    1

    Montgomery potential

    m**2 s**-2

    mont

    53

    T639 (T319)

    x


    2

    Pressure

    Pa

    pres

    54

    T639 (T319)

    x


    3

    Potential vorticity

    K m**2 kg**-1 s**-1

    pv

    60

    N320 (N160)

    x


    4

    U component of wind

    m s**-1

    u

    131

    T639 (T319)

    x


    5

    V component of wind

    m s**-1

    v

    132

    T639 (T319)

    x


    6

    Specific humidity

    kg kg**-1

    q

    133

    N320 (N160)

    x


    7

    Vorticity (relative)

    s**-1

    vo

    138

    T639 (T319)

    x


    8

    Divergence

    s**-1

    d

    155

    T639 (T319)

    x


    9

    Ozone mass mixing ratio

    kg kg**-1

    o3

    203

    N320 (N160)

    x



    Anchor
    Table11
    Table11
    Table 11: potential vorticity level parameters: instantaneous

    (not available from the CDS disks)
    (stream=oper/enda/mnth/moda/edmm/edmo, levtype=pv)
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA) or T639 spherical harmonics (T319 for the EDA), as indicated)

    count

    name

    units

    shortName

    paramId

    native grid

    an

    fc

    1

    Potential temperature

    K

    pt

    3

    T639 (T319)

    x


    2

    Pressure

    Pa

    pres

    54

    T639 (T319)

    x


    3

    Geopotential

    m**2 s**-2

    z

    129

    T639 (T319)

    x


    4

    U component of wind

    m s**-1

    u

    131

    N320 (N160)

    x


    5

    V component of wind

    m s**-1

    v

    132

    N320 (N160)

    x


    6

    Specific humidity

    kg kg**-1

    q

    133

    N320 (N160)

    x


    7

    Ozone mass mixing ratio

    kg kg**-1

    o3

    203

    N320 (N160)

    x



    Anchor
    Table12
    Table12
    Table 12: model level parameters: instantaneous

    (GRIB2 format)
    (not available from the CDS disks)
    (stream=oper/enda/mnth/moda/edmm/edmo, levtype=ml)
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA) or T639 spherical harmonics (T319 for the EDA), as indicated)

    count

    name

    units

    shortName

    paramId

    native grid

    an

    fc

    1

    Specific rain water content

    kg kg**-1

    crwc

    75

    N320 (N160)

    x

    x

    2

    Specific snow water content

    kg kg**-1

    cswc

    76

    N320 (N160)

    x

    x

    3

    Eta-coordinate vertical velocity

    s**-1

    etadot

    77

    T639 (T319)

    x

    x

    4

    Geopotential1

    m**2 s**-2

    z

    129

    T639 (T319)

    x

    x

    5

    Temperature

    K

    t

    130

    T639 (T319)

    x

    x

    6

    U component of wind

    m s**-1

    u

    131

    T639 (T319)

    x

    x

    7

    V component of wind

    m s**-1

    v

    132

    T639 (T319)

    x

    x

    8

    Specific humidity

    kg kg**-1

    q

    133

    N320 (N160)

    x

    x

    9

    Vertical velocity

    Pa s**-1

    w

    135

    T639 (T319)

    x

    x

    10

    Vorticity (relative)

    s**-1

    vo

    138

    T639 (T319)

    x

    x

    11

    Logarithm of surface pressure1

    ~

    lnsp

    152

    T639 (T319)

    x

    x

    12

    Divergence

    s**-1

    d

    155

    T639 (T319)

    x

    x

    13

    Ozone mass mixing ratio

    kg kg**-1

    o3

    203

    N320 (N160)

    x

    x

    14

    Specific cloud liquid water content

    kg kg**-1

    clwc

    246

    N320 (N160)

    x

    x

    15

    Specific cloud ice water content

    kg kg**-1

    ciwc

    247

    N320 (N160)

    x

    x

    16

    Fraction of cloud cover

    (0 - 1)

    cc

    248

    N320 (N160)

    x

    x

    1Only archived on level=1.

    ...

    (GRIB2 format)
    (not available from the CDS disks)
    (stream=oper/enda/mnth/moda/edmm/edmo, levtype=ml)
    (The native grid is the reduced Gaussian grid N320 (N160 for the EDA))

    1These parameters provide data for the model half levels - the interfaces of the model layers.

    ...

    Dataset nameObservation typeMeasurement
    SYNOPLand stationSurface Pressure, Temperature, wind, humidity
    METARLand stationSurface Pressure, Temperature, wind, humidity
    DRIBU/DRIBU-BATHY/DRIBU-TESAC/BUFR Drifting BuoyDrifting buoys10m-wind, Surface Pressure
    BUFR Moored BuoyMoored buoys10m-wind, Surface Pressure
    SHIPship stationSurface Pressure, Temperature, wind, humidity
    Land/ship PILOTRadiosondeswind profiles
    American Wind ProfilerRadarwind profiles
    European Wind ProfilerRadarwind profiles
    Japanese Wind ProfilerRadarwind profiles
    TEMP SHIPRadiosondesTemperature, wind, humidity profiles
    DROP SondeAircraft-sondesRadiosondesTemperature, wind, humidity profiles
    Land/Mobile TEMPRadiosondesTemperature, wind, humidity profiles
    AIREPAircraft dataTemperature, wind profiles
    AMDARAircraft dataTemperature, wind profiles
    ACARSAircraft dataTemperature, wind profiles, humidity
    WIGOS AMDARAircraft dataTemperature, wind, humidity
    TAMDARAircraft dataTemperature, wind
    ADS-CAircraft dataTemperature, wind profiles
    Mode-SAircraft dataWind
    Ground based radarRadar precipitation compositesRain rates


    Anchor
    Table16
    Table16
    Table 16: Snow data

    ...

    1. 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".
    2. ERA5: compute pressure and geopotential on model levels, geopotential height and geometric height
    3. ERA5: How to calculate wind speed and wind direction from u and v components of the wind?
    4. Sea surface temperature and sea-ice cover (sea ice area fraction), see Table 2 above, are available at the usual times, eg hourly for the HRES, but their content is only updated once daily. However, for inland water bodies (lakes, reservoirs, rivers and coastal waters) the FLake model calculates the surface temperature (ie the lake mixed-layer temperature or lake ice temperature) and does include diurnal variations.
    5. Mean rates/fluxes and accumulations at step=0 have values of zero because the length of the processing period is zerothe length of the processing period is zero.
    6. 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.

    7. Expand
      titleERA5: 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.



    8. Expand
      titleERA5: 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):

      1. the model bathymetry (Model bathymetry. Fig 1)
      2. 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:

      Model bathymetry fieldSea ice cover fieldCombined mask

      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
      titleToolbox workflow
      collapsetrue
      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', '24'01:00', '02:00',
                  '2503:00', '2604:00', '2705:00',
                  '2806:00', '2907:00', '3008:00',
                  '3109:00',
                  ]'10:00', '11:00',
                  'time': ['12:00', '13:00', '14:00',
                  '0015:00', '0116:00', '0217:00',
                  '0318:00', '0419:00', '0520:00',
                  '0621:00', '0722:00', '0823:00',
                  ],
      
                  }
                  )
          return        '09:00', '10:00', '11:00',
                  '12:00', '13:00', '14:00',
                  '15:00', '16:00', '17:00',
                  '18:00', '19:00', '20:00',
                  '21:00', '22:00', '23:00',
                  ],
      
                  }
                  )
          return m, s
      
      
      Expand
      titleAltimeter 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
      titleComputation 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), dew point temperature (Td), and surface pressure (sp) from which you can calculate specific and relative humidity at 2m.

      • Specific humidity can be calculated over water and ice 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: RH = 100 * es(Td)/es(T)

       Relative humidity can be calculate 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.

      Expand
      titleComputation 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
      titleERA5 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
      titleParameter "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
      titleActual 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):

      1. Evaporation from bare soil
      2. Evaporation from open water surfaces excluding oceans
      3. Evaporation from the top of canopy
      4. 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:

      ERA5 IFS cycle 41r2

      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.

    Known issues

    ...

    1. m, s
      
      




    2. Expand
      titleAltimeter 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. 



    3. Expand
      titleComputation of near-surface humidity

      Near-surface humidity is not archived directly in ERA datasets, but the archive contains near-surface (2m from the surface) temperature (T) and dew point temperature (Td), and also surface pressure (sp), from which you can calculate specific and relative humidity at 2m.

      • Specific humidity can be calculated using equations 7.4 and 7.5 from Part IV, Physical processes section (Chapter 7, section 7.2.1b) in the documentation of the IFS for CY41R2. Use the 2m dew point temperature and surface pressure (which is approximately equal to the pressure at 2m) in these equations. The constants in 7.4 are to be found in Chapter 12 (of Part IV: Physical processes) and the parameters in 7.5 should be set for saturation over water because the dew point temperature is being used.
      • Relative humidity should be calculated from: RH = 100 * es(Td)/es(T)

       Relative humidity can be calculated with respect to saturation over water, ice or mixed phase by defining es(T) with respect to saturation over water, ice or mixed phase (water and ice). The usual practice is to define near-surface relative humidity with respect to saturation over water. Note that in ERA5, the relative humidity on pressure levels has been calculated with respect to saturation over mixed phase.



    4. Expand
      titleComputation 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
      titleERA5 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).



    5. 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.



    6. Expand
      titleActual 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):

      1. Evaporation from bare soil
      2. Evaporation from open water surfaces excluding oceans
      3. Evaporation from the top of canopy
      4. 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:

      ERA5 IFS cycle 41r2

      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, 391406, doi:10.1080/02626660009492337.




    7. 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 surfaceEvaporation from liquid water to water vapourDew deposition from water vapour
      Cold vegetation surfaceEvaporation from liquid water to water vapourDew deposition from water vapour
      Ice surfaceSublimation from ice to water vapourIce deposition from water vapour
      Snow surfaceSublimation from snow to water vapourSnow deposition from water vapour



    Known issues

    Currently, we are aware of these issues with ERA5:

    1. ERA5T: from 1 September to 13 December 2021, the final ERA5 product is different to ERA5T due to the correction of the assimilation of incorrect snow observations in central Asia. Although the differences are mostly limited to that region and mainly to surface parameters, in particular snow depth and soil moisture and to a lesser extent 2m temperature and 2m dewpoint temperature, all the resulting reanalysis fields can differ over the whole globe but should be within their range of uncertainty (which is estimated by the ensemble spread and which can be large for some parameters). On the CDS disks, the initial, ERA5T, fields have been overwritten (with the usual 2-3 month delay), i.e., for these months, access to the original CDS disk, ERA5T product is not possible after it has been overwritten. Potentially incorrect snow observations have been assimilated in ERA5 up to this time, when the effects became noticeable. The quality control of snow observations has been improved in ERA5 from September 2021 and from 15 November 2021 in ERA5T.
    2. 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.
    3. ERA5 suffers from an overly strong equatorial mesospheric jet, particularly in the transition seasons.
    4. 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.
    5. 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.
    6. The analysed "2 metre temperature" can be larger than the forecast "Maximum temperature at 2 metres since previous post-processing".
    7. 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".
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Large values of CAPE: occasionally, the Convective available potential energy in ERA5 is unrealistically large.
    13. Ship tracks in the SST: prior to September 2007, in the period when HadISST2 was used, ship tracks can be visible in the SST.
    14. 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.
    15. 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.
    16. 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.
    17. 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.

    18. Expand
      titleThe 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
      titleERA5 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:

      Code Block
      languagepy
      titleRequest 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')

      Eventually, the data gap will be filled by the re-run of the ERA5 back extension.

    19. 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.

    20. Expand
      titleFour 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.

    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. ERA5 CDS: wind values are far too low on pressure levels at the poles in the CDS
    26. ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense
    27. ERA5 back extension 1950-1978 (Preliminary version): large bias in surface analysis over Australia prior to 1970
    28. ERA5 back extension 1950-1978 (Preliminary version): the deep soil moisture tends to be too dry

    Resolved issues

    ERA5 CDS: incorrect values of U/V on pressure levels in the CDS

    1. 's analysed.


    2. 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.


    3. 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.


    4. Expand
      titleFour 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.



    5. ERA5 sea-ice cover and 2 metre temperature: in the period 1979-1989, in a region just to the north of Greenland, the sea-ice cover outside of the melt season is too low and hence the 2 metre temperature is too high. For more information, see Section 3.5.4 of Low frequency variability and trends in surface air temperature and humidity from ERA5 and other datasets
    6. ERA5 sea-ice cover is missing in the Caspian Sea from late 2007 to 2013, inclusive.
    7. 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.
    8. 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.
    9. ERA5 CDS: wind values are far too low on pressure levels at the poles in the CDS
    10. Snow present in Iberia throughout 1978 due to assimilation of erroneous in situ snow data. This has an effect on 2m temperature, which shows negative anomalies of several degrees Celsius. There is no snow present in ERA5-Land, as this snow data is not assimilated. However, the 2m temperature anomaly is present, as the forcing comes from the erroneous ERA5 data. These figures show ERA monthly averaged 2m temperature (t2m) and snow depth (sd)  (38 to 43N, -8 to -6W), from 1940-2023, with 1978 highlighted in red. The same snow depth plot, limited to 1977-07 to 1979-04 shows more detail, with the period of erroneous snow depth in ERA5 extending from 1977-12 to 1979-03 in the monthly mean dataset.

    Resolved issues

    1. ERA5

    ...

    1. .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
      titleMore 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.


    2. 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.
    3. ERA5 CDS: incorrect values of U/V on pressure levels in the CDS
    4. ERA5 CDS: Data corruption

    User support

    There is a range of user support available for ERA5, including a Knowledge Base (where this article resides), a Forum and a ticketed system for questions - for more information see the C3S Help and Support Page.

    How to acknowledge, cite and refer to ERA5

    How to acknowledge and cite ERA5

    If you have downloaded For 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 , cite and reference data published on the Climate Data Store.

    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 'reanalysis-era5-complete-preliminary-back-extension') or via authorised direct access to MARS, please contact the C3S Helpdesk at ECMWF.

    and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it.

    References

    The ERA5 global reanalysis

    ...

    The ERA5 global reanalysis: Preliminary extension to 1950

    Global stratospheric temperature bias and other stratospheric aspects of ERA5 and ERA5.1

    ...

    Further ERA5 references are available from the ECMWF e-Librarywebsite.


    Info
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    This document has been produced in the context of the Copernicus Climate Change Service (C3S).

    The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation

    agreement

    Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.

    The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

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