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.

Historiska observationer av temperatur, vegetationsperiodens längd, nederbörd, snö, globalstrålning och geostrofisk vind i Sverige har analyserats. Längden på de tillgängliga tidsserierna varierar mellan de olika variablerna. Det finns dagliga temperaturobservationer från Uppsala så långt tillbaka som 1722, medan startåret för de globalstrålningsmätningar från åtta svenska stationer som analyserats här är så sent som 1983. Klimatindikatorer som baseras på dessa observationer visar att:• Sveriges årsmedeltemperatur har ökat med 1,9 °C jämfört med perioden 1861–1890. • Sveriges årsnederbörd har ökat sedan 1930 från 600 mm/år till nästan 700 mm/år. • Antalet dagar med snötäcke har minskat sedan 1950. • Globalstrålningen har ökat med cirka 10 % sedan mitten av 1980-talet. • Någon förändring av den geostrofiska vinden kan inte fastslås från 1940.De ovan listade förändringarna syftar alla till årliga genomsnitt för hela Sverige. De är statistiskt signifikanta i de flesta fall. Bilden blir mer tvetydig då genomsnitt för olika landsdelar eller säsonger undersöks. Exempelvis är den ökade årsnederbörden mest ett resultat av ökad nederbörd under vinter och höst, medan det inte finns någon tydlig trend för sommar och vår. Det är också generellt sett svårare att fastslå förändringar i extremvärden. Exempelvis finns ingen signifikant trend vad gäller vinterns största snödjup, trots en tydlig minskning i antalet dagar med snötäcke.

Snow-induced photovoltaic (PV)-energy losses (snow losses) in snowy and cold locations vary up to 100% monthly and 34% annually, according to literature. Levels that illustrate the need for snow loss estimation using validated models. However, to our knowledge, all these models build on limited numbers of sites and winter seasons, and with limited climate diversity. To overcome this limitation in underlying statistics, we investigate the estimation of snow losses using a PV system's yield data together with freely available gridded weather datasets. To develop and illustrate this approach, 263 sites in northern Sweden are studied over multiple winters. Firstly, snow-free production is approximated by identifying snow-free days and using corresponding data to infer tilt and azimuth angles and a snow-free performance model incorporating shading effects, etc. This performance model approximates snow-free monthly yields with an average hourly standard deviation of 6.9%, indicating decent agreement. Secondly, snow losses are calculated as the difference between measured and modeled yield, showing annual snow losses up to 20% and means of 1.5-6.2% for winters with data for at least 89 sites. Thirdly, two existing snow loss estimation models are compared to our calculated snow losses, with the best match showing a correlation of 0.73 and less than 1% bias for annual snow losses. Based on these results, we argue that our approach enables studying snow losses for high numbers of PV systems and winter seasons using existing datasets.