Climatological Z-R relationship for radar rainfall estimation in the upper Ping river basin

Abstract

ABSTRACT:Weather radar can potentially provide high-resolution spatial and temporal rainfall estimates bringing more accuracy to flood estimations as well as having some other applications in areas with insufficient rainfall stations like Thailand. Weather radar cannot be used to measure the rainfall depth directly; so an empirical relationship between the reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z = AR b ), is generally used to assess the rainfall depth using radar. In this study, an optimization approach was used to find a suitable climatological Z-R relationship for the upper Ping river basin, Northern Thailand. The reflectivity data between June and October in 2003 and 2004 at the Omkoi radar station located in Chiangmai Province, together with the daily rainfall depths at fifty rainfall stations located in and around the basin during the same periods were used. A climatological Z-R relationship in the form Z = 74R 1.6 shows acceptable statistical indicators, making it suitable for radar rainfall prediction for the upper Ping river basin.

Modifikasi Konstanta Persamaan Z-R Radar Surabaya Untuk Peningkatan Akurasi Estimasi Curah Hujan

“…Nilai dari a dan b dapat bervariasi pada setiap lokasi dan dapat dipengaruhi oleh beragam kondisi cuaca. Hubungan Z-R biasanya diturunkan dengan menggunakan beberapa metode seperti distribusi ukuran butiran hujan (Droplet Size Distribution) dan metode optimasi (teknik regresi) [5]. Pada metode pertama, nilai Z dan R dihitung secara langsung dengan menggunakan data distribusi ukuran butiran hujan yang dicatat menggunakan disdrometer.…”

Improving Runoff Estimates by Increasing Catchment Subdivision Complexity and Resolution of Rainfall Data in the Upper Ping River Basin, Thailand

“…Weather radar has been used as an alternative tool for providing high-resolution spatial and temporal rainfall estimates, especially in areas with insufficient rainfall stations, like Thailand, in order to enhance flood prediction accuracy. Mapiam and Sriwongsitanon (2008) first developed a climatological Z-R relationship (Z=74R 1.6 ) based on daily data for converting instantaneous reflectivity data into rainfall rate in the Upper Ping River Basin. However, found that using the daily (24-hour) Z-R relationship to estimate hourly radar rainfall can lead to significant errors when estimating extreme rainfall.…”

A terrain‐based weighted random forests method for radar quantitative precipitation estimation

“…By varying the co-efficients a and b in Equation (2), the performance of the Z-R relationship can be further improved. Hence, a total of 59 200 (the co-efficient a varies from 31 to 400 and b varies from 1.1 to 1.9) kinds of Z-R relationships (Mapiam and Sriwongsitanon, 2008) are tested in the testing set, and the results are shown in Figure 5. In this study, radar QPE using Z = 48R 1.77 outperforms other Z-R relationships for the RMSE.…”

“…For instance, Steiner et al (1995) and Qi et al (2013) investigated the segregation for stratiform and convective precipitation, and applied different Z-R relationships for different precipitation types. Mapiam and Sriwongsitanon (2008) and Ramli et al (2011) used an optimization method to obtain climatological Z-R relationships for local regions. Huang et al (2015) combined radial velocity with radar reflectivity and used a support vector machine to obtain a new rainfall estimation model.…”