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Table of Contents
maxLevel2

Introduction

Here we document the ERA-Interim dataset, which, covers the period from 1st January 1979 to 31st August 2019.

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Generally, the data are available at a sub-daily and monthly frequency and consist of analyses and 10-day forecasts, initialised twice daily at 00 and 12 UTC. Most analysed parameters are also available from the forecasts. There are a number of forecast parameters, e.g. mean rates and accumulations, that are not available from the analyses.

How to download ERA-Interim

The data are archived in the ECMWF data archive MARS and datasets are available through both the Web interface and the ECMWF WebAPI,  which is the programmatic way of retrieving data from the archive.

Documentation is available on How to download ERA-Interim data from the ECMWF data archive (Member State users can access the data directly from MARS, in the usual manner).

The IFS and data assimilation

The model documentation for CY31r2 (choose Cy31r1) is at https://www.ecmwf.int/en/publications/ifs-documentation.

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The model time step is 30 minutes.

Data organisation

The data can be accessed from MARS using the keywords class=ea and expver=0001. Subdivisions of the data are labelled using stream, type and levtype.

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For analyses date and time, specify the analysis time and step equal to 0 hours. For forecasts date and time, choose the forecast start time and then step specifies the number of hours since that start time. The combination of date, time and forecast step defines the validity time. For analyses, the validity time is equal to the analysis time. Refer to ERA-Interim: 'time' and 'steps', and instantaneous, accumulated and min/max parameters for further details.

Spatial grid

The horizontal grid spacing of ERA-Interim atmospheric model and reanalysis system is around 80 km (reduced Gaussian grid N128) which became around 83km (

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Longitudes range from 0 to 360, which is equivalent to -180 to +180 in Geographic coordinate systems.


Temporal frequency

Analyses of atmospheric fields on model levels, pressure levels, potential temperature and potential vorticity, are available every 6 hours at 00, 06, 12,  and 18 UTC. Forecasts run twice at 00 and 12 UTC and provide 3 hours output for surface and pressure level parameters up to 24 hours, with decreasing frequency to 10 days.


Wave spectra

The ERA-Interim atmospheric model is coupled ocean-wave model resolving 30 wave frequencies and 24 wave directions at the nodes of its reduced

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Expand
titleDecoding 2D wave spectra

Download from ERA-Interim

Wave data can be downloaded using the same mechanisms as atmospheric data. Please see How to download data via the ECMWF WebAPI

For wave spectra you need to specify the additional parameters 'direction' and 'frequency'.


Expand
titleClick here to see a sample script to download 2D wave spectra from ERA-Interim using the ECMWF WebAPI


Code Block
languagepy
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
server = ECMWFDataServer()
server.retrieve({
    "class": "ei",
    "dataset": "interim",
    "expver": "1",
    "stream": "wave",
    "type": "an",
    "date": "2016-01-01/to/2016-01-31",
    "time": "00:00:00/06:00:00/12:00:00/18:00:00",
    "param": "251.140",
    "direction": "1/2/3/4/5/6/7/8/9/10/11/12/13/14/15/16/17/18/19/20/21/22/23/24",
    "frequency": "1/2/3/4/5/6/7/8/9/10/11/12/13/14/15/16/17/18/19/20/21/22/23/24/25/26/27/28/29/30",
    "target": "2d_spectra_201601",
})

If you want to download the data in NetCDF format, please add the 'format' and 'grid' parameters:

Code Block
languagepy
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
server = ECMWFDataServer()
server.retrieve({
    ......
    "grid": "0.75/0.75", # Spatial resolution in degrees latitude/longitude
    "format": "netcdf"
    "target": "2d_spectra_201601.nc"
})


Decoding 2D wave spectra in GRIB

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

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

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

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

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

Decoding 2D wave spectra in NetCDF

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

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

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

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

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

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

Units of 2D wave spectra

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


Instantaneous and Accumulated parameters

Instantaneous parameters represent an average over the model time step (30min). Accumulated parameters are accumulated from the start of the forecast, ie. from 00 UTC or 12 UTC to the time step selected. All the analysed fields are instantaneous instead forecast data could be either instantaneous or accumulated, depending on the parameter. More detailed information on parameters are shown in Parameter listing.

Minimum/maximum since the previous post processing

In ERA-Interim there are some parameters named '...since previous post-processing', for example 'Maximum temperature at 2 metres since previous post-processing'. This represents the maximum temperature between the previous archived forecast 'Step' and the forecast 'Step'. For example, 'Maximum temperature at 2 metres since previous post-processing' with start time 00 UTC and Step=9, is the maximum 2m temperature in the 3-hour period between 06 UTC and 09 UTC.

Monthly means

ERA-interim sub-daily data are monthly averaged on data with valid times or accumulation periods that fall within the calendar month in question. The different monthly means are:

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See also Section 3 of the ERA-Interim archive documentation.

Data format

Model level fields are in GRIB2 format. All other fields are in GRIB1, unless otherwise indicated.?

Level listings

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

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Potential vorticity level:

Mathinline
PV=\pm 2PVU

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 is available from the ECMWF parameter database.

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Expand
titleTable 12


count

name

paramId

units

1

Potential temperature

3

K

2

Pressure

54

Pa

3

Geopotential

129

m2 s-2

4

Eastward wind component

131

m s-2

5

Northward wind component

132

m s-2

6

Specific humidity

133

kg/kg

79ozone mass mixing ratio203kg/kg


Observations

The observations (satellite and in-situ) used as input into ERA-Interim are listed in tables below.

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Expand
titleTable 13


SensorSatelliteSatellite agencyData provider+

Measurement

(sensitivities exploited in ERA5 / variables analysed)

Satellite radiances (infrared and microwave)



AIRSAQUANASANOAABT (T, humidity and ozone)
AMSREAQUAJAXA

BT  (column water vapour, cloud liquid water,

precipitation and ocean surface wind speed)

AMSUANOAA-15/16/17/18/19, AQUA, METOP-ANOAA,ESA,EUMETSAT
BT (T)
AMSUBNOAA-16/17NOAA
BT (humidity)
HIRSMETOP-A, NOAA-10/11/12/14/15/16/17/18/19NOAA
BT (T, humidity and ozone)
MHSNOAA-18/19, METOP-ANOAA, EUMETSAT/ESA
BT (humidity and precipitation)
MSUNOAA-10/11/12/14

BT (T)
SSM/IDMSP-8/10/11/13/14/15/16US NavyNOAA,CMSAF

BT (column water vapour, cloud liquid water,

precipitation and ocean surface wind speed)

SSMISDMSP-16US NavyNOAA

BT (T,  humidity,  column water vapour,

cloud liquid water, precipitation and ocean surface wind speed)

SSUNOAA-11/14NOAA
BT (T)
MVIRIMETEOSAT 5/7EUMETSAT/ESAEUMETSATBT (water vapour, surface/cloud top T)
SEVIRIMETEOSAT-8/9EUMETSAT/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-1RJMA
BT (water vapour, surface/cloud top T)
Satellite retrievals from radiance data



MVIRIMETEOSAT-4/5/7EUMETSAT/ESAEUMETSATwind vector
SEVIRIMETEOSAT-8/9/10EUMETSAT/ESAEUMETSATwind vector
GOES IMAGERGOES-8/9/10/12NOAACIMMS,NESDISwind vector
GMS IMAGERGMS-3/4/5JMA
wind vector
MTSAT IMAGERMTSAT-1RJMA
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
MIPASENVISAT*ESA
Ozone
MLSEOS-AURA*NASA
Ozone
OMIEOS-AURA*NASA
Ozone
SBUVNIMBUS-7*,NOAA*9/11/14/16/17/18NOAANASAOzone
SCIAMACHYENVISAT*ESA
Ozone
TOMSNIMBUS-7*,METEOR-3,ADEOS-1*,EARTH PROBENASA
Ozone
Satellite GPS-Radio Occultation data



BlackJackCHAMPDLR,NASA/DLR,NASA/COMAEGFZ,UCARBending angle
GRASMETOP-AEUMETSAT/ESAEUMETSATBending angle
IGORCOSMIC-1 to 6NSPO/NOAAGFZ,UCARBending angle
Satellite Altimeter data



RAERS-1*/2*ESA
Wave Height
RA-2ENVISAT*ESA
Wave Height
Poseidon-2JASON-1*CNES/NASACNESWave Height


In-situ data, provided by WMO WIS

Expand
titleTable 14


Dataset nameObservation typeMeasurement
SYNOPLand stationSurface Pressure, Temperature, wind, humidity
METARLand stationSurface Pressure, Temperature, wind,humidity
DRIBU/DRIBU-BATHY/DRIBU-TESAC/BUFR Drifting BuoyDrifting buoys10m-wind, Surface Pressure
BUFR Moored BuoyMoored buoys10m-wind, Surface Pressure
SYNOP 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


Computation of near-surface humidity and snow cover

Near-surface humidity

Near-surface humidity is not archived directly in ERA datasets, but the archive contains near-surface (2m from the surface) temperature (T), dew point temperature (Td), and surface pressure[1] (sp) from which you can calculate specific and relative humidity at 2m:

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Known issues

Please see the ERA-Interim known issues page for guidance and workarounds.

How to cite ERA-Interim

Please use this as the main scientific reference to ERA-Interim:

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If no specific advice is given by the journals, it is usually recommended that the above data citation is put in the acknowledgements section.

Reports

2004-2007

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References

  • Berrisford, P., P. Kållberg,  S. Kobayashi, D. Dee, S. Uppala, A. J. Simmons, P. Poli,  and H. Sato, 2011: Atmospheric conservation properties in ERA-Interim. Q.J.R. Meteorol. Soc., 137: 1381–1399. doi: 10.1002/qj.864
  • Dee, D. P., and Coauthos, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553–597. doi:10.1002/qj.828
  • Further references available from the ECMWF e-library

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