Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 2, pp. 213-224
Mesoscale convective systems forecast using global and mesoscale atmospheric models
A.V. Bykov
1 , A.N. Shikhov
1 1 Perm State University, Perm, Russia
Accepted: 07.03.2018
DOI: 10.21046/2070-7401-2018-15-2-213-224
The article is devoted to the evaluation of forecast of mesoscale convective systems (MCS) with hazardous weather events (squalls, large hail and heavy rainfall) according to the global and mesoscale atmospheric models data. Two approaches are implemented to perform this evaluation. They are the forecast based on the instability indices calculated from the GFS and SLAV global atmospheric models output, and explicit (cloud-resolving) modeling of the deep convection using WRF-ARW and WRF-NMM mesoscale models. New instability index is developed for MCS forecast on the global atmospheric models output data. This index is based on the Lifted Index (LI) modification. Terra/Aqua MODIS satellite images and ground-based weather station data are used to estimate the reliability of MCS and hazardous weather events forecasts respectively. It is shown, that the SLAV model forecasts are more reliable in comparison with GFS forecasts, according to comparison of simulated areas of maximum convective instability with satellite-observed MCS position. The estimation of the cloud-resolving MCS forecasts by the WRF-ARW and WRF-NMM models shows that the simulated MCS spatial position often did not coincide with the MODIS-observed position. This could be associated with errors in initial conditions (GFS forecast data). Besides, the WRF model does not reproduce the MCS which were formed in the absence of a dynamic (frontal) convection. It should be noted that WRF-NMM model significantly overestimates the convective precipitation intensity and the areas with heavy showers (≥ 30 mm/h). Because of this, the amount of correct forecasts is increasing; however, the commission error is also rising.
Keywords: global atmospheric models, mesoscale atmospheric models, MODIS data, severe weather events, mesoscale convective systems, Perm krai
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