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Calibration of weather radar using region probability matching method (RPMM)

Author:
Ayat, Hooman, Reza Kavianpour, M., Moazami, Saber, Hong, Yang, Ghaemi, Esmail
Source:
Theoretical and applied climatology 2018 v.134 no.1-2 pp. 165-176
ISSN:
0177-798X
Subject:
developing countries, radar, rain gauges, uncertainty, weather, Iran
Abstract:
This research aims to develop a novel method named region probability matching method (RPMM) for calibrating the Amir-Abad weather radar located in the north of Iran. This approach also can overcome the limitations of probability matching method (PMM), window probability matching method (WPMM), and window correlation matching method (WCMM). The employing of these methods for calibrating the radars in light precipitation is associated with many errors. Additionally, in developing countries like Iran where ground stations have low temporal resolution, these methods cannot be benefited from. In these circumstances, RPMM by utilizing 18 synoptic stations with a temporal resolution of 6 h and radar data with a temporal resolution of 15 min has indicated an accurate estimation of cumulative precipitation over the entire study area in a specific period. Through a comparison of the two methods (RPMM and traditional matching method (TMM)) on March 22, 2014, the obtained correlation coefficients for TMM and RPMM were 0.13 and 0.95, respectively. It is noted that the cumulative precipitation of the whole rain gauges and the calibrated radar precipitation at the same pixels were 38.5 and 36.9 mm, respectively. Therefore, the obtained results prove the inefficiency of TMM and the capability of RPMM in the calibration process of the Amir-Abad weather radar. Besides, in determining the uncertainty associated with the calculated values of A and B in the Z ₑ –R relation, a sensitivity analysis method was employed during the estimation of cumulative light precipitation for the period from 2014 to 2015. The results expressed that in the worst conditions, 69% of radar data are converted to R values by a maximum error less than 30%.
Agid:
6152401