Abstract
More recently, snow accumulation and snowmelt models for their calculations are forced to apply data from numerical weather prediction (NWP) models. This approach allows improvement the accuracy of calculating snow water equivalent (SWE) values especially in remote and mountain regions. In this study, we compared the numerical results of SWE calculations performed by two independent models. The first one is the SnoWE model and the second one is the ICON NWP model. During the period from November 2018 to May 2019, the simulation results of SWE compared with in-situ data from 64 snow surveys, which are located in the Kama river basin. We found that both models (SnoWE and ICON) allow getting satisfactory estimates of the maximum values of SWE (the accuracy of data is sufficient for their practical using). The root mean square error was equal 14-18% from the average measured SWE. Moreover, we got reliable maximum values of SWE for forested areas. At the same time, both models underestimate SWE values during spring snowmelt season. Probably, this underestimation is due to the shortcomings of the models and a sparse snow course-measuring network.
Highlights
Snow water equivalent (SWE) is one of the main characteristics of snow cover
3.1 Comparison of simulated SWE based on SnoWE and ICON models
We compared the values of SWE, averaged over all weather stations located in the Kama river basin (Fig. 1 A)
Summary
Snow water equivalent (SWE) is one of the main characteristics of snow cover. The reliable data about the spatial distribution of SWE are required for modelling of spring floods, which are caused by snow melting. Many experiments on the assessment of SWE spatial distribution based on NWP models [2,3,4,5] and satellite data [6] have been performed worldwide. In the SnoWE model is used a combination of short-term forecasts from the NWP model COSMO-Ru and in-situ observations on the weather stations It had been implemented in quasi-operational mode since 2015 with a spatial resolution 2.2 km (for the Central Federal District), 7 km (for European part of Russia) and 13 km (for the entire territory of the country). During the cold season of 2018-2019, the same technique was implemented with the use of daily forecasts of ICON model, developed by the weather service of Germany This model has the highest accuracy of winter precipitation forecast, comparing with other global NWP models [11]. We performed an inter comparison of such two datasets and estimated their accuracy based on field measurements data
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