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Note

title	ERA-Interim production stopped on 31st August 2019

For

the time being and until further notice,

ERA-Interim (1st January 1979 to 31st August 2019)

shall continue to be accessible

access through the ECMWF Web API stopped on 01 June 2023

Its successor . ERA5 is now available from the Climate Data Store (CDS) (What are the changes from ERA-Interim to ERA5?) and users are strongly advised to migrate to ERA5 (How to download ERA5).

For those users who still need access to ERA-Interim after 01 June 2023 (subject to further notice), they can do so via the Climate Data Store (CDS) API.

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title	Table of Contents

Table of Contents

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...

The data are archived in the ECMWF data archive MARS and datasets are available until 31 May 2023 through both the ECMWF Web interface and the ECMWF WebAPI, which is the programmatic way of retrieving data from the archive.

Until further notice, ERA-Interim is also available via the API of the C3S Climate Data Store.

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).

...

Expand

title	Decoding 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 WebAPIERA-Interim

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

Expand

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

Code Block

language	py

#!/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

language	py

#!/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"
})

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

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

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=2,30Also 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), . The data provided is the log10 of spectra density. To obtain the spectral density one has to take to 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

language	py

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:

Synoptic Monthly Means (stream=mnth) are the monthly averages:
- for each analysis time (at the four main synoptic hours - 00, 06, 12, and 18 UTC)
- for each forecast start time (00 and 12 UTC) and step (3, 6, 9, 12 etc).
Monthly Means of Daily Means (stream=moda) are only available for analysis and instantaneous forecast data.
Monthly Means of Daily Forecast Accumulations (stream=mdfa) are similar to Monthly Means of Daily Means but are for accumulated fields (eg precipitation, radiation) and three step ranges are available.

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

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

Model levels: 1/to/60, which are described at L60 model level definitions

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.

Parameters described as "instantaneous" below, are valid at the specified time.

...

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

language	py

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:

Synoptic Monthly Means (stream=mnth) are the monthly averages:
- for each analysis time (at the four main synoptic hours - 00, 06, 12, and 18 UTC)
- for each forecast start time (00 and 12 UTC) and step (3, 6, 9, 12 etc).
Monthly Means of Daily Means (stream=moda) are only available for analysis and instantaneous forecast data.
Monthly Means of Daily Forecast Accumulations (stream=mdfa) are similar to Monthly Means of Daily Means but are for accumulated fields (eg precipitation, radiation) and three step ranges are available.

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

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

Model levels: 1/to/60, which are described at L60 model level definitions

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.

Parameters described as "instantaneous" below, are valid at the specified time.

Instantaneous, invariant, surface and single level parameters table

Expand

title	Table 1

count	name	an	fc	paramId	units
1	Low vegetation cover	x		27	(0-1)
2	High vegetation cover	x		28	(0-1)
3	Low vegetation type (table index - see Table 10.1 in IFS CY31R1 Documentation)	x		29	index
4	High vegetation type (table index - see Table 10.1 in IFS CY31R1 Documentation)	x		30	index
5	Standard deviation of filtered subgrid orography	x		74	m
6	Surface geopotential	x	x	129	m²s^-2
7	Standard deviation of orography	x		160	m
8	Anisotropy of orography X	x		161	~
9	Angle of sub-grid scale orography	x		162	~
10	Slope of sub-grid scale orography	x		163	~
11	Land-sea mask	x	x	172	(0 - 1)

Instantaneous, varying, surface and single level parameters table

Expand

title	Table 12

count	name	an	fc	paramId	units
1

Low vegetation cover

Sea ice fraction

x

27

31	(0 - 1)
2

High vegetation cover

Snow albedo

x

28

32	(0 - 1)
3

Low vegetation type (table index - see Table 10.1 in IFS CY31R1 Documentation)

Snow density

x

29

index

4

High vegetation type (table index - see Table 10.1 in IFS CY31R1 Documentation)

x

30

index

5

Standard deviation of filtered subgrid orography

x

74

m

33	kg m^-3
4	Sea surface temperature	x	x	34	K
5	Sea ice temperature layer 1¹	x	x	35	K
6	Sea ice temperature layer 2¹

6

Surface geopotential

x

129

36

K

m²s^-2

7

Standard deviation of orography

Sea ice temperature layer 3¹

x

160

m

8

Anisotropy of orography X

x

161

37

~

K

9

Angle of sub-grid scale orography

x

162

~

10

Slope of sub-grid scale orography

x

163

~

11

Land-sea mask

8	Sea ice temperature layer 4¹	x	x	38	K
9	Volumetric soil water level 1²	x	x

172

(0 - 1)

²	half levels
2	Downdraught mass flux	x	x	105	kg m^-2	half levels
3	Updraught detrainment rate	x	x	106	kg m^-2	full levels
4	Downdraught detrainment rate	x	x	107	kg m^-2	full levels
5	Total precipitation profile	x	x	108	kg m^-2	half levels
6	Turbulent diffusion coefficient for heat	x	x	109	m²	half levels

	60	m² s^-1 K kg^-1
4	Eastward wind component			131	m s^-2
5	Northward wind component			132	m s^-2
6	Specific humidity	x		133	kg/kg
7	Vorticity		x	138	s^-1
8	Divergence		x	155	s^-1
9	ozone mass mixing ratio	x		203	kg/kg

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Key

Instantaneous and Accumulated parameters

Minimum/maximum since the previous post processing

Monthly means

Data format

Level listings

Parameter listings

Instantaneous and Accumulated parameters

Minimum/maximum since the previous post processing

Monthly means

Data format

Level listings

Parameter listings

Observations

Observations

In-situ data, provided by WMO WIS

Computation of near-surface humidity and snow cover

Notes

Known issues

How to acknowledge and cite ERA-Interim

In-situ data, provided by WMO WIS

Computation of near-surface humidity and snow cover

Notes

Known issues

How to acknowledge and cite ERA-Interim

Reports

Space shortcuts

Page tree

Page History

Versions Compared

Old Version 26

New Version Current

Key

Instantaneous and Accumulated parameters

Minimum/maximum since the previous post processing

Monthly means

Data format

Level listings

Parameter listings

Instantaneous and Accumulated parameters

Minimum/maximum since the previous post processing

Monthly means

Data format

Level listings

Parameter listings

Observations

Observations

In-situ data, provided by WMO WIS

Computation of near-surface humidity and snow cover

Surface elevation and orography

Notes

Known issues

How to acknowledge and cite ERA-Interim

In-situ data, provided by WMO WIS

Computation of near-surface humidity and snow cover

Surface elevation and orography

Notes

Known issues

How to acknowledge and cite ERA-Interim

Reports