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شناسایی ابرهای بارش‌زا در جنوب و جنوب‌غرب ایران با استفاده از مشاهدات ماهواره CALIPSO و CloudSat | ||
| فیزیک زمین و فضا | ||
| مقاله 7، دوره 47، شماره 2، مرداد 1400، صفحه 301-314 اصل مقاله (1018.11 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2021.314784.1007266 | ||
| نویسندگان | ||
| فاطمه فلاح‌زاده1؛ حسن لشکری* 2؛ علیرضا محمودیان3؛ علی‌اکبر متکان4 | ||
| 1دانشجوی دکتری، گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران | ||
| 2استاد، گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران | ||
| 3استادیار، گروه فیزیک فضا، مؤسسه ژئوفیزیک، دانشگاه تهران، تهران، ایران | ||
| 4استاد، گروه سنجش از دور و GIS، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران | ||
| چکیده | ||
| هدف اصلی این مطالعه، تشخیص ابرهای بارش‌زا و تحلیل ساختار قائم آنها در جنوب و جنوب‌غرب ایران با استفاده از مشاهدات ماهواره CALIPSO و CloudSat است. نخست با استفاده از بارش روزانه ایستگاه‌های همدیدی منطقه مطالعاتی طی دوره آماری ۲۰۰۶ تا ۲۰۱۶ نمونه‌های بارشی و روزهای اوج بارش آنها استخراج شد. سپس جهت اطمینان از وقوع بارش همزمان با لحظه گذر مدار ماهواره‌ها از روی منطقه، از بارش شبکه‌ای ماهواره TRMM استفاده شد. با بررسی مقادیر بارش شبکه‌ای روزهای اوج، سه نمونه بارشی که بارش منطبق بر مسیر ماهواره‌ها رخ داده بود، برای تحلیل ساختار ابر آنها انتخاب شد. چهار ویژگی شامل لایه‌های تضعیف مجموع بازپراکنش در طول‌موج ۵۳۲ نانومتر، نسبت دیپلاریزاسیون، نسبت رنگی و بازپراکنش رادار با استفاده از داده‌های سنجنده CALIOP و CPR تهیه شد. نتایج تحلیل‌ها نشان داد که در نمونه اول (مسیر A) برخلاف ضخامت زیاد ابر (تقریباً ۱۰ کیلومتر)، حجم بارش کمتر از دو نمونه دیگر است. لایه‌های ابر در راستای قائم به اندازه کافی متراکم و یکپارچه نیست. همچنین ذرات هواویز و بلورهای یخ موجود در ابر به لحاظ تعداد کمتر و از نظر اندازه نیز کوچک‌تر است. در حالی‌که در دو نمونه دیگر به‌خصوص در مسیر C ضمن این‌که ابر ضخیم و متراکمی جو منطقه را پوشانده است، غلظت هواویزها و کریستال‌های یخ نیز به مراتب بیشتر است. در مجموع یافته‌های تحقیق نشان داد که با استفاده از مشاهدات ماهواره CloudSat تشخیص ابرهای بارش‌زا و شدت بارش امکان‌پذیر است و داده‌های ماهواره CALIPSO جهت شناسایی دقیق ارتفاع قله ابر و به‌خصوص تمایز ابر از هواویز کاربرد بهتری دارد. | ||
| کلیدواژه‌ها | ||
| ابر بارش‌زا؛ بازپراکنش رادار؛ کالیپسو؛ کلودست | ||
| عنوان مقاله [English] | ||
| Identification of precipitating clouds in the south and southwest of Iran using CALIPSO and CloudSat satellite observations | ||
| نویسندگان [English] | ||
| Fateme Fallahzade1؛ Hassan Lashkari2؛ Ali Reza Mahmoudian3؛ Ali Akbar Matkan4 | ||
| 1Ph.D. Student, Department of Natural Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran | ||
| 2Professor, Department of Natural Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran | ||
| 3Assistant Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran | ||
| 4Professor, Remote Sensing and GIS Center, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran | ||
| چکیده [English] | ||
| The main purpose of this study is to detect precipitating clouds and to analyze their vertical structures in the south and southwest of Iran using CALIPSO and CloudSat satellite observations. At First, events with high precipitation rates using the daily precipitation data of the synoptic stations in the area of interest during the statistical period from 2006 to 2016 were selected. The selection of these samples is based on two parameters: the average precipitation of the synoptic system and the number of stations involved in precipitation. The average precipitation of the system was calculated by the ratio of the total precipitation of all stations in one day to the number of stations involved in precipitation on the same day. In order to eliminate light precipitating samples, a precipitation threshold was set for the mentioned parameters. So that at least in one of the days of precipitating system activity, the number of stations involved in precipitation is not less than 15 stations and the average precipitation of the system is not less than 15 mm. This threshold is defined as the day of peak precipitation. In total, 74 precipitating systems that lasted from one day to one week were determined and 107 days of precipitation with the above specifications were selected. In order to ensure the occurrence of precipitation at the same time as the satellite orbit passing through the area, TRMM satellite level 3B precipitation data was used. These data have precipitation values in a temporal interval of 30 minutes and spatial resolution of 0.1 by 0.1 degrees. Considering the network precipitation values of peak days, three precipitating samples in three different paths where the precipitation occurred along the satellite path, were selected to analyze their cloud structures. Precipitation characteristics of the mentioned systems were extracted based on station and network precipitation values. In the next stage, three features including the total attenuated backscatter at 532 nm, the depolarization ratio and the color ratio were obtained by the use of CALIOP lidar level 1B data. The radar reflectivity feature was also extracted using data of CPR sensor of CloudSat. Then, using layers extracted from CALIOP and CPR sensors, the clouds of these samples were compared and analyzed in terms of cloud thickness and precipitation intensity. The results of the analysis showed that in the first sample (Path A), despite the large thickness of the cloud (approximately 10 km), the amount of precipitation is less than the other two samples. The cloud of this sample is different from the other two samples. Cloud layers in the vertical direction are not dense and integrated enough. Also, aerosol particles and ice crystals in the cloud are fewer and smaller. While in the other two samples, especially in path C, while the thick and dense cloud covers the atmosphere of the region, the concentrations of aerosols and ice crystals are much higher. | ||
| کلیدواژه‌ها [English] | ||
| precipitating cloud, radar reflectivity, CALIPSO, CloudSat | ||
| مراجع | ||
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Adler, R. F., Huffman, G. J., Bolvin, D. T., Curtis, S. and Nelkin, E. J., 2000, Tropical rainfall distributions determined using TRMM combined with other satellite and rain gauge information. Journal of Applied Meteorology, 39(12), 2007-2023. Brakhasi, F., Matkan, A., Hajeb, M. and Khoshelham, K., 2018, Atmospheric scene classification using CALIPSO spaceborne lidar measurements in the Middle East and North Africa (MENA), and India. International Journal of Applied Earth Observation and Geoinformation, 73, 721–735. Eric, E., Wandjie, B. B. S., Lenouo, A., Monkam, D. and Manatsa, D., 2020, African summer monsoon active and break spells cloud properties: Insight from CloudSat-CALIPSO. Atmospheric Research, 237, 104842. Haynes, J. M., L’Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu, C., Wood, N. B. and Tanelli, S., 2009, Rainfall retrieval over the ocean with spaceborne W-band radar. Journal of Geophysical Research Atmospheres, 114(8), 1–18. Hostetler, C. A., Liu, Z., Reagan, J., Vaughan, M., Winker, D., Osborn, M., Hunt, W. H., Powell, K. A. and Trepte, C., 2006, CALIOP algorithm theoretical basis document, calibration and level 1 data products: Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations PC-SCI-201, 1–66. Kawamoto, K. and Suzuki, K., 2013, Comparison of water cloud microphysics over mid-latitude land and ocean using CloudSat and MODIS observations. Journal of Quantitative Spectroscopy and Radiative Transfer, 122, 13–24. Kawamoto, K. and Suzuki, K., 2015, Distributional correspondence of 94-GHz radar reflectivity with the variation in water cloud properties over the northwestern Pacific and China. Journal of Quantitative Spectroscopy and Radiative Transfer, 153, 38–48. Keikhosravi Kiany, M.S., Masoodian, S.A., Balling, R. C. and Montazeri, M., 2020, Evaluation of the TRMM 3B42 product for extreme precipitation analysis over southwestern Iran. Advances in Space Research, 66(9), 2094–2112. Kikuchi, M. and Suzuki, K., 2019, Characterizing vertical particle structure of precipitating cloud system from multiplatform measurements of A-train constellation. Geophysical Research Letters, 46(2), 1040–1048. Kubar, T. L., Hartmann, D. L. and Wood, R., 2009, Understanding the importance of microphysics and macrophysics for warm rain in marine low clouds. Part I: Satellite observations. Journal of the Atmospheric Sciences, 66(10), 2953–2972. Kuma, P., 2010, Visualising data from CloudSat and CALIPSO satellites. 1–73. Kummerow, C., Simpson, J., Thiele, O., Barnes, W., Chang, A. T. C., Stocker, E., Adler, R. F., Hou, A., Kakar, R., Wentz, F., Ashcroft, P., Kozu, T., Hong, Y., Okamoto, K., Iguchi, T., Kuroiwa, H., Im, E., Haddad, Z., Huffman, G. and Nakamura, K., 2000, The status of the tropical rainfall measuring mission (TRMM) after two years in orbit. Journal of Applied Meteorology, 39(12), 1965–1982. Li, S., Li, Y., Sun, G. and Lu, Z., 2018, Macro- and microphysical characteristics of precipitating and non-precipitating stratocumulus clouds over Eastern China, Atmosphere, 9(7), 1–14. Liu, B., Ma, Y., Gong, W. and Zhang, M., 2017, Observations of aerosol color ratio and depolarization ratio over Wuhan. Atmospheric Pollution Research, 8(6), 1113–1122. Liu, Z., Omar, A., Hu, Y., Vaughan, M. and Winker, D., 2005, Part 3: Scene classification algorithms: CALIOP Algorithm and Theoretical …, 1–56. Luo, Z. J., Anderson, R. C., Rossow, W. B. and Takahashi, H., 2017, Tropical cloud and precipitation regimes as seen from near-simultaneous TRMM, cloudsat, and CALIPSO observations and comparison with ISCCP. Journal of Geophysical Research, 122(11), 5988–6003. Marchand, R., Mace, G. G., Ackerman, T. and Stephens, G., 2008, Hydrometeor detection using Cloudsat-An earth-orbiting 94-GHz cloud radar. Journal of Atmospheric and Oceanic Technology, 25(4), 519–533. Stephens, G. L. and Haynes, J. M., 2007, Near global observations of the warm rain coalescence process. Geophysical Research Letters, 34(20), 1–5. Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O’Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A. and Mitrescu, C., 2002, The cloudsat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation. Bulletin of the American Meteorological Society, 83(12). Suzuki, K., Nakajima, T. Y. and Stephens, G. L., 2010, Particle growth and drop collection efficiency of warm clouds as inferred from joint cloudsat and MODIS observations. Journal of the Atmospheric Sciences, 67(9), 3019–3032. Suzuki, K. and Stephens, G. L., 2008, Global identification of warm cloud microphysical processes with combined use of A-Train observations. Geophysical Research Letters, 35(8), 1–5. Suzuki, K. and Stephens, G. L., 2009, Relationship between radar reflectivity and the time scale of warm rain formation in a global cloud-resolving model. Atmospheric Research, 92(4), 411–419. Suzuki, K., Stephens, G. L., Van Den Heever, S. C. and Nakajima, T. Y., 2011, Diagnosis of the warm rain process in cloud-resolving models using joint cloudsat and MODIS observations. Journal of the Atmospheric Sciences, 68(11), 2655–2670. Vaughan, M. A., Winker, D. M. and Powell, K. A., 2005, CALIOP algorithm theoretical basis document part 2 : feature detection and layer properties algorithms. Science, 1–87. Wang, Y., Chen, Y., Fu, Y. and Liu, G., 2017, Identification of precipitation onset based on Cloudsat observations. Journal of Quantitative Spectroscopy and Radiative Transfer, 188, 142–147. Winker, D. M., Hunt, W. H. and McGill, M. J., 2007, Initial performance assessment of CALIOP. Geophysical Research Letters, 34(19), 1–5. Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H. and Young, S. A., 2009, Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology, 26(11), 2310–2323. Wood, R., Kubar, T. L. and Hartmann, D. L., 2009, Understanding the importance of microphysics and macrophysics for warm rain in marine low clouds. Part II: Heuristic models of rain formation. Journal of the Atmospheric Sciences, 66(10), 2973–2990.
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