Abstract
Climate modelling, either global or regional, is usually treated as a typical large-scale scientific computational problem. The regional climate model RegCM, well-known within the meteorological community, is applied in the study to estimate quantitatively the snow water equivalent, which is the most consistent snow cover parameter. Multiple runs for a time window of 14 consecutive winters with different model configurations, in particular with various initial and boundary conditions, have been performed, in an attempt to obtain most adequate representation of the real snow cover. The results are compared with stations’ measurements from the network of the National Institute of Meteorology and Hydrology. Generally all runs yield similar results, where the overall (i.e. over the whole time span) biases are acceptable, but, however, with large discrepancies in the day-by-day comparisons, which is typical for climate modelling studies.
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Deep gratitude to the organizations and institutes (ICTP, ECMWF, NCEP-NCAR, Unidata, MPI-M and all others), which provides free of charge software and data. Without their innovative data services and tools this study would be not possible.
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Chervenkov, H., Todorov, T., Slavov, K. (2015). Snow Cover Assessment with Regional Climate Model - Problems and Results. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2015. Lecture Notes in Computer Science(), vol 9374. Springer, Cham. https://doi.org/10.1007/978-3-319-26520-9_36
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