Snow cover climatology over the Baltic States based on satellite data Justinas Kilpys Climatology Division at Lithuanian Hydrometeorological Service, Vilnius, Lithuania Introduction Methodology Snow is one of the essential climate variables. For a long time in the Baltic States in-situ measurements were the only data source for snow monitoring. This study was an effort to apply satellite data for snow cover monitoring in this region. Time series of snow cover days and snow water equivalent were generated using MODIS and GlobSnow project data. The MODIS daily snow cover product in climate modelling grid of 0.05 degree (MOD10C1) was used to calculate annual and monthly snow cover days in the Baltic States for the period 2000–2013. The main limitation of MODIS data is that surface of the Earth is often obscured by clouds. Simple backward and forward temporal gap filling technique (Foppa, Seiz, 2012) was applied to fill the data gaps due to the cloud cover. The time series of snow water equivalent (SWE) was generated using SWE product (25×25 km) from ESA DUE GlobSnow project (Luojus et al., 2010). Satellite based number of snow cover days and SWE were validated with the observations from ground stations. Kuusiku Dobele Vilnius Fig. 1 Elevation model of the Baltic States with marked ground stations used for validation Fig. 2 Mean annual number of snow cover days based on MOD10C1 data, 2000–2013 Fig. 3 Mean maximum snow water equivalent based on GlobSnow project data, 2000–2013 Validation Validation with in-situ measurements from Vilnius (Lithuania), Dobele (Latvia) and Kuusiku (Estonia) meteorological stations showed that there is a good agreement between ground observations and satellite data. The hit rate for MODIS based snow cover days (SCD) was 0.88–0.92 indicating high accuracy, although critical success index (CSI) was lower (0.68–0.72) indicating that in case of rare events snow covered pixels were either overestimated or underestimated. Validation of GlobSnow SWE product with in-situ measurements showed that the overall accuracy was lower than for SCD. The corr. coef. of SWE estimates varied from 0.64 at Dobele station to 0.91 at Kuusiku. At Dobele station GlobSnow product showed underestimation error, while at other stations there was a tendency to overestimate SWE. The lower validation scores for satellite based SCD and SWE were determined during the transition of seasons (November, March–April) when there are many thaws and snow cover is ephemeral and patchy. Fig. 5 Time series of maximum snow water equivalent at Vilnius based on GlobSnow and insitu data. Fig. 6 Scatter plots of SWE estimates at Vilnius based on GlobSnow and insitu data. Fig. 4 Comparison of SCD derived from MOD10C1 and in-situ measurements Contact details Justinas Kilpys Climatology Division Lithuanian Hydrometeorological Service under the Ministry of Environment Rudnios g. 6, LT-09300 Vilnius Tel.: +370 648 06320 E-mail: [email protected] Table 1 Validation scores for snow cover days (SCD) derived from MOD10C1 Mean In-situ Corr. Hit Station abs. FAR POD CSI SSclim SCD coef. rate diff. Kuusiku 102.3 22.1 0.75 0.88 0.25 0.88 0.68 -0.01 Dobele 76.1 8.3 0.94 0.92 0.22 0.86 0.70 0.82 Vilnius 93.1 8.9 0.89 0.92 0.15 0.83 0.72 0.79 Table 2 Summary of GlobSnow SWE product performance over the Baltic States Station RMSE (mm) Bias (mm) Corr. Coef. Sample size Kuusiku 17.9 3.7 0.91 79 Dobele 28.0 -4.1 0.64 35 Vilnius 11.6 4.9 0.80 85 Conclusions This study showed that MOD10C1 snow product with 0.05 degree grid is adequate to describe the temporal and spatial variation of snow cover days over the Baltic States. Data gaps due to the cloud cover can be reduced using temporal gap filling, but this method is applicable only for data re-processing. GlobSnow SWE product has a coarse resolution (25×25 km) and thus it is not possible to accurately determine local features. However SWE product is relevant for general overview of snow conditions in the region. Applying satellite data for snow cover evaluation in the Baltic States is of interest on the national border regions and trans-boundary river basins were measurement networks are scarce.