This document describes the CF conventions for climate and forecast metadata designed to promotethe processing and sharing of files created with the netCDF Application Programmer Interface[NetCDF]. The conventions define metadata that provide a definitive description of what the data ineach variable represents, and of the spatial and temporal properties of the data. This enables usersof data from different sources to decide which quantities are comparable, and facilitates buildingapplications with powerful extraction, regridding, and display capabilities.The CF conventions generalize and extend the COARDS conventions [COARDS]. The extensionsinclude metadata that provides a precise definition of each variable via specification of a standardname, describes the vertical locations corresponding to dimensionless vertical coordinate values,and provides the spatial coordinates of non-rectilinear gridded data. Since climate and forecastdata are often not simply representative of points in space/time, other extensions provide for thedescription of coordinate intervals, multidimensional cells and climatological time coordinates, andindicate how a data value is representative of an interval or cell. This standard also relaxes theCOARDS constraints on dimension order and specifies methods for reducing the size of datasets.

This document describes the CF conventions for climate and forecast metadata designed to promote the processing and sharing of files created with the netCDF Application Programmer Interface. The conventions define metadata that provide a definitive description of what the data in each variable represents, and of the spatial and temporal properties of the data. This enables users of data from different sources to decide which quantities are comparable, and facilitates building applications with powerful extraction, regridding, and display capabilities.

The CF conventions generalize and extend the COARDS conventions. The extensions include metadata that provides a precise definition of each variable via specification of a standard name, describes the vertical locations corresponding to dimensionless vertical coordinate values, and provides the spatial coordinates of non-rectilinear gridded data. Since climate and forecast data are often not simply representative of points in space/time, other extensions provide for the description of coordinate intervals, multidimensional cells and climatological time coordinates, and indicate how a data value is representative of an interval or cell. This standard also relaxes the COARDS constraints on dimension order and specifies methods for reducing the size of datasets.

This document describes the CF conventions for climate and forecast metadata designed to promotethe processing and sharing of files created with the netCDF Application Programmer Interface[NetCDF]. The conventions define metadata that provide a definitive description of what the data ineach variable represents, and of the spatial and temporal properties of the data. This enables usersof data from different sources to decide which quantities are comparable, and facilitates buildingapplications with powerful extraction, regridding, and display capabilities.The CF conventions generalize and extend the COARDS conventions [COARDS]. The extensionsinclude metadata that provides a precise definition of each variable via specification of a standardname, describes the vertical locations corresponding to dimensionless vertical coordinate values,and provides the spatial coordinates of non-rectilinear gridded data. Since climate and forecastdata are often not simply representative of points in space/time, other extensions provide for thedescription of coordinate intervals, multidimensional cells and climatological time coordinates, andindicate how a data value is representative of an interval or cell. This standard also relaxes theCOARDS constraints on dimension order and specifies methods for reducing the size of datasets.

A gridded dataset (SMHI Gridded Climatology - SMHIGridClim) has been produced forthe years 1961 - 2018 over an area covering the Nordic countries on a grid with 2.5 kmhorizontal resolution. The variables considered are the two meter temperature and twometer relative humidity on 1, 3 or 6 hour resolution, varying over the time periodcovered, the daily minimum and maximum temperatures, the daily precipitation and thedaily snow depth. The gridding was done using optimal interpolation with the gridppopen source software from the Norwegian Meteorological Institute.Observations for the analysis are provided by the Swedish, Finish and Norwegianmeteorological institutes, and the ECMWF. The ECA&D observation data set (e.g. usedfor the gridded E-OBS dataset) was considered for inclusion but was left out because ofcomplications with time stamps and accumulation periods varying between countries andperiods. Quality check of the observations was performed using the open source softwareTITAN, also developed at the Norwegian Meteorological Institute.The first guess to the optimal interpolation was given by statistically downscaledforecasts from the UERRA-HARMONIE reanalysis at 11 km horizontal resolution. Thedownscaling was done to fit the output from the operational MEPS NWP system at 2.5km with a daily and yearly variation in the downscaling parameters.The quality of the SMHIGridClim dataset, in terms of annual mean RMSE, was shown tobe similar to that of gridded datasets covering the other Nordic countries; “seNorge”from Norway and the dataset “FMI_ClimGrid” from Finland.

Ett klimatologiskt griddat datasett (SMHI Gridded Climatology - SMHIGridClim) hartagits fram för åren 1961 – 2018. Data täcker de nordiska länderna med en horisontellupplösning av 2,5 km. Variablerna som tagits fram är lufttemperatur och relativluftfuktighet vid 2m höjd med en upplösning av1,3 eller 6 timmar beroende av tidsperiod,samt dygnsupplöst min- och maxtemperatur, nederbörd och snödjup. Datasetet ärframtaget med optimal interpolation av stationsdata genom analysverktyget gridpp, somär en öppet tillgänglig programvara från Norska Meteorologiska Institutet.Observationer till analysen har erhållits från de svenska, norska och finskameteorologiska instituten, samt ECMWF. En ansats gjordes också att användaobservationer från datasetet ECA&D från KNMI, men på grund av svårigheter med atttidsstämplarna för data från olika länder inte överensstämde, uteslöts datasetet uranalysen. Kvalitetskontroll av observationerna gjordes med programvaran TITAN, somäven den finns tillgänglig från och utvecklats av Norska Meteorologiska Institutet.Som en första gissning till interpolationen användes statistiskt nerskalade prognosfält(från 11 km till 2,5 km upplösning) från UERRA-HARMONIE. Nerskalningen gjordesmot fält från den operationella numeriska väderprognosmodellen MEPS. Anpassningengjordes med nedskalningsparametrar som varierar över året och dygnet.Kvalitén hos ”SMHIGridClim med avseende på genomsnittligt RMSE är liknande densom tagits fram för griddade data för andra nordiska länderna med varierandeanalysmetoder; “seNorge” från Norge och “FMI_ClimGrid” från Finland.