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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

-changing climate. Despite a long, albeit sparse, record [first known observations date back 2000 BCE ( Wang and Zhang 1988 )], globally complete precipitation measurements did not become available until the modern era of satellite Earth-observing systems that employ infrared and microwave radiometric techniques (e.g., Atlas and Thiele 1981 ). Achieving measurement standards of rainfall in atypical (i.e., extreme) environments on small spatiotemporal scales across the globe, however, has turned out to be more

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Sybille Y. Schoger, Dmitri Moisseev, Annakaisa von Lerber, Susanne Crewell, and Kerstin Ebell

retrieval development is explained in detail together with an uncertainty analysis based on data from Hyytiälä. Second, because there are no reliable MRR observations available in Hyytiälä during the same time when a cloud radar has been measuring, we test the performance of the new snowfall-rate retrieval methods with measurements from AWIPEV at Ny-Ålesund. At this site, the retrieved parameters are applied to measured Z e values from an MRR and the W-band Microwave Radar for Arctic Clouds (MiRAC

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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

global snowfall properties is to use spaceborne microwave radars since ground-based observations are limited to easily accessible locations and passive spaceborne sensors have additional ambiguity in determining the vertical distribution of hydrometeors. Currently, there exist two NASA missions with spaceborne radars designed to sample hydrometeors. The first mission, launched in 2006, is CloudSat ( Stephens et al. 2002 ), which consists of a highly sensitive 94 GHz nonscanning cloud radar in a 98

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E. F. Stocker, F. Alquaied, S. Bilanow, Y. Ji, and L. Jones

establishing consistent reflectivities, brightness temperatures ( T b ), and precipitation retrievals between the two missions. The TRMM Microwave Imager (TMI), a conical scanning microwave radiometer, is composed of nine channels with five different frequencies, namely, 10.65, 19.35, 21.30, 37.0, and 85.5 GHz. All frequencies were dual polarized (pol)—vertical (V) and horizontal (H)—except for 21.3 GHz, which was V-pol only ( Kummerow et al. 1998 ). The GPM Microwave Imager is a conically scanning

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Sara Q. Zhang, T. Matsui, S. Cheung, M. Zupanski, and C. Peters-Lidard

with satellite observations and physical constraints of the underlying processes, with fully realized dynamic interaction and feedback through explicit microphysics and mesoscale dynamics. Using an advanced ensemble data assimilation system developed for the NASA Unified Weather Research and Forecasting (NU-WRF; Peters-Lidard et al. 2015 ) Model, precipitation-sensitive microwave radiances are directly assimilated into a storm-scale NU-WRF simulation of the WAM. Assimilation of precipitation

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

1980 ; Hallikainen et al. 1986 , 1987 ). Hence, snow cover has a time-varying effect on snowfall upwelling signal. Physical and empirical approaches have been developed for microwave retrievals of snowfall. Skofronick-Jackson et al. (2004) presented a physical method to retrieve snowfall during a blizzard over the eastern United States using high-frequency observations from the Advanced Microwave Sounding Unit B (AMSU-B) instrument. Kim et al. (2008) simulated atmospheric profiles of a

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Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

regions, and are fraught with problems like undercatch and wind-blown snow biases ( Fassnacht 2004 ). This measurement gap can be bridged by spaceborne active and passive microwave (PMW) sensors that are tailored to detect and quantify snowfall thanks to their ability to probe within clouds ( Levizzani et al. 2011 ; Skofronick-Jackson et al. 2017 ). Two spaceborne radars paved the way toward ground-breaking vertically resolved observations of falling snow over much of the globe: the CloudSat Cloud

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Gail Skofronick-Jackson, Walter A. Petersen, Wesley Berg, Chris Kidd, Erich F. Stocker, Dalia B. Kirschbaum, Ramesh Kakar, Scott A. Braun, George J. Huffman, Toshio Iguchi, Pierre E. Kirstetter, Christian Kummerow, Robert Meneghini, Riko Oki, William S. Olson, Yukari N. Takayabu, Kinji Furukawa, and Thomas Wilheit

distribution in light of advanced observations from space . J. Climate , 27 , 3957 – 3965 , doi: 10.1175/JCLI-D-13-00679.1 . 10.1175/JCLI-D-13-00679.1 Berg , W. , and Coauthors , 2016 : Intercalibration of the GPM microwave radiometer constellation . J. Atmos. Oceanic Technol. , 33 , 2639 – 2654 , doi: 10.1175/JTECH-D-16-0100.1 . 10.1175/JTECH-D-16-0100.1 Carr , N. , and Coauthors , 2015 : The influence of surface and precipitation characteristics on TRMM Microwave Imager rainfall retrieval

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

.g., setting a horizontal grid spacing significantly smaller than the gate size of the ground-based scanning radars would not be a wise practice). Generally, we recommend SIMBA column grid spacing be set to at least 500 m in the horizontal and at least 250 m in the vertical planes, and we note that for some applications larger grid spacing on the order of 1 km may be more relevant (e.g., comparisons of ground-based radar and satelliteborne passive and active microwave observations at the pixel scale or

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Jackson Tan, George J. Huffman, David T. Bolvin, Eric J. Nelkin, and Manikandan Rajagopal

Abstract

A key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN, which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.

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