Recognition of Atmospheric Formations by Adaptive Lattice Filter' Parameters
DOI:
https://doi.org/10.20535/RADAP.2022.88.15-23Keywords:
meteorological radar, turbulence, recognition meteorological formations, adaptive lattice filter, non-energy parameters, correlation coefficient, order of autoregressive processAbstract
The paper deals with algorithms for recognizing atmospheric formations with various coherence meteorological radars. It shows that the known recognition algorithms differ in the degree of complexity, and in the completeness of the vector of phenomena and meteorological formation (MF) types to be recognized. Besides, no single structural and algorithmic basis that allows unifying the measurement and recognition problems. To solve this problem, we propose to use the parameters of adaptive lattice filters (ALF), obtained at a stage of ALF tuning with the help of radar returns from MFs. The proposed algorithm is tested using an annual cycle of experimental data on the amplitude fluctuations of incoherent 3-cm radiowave signals reflected from different cloud types. The recognition statistical characteristics obtained with known and proposed methods are compared. It is demonstrated that the proposed way is practically not inferior to the known ones in terms of the accuracy of recognition of returns from MF but it is directly realized while measuring the amplitude fluctuations spectrum of the returns, and this favorably distinguishes it from the others. The tests confirmed the proposed algorithm effectiveness. A unified structural and algorithmic basis for practical realization of the ALF-based measurements of MF parameters and for recognition of dangerous meteorological phenomena is proposed. We show that the proposed algorithm and its practical implementation can, with minor changes, be used in coherent and incoherent radars, as well as in meteorological channels of non-meteorological radars.
References
References
Kollias P., Bharadwaj N., Clothiaux E. E., et al. (2020). The ARM Radar Network: At the Leading Edge of Cloud and Precipitation Observations. Bulletin of the American Meteorological Society, Vol. 101, Iss. 5. DOI: 10.1175/BAMS-D-18-0288.1.
Dzung Nguyen-Le, Tomohito J. Yamada. (2019). Using Weather Pattern Recognition to Classify and Predict Summertime Heavy Rainfall Occurrence over the Upper Nan River Basin, Northwestern Thailand. Weather and Forecasting, Vol. 34, Iss. 2, pp. 345–360. DOI: 10.1175/WAF-D-18-0122.1.
Qing Meng, Wen Yao, Liangtao Xu. (2019). Development of Lightning Nowcasting and Warning Technique and Its Application. Advances in Meteorology, Article ID 2405936. doi:10.1155/2019/2405936.
Bazlova T. A., Bocharnikov N. V., Brylyov G. B., Solonin A. S., et al., exec. ed. A. S. Solonin. (2015). Radar meteorological information in aero navigation. St. Petersburg: Russian State Hydro Meteorological University (RSHMU), 363 p.
Bolelov E. A. (2018). Meteorological service for civil aviation: problems and ways of their solution. Civil Aviation High Technologies, Vol. 21, No. 5, pp. 117-129. doi:10.26467/2079-0619-2018-21-5-117-129. (In Russian).
Bazlova T. A., et al., ed. by Solonin A. S. (2010). Radar meteorological observations. V. II: Issues of practical application of the radar meteorological information. St. Petersburg: Nauka. 517 p.
Khyong N. V. (2020). Algorithm for detection of meteorological formations by results of primary processing in landing airfield radar. Proceedings of MPTI, Vol. 12, No. 2, pp. 117-125.
Zhukov V. Yu. (2019). Recognition and investigations of dangerous weather phenomena in multiparametric meteorological radar. Thesis for Doctor of phys. and math. sciences degree: 25.00.30 / A. F. Mozhaisky Military Space Academy, St. Petersburg, 312 p. http://www.rshu.ru/university/dissertations/files/230.
Song K., Liu X., Gao T., He B. (2019). Raindrop Size Distribution Retrieval Using Joint Dual-Frequency and Dual-Polarization Microwave Links. Advances in Meteorology, Article ID 7251870. DOI: 10.1155/2019/7251870.
K. Lamer , B. Puigdomènech Treserras, Z. Zhu, B. Isom, N. Bharadwaj, and P. Kollias. (2019). Characterization of shallow oceanic precipitation using profiling and scanning radar observations at the Eastern North Atlantic ARM observatory. Atmospheric Measurement Techniques, Vol. 12, Iss. 9, pp. 4931–4947. DOI: 10.5194/AMT-12-4931-2019.
Zheng X. et al. (2019). Detecting Comma-Shaped Clouds for Severe Weather Forecasting Using Shape and Motion. IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 6, pp. 3788-3812. DOI: 10.1109/TGRS.2018.2887206.
Skripniková K., Rezácová D. (2019). Comparison of Radar-Based Hail Detection Using Single- and Dual-Polarization. Remote Sensing, Vol. 11(12), pp. 1436-1448. DOI: 10.3390/rs11121436.
Naumenko V., Totsky A. V., Khlopov G., Voitovych O., Astola J. T. (2016). Classification of the atmospheric formations by using bicoherence-based features extracted from weather radar backscattering signals. Telecommunications and Radio Engineering, Vol. 75(5), pp. 463-475. DOI:10.1615/TelecomRadEng.v75.i5.70.
Bezruk V. M., Belov E. N., Voitovich O. A., et al. (2011). Radar recognition of meteorological objects by echo-signal fluctuations intensity. Proc. of IV International Radioelectronic forum ''Applied Radioelectronics. State-of-the-art and prospects of development'', Kharkiv. V.1. 18-21 October 2011, pp. 83-86.
Voitovich O. A., Zatserklyana A. V., Rudnev G., et al. (2015). Investigations of fluctuations of incoherent signals scattered by clouds. Radiofizika I elektronika, Vol. 6(20), No. 2, pp. 48-52.
Masalov E. V., Krivin N. N. (2019). Precision characteristics of the differential radar reflectivity meter. Journal of Siberian Federal University. Engineering & Technologies, Vol. 12(1), pp. 97–105. DOI:10.17516/1999-494X-0028.
Gagne II D. J., Haupt S. E., Nychka D. W., Thompson G. (2019). Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms. Monthly Weather Review, Volume 147, Issue 8, Page(s): 2827–2845. DOI: 10.1175/MWR-D-18-0316.1.
Wang, X., Wang, J., Miao, C., Zeng K. (2020). Forewarning method of downburst based on feature recognition and extrapolation. Natural Hazards, Vol. 103(1), pp. 903–921. DOI: 10.1007/s11069-020-04018-4.
Kalesse, H., Vogl, T., Paduraru, C., Luke, E. (2019). Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm. Atmospheric Measurement Techniques, Vol. 12, Iss. 8, pp. 4591–4617. doi:10.5194/amt-12-4591-2019.
Zhou L., Dong X., Fu Z. et al. (2020). Vertical Distributions of Raindrops and Z-R Relationships Using Microrain Radar and 2-D-Video Distrometer Measurements During the Integrative Monsoon Frontal Rainfall Experiment (IMFRE). Journal of Geophysical Research, Vol. 125, Iss. 3. DOI: 10.1029/2019JD031108.
Qing H., Chu Y., Zhao Z., Su C., Zhou C., and Zhang Y. (2017). Observation and analysis of atmospheric rainfall based on the very high frequency radar . IET Radar, Sonar & Navigation, Vol. 11, Iss. 4, pp. 616–620. doi:10.1049/iet-rsn.2016.0089.
Doviak, R. J., and D. S. Zrnić (1993). Doppler Radar and Weather Observations. 2nd ed. Academic Press, 562 p.
The order of applying the automated meteorological radar complex ''ASU-MRL'' in practice of storm warning and anti-hail protection. Methodological instructions. RD 52.37.XXX-2008. Moscow: Rosgidromet, 2008. 84 p.
Rachkov D. S., Lekhovytskiy D. I. (2015). Lattice-filter-based unified structure of system for interperiod processing of weather radar signals. 2015 IEEE Radar Conference (RadarCon), pp. 1234–1239. DOI: 10.1109/RADAR.2015.7131183.
Lekhovitskiy D. I., Riabukha V. P., Atamanskiy D. V., Semeniaka A. V., Rachkov D. S. (2021). Lattice filtration theory. Part I: One-dimensional lattice filters. Telecommunications and Radio Engineering, Vol. 80, Iss. 5, pp. 41-79. DOI:10.1615/TelecomRadEng.2021039186.
Lekhovitskiy D. I. (2018). Adaptive lattice filters for systems of space-time processing of non-stationary Gaussian processes. Radioelectronics and communications systems, Vol. 61, No. 11, pp. 607–644. DOI: 10.3103/S0735272718110018.
Lekhovitskiy D. I., Rachkov D. S., Semeniaka A. V. (2015). K-rank modification of adaptive lattice filter parameters. 2015 IEEE Radar Conference (RadarCon), pp. 127–132. DOI: 10.1109/radar.2015.7130983.
Atamanskiy, D. V., Semeniaka, A. V. & Krasnoshapka, I. V. (2021). Width Estimation of Non-Gaussian Doppler Velocity Spectra of Meteorological Formations. Radioelectronics and Communications Systems, Vol. 64, pp. 1–13. DOI: 10.3103/S0735272721010015.
Lekhovytskiy, D. I., Atamanskiy, D. V., Rachkov, D. S., Semeniaka A. V. (2017). Estimation of the energy spectrums of reflections in pulse doppler weather radars. Part 3. Statistical analysis of the reconstruction techniques of continuous spectrums of the reflections from meteorological objects. Radioelectronics and Communications Systems, Vol. 60, No. 2, pp. 47–79. DOI: 10.3103/S0735272717020017.
Riabukha V. P., Semeniaka A. V., Katushin E. A., Zaritskiy V. I., Golovin O. O. (2019). Adaptive lattice filter-based digital adaptive system of radar protection against masking clutters. Armour and Military Technology, No. 4 (24), pp. 32-40. DOI: 1034169/2414-0651.2019.3(23).32-40.
Averyanova Yu. A., Prokopenko I. G., Prokopenko K. I., Yanovsky F. J. (2004). Algorithms of turbulance detection with weather radars. Proceedings of the NAU [Visnyk NAU], Vol. 19, No. 1, pp. 41–51. DOI: 10.18372/2306-1472.19.952. [In Ukrainian].
Bazlova T. A., Bocharnikov N. V., Brylev G. B. (2002). Meteorological automated radar networks, exec. ed. G. B. Brylev, St. Petersburg: Gidrometeoizdat, 332 p.
Skolnik, W. E. Ed. (2014). Handbook of radar. Book 1. Moscow: Tekhnosfera, 672p. [in Russian].
Recommendations for working with weather radars ''Kontur-10Ts'' series 5 for flight crews [Rekomendacii po rabote s meteoradiolokatorami «Kontur-10C» seriya 5 dlya letnyh ekipazhej]. https://www.kontur-niirs.ru/static/documents/97.pdf (accessed 29/12/20) [In Russian].
Zhukov V. Yu., Bychkov A. A., Shchukin G. G. (2015). Additional informative capabilities of the small-sized ''Contour METEO-01'' meteorological radar. V All-Russian Armand Readings: Ultra-wideband signals in radiolocation, communication and acoustics. Proceedings of the V All-Russian Scientific Conference, Murom, Russia, 29 June-01 July, pp. 134-139.
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