Introduction

Here, we document the ERA5 dataset, which covers the period from January 1940 to the present and continues to be extended forward in near real time. For up to date information on ERA5, please consult the C3S Announcements on the Copernicus user forum.

ERA5 is produced using 4D-Var data assimilation and model forecasts in CY41R2 of the ECMWF Integrated Forecast System (IFS), with 137 hybrid sigma/pressure (model) levels in the vertical and the top level at 0.01 hPa. Atmospheric data are available on these levels and they are also interpolated to 37 pressure, 16 potential temperature and 1 potential vorticity level(s) by FULL-POS in the IFS. "Surface or single level" data are also available, containing 2D parameters such as precipitation, top of atmosphere radiation and vertical integrals over the entire depth of the atmosphere. The atmospheric model in the IFS is coupled to a land-surface model (HTESSEL), which produces parameters such as 2m temperature and soil temperatures, and an ocean wave model (WAM), the parameters of which are also designated as "Surface or single level" parameters.

The ERA5 dataset contains one (hourly, 31 km) high resolution realisation (referred to as "reanalysis" or "HRES") and a reduced resolution ten member ensemble (referred to as "ensemble" or "EDA"). The ensemble is required for the data assimilation procedure, but as a by-product also provides an estimate of the relative, random uncertainty. Generally, the data are available at a sub-daily and monthly frequency and consist of analyses and short (18 hour) forecasts, initialised twice daily from analyses at 06 and 18 UTC. Most analysed parameters are also available from the forecasts. However, there are a number of forecast parameters, e.g. mean rates/fluxes and accumulations, that are not available from the analyses.

The data are archived in the ECMWF data archive (MARS) and a pertinent sub-set of the data, interpolated to a regular latitude/longitude grid, has been copied to the C3S Climate Data Store (CDS) disks. On the CDS disks, where single level and pressure level data are available, analyses are provided rather than forecasts, unless the parameter is only available from the forecasts. The interpolation software (MIR) was updated when the ERA5 production was moved to the new ATOS HPC on 24 October 2022.

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

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

An ERA5 back extension 1950-1978 (Preliminary version) was produced. Although in many other respects the quality was relatively good, this preliminary data did suffer from excessively intense tropical cyclones. This dataset is now deprecated.

Data update frequency

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

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

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

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

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

In the event that serious flaws are detected in ERA5T, the latter could be different to the final ERA5 data. Based on experience with the production of ERA5 so far (and ERA-Interim in the past), our expectation is that such an event would not occur often. So far, it has only occurred once:

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

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

The IFS and data assimilation

For ERA5, the IFS documentation for CY41R2 should be used.

The twice daily, short (18 hour) forecasts are run from the 06 and 18 UTC analyses.

The 4D-Var data assimilation uses 12 hour windows from 09 UTC to 21 UTC and 21 UTC to 09 UTC (the following day).

The model time step is 12 minutes for the HRES and 20 minutes for the EDA, though occasionally these numbers are adjusted to cope with instabilities.

Data assimilation is a process whereby a model forecast is blended with observations to obtain the best fit to both the forecast and the observations, given the known uncertainties of both. The result is called an analysis (of the state of the atmosphere). For the atmospheric parameters in ERA5, the 4D-Variational (4D-Var) data assimilation windows are 12 hours long, commencing after the first 3 hours of the short forecasts. All the available observations within each 12 hour window are considered by the system, though some might be discarded for various reasons, such as quality control. Some of the parameters under the category "Surface or single level" parameters, are produced by the Land-surface scheme, which uses 1D and 2D Optimal Interpolation and Extended Kalman Filter, data assimilation. The ERA5 MARS archive contains both the analyses and short forecasts. On the CDS disks, where single level and pressure level data are available, analyses are provided rather than forecasts, unless the parameter is only available from the forecasts.

The above data assimilation process, or something similar, is performed for Numerical Weather Prediction (NWP), which provides real time forecasts (and analyses) for many purposes and applications. It would be tempting to use the data produced therein, for climate purposes. However, NWP systems are being improved on a regular basis - typically twice per year at ECMWF. Therefore, the NWP data contain various abrupt changes, due to system improvements, which are mixed in with changes in the climate. Reanalysis avoids this problem by using a fixed NWP system to "re-analyse" the state of the atmosphere for long periods in the past. It should be remembered, however, that spurious changes will still be included in the reanalysis, due to changes in the observing system. The ERA5 data assimilation and forecasting system was used operationally for NWP in 2016. Once this fixed system becomes too old, the reanalysis should be re-done with a more modern, fixed system. Although "reanalysis" suggests that only analyses are provided, the short forecasts are also made available, as noted above.

Data format

Model level parameters are archived in GRIB2 format. All other parameters are in GRIB1 unless otherwise indicated, see Parameter listings.

In the CDS, there is the option of retrieving the data in netCDF format.

For GRIB, ERA5T data can be identified by the key expver=0005 in the GRIB header. ERA5 data is identified by the key expver=0001.

For netCDF data requests which return just ERA5 or just ERA5T data, there is no means of differentiating between ERA5 and ERA5T data in the resulting netCDF files.

For netCDF data requests which return a mixture of ERA5 and ERA5T data, the origin of the variables (1 or 5) will be identifiable in the resulting netCDF files. See this link for more details.

Data organisation and how to download ERA5

The full ERA5 and ERA5T datasets are held in the ECMWF data archive (MARS) and a pertinent sub-set of these data, interpolated to a regular latitude/longitude grid, has been copied to the C3S Climate Data Store (CDS) disks. ERA5.1 is not available from the CDS disks, but is available from MARS (for advice on using ERA5.1 in conjunction with ERA5, CDS data, see "ERA5: mixing CDS and MARS data" in Guidelines). On the CDS disks, where most single level and pressure level parameters are available, analyses are provided rather than forecasts, unless the parameter is only available from the forecasts.

ERA5 (and recent ERA5T) data on the CDS disks can be downloaded either from the relevant CDS download page or using the CDS API.

Getting data from the CDS disks provides the fastest access to ERA5.

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

Documentation is available on How to download ERA5.

Date and time specification

In MARS: the date and time of the data is specified with three MARS keywords: date, time and (forecast) step. For analyses, step=0 hours so that date and time specify the analysis date/time. For forecasts, date and time specify the forecast start time and step specifies the number of hours since that start time. The combination of date, time and step defines the validity date/time. For analyses, the validity date/time is equal to the analysis date/time.

In the CDS: analyses are provided rather than forecasts, unless the parameter is only available from the forecasts. The date and time of the data is specified using the validity date/time, so step does not need to be specified. For forecasts, steps between 1 and 12 hours have been used to provide data for all the validity times in each 24 hours, see Table 0 below.

Temporal frequency

For sub-daily data for the HRES (stream=oper/wave) the analyses (type=an) are available hourly. The short forecasts, run twice daily from 06 and 18 UTC, provide hourly output forecast steps from 0 to 18 hours (only steps 1 to 12 hours are available on the CDS disks). For the EDA, the sub-daily non-wave data (stream=enda) are available every 3 hours but the sub-daily wave data (stream=ewda) are available hourly in MARS and 3 hourly on the CDS disks.

Spatial grid

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

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

ERA5 data available from the CDS disks has been pre-interpolated to a regular latitude/longitude grid appropriate for that data.

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

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

Surface elevation datasets used by ERA5

In order to define the surface geopotential in ERA5, the IFS uses surface elevation data interpolated from a combination of SRTM30 and other surface elevation datasets. For more details please see the IFS documentation, Cycle 41r2, Part IV. Physical processes, section 11.2.2 Surface elevation data at 30 arc seconds.

Spatial reference systems and Earth model

The IFS assumes that the underlying shape of the Earth is a perfect sphere, of radius 6371.229 km, with the surface elevation specified relative to that sphere. The geodetic latitude/longitude of the surface elevation datasets are used as if they were the spherical latitude/longitude of the IFS.

ERA5 data is referenced in the horizontal with respect to the WGS84 ellipse (which defines the major/minor axes) and in the vertical it is referenced to the EGM96 geoid over land but over ocean it is referenced to mean sea level, with the approximation that this is assumed to be coincident with the geoid. For more information on the relationship between mean sea level and the geoid, see for example Gregory et al. (2019).

For data in GRIB1 format the earth model is a sphere with radius = 6367.47 km (note, this is inconsistent with what is actually used in the IFS),, as defined in the WMO GRIB Edition 1 specifications, Table 7, GDS Octet 17.

For data in GRIB2 format the earth model is a sphere with radius = 6371.2229 km (note, this is consistent with what is actually used in the IFS), as defined in the WMO GRIB2 specifications, section 2.2.1, Code Table 3.2, Code figure 6.

For data in NetCDF format (i.e. converted from the native GRIB format to NetCDF), the earth model is inherited from the GRIB data.

Production experiments

In order to speed up production, the historic ERA5 data was produced by running several parallel experiments which were then spliced together to form the final product.

A discontinuity can occur at the transition between the different experiments. Please see the Known issues for an example. The degree of discontinuity depends on how well the experiments were "spun-up". How well "spun-up" an experiment is, depends on the initial, chosen, state of the atmosphere and land surface at the beginning of the experiment, how long the experiment is run for, before being used for production, and the parameter(s) of interest - some parameters, such as those for the deeper soil and for the higher atmospheric levels, take longer to spin-up than others.

The information below gives the date ranges for the various production experiments (and hence the transition points) for the final version of ERA5 and also indicates when the computing system changed from the Cray to the ATOS.

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).

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')

Monthly means

In addition to the sub-daily data, most analysed and forecast parameters are also available as monthly means. For the surface and single level parameters, there are some exceptions which are listed in Table 8.

Monthly means are available in two forms:

• Synoptic monthly means, for each particular time and forecast step (stream=mnth/wamo/edmm/ewmm) - in the CDS, referred to as "monthly averaged by hour of day".
• Monthly means (of daily means, stream=moda/wamd/edmo/ewmo) for the month as a whole - in the CDS, referred to as "monthly averaged". These monthly means are created from all the hourly (3 hourly for the ensemble) data in the month.

Monthly means for:

• forecast parameters are created using the first 12 hours of the twice daily short forecasts (beginning at 06 and 18 UTC).
• analysis and instantaneous forecast parameters are created from data with a validity time in the month, between 00 and 23 UTC, which excludes the time 00 UTC on the first day of the following month.
• accumulation and mean rate/flux forecast parameters are created from data with processing periods that fall within the month.
• monthly means of daily means, for accumulations and mean rates/fluxes are created from contiguous data with processing periods spanning from 00 UTC on the first day of the month to 00 UTC on the first day of the following month i.e. they are accumulations or mean rates/fluxes for the complete, whole month.

The accumulations in monthly means (of daily means, stream=moda/edmo) have been scaled to have an "effective" processing period of one day, so for accumulations in these streams:

• The hydrological parameters have effective units of "m of water per day" and so they should be multiplied by 1000 to convert to kgm^-2day^-1 or mmday^-1.
• The energy (turbulent and radiative) and momentum fluxes should be divided by 86400 seconds (24 hours) to convert to the commonly used units of Wm^-2 and Nm^-2, respectively.

Ensemble means and standard deviations

For the EDA sub-daily data (stream=enda/ewda), compared with HRES sub-daily data (stream=oper/wave), ensemble means and standard deviations (type=em/es) are also available. Both these quantities are calculated from all the 10-members (i.e., including the control).

Ensemble standard deviation is often referred to as ensemble spread and is calculated with respect to the ensemble mean. The ensemble standard deviation is not the sample stdv, so we divide by 10 rather than 9 (N-1). 

Ensemble means and standard deviations contain analysed parameters when step=0, otherwise they contain forecast parameters. However, only surface and pressure level data (levtype=sfc/pl) contain forecast steps beyond 3 hours. There are no monthly means for ensemble means and standard deviations.

Level listings

Pressure levels (hPa): 1000/975/950/925/900/875/850/825/800/775/750/700/650/600/550/500/450/400/350/300/250/225/200/175/150/125/100/70/50/30/20/10/7/5/3/2/1

Potential temperature levels (K): 265/275/285/300/315/320/330/350/370/395/430/475/530/600/700/850

Potential vorticity level (10^-9 K m² kg^-1 s^-1 or 10^-3 PVU): 2000 (which is representative of the dynamical tropopause)

Model levels: 1/to/137, which are described at L137 model level definitions and ERA5: compute pressure and geopotential on model levels, geopotential height and geometric height. The model levels are hybrid pressure/sigma. For more information, see the documentation of the underlying model, ECMWF's IFS, CY41R2, Part III. Dynamics and numerical procedures, Chapter 2 Basic equations and discretisation.

Parameter listings

Tables 1-6 below describe the surface and single level parameters (levtype=sfc), Table 7 describes wave parameters, Table 8 describes the monthly mean exceptions for surface and single level and wave parameters and Tables 9-13 describe upper air parameters on various levtypes.

Information on all ECMWF parameters (e.g. columns shortName and paramId) is available from the ECMWF parameter database

Table 1: surface and single level parameters: invariants (in time)

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

¹Soil type (texture) determines the saturation, field capacity and permanent wilting point at all the soil levels, see Table 8.9 in Chapter 8 Surface parametrization, Part IV Physical Processes of the IFS documentation (CY41R2 for ERA5).

Table 2: surface and single level parameters: instantaneous

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

²GRIB2 format

³Leaf Area Index (LAI) parameters are based on a monthly climatology. Users will only see monthly variability, but not inter-annual variability.

Table 3: surface and single level parameters: accumulations

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

The accumulations in monthly means of daily means (stream=moda/edmo), see monthly means, have been scaled to have units that include "per day", so for accumulations in these streams:

• Most hydrological parameters are in units of "m of water per day", so these should be multiplied by 1000 to convert to kg m^-2 day^-1 or mm day^-1.
• Energy (turbulent and radiative) and momentum fluxes should be divided by 86400 seconds (24 hours) to convert to the commonly used units of W m^-2 and N m^-2, respectively.

Table 4: surface and single level parameters: mean rates/fluxes

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

The mean rates/fluxes in Table 4 provide similar information to the accumulations in Table 3, except they are expressed as temporal averages, and so have units of "per second". The mean rate hydrological parameters have units of "kg m^-2 s^-1" and so they can be multiplied by 86400 seconds (24 hours) to convert to kg m^-2 day^-1 or mm day^-1.

Table 5: surface and single level parameters: minimum/maximum

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

Table 6: surface and single level parameters: vertical integrals and total column: instantaneous

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

Table 7: wave parameters: instantaneous

(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))

¹for 30 frequencies and 24 directions

Table 8: monthly mean surface and single level and wave parameters: exceptions from Tables 1-7

(stream=mnth/moda/edmm/edmo, levtype=sfc or wamo/wamd/ewmm/ewmo)

¹Accumulated parameter
²Mean rate/flux parameter
³Instantaneous parameter

Table 9: pressure level parameters: 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)

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)

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)

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)

¹Only archived on level=1.

Table 13: model level parameters: mean rates/fluxes

(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.

Observations

The observations (satellite and in-situ) used as input to ERA5 are listed below. For more information on the observational input to ERA5, including dates when particular sensors or observation types were used, please see Section 5 in the ERA5 journal article, The ERA5 global reanalysis.

Table 14: Satellite Data

* reprocessed dataset
+ when different than the satellite agency

Table 15: In-situ data, provided by WMO WIS

Table 16: Snow data

Guidelines

The following advice is intended to help users understand particular features of the ERA5 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 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. 
   

   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. 
   

   The land-sea mask in ERA5 is an invariant field. This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.  The ERA5 land-sea mask provided is not suitable for direct use with wave parameters, as the time variability of the sea-ice cover needs to be taken into account and wave parameters are undefined for non-sea points. In order to produce a land-sea mask for use with wave parameters, users need to download the following ERA5 data (for the required period): the model bathymetry (Model bathymetry. Fig 1) the sea-ice cover (Sea ice area fraction, Fig 2) and combine these data to produce the land-sea mask (Fig 3). See attached pictures: Fig 1: Model bathymetry                                                 Fig 2: Sea-ice cover                                                          Fig 3: Combined mask 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: import cdstoolbox as ct @ct.application(title='Download data') @ct.output.download() @ct.output.carousel() def download_application(): count = 0 years=['1980'] months = [ '01', #'02', '03', # '04', '05', '06', # '07', '08', '09', # '10', '11', '12' ] # For hourly data hourly=True # For monthly data monthly=True hourly = True monthly = False for yr in years: for mn in months: if hourly == True: mb,si = get_hourly_data(yr, mn) elif monthly == True: mb,si = get_monthly_data(yr, mn) print(mb) # Check values are >= 0.0 in the model bathymetry mask compare_ge_mb = ct.operator.ge(mb, 0.0) print(si) # Check values are > 0.5 in the sea ice mask compare_ge_si = ct.operator.gt(si, 0.500) # Invert model bathymetry mask new = ct.operator.add(compare_ge_mb, -1.0) new1 = ct.operator.mul(new, -1.0) # Add the Bathymetry Mask to the Sea Ice Mask new_all = ct.operator.add(compare_ge_si,new1) # Reset scale to land=1, ocean=0 new_all_final = ct.operator.ge(new_all, 1.0) print(new_all_final) if count == 0: combined_mask = new_all_final else: combined_mask = ct.cube.concat([combined_mask, new_all_final], dim = 'time') count = count + 1 renamed_data = ct.cdm.rename(combined_mask, "wavemask") new_data = ct.cdm.update_attributes(renamed_data, attrs={'long_name': 'Wave Land Sea Mask'}) combined_mask = new_data print("combined_mask") print(combined_mask) # Plot mask for first timestep fig_list = ct.cdsplot.geoseries(combined_mask) return combined_mask, fig_list def get_monthly_data(y,m): m,s = ct.catalogue.retrieve( 'reanalysis-era5-single-levels-monthly-means', { 'product_type': 'monthly_averaged_reanalysis', 'variable': [ 'model_bathymetry', 'sea_ice_cover', ], 'year': y, 'month': m, 'time': '00:00', } ) return m, s def get_hourly_data(y,m): m,s = ct.catalogue.retrieve( 'reanalysis-era5-single-levels', { 'product_type': 'reanalysis', 'variable': [ 'model_bathymetry', 'sea_ice_cover', ], 'year': y, 'month': m, 'day': [ '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', ], 'time': [ '00:00', '01:00', '02:00', '03:00', '04:00', '05:00', '06:00', '07:00', '08:00', '09:00', '10:00', '11:00', '12:00', '13:00', '14:00', '15:00', '16:00', '17:00', '18:00', '19:00', '20:00', '21:00', '22:00', '23:00', ], } ) return m, s

   
   

9. 
   

   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)

   
   

10. 
    

    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.

    
    

11. 
    

    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: 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).

    
    

12. 
    

    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.

    
    

13. 
    

    Actual evapotranspiration in the ERA5 single levels datasets is called "Evaporation" (param ID 182) and is the sum of the following four evaporation components (which are not available separately in ERA5 but only for ERA5-Land): Evaporation from bare soil Evaporation from open water surfaces excluding oceans Evaporation from the top of canopy Evaporation from vegetation transpiration For the ERA5 single levels datasets, actual evapotranspiration can be downloaded from the C3S Climate Data Store (CDS) under the category heading "Evaporation and Runoff", in the "Download data" tab. For details about the computation of actual evapotranspiration, please see Chapter 8 of Part IV : Physical processes, of the IFS documentation: 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. 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. Please note that based on ERA5 atmospheric forcing, other independent (offline) methods such us "Priesley-Taylor1 (1972) , Schmidt2 (1915) or de Bruin3 (2000)" can also be used to estimate Potential evapotranspiration. 1PRIESTLEY, C. H. B., & TAYLOR, R. J. (1972). On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Monthly Weather Review, 100(2), 81-92. Retrieved Aug 27, 2021, from https://journals.ametsoc.org/view/journals/mwre/100/2/1520-0493_1972_100_0081_otaosh_2_3_co_2.xml  2Schmidt, W., 1915: Strahlung und Verdunstung an freien Wasserflächen; ein Beitrag zum Wärmehaushalt des Weltmeers und zum Wasserhaushalt der Erde (Radiation and evaporation over open water surfaces; a contribution to the heat budget of the world ocean and to the water budget of the earth). Ann. Hydro. Maritimen Meteor., 43, 111–124, 169–178. 3de Bruin, H. A. R., , and Stricker J. N. M. , 2000: Evaporation of grass under non-restricted soil moisture conditions. Hydrol. Sci. J., 45, 391–406, doi:10.1080/02626660009492337.

    
    

14. 
    

    The "Instantaneous moisture flux" (units: kg m-2 s-1; paramId=232) incorporates the same processes as "Evaporation" (units: m of water equivalent; paramId=182), but the latter is accumulated over a particular time period (during the hour preceeding the validity date/time, in the ERA5 HRES), whereas the former is an instantaneous parameter. Note, the different units of these two parameters. For the atmosphere, these two parameters only involve water vapour. Cloud liquid does not sediment and the cloud ice sedimentation flux is included in the snowfall flux. Here are some further details about the processes in the "Instantaneous moisture flux" and "Evaporation": Surface characteristics Process from surface to atmosphere (defined to be negative) Process from atmosphere to surface (defined to be positive) Warm surface Evaporation from liquid water to water vapour Dew deposition from water vapour Cold vegetation surface Evaporation from liquid water to water vapour Dew deposition from water vapour Ice surface Sublimation from ice to water vapour Ice deposition from water vapour Snow surface Sublimation from snow to water vapour Snow deposition from water vapour