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Francesco Silvestro, Nicola Rebora, and Luca Ferraris

task is to analyze if a relatively simple algorithm, which involves polarimetric variables for estimating rain rate, can perform well in a real-time framework. The main objective of this work was to investigate the possible improvements to operational polarimetric rainfall estimation and to make a comparison with the use of traditional Z–R techniques. In addition, this work has enabled polarimetric capabilities to be tested on a C-band operational system. From September 2005 to October 2007, nine

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Robert J. Kuligowski, Yaping Li, Yan Hao, and Yu Zhang

an operational forecasting environment, precipitation information needs to be available with very little delay and updated frequently (e.g., Zhang et al. 2013 ), and this has made it challenging to directly use PMW information in an operational environment. Consequently, despite the challenges of using IR data for estimating rainfall, it remains an important source of information for real-time forecasting applications. As a legacy algorithm based on concepts from the 1970s [before water vapor

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Yalei You, Nai-Yu Wang, Ralph Ferraro, and Patrick Meyers

). Researchers have historically focused more on the precipitation retrieval algorithm development for imagers. For example, numerous algorithms, either using regression model or Bayesian technique, have been developed for the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS; e.g., Spencer et al. 1989 ; Liu and Curry 1992 ; Petty 1994 ; Ferraro and Marks 1995 ; McCollum and Ferraro 2003 ; Sanò et al. 2013 ; You et al. 2015 ). Similarly, a variety of the

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Haonan Chen, V. Chandrasekar, and Renzo Bechini

DPR and passive radiometer on board GPM extend the observation range attained by TRMM from tropics to most of the globe and provide accurate measurement of rainfall, snowfall, and other precipitation types. Through improved measurements of precipitation, the GPM mission is helping to advance our understanding of Earth’s water and energy cycle, as well as climate changes. As an indispensable part of the GPM mission, ground validation helps to develop the radar and radiometer retrieval algorithms by

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Marios N. Anagnostou, Emmanouil N. Anagnostou, Jothiram Vivekanandan, and Fred L. Ogden

(2002) argue that the redundancy among Z H , Z DR , and K DP precludes the retrieval of all three DSD parameters in Eq. (1) with this parameter set. Bringi et al. (2002) applied the method only to situations in which K DP ≥ 0.3° km −1 (rain rates >20 mm h −1 ). Hence, the technique has limited application for general DSD retrievals. In section 2 we provide background information on radar observations and a brief review of the attenuation correction algorithm. Section 3 gives an

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Jian Zhang and Youcun Qi

small improvement in the RMAE for KFWS 20080527 event, and in RMB scores for KLBB20090209, KMAF, and KFWS20090311 events were due to the imperfect convective–stratiform segregation as mentioned earlier in this paper (see section 2 ). 4. Summary and future work A real-time algorithm for the correction of brightband (BB) effects in radar precipitation estimation was developed. The correction was based on the radar observed (“apparent”) vertical profiles of reflectivity (AVPRs) from volumetric

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Tim Bellerby, Kuo-lin Hsu, and Soroosh Sorooshian

) information on cloud patterns but are not directly sensitive to precipitation processes within the clouds. Most high-resolution satellite rainfall algorithms combine information from GEO imagery and LEO MW sensors. These algorithms may be divided into two categories: microwave-calibrated and morphing algorithms. Microwave-calibrated algorithms dynamically calibrate an empirical GEO rainfall algorithm against local microwave data ( Bellerby et al. 2000 ; Bellerby 2004 ; Huffman et al. 2007 ; Kidd et al

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Isidora Jankov, Jian-Wen Bao, Paul J. Neiman, Paul J. Schultz, Huiling Yuan, and Allen B. White

above BB precipitation is graupel when using the Lin, WSM6, and Thompson microphysics schemes. But that same water mass is considered to be snow in the Schultz algorithm. There, graupel is produced only when snow interacts with (supercooled) cloud liquid, which occurs in a thin layer just above the 0°C isotherm. This is a realistic mechanism for producing a simulated BB; however, in nature, the dominant mechanism is probably caused by melting aggregates. This may explain why synthetic reflectivity

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Huihui Zhang, Hugo A. Loáiciga, Da Ha, and Qingyun Du

-model based downscaling algorithm performed better than the traditional downscaling algorithms (univariate regression, multivariate regression and exponential function) when applied to the TRMM3B43 V7 at the monthly scale ( Chen et al. 2015 ; Xu et al. 2015 ). The artificial neural network method was introduced for the estimation of precipitations in recent years. The performance of the genetic algorithm (GA)-based back-propagation (BP) artificial neural network (GA+BP_ANN) model has been evaluated by

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Bong-Chul Seo, Witold F. Krajewski, and Alexander Ryzhkov

1. Introduction Quantitative precipitation estimation (QPE) using weather radar has become common and important for many meteorological and hydrological applications such as severe weather warnings and flood forecasting and management (e.g., Zhang et al. 2016 ; Krajewski et al. 2017 ). Since its initial deployment in the early 1990s, the QPE algorithm for the U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) network has evolved according to its hardware and polarimetric upgrades (e

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