The aim of this article is to describe the reference configuration of the convection-permitting numerical weather prediction (NWP) model HARMONIE-AROME, which is used for operational short-range weather forecasts in Denmark, Estonia, Finland, Iceland, Ireland, Lithuania, the Netherlands, Norway, Spain, and Sweden. It is developed, maintained, and validated as part of the shared ALADIN-HIRLAM system by a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale NWP. HARMONIE-AROME is based on the model AROME developed within the ALADIN consortium. Along with the joint modeling framework, AROME was implemented and utilized in both northern and southern European conditions by the above listed countries, and this activity has led to extensive updates to the model's physical parameterizations. In this paper the authors present the differences in model dynamics and physical parameterizations compared with AROME, as well as important configuration choices of the reference, such as lateral boundary conditions, model levels, horizontal resolution, model time step, as well as topography, physiography, and aerosol databases used. Separate documentation will be provided for the atmospheric and surface data-assimilation algorithms and observation types used, as well as a separate description of the ensemble prediction system based on HARMONIE-AROME, which is called HarmonEPS.

A B S T R A C TThe seasonal evolution of snow and ice on Lake Oraja¨rvi, northern Finland, was investigated for three consecutive winter seasons. Material consisting of numerical weather prediction model (HIRLAM) output, weather station observations, manual snow and ice observations, high spatial resolution snow and ice temperatures from ice mass balance buoys (SIMB), and Moderate Resolution Imaging Spectroradiometer (MODIS) lake ice surface temperature observations was gathered. A snow/ice model (HIGHTSI) was applied to simulate the evolution of the snow and ice surface energy balance, temperature profiles and thickness. The weather conditions in early winter were found critical in determining the seasonal evolution of the thickness of lake ice and snow. During the winter season (Nov.ÁApr.), precipitation, longwave radiative flux and air temperature showed large inter-annual variations. The uncertainty in snow/ice model simulations originating from precipitation was investigated. The contribution of snow to ice transformation was vital for the total lake ice thickness. At the seasonal time scale, the ice bottom growth was 50Á70% of the total ice growth. The SIMB is suitable for monitoring snow and ice temperatures and thicknesses. The Mean Bias Error (MBE) between the SIMB and borehole measurements was (0.7 cm for snow thicknesses and 1.7 cm for ice thickness. The temporal evolution of MODIS surface temperature (three seasons) agrees well with SIMB and HIGHTSI results (correlation coefficient, R 00.81). The HIGHTSI surface temperatures were, however, higher (2.88C5MBE53.98C) than the MODIS observations. The development of HIRLAM by increasing its horizontal and vertical resolution and including a lake parameterisation scheme improved the atmospheric forcing for HIGHTSI, especially the relative humidity and solar radiation. Challenges remain in accurate simulation of snowfall events and total precipitation.

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A B S T R A C T Snow and ice thermodynamics of Bear Lake (Alaska) are investigated with a simple freshwater lake model (FLake) and a more complex snow and ice thermodynamic model (HIGHTSI). A number of sensitivity experiments have been carried out to investigate the influence of snow and ice parameters and of different complexity on the results. Simulation results are compared with observations from the Alaska Lake Ice and Snow Observatory Network. Adaptations of snow thermal and optical properties in FLake can largely improve accuracy of the results. Snow-to-ice transformation is important for HIGHTSI to calculate the total ice mass balance. The seasonal maximum ice depth is simulated in FLake with a bias of (0.04 m and in HIGHTSI with no bias. Correlation coefficients between ice depth measurements and simulations are high (0.74 for FLake and 0.9 for HIGHTSI). The snow depth simulation can be improved by taking into account a variable snow density. Correlation coefficients for surface temperature are 0.72 for FLake and 0.81 for HIGHTSI. Overall, HIGHTSI gives slightly more accurate surface temperature than FLake probably due to the consideration of multiple snow and ice layers and the expensive iteration calculation procedure.

A B S T R A C T When the resolution of numerical weather prediction (NWP) and climate models increases, it becomes more and more important to correctly account for the lakeÁatmosphere interactions. One possible way to handle lake effects is to use a lake model, which treats the lake surface temperature and ice conditions as prognostic variables. Such a parametrisation eliminates the traditional for NWP need to prescribe lake characteristics based on long-term climate averages. At the same time, new in situ and satellite measurements are becoming available for an operational practice. This offers the possibility to assimilate lake observations into the NWP models. We study the applicability of the prognostic and observation-based approaches and compare both. As a first step towards integrated lake data assimilation and forecasting in NWP, we suggest using the results of the prognostic lake parametrisation as the background for objective analysis (spatialisation) of the lake water surface temperature observations. We run NWP experiments in the Nordic conditions, where the freezing and melting of lakes can significantly influence local weather. Our results indicate that a lake model, usually used in climate studies, works well also in the NWP model even without assimilation of observations. However, it is possible to improve the description of the changing lake surface state by using good observation data. In this case, the lake model provides a better background for the data assimilation than a lake surface temperature climatology.