M. V. Shatalina

Bio: M. V. Shatalina is an academic researcher from Russian Academy of Sciences. The author has contributed to research in topics: Electric field & Lightning. The author has an hindex of 5, co-authored 15 publications receiving 47 citations. Previous affiliations of M. V. Shatalina include N. I. Lobachevsky State University of Nizhny Novgorod.

Skills of Thunderstorm Prediction by Convective Indices over a Metropolitan Area: Comparison of Microwave and Radiosonde Data

Abstract: 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.

On Statistical Characteristics of Electric Fields of the Thunderstorm Clouds in the Atmosphere

Abstract: A series of features of the spectral characteristics of thunderstorm-cloud field perturbations is specified on the basis of long-term ground-based measurements of the electric field at remote locations A significant increase in the spectral density of the electric-field variations during the thunderstorm has been observed Maximum increase due to the pulsed field component, which is related to the lightning discharges, is observed in the fluctuation-period range from tens of seconds to several minutes A significant increase is also observed in the range 05–15 mHz (10–30-min periods) in which the spectral density is increased by more than a factor of 104, whereas the increase factor at the lower frequencies is equal to 100 (about 10 times for the field) Quasimonochromatic components (with 10–20-min periods) in the frequency fluctuation spectra of an electric field of the powerful thunderstorm clouds, which drift by frequency at the cloud initiation, maturity, and dissipation stages are found It is shown that presentation of the sequence of the pulsed field perturbations related to the discharges in the form of a pulse flow with independent intervals (Poisson flow) agrees with the form of the fluctuation spectrum of the observed field and leads to an estimate of 10 s for the average relaxation (regeneration) time of the field in the thunderstorm-cloud vicinity after the discharge

Aeroelectric structures and turbulence in the atmospheric boundary layer

Abstract: . Complex electrical measurements with the use of sodar data show that electric field pulsation analysis is useful for electrodynamics/turbulence monitoring under different conditions. In particular, the number of aeroelectric structures (AES) generated per hour is a convenient measure of the turbulence intensity. During convectively unstable periods, as many as 5–10 AES form per hour. Under stable conditions, AES occasionally form as well, indicating the appearance of occasional mixing events reflected in the electric field perturbations. AES magnitudes under stable conditions are relatively small, except in special cases such as high humidity and fog. The analysis of electric field (EF) spectra gives additional useful information on the parameters of the atmospheric boundary layer and its turbulence. A rather sharp change in the spectrum slope takes place in the vicinity of 0.02 Hz under stable conditions. The characteristic slope of the spectrum and its change are reproduced in a simple model of EF formation.

Experimental Study of Diurnal and Seasonal Variations in the Atmospheric Electric Field

Abstract: In order to separate global and local effects of atmospheric electricity, measurements of the fair-weather electric field were performed in Nizhny Novgorod in 2013-2018. As a result of processing 139 diurnal records from four observation points spaced 6–8 km apart, diurnal variations in the fair-weather atmospheric electric field for different seasons and weekdays (working days and weekends) were studied. The curve of the local diurnal variation is shown to always have two maxima. The evening maximum of the diurnal variation (19:00–20:00 UT) coincides in time with the maximum of the Carnegie curve, which is a characteristic of the global electrical circuit. The highest values of the field amplitude are reached in the winter period. The field-intensity maximum in the first half of the day (09:00–11:00 LT) is characteristic of the urban environment and shows that local effects associated with the presence of aerosol particles in the air significantly contribute to the formation of diurnal variation, especially in summer. According to the 2013–2018 measurements, the seasonal variation in the monthly-average values of the atmospheric electric field is revealed and analyzed compared with the results of measurements of seasonal variation in other regions of the globe. The obtained results allow one to reveal the role of local effects in the formation of diurnal variation in the mid-latitude areas with temperate continental climate and provide a basis for developing a theory which can explain the physical mechanisms of local effects and suggest appropriate parametrization for finding the surface electric field in the weather and climate models.

Pan-Eurasian Experiment (PEEX): towards a holistic understanding of the feedbacks and interactions in the land–atmosphere–ocean–society continuum in the northern Eurasian region

Abstract: . The northern Eurasian regions and Arctic Ocean will very likely undergo substantial changes during the next decades. The Arctic–boreal natural environments play a crucial role in the global climate via albedo change, carbon sources and sinks as well as atmospheric aerosol production from biogenic volatile organic compounds. Furthermore, it is expected that global trade activities, demographic movement, and use of natural resources will be increasing in the Arctic regions. There is a need for a novel research approach, which not only identifies and tackles the relevant multi-disciplinary research questions, but also is able to make a holistic system analysis of the expected feedbacks. In this paper, we introduce the research agenda of the Pan-Eurasian Experiment (PEEX), a multi-scale, multi-disciplinary and international program started in 2012 ( https://www.atm.helsinki.fi/peex/ ). PEEX sets a research approach by which large-scale research topics are investigated from a system perspective and which aims to fill the key gaps in our understanding of the feedbacks and interactions between the land–atmosphere–aquatic–society continuum in the northern Eurasian region. We introduce here the state of the art for the key topics in the PEEX research agenda and present the future prospects of the research, which we see relevant in this context.

Observations of the connection of positive and negative leaders in meter-scale electric discharges generated by clouds of negatively charged water droplets

Abstract: Detailed observations of the connection between positive and negative leaders in meter-scale electric discharges generated by clouds of negatively-charged water droplets are presented and their possible implications for the attachment process in lightning are discussed. Optical images obtained with three different high-speed cameras (visible-range with image enhancement, visible-range regular, and infrared) and corresponding current recordings were used. Two snapshots of the break-through phase of the leader connection, showing significant leader branching inside the common streamer zone, are presented for the first time. Positive and negative leader speeds inside the common streamer zone for two events were found to be similar. Higher leader speeds were generally associated with higher leader currents. In the case of head-to-head leader connection, the infrared brightness of the junction region (probably representing the gas temperature and, hence, the energy input) was typically a factor of 5 or so higher than for channel sections either below or above that region. In 16% of cases, the downward negative leader connected to the upward positive leader below its tip (attached to the lateral surface of the positive leader), with the connection being accomplished via a channel segment that appeared to be perpendicular to one or both of the leader channels.

Skills of Thunderstorm Prediction by Convective Indices over a Metropolitan Area: Comparison of Microwave and Radiosonde Data

Abstract: 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.