Article

Article title RECOGNITION AND SUPPORT OF CLOUD CONVECTIVE CELLS WITH THE AIM OF THE CURRENT FORECAST OF DANGEROUS WEATHER PHENOMENA
Authors V. A. Shapovalov, Kh. A. Tumgoeva
Section SECTION I. METHODS AND ALGORITHMS OF INFORMATION PROCESSING
Month, Year 03, 2018 @en
Index UDC 004.93:551.5
DOI
Abstract To date, the means of remote sensing of the atmosphere have achieved great success, at the same time, there are modern methods of operational data processing, all this requires updating and development of existing meteorological software. The current forecast of dangerous weather events is necessary at the stage of obtaining and processing the operational data of observations, the period of its relevance is the time between updating the information of most remote sensing methods in meteorology (3–30 min). The paper deals with the problem of detection, recognition and tracking for the current forecast of mesoscale meteorological conditions by radar, satellite and ground-penetrating data. The methods and algorithms of analysis of cloud convective cells dynamics are proposed. The problems of data integration as the basis of sustainable recognition of rapidly developing dangerous weather events are considered. Comprehensive studies of microphysical and electrical processes in the atmosphere were carried out using the active-passive geophysical monitoring complex constructed by FGBU "VGI", consisting of a meteorological radar and a network of sensors of the automatic thunder direction finder, rangefinder LS 8000, which work in comparison with the data of the network of ground meteorological observation points allowed to significantly improve the quality of detection and prediction of natural hazards. It is established that electric processes precede the appearance of dangerous hydrometeorological phenomena and thus are predictors of stages and trends of their development in the atmosphere. These predictors include: occurrence in the cloud of inter-cloud and cloud – earth discharges, the intensity of discharges per unit of time, the amplitude of the current of lightning, the rise time of the current, the sign of lightning discharge, the location of discharges in the cloud, etc. For example, a sharp increase in the intensity of intra-cloud discharges in the cloud (up to 60 times./min) indicates that a tornado or flurry of flocks dangerous to aviation may occur after 10-15 minutes. The reverse polarity of the lightning, mainly from negative to positive, indicates the beginning of the period of formation of hail particles in the cloud and the beginning of their loss, and, after the end of the hazardous stage, the polarity is restored. Examples of the developed system of information aggregation and current forecast of convective cloud cells movement are given. The high efficiency of automatic data processing in increasing the advance warning about the dangerous phenomena of convective origin is shown.

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Keywords Detection and recognition; convective clouds; extreme weather events; nowcasting; weather radar; lightning sensor.
References 1. Akimov L.M., Novikova S.S., Lisichenko E.A. Avtomatizirovannaya sistema rascheta opasnykh yavleniy pogody [Automated system of calculation of dangerous weather events], Materialy Mezhdunarodnoy nauchnoy konferentsii «Regional'nye effekty global'nykh izmeneniy klimata (prichiny, posledstviya, prognozy)» [Proceedings of the International scientific conference "Regional effects of global climate change (causes, consequences, forecasts)"], 2012, pp. 455-458.
2. Mazur I.I, Ivanov O.P. Opasnye prirodnye protsessy [Dangerous natural processes]. Moscow: Ekonomika, 2004, 702 p.
3. Atlas prirodnykh i tekhnogennykh opasnostey i riskov chrezvychaynykh situatsiy v Rossiyskoy Federatsii [Atlas of natural and technogenic dangers and risks of emergencies in the Russian Federation], under the General editorship of Sergei Shoigu]. Moscow: IPTs «Dizayn. Informatsiya. Kartografiya», 2005, 270 p.
4. Adzhieva A.A., Shapovalov V.A., Mashukov I.Kh., Skorbezh N.N., Shapovalov M.A. Obnaruzhenie i raspoznavanie opasnykh konvektivnykh protsessov radiotekhnicheskimi sredstvami [Detection and recognition of dangerous convective processes by radio engineering means], Izvestiya vysshikh uchebnykh zavedeniy. Severo-Kavkazskiy region. Seriya: Estestvennye nauki [Proceedings of higher educational institutions. North Caucasus region. Series: Natural Sciences], 2014, No. 1 (179), pp. 59-62.
5. Adzhiev A.Kh., Stasenko V.N., Shapovalov A.V., Shapovalov V.A. Napryazhennost' elektricheskogo polya atmosfery i grozovye yavleniya na Severnom Kavkaze [The intensity of atmospheric electric field and thunderstorm phenomena in the North Caucasus], Meteorologiya i gidrologiya [Meteorology and hydrology], 2016, No. 3, pp. 46-54.
6. Verbitskaya E.M., Romanskiy S.O. Primenenie vysokoproizvoditel'nykh sredstv dlya prognoza opasnykh yavleniy pogody konvektivnoy prirody [The use of high-performance tools for the forecast of dangerous weather phenomena of convective nature], Materialy III vserossiyskoy nauch.-prakt. konf. «Informatsionnye tekhnologii i vysokoproizvoditel'nye vychisleniya» [Materials of the III all-Russian scientific.-prakt. conf. “Information technology and high-performance computing”], 2015, p. 18-21.
7. Adzhieva A.A, Shapovalov V.A., Mashukov I.Kh. Metody obrabotki i predstavleniya radiolokatsionnoy meteorologicheskoy informatsii na territorii Severnogo Kavkaza [Methods of processing and presentation of radar meteorological information to the North Caucasus], Izvestiya vuzov. Severo-Kavkazskiy region. Estestvennye nauki. Fizika atmosfery. Spetsvypusk [News universities. North Caucasus region. Natural science. Atmospheric physics. Special issue], 2010, pp. 12-17.
8. Shapovalov V.A. Chislennoe issledovanie mikrostrukturnykh i elektricheskikh kharakteristik konvektivnykh oblakov [Numerical study of the microstructural and electrical characteristics of convective clouds], Protsessy v geosredakh [Processes in GeoMedia], 2018, No. 1 (14),
pp. 804-810.
9. Shapovalov V.A., Shapovalov M.A. Raspoznavanie opasnykh konvektivnykh protsessov s primeneniem algoritmov neyronnykh setey (Neural Network) i komp'yuternogo zreniya (Computer Vision) [Recognition of dangerous convective processes using neural network (Neural Network) and computer vision (Computer Vision) algorithms], Materialy Vserossiyskoy otkrytoy konferentsii po fizike oblakov i aktivnym vozdeystviyam na gidrometeorologicheskie protsessy, posvyashchennoy 80-letiyu El'brusskoy vysokogornoy kompleksnoy ekspeditsii AN SSSR [Materials of the all-Russian open conference on cloud physics and active effects on hydrometeorological processes, dedicated to the 80th anniversary of the Elbrus complex high-mountain expedition of the USSR Academy of Sciences]. Nal'chik, 2014, pp. 148-154.
10. Meyer V.K., Holler H., Betz H.D. Automated thunderstorm tracking: utilization of three-dimensional lightning and radar data, Atmos. Chem. Phys., 2013, No. 13, pp. 5137-5150.
11. Yu Liu, Du-Gang Xi, Zhao-Liang Li, Chun-Xiang Shi. Automatic Tracking and Characterization of Cumulonimbus Clouds from FY-2C Geostationary Meteorological Satellite Images, Advances in Meteorology, 2014, Vol. 2014, pp. 18.
12. Shah S., Notarpietro R., Branca M. Storm Identification, Tracking and Forecasting Using High-Resolution Images of Short-Range X-Band Radar, Atmosphere, 2015, No. 6, pp. 579-606.
13. Johnson J.T., MacKeen P.L., Witt A., et al. The storm cell identification and tracking algorithm: an enhanced WSR-88D algorithm, Weather Forecast, 1998, No. 13, pp. 263-276.
14. Novo S., Martínez D., Puentes O. Tracking, analysis, and nowcasting of Cuban convective cells as seen by radar, Met. Apps., 2014, Vol. 21, pp. 585-595.
15. Lakshmanan V., Rabin R., DeBrunner V. Multiscale Storm Identification and Forecast, Elsevier Atmospheric Research, 2003, pp. 67-68, 367-380.
16. Dixon M., Weiner G. TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting–A Radar-based Methodology, Journal of Atmospheric and Oceanic Technology, 1993, Vol. 10 (6), pp. 785-797.
17. del A. Moral, T. Rigo, M. Llasat. Identification of anomalous motion of thunderstorms using radar and satellite data: the splitting thunderstorm of the 10th July 2013 in Catalonia, 15th Plinius Conference on Mediterranean Risks, 2016, Vol. 15, pp. 1-20.
18. Han L., Fu S., Zhao L., Zheng Y., Wang H., Lin Y. 3D Convective Storm Identification, Tracking and Forecasting-An Enhanced TITAN Algorithm, J. Atmos. Oceanic Technol., 2009,
Vol. 26, pp. 719-732.
19. Bally J. The Thunderstorm Interactive Forecast System: Turning Automated Thunderstorm Tracks into Severe Weather Warnings, Wea. Forecasting, 2004, No. 19, pp. 64-72.
20. Adzhieva A.A., Shapovalov V.A., Boldyreff A.S. Development of thunderstorm monitoring technologies and algorithms by integration of radar, sensors and satellite images, Proc. SPIE 10424, Remote Sensing of Clouds and the Atmosphere XXII, 104240H (20 October 2017); doi: 10.1117/12.2299289.
21. Herzegh P., Wiener G., Bateman R., Cowie J., Black J. Data fusion enables better recognition of ceiling and visibility hazards in aviation, Bulletin of the American Meteorological Society, 2015, Vol. 96 (4), pp. 526-532.
22. Nayak M.A., Ghosh S. Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier, Theoretical and Applied Climatology, 2013, Vol. 114,
I. 3-4, pp. 583-603.
23. Matveev L.T. Dinamika oblakov [Dynamics of clouds]. Leningrad: Gidrometeoizdat, 1981, 311 p.
24. Mazin I.P., Shmeter S.M. Oblaka, stroenie i fizika obrazovaniya [Shmeter Clouds, structure and physics of formation]. Leningrad: Gidrometeoizdat, 1983, 280 p.
25. Ivanova A.R., Shakina N.P. Perspektivy razvitiya naukastinga dlya meteorologicheskogo obespecheniya aviatsii v ramkakh realizatsii global'nogo aeronavigatsionnogo plana (GANP) [Prospects for the development of science casting for meteorological support of aviation in the framework of the implementation of the global air navigation plan (GANP)], Trudy Gidrometeorologicheskogo nauchno-issledovatel'skogo tsentra Rossiyskoy Federatsii [Proceedings of the Hydrometeorological research center of the Russian Federation], 2016,
No. 360, pp. 113-134.

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