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. The High Resolution Limited Area Model (HIRLAM), used for the operationalnumerical weather prediction in the Finnish Meteorological Institute(FMI), includes prognostic treatment of lake surface state since2012. Forecast is based on the Freshwater Lake (FLake) modelintegrated into HIRLAM. Additionally, an independent objectiveanalysis of lake surface water temperature (LSWT) combines the shortforecast of FLake to observations from the Finnish EnvironmentInstitute (SYKE). The resulting description of lake surface state –forecast FLake variables and analysed LSWT – was compared to SYKEobservations of lake water temperature, freeze-up and break-up dates,and the ice thickness and snow depth for 2012–2018 over 45lakes in Finland. During the ice-free period, the predicted LSWTcorresponded to the observations with a slight overestimation, with asystematic error of +0.91  K. The colder temperatures wereunderrepresented and the maximum temperatures were too high. Theobjective analysis of LSWT was able to reduce the bias to +0.35  K. The predicted freeze-up dates corresponded well to the observeddates, mostly within the accuracy of a week. The forecast break-updates were far too early, typically several weeks ahead of theobserved dates. The growth of ice thickness after freeze-up wasgenerally overestimated. However, practically no predicted snowappeared on lake ice. The absence of snow, presumably due to anincorrect security coefficient value, is suggested to be also the mainreason for the inaccurate simulation of the lake ice melting in spring.

Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland