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Abstract. The formation of convective sea-effect snowfall (i.e., snow bands) is triggered by cold air outbreaks over a relatively warm and open sea. Snow bands can produce intense snowfall which can last for several days over the sea and potentially move towards the coast depending on wind direction. We defined the meteorological conditions which statistically favor the formation of snow bands over the north-eastern Baltic Sea of the Finnish coastline and investigated the spatio-temporal characteristics of these snow bands. A set of criteria, which have been previously shown to be able to detect the days favoring sea-effect snowfall for Swedish coastal area, were refined for Finland based on four case study simulations, utilizing a convection-permitting numerical weather prediction (NWP) model (HARMONIE-AROME). The main modification of the detection criteria concerned the threshold for 10 m wind speed: the generally assumed threshold value of 10 m s−1 was decreased to 7 m s−1. The refined criteria were then applied to regional climate model (RCA4) data, for an 11-year time period (2000–2010). When only considering cases in Finland with onshore wind direction, we found on average 3 d yr−1 with favorable conditions for coastal sea-effect snowfall. The heaviest convective snowfall events were detected most frequently over the southern coastline. Statistics of the favorable days indicated that the lower 10 m wind speed threshold improved the representation of the frequency of snow bands. For most of the favorable snow band days, the location and order of magnitude of precipitation were closely captured, when compared to gridded observational data for land areas and weather radar reflectivity images. Lightning were observed during one third of the favorable days over the Baltic Sea area.

Abstract.A new national daily snowfall record was measured in Finland on 8 January 2016 when it snowed 73 cm (31 mm as liquid water) in less than a day in Merikarvia on the western coast of Finland. The area of the most intense snowfall was very small, which is common in convective precipitation. In this work we used hourly weather radar images to identify the sea-effect snowfall case and to qualitatively estimate the performance of HARMONIE, a non-hydrostatic convection-permitting weather prediction model, in simulating the spatial and temporal evolution of the snowbands. The model simulation, including data assimilation, was run at 2.5 km horizontal resolution and 65 levels in vertical. HARMONIE was found to capture the overall sea-effect snowfall situation quite well, as both the timing and the location of the most intense snowstorm were properly simulated. Based on our preliminary analysis, the snowband case was triggered by atmospheric instability above the mostly ice-free sea and a low-level convergence zone almost perpendicular to the coastline. The simulated convective available potential energy (CAPE) reached a value of 87 J kg −1 near the site of the observed snowfall record.

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<p>Rapidly updating nowcasting system, Smartmet nowcast, has been developed at Finnish Meteorological Institute (FMI). The system combines information from multiple sources to operationally produce accurate and timely short range forecasts and a detailed description of the present weather to the end-users. The information sources combined are 1) Rapidly-updating high-resolution numerical weather prediction (NWP) MetCoOp nowcast (MNWC) forecast 2) radar-based nowcast 3) 10-day operational forecast. The Smartmet nowcast is currently produced for parameters 2-m temperature, 10-m wind speed, relative humidity, total cloud cover and accumulated 1-hour precipitation.</p><p>The system produces hourly updating nowcast information over the Scandinavian forecast domain and combines it seamlessly with the 10-day operational forecast information. Prior the combination a simple bias correction scheme based on recent forecast error information is applied to MNWC model analysis and forecast fields of 2-m temperature, relative humidity and 10-m wind speed. The blending of the nowcast and the 10-day operational forecast information is done using Optical-flow based image morphing method, which provides visually seamless forecasts for each forecast variable.</p><p>FMI has operationally produced Smartmet nowcast forecasts since September 2020. The validation of the data is in progress. The available results show that the Smartmet nowcast is improving the quality of short range forecasts and producing seamless and consistent forecasts. The method is also reducing the delay of forecast production. The Smartmet nowcast method will be automated in FMI forecast production in the near future.</p>