Том 42, № 6 (2017)

The results of numerical experiments with the data assimilation system including the WRF-ARW mesoscale atmospheric model and WRFDA analysis package in the 3D-Var mode are considered. The focus is on the impact of Doppler weather radar data on the quality of short-range weather forecasting. The maps of weather events and cloud top constructed from the modeling by GIMET-2010 software package are analyzed using the web-GIS METEORAD. The experiments in the Moscow region demonstrated that if only radial wind is assimilated, the intensity of simulated cloud and precipitation formation is the closest to the observed one; however, the spatial mismatches of simulated and observed cloud systems are possible. If Doppler radar data on radial wind and reflectivity are assimilated, the general pattern and location of clouds and precipitation are simulated more accurately, but there is a certain overestimation of convection intensity which leads to the overestimation of the number of thunderstorms and rainfall rate.

The results of observation of the development of a high-depth thunder-hail storm is presented. The measurements were carried out with the MRL-5 weather radar and LS8000 lightning detection system. The electrical parameters of the investigated cloud obtained with LS8000 as well as their relations to radar-derived cloud characteristics and to the indirect criteria of electrical conditions computed on their basis are analyzed. The possibility to forecast thunderstorm based on different thermodynamic criteria is investigated. The high correlation was revealed between the total lightning current in the LF range and the lightning flash rate in the LF and VHF ranges. The total charge transferred by negative lightnings from this cloud to the ground is equal to 387 C; the average value of charge per one lightning is 0.44 C. Regression equations linking the radar criteria of lightnings and the lightning flash rate are presented.

The implementation of the Simplified Extended Kalman Filter (SEKF) for the deep soil moisture initialization in the SL-AV global atmosphere model is described. Special attention is paid to the calculation of the observation operator and analysis increment. SL-AV screen-level parameters forecasts are estimated with SEKF and optimal interpolation initialization methods. It is demonstrated that the implementation of the assimilation algorithm improves the model forecast quality for screen-level temperature and relative humidity.

Spatial variations in extreme precipitation events make hydrological, climatological, social, environmental and agricultural effects on a country. This study presents the spatiotemporal autocorrelation analysis of extreme precipitation events over Iran using gridded data on daily precipitation for the period 1961–2010. The 95th percentile is considered as extreme precipitation factor. The spatial autocorrelation of extreme precipitation is examined by three commonly used spatial autocorrelation statistics, the G_i statistic index, Moran’s I global index, and Local Moran’s I (LISA) index, at the 95 and 99% significant confidence level. The results showed a strong significant spatial autocorrelation for extreme precipitation events with the highest Moran’s I value in January. The positive significant autocorrelation of extreme precipitation is observed over the southern parts of the Caspian Sea and Zagros Mountains ranges, while the negative significant autocorrelation is observed over the central and eastern parts of country. In spring and summer the positive autocorrelation cores displace from the Zagros Mountains ranges to the northwestern and southeastern parts.