Assessment and Calibration of Gilan Radar Precipitation intensity using ground station data

Authors

1 Assistant Professor, Atmospheric Survey Research Group, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran

2 Expert in the Meteorological Office of Gilan, Rasht, Iran

3 Head of the Applied Meteorological Research Group of Gilan, Rasht, Iran

4 Expert in Department of Applied Meteorology, Gilan, Rasht, Iran

Abstract

Radar is a remote sensing instrument that sends electromagnetic waves with specific power to atmosphere and evaluates the amount of return power. It can then measure the difference between the send and retune powers and detect atmospheric phenomena as clouds. Using this tool, there is a wide, continuous and integrated monitoring of atmospheric phenomena. Like any remote sensing device that has, data of weather radars can also have errors. One of the most important measures to eliminate or minimize the radar data errors is calibration, and correction of radar index coefficients. The purpose of this paper is to extract an appropriate relationship for precipitation intensity related to radar reflectivity in Gilan. The Gilan ground based radar installed at the Kiashahr Port is a German-made GEMATRONIK MTEOR1600C type operating in the dual-polarization Doppler radar frequency band (c-band). In general, in order to calibrate the weather radars, “a” and “b” coefficients are required to modify in the Marshall Palmer initial formula for the target area. For this purpose, we tried to estimate the coefficients of this relationship (the relationship between precipitation intensity and radar reflection intensity) in a three years period (2012-2015) and to find new coefficients. In this study, the Doppler filter method (IIR Doppler Filter) was used to remove clutters. This filter was installed in the signal processor. In order to calibrate the Gilan radar, the rain gauge data of the Rasht airport synoptic station was compared with radar data. In this way, the precipitation statistics of the meteorological station were extracted using available meteorological and scdata software in the selected period and were separated based on two views of the season and precipitation severity.
Then the precipitation intensity was calculated based on radar data. Due to the large amount of raw data in Rainbow software, the data format was converted from binary to text. In the next step, the power regression is made between meteorological radar data ( ) and the automatic rain gauge (in mm / h), based on the existing default coefficients. Then, the new coefficient (a’ and b’) were determine by introducing the linear equation, a (a') and a new b (b'). In the third step, the precipitation intensity was re-calculated by applying new coefficients in radar measuremets. Now, there are 2 precipitation intensity values which are obtained by default and new coefficients. The intensity precipitation values were compared with observation of meteorological data using root mean square error in different seasons regardless of intensity. The same process was performed for the severity of observed precipitation and calculated precipitation by the radar regardless of seasons. The most important results of this study are relative improvement of radar estimation from precipitation intensity after correction of coefficients, which was 38% in March to May (spring) and 22% in December to February (winter). During the months of July to November (summer and autumn), there was almost no improvement. Also, based on precipitation intensity (regardless of seasons), average accuracy of precipitation was increased by 25% and severe precipitation by 47%. While in gentle precipitation, this method did not work and there was no improvement.

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References

آخوندعلی، ع.، رادمنش، ف. و شریفی، م.، 1392، ارزیابی و بهبود عدم قطعیت‌های موجود در داده‌های رادار اهواز با تأکید بر کالیبراسیون رابطه R-Z؛ مجله زمین‌شناسی کاربردی پیشرفته؛ شماره 9.
ذاکری، ز.، آزادی، م. و قادر، س.، ١٣٩٤، بررسی اثر داده‌گواری داده­های ماهواره و ایستگاه‌های دیدبانی بر روی پیش­بینی­های مدل WRF، کنفرانس ملی هواشناسی ایران، اردیبهشت ٩٤، یزد.
ریحانی پرور، م.، 1388، خطاهای رادار در تخمین بارش و روش‌های کاهش خطا. نخستین سمینار فناوری سیستم‌های راداری؛ 4 تا 6 اسفند؛ دانشگاه صنعتی شریف.
صفر، م.، احمدی گیوی، ف. و محب‌الحجه، ع.، ١٣٩١، بررسی اثر گوارد داده­های رادار در مدل عددی ARPS در شبیه­سازی بارش حاصل از سامانه همدیدی ٣١ مارس ٢٠٠٩ در منطقه تهران، مجله ژئوفیزیک ایران، 6(3)، ١١٢-٩٤.
صفر، م.، احمدی گیوی، ف. و گلستانی، ی.، 1395، کنترل کیفی داده‌های رادار هواشناسی با استفاده از ساختار افقی و قائم برگشت‌پذیری، مجله ژئوفیزیک ایران، 10(2)، 120-131.
صفر، م.، احمدی گیوی، ف.، 1396، گزینش طرح‌واره همرفت بهینه بر مبنای داده‌‌‌های رادار در حین اجرای مدل WRF برای پیش‌‌‌بینی کوتاه‌مدت بارش، مجله فیزیک زمین و فضا، 43(3)، 585-600
محمدیها، ا.، معماریان، م. و ریحانی پروری، م.، 1392، ارزیابی برآوردهای رادار هواشناسی تهران از کمیت بارش به روش  Z-Rبرای سه رویداد بارش سال‌های 2010 و 2011، فصلنامه فیزیک زمین و فضا، شماره 372، ص 187-204.
نیستانی، الف.، قادر، س. و محب‌الحجه، ع.، 1391، کاربست داده‌گواری در مدل WRF برای شبیه‌سازی بارش ناشی از یک سامانه همدیدی در غرب ایران، مجله فیزیک زمین و فضا، 11(1)،101-123.
Bellon, A. and Fabry, F., 2014, Real-Time Radar Reflectivity Calibration from Differential Phase Measurements. Journal of Atmospheric and Oceanic Technology, 31(5), 1089-1097.
McRoberts, B. and Nielsen-Gammon, J. W., 2017, Detecting of Beam Blockage in Radar-Based Precipitation Estimates. Journal of Atmospheric and Oceanic Technology, 34(7), https://doi.org/10.1175/JTECH-D-16-0174.
Cremonini, R. and Behini, R., 2010, Heavy rainfall monitoring by polarimetric c-band weather radars. Water, 2, 838-848.
Einfalt, T. J. M. and Mehlig, B., 2005, Comparison of radar and raingauge measurements during heavy rainfall. Water Science and Technology, 51, 195-201.
Fisher, O., GyuWan, Z., Zawadi, L. and Zak, I., 2006, Radar calibration by gauge, disdrometer, and polarmetry: Theoretical limit caused by the variability of drop size distribution and application to faset scanning operational radar data. Journal of Hydrology, 328, 83-97.
Keränen, R. and Chandrasekar, V., 2014, Detection and Estimation of Radar Reflectivity from Weak Echo of Precipitation in Dual-Polarized Weather Radars. Atmospheric and Oceanic Technology, 31(8), 1677-1693.
Lee, G. W., 2006, Sources of Errors in Rainfall Measurements by Polarimetric Radar: Variability of Drop Size Distributions, Observational Noise, and Variation of Relationships between R and Polarimetric Parameters. Journal of Atmospheric and Oceanic Technology, 23(8), 2005-2026.
Lynn Baeck, M. and Smith, J. A., 1998, Rainfall Estimation by the WSR-88D for Heavy Rainfall Events, AMS Journal, 13, 416-436.
Marshall, J. S. and Palmer, W. M., 1948, Shorter Contributions, the Distribution of Raindrops with Size, Journal of Meteorology, 5, 165-167.
Qi, Y., Zhang, J., Kaney, B., Langston, C. and Howard, K., 2014, Improving WSR-88D Radar QPE for Orographic Precipitation Using Profiler Observations, 15(3), 1135-1151.
Speirs, P., Marco Gabella, M. and Berne, A., 2017, A Comparison between the GPM Dual-Frequency Precipitation Radar and Ground-Based Radar Precipitation Rate Estimates in the Swiss Alps and Plateau, Journal of Hydrometeorology, 18(5), 1247-1269.
Suzana, R. and Wardah, T., 2011, Radar Hydrology: New Z/R Relationships for Klang River Basin, Malaysia, International Conference on Environment Science and Engineering IPCBEE vol.8, IACSIT Press, Singapore.

Thompson, E. J. S.,  Rutledge, A., Dolan, B., Chandrasekar, V. and Leng Cheong, B., 2014, A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation. Journal of Atmospheric and Oceanic Technology, 31(7), 1457-1481.

Tokay, A., Hartmann, P., Battaglia, A., Gage, K. S., Clark, W. L. and Williams, Ch. R., 2009, A Field Study of Reflectivity and Z–R Relations Using Vertically Pointing Radars and Disdrometers. Journal of Atmospheric and Oceanic Technology, 26(6), 1120-1134.

WMO No. 306, 2014, Manual on Codes, International Codes, Vol. 1, Part 1.
Wolff, D. B.,  Marks, D. A. and Petersen, W. A., 2015, General Application of the Relative Calibration Adjustment (RCA) Technique for Monitoring and Correcting Radar Reflectivity Calibration. Journal of Atmospheric and Oceanic Technology, 32(3), 496-506.
Wu, W., Kitzmiller, D. and Wu, Sh., 2012, Evaluation of Radar Precipitation Estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the Conterminous United States. Journal of Hydrometeorology, 13(3), 1080-1093.
Journal of the Earth and Space Physics
Volume 44, Issue 3
November 2018
Pages 679-695

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