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Bin Pei and Firat Y. Testik

1. Introduction The improvement of radar rainfall estimation has long been an active topic, as radar measurements play an important role in various applications related to meteorology, hydrology, and agriculture, among others ( Sene 2009 ; Testik and Gebremichael 2010 ). Over the years a large number of dual-polarization radar algorithms for rain-rate estimations have been developed and tested (e.g., Seliga and Bringi 1976 ; Bringi and Chandrasekar 2001 ). These algorithms can be classified

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Minda Le and V. Chandrasekar

melting-layer detection ( Le and Chandrasekar 2013 ). Using a similar philosophy, Le et al. (2017) developed an algorithm to identify surface snowfall at each DPR matched footprint. It uses vertical features of dual-frequency reflectivity profiles of different precipitation types. These features include reflectivity amplitude, dual-frequency ratio, slope with respect to height, and storm-top height. An effective “snow index” is then built that can separate surface snowfall from rain at a 97% success

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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

variations of surface emissivity ( Kulie et al. 2010 ; Skofronick-Jackson and Johnson 2011 ; Gong and Wu 2017 ; You et al. 2017 ). Among high-frequency channels, Bennartz and Bauer (2003) found that frequencies around and above 150 GHz provide a strong polarization signal for snowfall detection ( Gong and Wu 2017 ; You et al. 2017 ; Panegrossi et al. 2017 ). Remote sensing of snowfall is among the most challenging tasks in precipitation retrieval algorithms ( Bennartz and Bauer 2003 ; Skofronick

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Yagmur Derin, Emmanouil Anagnostou, Marios Anagnostou, and John Kalogiros

of ϕ DP to attenuation from rainfall, or other atmospheric sources, and its independence to radar calibration, make QPE using ϕ DP a powerful method ( E. N. Anagnostou et al. 2006 ; M. N. Anagnostou et al. 2006 ; Bringi and Chandrasekar 2001 ; Chandrasekar et al. 1990 ; Matrosov et al. 2002 , 2005 ; Testud et al. 2000 ). Recent studies by Anagnostou et al. (2009 , 2010 ) and Kalogiros et al. (2013a) have led to the development and demonstration of a new algorithm for attenuation

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Ousmane O. Sy, Simone Tanelli, Stephen L. Durden, Andrew Heymsfield, Aaron Bansemer, Kwo-Sen Kuo, Noppasin Niamsuwan, Robert M. Beauchamp, V. Chandrasekar, Manuel Vega, and Michael P. Johnson

collocation margins ( δ r = 0.5 km, δ h = 50 m, δ t = 5 min). GCPEx and OLYMPEx were ground-validation experiments to improve the snowfall retrieval algorithms of GPM. In both experiments, the CIT measured the state of the atmosphere using sensors for temperature, King and Nevzorov probes for the water content, and High Volume Precipitation Spectrometer, version 3 (HVPS3), and two-dimensional stereo (2DS) particle imaging probes. The particle images were processed to derive PSD and aspect ratio

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Aaron R. Naeger, Brian A. Colle, Na Zhou, and Andrew Molthan

position indicator (PPI) and range–height indicator (RHI) scans from the NASA S-band radar (NPOL; Wolff et al. 2017 ) at the coast and X-band Doppler on Wheels (DOW; Houze et al. 2018 ) along the windward slopes for evaluating the simulated precipitation structures from the BMPs. Eddy dissipation rate (EDR) is calculated based on spectrum width measurements from NPOL ( Bohne 1982 ) using the Python Turbulence Detection Algorithm ( Lang and Guy 2017 ) for detecting turbulent motions. The dual

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Joseph P. Zagrodnik, Lynn McMurdie, and Robert Conrick

, P. J. Neiman , P. J. Schultz , H. Yuan , and A. B. White , 2009 : Evaluation and comparison of microphysical algorithms in ARW-WRF Model simulations of atmospheric river events affecting the California coast . J. Hydrometeor. , 10 , 847 – 870 , https://doi.org/10.1175/2009JHM1059.1 . 10.1175/2009JHM1059.1 Jiang , Q. , and R. B. Smith , 2003 : Cloud timescales and orographic precipitation . J. Atmos. Sci. , 60 , 1543 – 1559 , https://doi.org/10.1175/2995.1 . 10

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Robert Conrick, Joseph P. Zagrodnik, and Clifford F. Mass

by latitude. The results of the above studies and others suggest that DSD radar retrieval algorithms developed for convective storms may not be applicable to locations where stratiform precipitation is dominant or where topography is a key controller of regional precipitation distributions, such as the Pacific Northwest ( Chow et al. 2013 ). Zagrodnik et al. (2018) , using DSD observations from the Olympic Mountains Experiment (OLYMPEX; Houze et al. 2017 ), showed that DSDs varied considerably

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Stephanie M. Wingo, Walter A. Petersen, Patrick N. Gatlin, Charanjit S. Pabla, David A. Marks, and David B. Wolff

. Overview of sensors and data fields included in the SIMBA data fusion framework. Fields available in these PPS products are described by the algorithm theoretical basis documents (ATBDs) for each product (e.g., GSFC 2017a , b ; Iguchi et al. 2017 ); also see main text. This paper documents the initial development of the SIMBA data fusion framework as a tool aimed at making more efficient precipitation research possible. Section 2 provides an overview of SIMBA, including supported platforms, system

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Andrew Heymsfield, Aaron Bansemer, Norman B. Wood, Guosheng Liu, Simone Tanelli, Ousmane O. Sy, Michael Poellot, and Chuntao Liu

. 2010 ; Wood et al. 2015 ). The satellite-based retrieval algorithms also make assumptions about the PSD, the ice density ( ρ b ), the ice particle terminal velocities ( V t ), and σ . The CloudSat Snow Profile product (2C-SNOW-PROFILE) provides estimates of vertical profiles of snowfall rate along with snow size distribution parameters and snow water content for radar reflectivity profiles observed by the CloudSat Cloud Profiling Radar (CPR). The retrieval algorithms for GPM and Tropical

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