Abstract
Abstract. Sodankylä, in the heart of Arctic Research Centre of the Finnish Meteorological Institute (FMI ARC) in northern Finland, is an ideal site for atmospheric and environmental research in the boreal and sub-Arctic zone. With temperatures ranging from −50 to +30 °C, it provides a challenging testing ground for numerical weather forecasting (NWP) models as well as weather forecasting in general. An extensive set of measurements has been carried out in Sodankylä for more than 100 years. In 2000, a 48 m-high micrometeorological mast was erected in the area. In this article, the use of Sodankylä mast measurements in NWP model verification is described. Starting in 2000, with the NWP model HIRLAM and Sodankylä measurements, the verification system has now been expanded to include comparisons between 12 NWP models and seven measurement masts, distributed across Europe. A case study, comparing forecasted and observed radiation fluxes, is also presented. It was found that three different radiation schemes, applicable in NWP model HARMONIE-AROME, produced somewhat different downwelling longwave radiation fluxes during cloudy days, which however did not change the overall cold bias of the predicted screen-level temperature.
Highlights
Nocturnal and wintertime surface temperature inversions still pose a difficult challenge to weather forecast models
Seasonal statistics compiled for individual observatories, or mast sites, containing the models available at each respective station are calculated in the mast comparison as well
For a model–observation comparison, six components of radiation fluxes measured in the 18 m-high Sodankylä radiation tower are available (Table 3): shortwave downwards (SWDN or global radiation) and upwards; direct normal solar irradiance (DNI); diffuse shortwave solar radiation; and longwave radiation downwards (LWDN) and upwards (LWUP)
Summary
Nocturnal and wintertime surface temperature inversions still pose a difficult challenge to weather forecast models. In the weather model verification, the traditional way is to perform detailed studies of model analyses and forecasts by comparing them with measurements afterwards. M. Kangas et al.: Weather model verification using Sodankylä mast measurements vides valuable information about model behaviour and, when monitored frequently, can act as a kind of alarm bell, alerting model developers when there are apparent problems with model forecasts. Kangas et al.: Weather model verification using Sodankylä mast measurements vides valuable information about model behaviour and, when monitored frequently, can act as a kind of alarm bell, alerting model developers when there are apparent problems with model forecasts Data collected this way can be used in model performance studies (Atlaskin and Kangas, 2006).
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