In this work, we compare the values of 15 convective indices obtained from radiosonde and microwave temperature and water vapor profiles simultaneously measured over Nizhny Novgorod (56.2°N, 44°E) during 5 convective seasons of 2014–2018. A good or moderate correlation (with coefficients of ~0.7–0.85) is found for most indices. We assess the thunderstorm prediction skills with a lead time of 12 h for each radiosonde and microwave index. It is revealed that the effectiveness of thunderstorm prediction by microwave indices is much better than by radiosonde ones. Moreover, a good correlation between radiosonde and microwave values of a certain index does not necessarily correspond to similar prediction skills. Eight indices (Showalter Index, Maximum Unstable Convective Available Potential Energy (CAPE), Total Totals index, TQ index, Jefferson Index, S index, K index, and Thompson index) are regarded to be the best predictors from both the true skill statistics (TSS) maximum and Heidke skill score (HSS) maximum points of view. In the case of radiosonde data, the best indices are the Jefferson Index, K index, S index, and Thompson index. Only TSS and HSS maxima for these indices are close to the microwave ones, whereas the prediction skills of other radiosonde indices are essentially worse than in the case of microwave data. The analysis suggests that the main possible reason of this discrepancy is an unexpectedly low quality of radiosonde data.

Ground-based microwave radiometers are increasingly used in operational meteorology and nowcasting. These instruments continuously measure the spectra of downwelling atmospheric radiation in the range 20–60 GHz used for the retrieval of tropospheric temperature and water vapor profiles. Spectroscopic uncertainty is an important part of the retrieval error budget, as it leads to systematic bias. In this study, we analyze the difference between observed and simulated microwave spectra obtained from more than four years of microwave and radiosonde observations over Nizhny Novgorod (56.2° N, 44° E). We focus on zenith-measured and elevation-scanning data in clear-sky conditions. The simulated spectra are calculated by a radiative transfer model with the use of radiosonde profiles and different absorption models, corresponding to the latest spectroscopy research. In the case of zenith-measurements, we found a systematic bias (up to ~2 K) of simulated spectra at 51–54 GHz. The sign of bias depends on the absorption model. A thorough investigation of the error budget points to a spectroscopic nature of the observed differences. The dependence of the results on the elevation angle and absorption model can be explained by the basic properties of radiative transfer and by cloud contamination at elevation angles.

An increase in the frequency of atmospheric hazards in a changing climate has attracted interest in the study of regional features of mesoscale convective systems and trends of lightning activity. Severe convective storms are the most destructive weather events causing substantial damage and fatalities. In this paper, we analyze general trends in the lightning activity in the Upper Volga region and identify the particular features of a severe thunderstorm on 13–14 July 2020. The analysis of the annual variability of the lightning activity in the Upper Volga region, carried out according to the World-Wide Lightning Location Network (WWLLN) data, has shown that the total number of discharges during the convective seasons of 2016–2021 increased. For the convective season of 2020, the daily number of discharges in the neighborhood of Nizhny Novgorod (the center of the region) is calculated according to the WWLLN data and the electric field mill (EFM) measurements. It is revealed that the most powerful (per convective season) thunderstorms have a number of similarities both according to observations and numerical simulation. The thunderstorm on 13–14 July was numerically simulated using the Weather Research and Forecasting (WRF) model in combination with calculations of the electric parameters (such as electric potential and intracloud electric field). The simulations were carried out using two parametrizations of microphysical processes to reveal the features associated with allowance for aerosol particles. The data from the meteorological radar and WWLLN were used to validate the radar reflectivity simulation results of the WRF model. Some features of the thunderstorm evolution characteristic for each of the microphysical parametrizations were identified. In general, the non-aerosol parametrization gives a more correct description of the development of severe thunderstorms in the Upper Volga region (with the exception of the final stage of the convective system development). For a convective event to have the required duration (more than 6 h), aerosol particles should be taken into account.