Version 83 (modified by gutierjm, 7 years ago) (diff)


The ECOMS UDG collects and provides information (mainly at 6-hourly and/or daily resolution) for a reduced number of variables from a number of datasets (seasonal hindcasts, reanalysis and observations) obtained from different data providers. The following list of variables has been identified according to the user's needs, receiving feedback from EUPORIAS WP22 (climate information indices, CIIs), WP23 (impact models), WP21 (calibration and downscaling) and SPECS WP61 (pilot applications) and WP52 (calibration and downscaling). See the section on the assessment of user's needs for more details.

Note that the R names below correspond to the vocabulary names used in the R data access package for homogenization purposes. Note that, data homogenization and aggregation (i.e. daily means from 6h data) is only provided through the R data access package.

In order to specify the particular temporal frequency/aggregation available for the variables in the different datasets, the following codes are used in the table below: 6h (6-hourly instantaneous data). 12h (12-hourly instantaneous data). 24h (24-hourly instantaneous data). DM (daily mean value). DX (daily maximum value). DN (daily minimum value). DA (daily accumulated data). DAr (accumulated since the initialization time –runtime) NOTE: The R package performs deaccumulation on a daily basis to match the standard definition. fx (static field)

In the table below, boldface codes (e.g. 6h) indicate variables already available through the ECOMS UDG. Italics are used for work in progress (variables to be included in the next update). e indicates that a variable exists in the original dataset but it is not planned to be included yet in ECOMS-UDG; blanks indicate that the variables do not exist in the original dataset. Codes ended by (*) indicate variables which do NOT exist in the dataset, but are derived/approximated from other available ones through the R data access package . More information on the particular approximations used are given in the variables-datasets mapping. Variables ended by (#) indicate daily aggregated values obtained from the corresponding original 3-hourly data.

Observations: Reanalysis: Seasonal forecasting models:
R name Variable description WFDEI NCEP_ reanalysis1 ERA_ Interim System4_ seasonal_15 System4_ seasonal_51 System4_ annual_15 CFSv2_ seasonal_15 SPECS_ESGF
Surface variables
tasNear-Surface air temperature DM 6h DM 6h/DM DM e e
tasmaxDaily Maximum Near-Surface Air Temperature DX(#) 6h DX DX DX DX 6h e
tasminDaily Minimum Near-Surface Air Temperature DN(#) 6h DN DN DN DN 6h e
tpTotal precipitation amount DA 6hA DA DAr DAr DAr 6h e
pslSea Level Pressure 6h DM 6h 6h 12h 6h e
psSurface air pressure DM e 6h(*) e
wssWind speed (at 10m) DM e 6h(*) e e 6h(*)
tdps2m Dewpoint Temperature e 6h e e
hussSurface (2m) specific humidity DM 6h e 6h(*) e
rsdsSurface Downwelling Shortwave Radiation DA 6hA e DAr e e e
rldsNet Longwave Surface Radiation DA 6hA e DAr e e e
sstSea surface temperature e e e e
uasEastward Near-Surface Wind 6h e 6h e e 6h e
vasNorthward Near-Surface Wind 6h e 6h e e 6h e
wssmaxWind speed (at 10m) e e e e e
wgustWind gust e e e
mrsoTotal Soil Moisture Content e e
mrrosSurface runoff flux e e e
mrroTotal Runoff e e e
ssroSub-surface runoff rate e e
wcslWater Content of Soil Layer e e
prsnSnowfall amount DA e e e
sdSnow Depth 24h e e
3D vars @ isobaric surface levels
uaEastward Wind 6h @ standard levels DM @ standard levels 12h @ standard levels e e e
vaNorthward Wind 6h @ standard levels DM @ standard levels 12h @ standard levels e e e
zgGeopotential height 6h @ standard levels DM @ standard levels 12h @ standard levels e e e
taAir temperature 6h @ standard levels DM @ standard levels 12h @ standard levels e e e
husSpecific humidity 6h @ standard levels DM @ standard levels 12h @ standard levels e e e
Static fields
zgsSurface geopotential height fx e e

@ standard Levels: 1000,850,700,500,300,200 mb, except for hus, which is not available at 200mb in some models

Data Homogeneization: The different nature of the datasets, and the idiosyncratic naming and storage conventions often applied by the modelling centres, makes necessary an homogenization across datasets in order to implement a truly user-friendly toolbox for data access. To this aim, the R package for data access has been developed. Data homogenization is achieved through the creation of a common vocabulary. The particular variables of each dataset are then translated -and transformed if necessary- into the common vocabulary by means of a dictionary. Both features -vocabulary and dictionary- are described here. In particular, some typical transformations performed by the loadECOMS interface are deaccumulation of initialization-accumulated variables to daily accumulated (i.e.: DAr --> DA) and scaling and/or offset of variables to match standard units (e.g. -273.15 for conversion K --> ºC).


WFDEI (provided by the EU-funded WATCH project)

The WATCH-Forcing-Data-ERA-Interim: WFDEI was produced post-WATCH using WFD methodology applied to ERA-Interim data. It is a meteorological forcing dataset extending into early 21st C (1979 – 2012). Eight meteorological variables at 3-hourly time steps, and as daily averages, for the global land surface at 0.5º x 0.5º resolution.

​NCEP_ reanalysis1 (provided by NCEP/NCAR)

A subset of predictors commonly used in statistical downscaling.

ERA_Interim (provided by ECMWF)

A subset of predictors commonly used in statistical downscaling (daily means at 2º resolution). This information has been downloaded from the ECMWF's MARS, degraded to a common 2 degrees grid and post-processed computing daily means based on the original 6 hourly fields when required. Therefore, this dataset is a degraded and subset of the original ERA-Interim reanalysis dataset, which is freely available via ECMWF servers at original resolution.

This dataset is used in the framework of different international initiatives (such as CORDEX-ESD and VALUE) in order to have a standard predictor dataset and to facilitate the work of the contributing downscaling groups. More information here.

System4 (provided by ECMWF)

The System 4 seasonal forecasting system became operational in November 2011. The corresponding hindcast is archived in the Meteorological Archival and Retrieval System (MARS), the main data repository at the ECMWF, as a colection of GRIB-1 files at 0.75º spatial resolution. The downloaded data has been exposed as three different virtual datasets (see the available variables? for these datasets):

  • System4_seasonal_15: There are twelve initializations (hereafter called runtimes) per year (the first of January, February, ...), each with 15 members running for 7 months (hereafter called simply times). Period: 1981-2010.
  • System4_seasonal_51: There are only four runtimes per year (the first of February, May, August and November), each with 51 members running for 7 months. Period: 1981-2010.
  • System4_annual_15: There are four runtimes per year each with 15 members, but the forecasts run for 13 months. Period: 1981-2010.

A preliminary validation report produced in SPECS (milestone MS22) is available for precipitation (System4_seasonal_15). The reports for all datasets and variables will be produced after feedback with end-users.

CFSv2 (provided by NCEP)

The CFS version 2 seasonal forecasting model became operational at NCEP in March 2011. The corresponding retrospective CFSv2 forecast dataset is stored in the NOMADS server as a collection of GRIB-2 files at 1º spatial resolution. The downloaded data is exposed as a single virtual dataset (see the available variables? for this dataset):

  • CFSv2_seasonal. There are four initializations (4 cycles) from every 5th day (thus providing on average 24 members per month) running for 9 moths (see CFSv2 members? for more detailed information of members' construction for this dataset). Period: 1982-2010. Note: For better comparability with other hindcasts, the R data access package defines by default an ensemble of 15 members for each lead month and forecast season.

See the UDG wiki for additional groups and datasets provided by the UDG.