The present study is aimed at the development of a novel empirical base for application to ground-based astronomical telescopes. A Weather Research and Forecasting (WRF) model is used for description of atmospheric flow structure with a high spatial resolution within the Baikal Astrophysical Observatory (BAO) region. Mesoscale vortex structures are found within the atmospheric boundary layer, which affect the quality of astronomical images. The results of simulations show that upward air motions in the lower atmosphere are suppressed both above the cold surface of Lake Baikal and inside mesoscale eddy structures. A model of the outer scale of turbulence for BAO is developed. In this work, we consider the seeing parameter that represents the full width at half-maximum of the point spread function. Optical turbulence profiles are obtained and daytime variations of seeing are estimated. Vertical profiles of optical turbulence are optimized taking into account data from direct optical observations of solar images.

The spatial structure of land use and biophysical characteristics of land surface (albedo, leaf index, and vegetation cover) are updated using the GLASS (Global Land Surface Satellite) and GLC2019 (Global Land Cover, 2019) modern satellite databases for mesoscale numerical weather prediction with the WRF model for the territory of Belarus. The series of WRF-based numerical experiments was performed to verify the influence of the updated characteristics on the forecast quality for some difficult to predict winter cases. The model was initialized by the GFS (Global Forecast System, NCEP) global numerical weather prediction model. It is shown that the use of high-resolution land use data in the WRF and the consideration of the new albedo and leaf index distribution over the territory of Belarus can reduce the root-mean-square error (RMSE) of short-range (to 48 hours) forecasts of surface air temperature by 16–33% as compared to the GFS. The RMSE of the temperature forecast for the weather stations in Belarus for a forecast lead time of 12, 24, 36, and 48 hours decreased on average by 0.40°С (19%), 0.35°С (10%), 0.68°С (23%), and 0.56°С (15%), respectively. The most significant decrease in RMSE of the numerical forecast of temperature (up to 2.1 °С) was obtained for the daytime (for a lead time of 12 and 36 hours), when positive feedbacks between albedo and temperature of the land surface are manifested most. Keywords: numerical weather prediction, WRF, digital land surface model, albedo, leaf area index, forecast model validation

The influence of interannual and long-term climate change impacts on net primary production (NPP) of terrestrial ecosystems in the central part of the Bela

This article discusses the expected climate changes in Belarus as a result of two types of land transformation—rewetting degraded peatlands and increasing forest cover. The analysis was performed for the growing season (May–September) based on long-term earth remote sensing data, mesoscale modeling of atmospheric processes, and balance calculations using ERA5 reanalysis. It is shown that, as a result of waterlogging, the daytime temperature of the land surface for the southern part of Belarus (below the latitude of Minsk) decreases within 1.5°С due to increased evaporation and, for the northern part, 0.5 increased due to albedo regularity. At night, waterlogging, depending on the soil and climatic conditions, can cause both an increase and decrease a specific value of the land surface temperature (LST) within 1°C. Evapotranspiration due to waterlogging in the northern regions of Belarus decreases and it increases in the southern regions, which is associated with a significant ratio between evaporation and transpiration in these regions. During the afforestation of cropland, the daytime LST of Belarus decreases within 2°C and, at night, increase. The total evapotranspiration for the growing season due to the increase in forest cover reaches 100 mm, and potential (maximum possible) evaporation remains at the same level, which contributes to an increase in soil moisture at an increased amount of atmospheric precipitation. The above changes in the physical characteristics of the land surface as a result of reclamation cause a decrease in the surface air temperature in the reclamated region within 0.4°C and an increase in the amount of atmospheric precipitation within 2% of the climatic normals. At the same time, the maximum in the spatial distribution of secondary precipitation due to western transport shifts to the east relative to the reclaimed region.

Two approaches are proposed for updating the geophysical characteristics of the surface (land use, albedo, leaf index, fraction of absorbed photosynthetically active radiation) in the Weather Research and Forecasting (WRF) numerical model for the territory of Belarus: updating the monthly averages based on modern Earth remote sensing databases GLASS (Global Land Surface Satellite), GLC2019 (Global Land Cover, 2019); daily update based on operational MODIS satellite composite products. To estimate the impact of the initial geophysical data on the quality of numerical prediction of surface temperature, a number of numerical experiments were carried out to predict various synoptic situations in the summer period. To assess the influence of on the quality of the numerical prediction of the surface temperature, a number of numerical experiments were performed to predict various synoptic situations in the summer period. A correction factor for the land surface albedo in the WRF model was calculated, which makes it possible to reduce the root-mean-square error of temperature forecast for the lead time of +12, +24, +36 and +48 h by 0.30 ºС, 0.10 ºС, 0.15 ºС and 0.16 ºC respectively. In numerical experiments the initialization of the WRF model using operational satellite products had the most positive effect on the surface temperature forecast for nighttime periods: for the lead time of +24 and +48 h the standard error decreased by 0.11 ºС and 0.14 ºС respectively.

Heavy precipitation forecast remains one of the biggest problems in numerical weather prediction. Modern remote sensing systems allow tracking of rapidly developing convective processes and provide additional data for numerical weather models practically in real time. Assimilation of Doppler weather radar data also allows to specify the position and intensity of convective processes in atmospheric numerical models.The primary objective of this study is to evaluate the impact of Doppler  radar reflectivity and velocity assimilation in the WRF-ARW mesoscale model for the territory of Belarus in different seasons of the year. Specifically, we focus on the short-range numerical forecasting of mesoscale convective systems passage over the territory of Belarus in 2017-2019 with assimilated radar data.Proceeding with weather radar observations available for our cases, we first perform the necessary processing of the raw radar data to eliminate noise, reflections and other kinds of clutter. For identification of non-meteorological noise fuzzy echo classification was used. Then we use the WRF-DA (3D-Var) system to assimilate the processed radar observations from 3 Belarusian Doppler weather radar in the WRF model. Assimilating both radar reflectivity and radial velocity data in the model we aim to better represent not only the distribution of clouds and their moisture content, but also the detailed dynamical aspects of convective circulation. Finally, we analyze WRF modelling output obtained with assimilated radar data and compare it with available meteorological observations and with other model runs (including control runs with no data assimilation or with assimilation of conventional weather stations data only), paying special attention to the accuracy of precipitation forecast 12 hours in advance.