Assessing specific differential phase (KDP)-based quantitative precipitation estimation for the record- breaking rainfall over Zhengzhou city on 20 July 2021

Abstract

Abstract. Although radar-based quantitative precipitation estimation (QPE) has been widely investigated from various perspectives, very few studies have been devoted to extreme-rainfall QPE. In this study, the performance of specific differential phase (KDP)-based QPE during the record-breaking Zhengzhou rainfall event that occurred on 20 July 2021 is assessed. Firstly, the OTT Parsivel disdrometer (OTT) observations are used as input for T-matrix simulation, and different assumptions are made to construct R(KDP) estimators. KDP estimates from three algorithms are then compared in order to obtain the best KDP estimates, and gauge observations are used to evaluate the R(KDP) estimates. Our results generally agree with previous known-truth tests and provide more practical insights from the perspective of QPE applications. For rainfall rates below 100 mm h−1, the R(KDP) agrees rather well with the gauge observations, and the selection of the KDP estimation method or controlling factor has a minimal impact on the QPE performance provided that the controlling factor used is not too extreme. For higher rain rates, a significant underestimation is found for the R(KDP), and a smaller window length results in a higher KDP and, thus, less underestimation of rain rates. We show that the QPE based on the “best KDP estimate” cannot reproduce the gauge measurement of 201.9 mm h−1 with commonly used assumptions for R(KDP), and the potential factors responsible for this result are discussed. We further show that the gauge with the 201.9 mm h−1 report was in the vicinity of local rainfall hot spots during the 16:00–17:00 LST period, while the 3 h rainfall accumulation center was located southwest of Zhengzhou city.