Page tree

Skip to end of metadata
Go to start of metadata

Table of Contents

Introduction

Here we document the ERA5 dataset, which, eventually, will cover the period from January 1950 onwards. Complete ERA5 data released so far covers the period from 1979 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.

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.

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.

 An ERA5 back extension 1950-1978 (Preliminary version) has been produced. Although in many other respects the quality is relatively good, this preliminary data does suffer from excessively intense tropical cyclones. This dataset is available as a separate entry in the CDS catalogue (and in MARS) for a short period of time, after which it will be deprecated and replaced by a new updated version which will be accessible through the main ERA5 entry. The main entry currently contains data from 1979 onwards.

Data update frequency

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

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

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.

The IFS and data assimilation

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

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 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 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 (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.

ERA5 data in MARS can be accessed with the CDS API by specifying dataset whereas member state users can access data in MARS by specifying class and expver, according to the following table:


CDS API access to MARS

(specify the dataset)

Member state access to MARS

(specify class and expver)

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

reanalysis-era5.1-complete

class=ea, expver=0051
ERA5TNOT available at the momentclass=ea, expver=0005

ERA5 back extension 1950-1978

(Preliminary version)

reanalysis-era5-complete-preliminary-back-extensionclass=ea, expver=0098


Subdivisions of the data are labelled using the keywords stream, type and levtype:

Stream:

  • oper (HRES sub-daily)
  • wave (HRES sub-daily, for waves)
  • mnth (HRES synoptic monthly means, ie by hour of day)
  • moda (HRES monthly means of daily means)
  • wamo (HRES synoptic monthly means, ie by hour of day, for waves)
  • wamd (HRES monthly means of daily means, for waves)
  • enda (EDA sub-daily)
  • ewda (EDA sub-daily, for waves)
  • edmm (EDA synoptic monthly means, ie by hour of day)
  • edmo (EDA monthly means of daily means)
  • ewmm (EDA synoptic monthly means, ie by hour of day, for waves)
  • ewmo (EDA monthly means of daily means, for waves)

Type:

  • an: analyses
  • fc: forecasts
  • em: ensemble mean
  • es: ensemble standard deviation

Levtype:

  • sfc: surface or single level
  • pl: pressure levels
  • pt: potential temperature levels
  • pv: potential vorticity level
  • ml: model levels

Documentation is available on How to download ERA5.

Date and time specification

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

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


CDS

date  time

MARS

date      time  step


CDS

date  time

MARS

date   time  step

date  00

date-1  18        06


date  12

date   06      06

date  01

date-1  18        07


date  13

date   06      07

date  02

date-1  18        08


date  14

date   06      08

date  03

date-1  18        09


date  15

date   06      09

date  04

date-1  18        10


date  16

date   06      10

date  05

date-1  18        11


date  17

date   06      11

date  06

date-1  18        12


date  18

date   06      12

date  07

date      06        01


date  19

date   18      01

date  08

date      06        02


date  20

date   18      02

date  09

date      06        03


date  21

date   18      03

date  10

date      06        04


date  22

date   18      04

date  11

date      06        05


date  23

date   18      05

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 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

The IFS assumes the Earth is a perfect sphere, but the geodetic latitude/longitude of the surface elevation datasets are used as if they were the spherical latitude/longitude of the IFS.

ECMWF data is referenced in the horizontal with respect to the WGS84 ellipse (which defines the major/minor axes) but in the vertical it is referenced to the Geoid (EGM96) over land and the mean sea level over ocean.

Earth model

For data in GRIB1 format the earth model is a sphere with radius = 6367.47 km, 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 = 6371.2290 km, 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.

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).

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:

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

Monthly means

In addition to the sub-daily data, most 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 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.

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))

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 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

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))

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


LayerRange
Layer 10 - 7 cm
Layer 27 - 28 cm
Layer 328 - 100 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.

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))

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

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))

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

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))

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


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))

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


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))

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

dimensionless

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

dimensionless

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

dimensionless

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

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


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)

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


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)

count

name

units

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


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



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



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.

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

SensorSatelliteSatellite agencyData provider+

Measurement

(sensitivities exploited in ERA5 / variables analysed)

Satellite radiances (infrared and microwave)



AIRSAQUANASANOAABT (T, humidity and ozone)
AMSR-2GCOM-W1*JAXA

BT  (column water vapour, cloud liquid water,

precipitation and ocean surface wind speed)

AMSREAQUA*JAXA

BT  (column water vapour, cloud liquid water,

precipitation and ocean surface wind speed)

AMSUANOAA-15/16/17/18/19, AQUA, METOP-A/BNOAA,ESA,EUMETSAT
BT (T)
AMSUBNOAA-15/16/17NOAA
BT (humidity)
ATMSNPPNOAA
BT (T and humidity)
CRISNPPNOAA
BT (T, humidity and ozone)
HIRSTIROS-N, NOAA-6 /7/8/9/11/14NOAA
BT (T, humidity and ozone)
IASIMETOP-A/BEUMETSAT/ESAEUMETSATBT (T, humidity and ozone)
GMIGPMNASA/JAXA

BT (humidity, column water vapour,

cloud liquid water, precipitation,

ocean surface wind speed)

MHSNOAA-18/19, METOP-A/BNOAA, EUMETSAT/ESA
BT (humidity and precipitation)
MSUTIROS-N, NOAA-6 to 12, NOAA-14

BT (T)
MWHSFY-3-A/BNRSCC
BT (humidity)
MWHS2FY-3-CCMA
BT (T, humidity and precipitation)
MWTSFY-3A/BNRSCC
BT (T)
MWTS2FY-3CCMA
BT (T)
SSM/IDMSP-08*/10*/11*/13*/14*/15*US NavyNOAA,CMSAF*

BT (column water vapour, cloud liquid water,

precipitation and ocean surface wind speed)

SSMISDMSP-16/17/18US NavyNOAA

BT (T,  humidity,  column water vapour,

cloud liquid water, precipitation and ocean surface wind speed)

SSUTIROS-N, NOAA-6/7/8/9/11/14NOAA
BT (T)
TMITRMMNASA/JAXA
BT (column water vapour, cloud liquid water,

precipitation, ocean surface wind speed)

MVIRIMETEOSAT 5/7EUMETSAT/ESAEUMETSATBT (water vapour, surface/cloud top T)
SEVIRIMETEOSAT-8*/9*/10EUMETSAT/ESAEUMETSATBT (water vapour, surface/cloud top T)
GOES IMAGERGOES-8/9/10/11/12/13/15NOAACIMMS,NESDISBT (water vapour, surface/cloud top T)
MTSAT IMAGERMTSAT-1R/MTSAT-2JMA
BT (water vapour, surface/cloud top T)
AHIHimawari-8JMA
BT (water vapour, surface/cloud top T)
Satellite retrievals from radiance data



MVIRIMETEOSAT-2*/3*/4*/5*/7*EUMETSAT/ESAEUMETSATwind vector
SEVIRIMETEOSAT-8*/9*/10EUMETSAT/ESAEUMETSATwind vector
GOES IMAGERGOES-4-6/8*/9*/10*/11*/12*/13*/15*NOAACIMMS*,NESDISwind vector
GMS IMAGERGMS-1*/2/3*/4*/5*JMA
wind vector
MTSAT IMAGERMTSAT-1R*/MTSAT2JMA
wind vector
AHIHimawari-8JMAJMAwind vector
AVHRRNOAA-7 /9/10/11/12/14 to 18, METOP-ANOAACIMMS,EUMETSATwind vector
MODISAQUA/TERRANASANESDIS,CIMMSwind vector
GOMEERS-2*ESA
Ozone
GOME-2METOP*-A/BESA/EUMETSAT
Ozone
MIPASENVISAT*ESA
Ozone
MLSEOS-AURA*NASA
Ozone
OMIEOS-AURA*NASA
Ozone
SBUV,SBUV-2NIMBUS-7*,NOAA*9/11/14/16/17/18/19NOAANASAOzone
SCIAMACHYENVISAT*ESA
Ozone
TOMSNIMBUS-7*,METEOR-3-5,ADEOS-1*,EARTH PROBENASA
Ozone
Satellite GPS-Radio Occultation data



BlackJackCHAMP,GRACE*-A/B,SAC-C*DLR,NASA/DLR,NASA/COMAEGFZ,UCAR*Bending angle
GRASMETOP-A/BEUMETSAT/ESAEUMETSATBending angle
IGORTerraSAR-X*, TanDEM-X, COSMIC*-1 to 6NSPO/NOAAGFZ,UCAR*Bending angle
Satellite scatterometer data



AMIERS-1,ERS-2ESA
Backscatter sigma0, soil moisture
ASCATMETOP-A/B*EUMETSAT/ESAEUMETSAT/TU WienBackscatter sigma0, soil moisture
OSCATOCEANSAT-2ISROKNMIBackscatter sigma0, vector wind
SEAWINDSQUIKSCATNASANASABackscatter sigma0
Satellite Altimeter data



RAERS-1*/2*ESA
Wave Height
RA-2ENVISAT*ESA
Wave Height
Poseidon-2JASON-1*CNES/NASACNESWave Height
Poseidon-3JASON-2CNES/NOAA/NASA/EUMETSATNOAA/EUMETSATWave Height
SIRALCRYOSAT-2ESA
Wave Height
AltiKaSARALCNES/ISROEUMETSATWave Height

* reprocessed dataset
+ when different than the satellite agency

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

Dataset nameObservation typeMeasurement
SYNOPLand stationSurface Pressure, Temperature, humidity
METARLand stationSurface Pressure, Temperature, 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-sondesTemperature, wind profiles
Land/Mobile TEMPRadiosondesTemperature, wind, humidity profiles
AIREPAircraft dataTemperature, wind profiles
AMDARAircraft dataTemperature, wind profiles
ACARSAircraft dataTemperature, wind profiles, humidity
WIGOS AMDARAircraft dataTemperature, wind profiles
Ground based radarRadar precipitation compositesRain rates

Table 16: Snow data

Dataset nameObservation typeMeasurement
SYNOPLand stationSnow depth
Additional national reportsLand stationSnow depth
NOAA/NESDIS IMSMerged satelliteSnow cover (NH only)

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.
  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):

    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

    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:

    Toolbox workflow
    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:

    ERA5 Snow cover formula

    snow_cover (SC) = min(1, (RW*SD/RSN) / 0.1 )

    where RW is density of water equal to 1000 and RSN is density of snow (parameter 33.128).


    ERA5 physical depth of snow where there is snow cover is equal to RW*SD/(RSN*SC).

  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):

    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.

    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, 391406, doi:10.1080/02626660009492337.

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 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: ERA5 is produced by several parallel experiments, each for a different period, which are then appended 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. 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.

  19. 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:

    Request for total precipitation forecast hourly data for 1st January 00UTC-06UTC
    #!/usr/bin/env python3
    import cdsapi
    c = cdsapi.Client()
    c.retrieve('reanalysis-era5-complete-preliminary-back-extension', {
        'date': '1978-12-31',
        'levtype': 'sfc',
        'param': '228.128',
        'time':'18:00:00',
        'step':'6/7/8/9/10/11/12',                 
        'stream': 'oper',                     
        'type': 'fc',
        'grid': '0.25/0.25',
        'format': 'netcdf',
    }, 'era5.preliminary-back-extension-temperature-tp.nc')

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

  20. 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.

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

Resolved issues

  1. 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

    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


For ERA5 data on the "CDS disks"

All users of 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) .

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 to present

For complete details, please refer to How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it.


For ERA5 data in MARS,

If you have downloaded ERA5 data in MARS, using either the CDS API ('reanalysis-era5-complete' or 'reanalysis-era5.1-complete' or 'reanalysis-era5-complete-preliminary-back-extension') or via authorised direct access to MARS, please contact us.

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

Low frequency variability and trends in surface air temperature and humidity from ERA5 and other datasets

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


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

The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement signed on 11/11/2014). 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.