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  • Global Precipitation Measurement (GPM): Science and Applications x
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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

atmospheric column, the environmental parameters to be used as cloud morphology predictors in the a priori database are chosen to correspond to the time step preceding their coupled precipitation rates. f. Database The above datasets are grouped to build the a priori knowledge for GPROF retrieval. Each of 14 surface types is treated separately. Data count distributions of eight land surface classes occurring over the domain of this study are given in Fig. 3 as a function of TPW and 2-m temperature

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Yalei You, S. Joseph Munchak, Christa Peters-Lidard, and Sarah Ringerud

radiometers on board the Soil Moisture Active Passive (SMAP) satellite and the Soil Moisture and Ocean Salinity (SMOS) satellite have a frequency of 1.4 GHz. The Advanced Scatterometer (ASCAT) on board the MetOp satellites operates at ~5.2 GHz. In contrast, the primary frequencies to measure the ice scattering over land from passive microwave radiometers are around 85 GHz and higher (e.g., 150 and 183 GHz). The lower frequencies used for soil moisture measurement can penetrate a thicker layer of soil and

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Hooman Ayat, Jason P. Evans, Steven Sherwood, and Ali Behrangi

land radiation at higher frequencies (e.g., 85 GHz) is used though it is strongly affected by ice scattering near the top of the clouds. ( Petković and Kummerow 2017 ). IR sensors contributing to satellite precipitation products use the information of cloud-top temperature to estimate the surface precipitation. Thus, the top-down view of satellites leads to strong consideration of information in upper atmospheric levels to estimate surface rainfall potentially missing evaporation effects in PMW

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

blizzard storm with the mesoscale MM5 model and a delta-Eddington-type radiative transfer (RT) model to produce a storm-scale database for snowfall retrieval using AMSU-B observations. Noh et al. (2009) used a large number of snowfall profiles from airborne, surface, and satellite radars, as well as an atmospheric RT model ( Liu 1998 ) to generate a regional database for snowfall retrievals using the AMSU-B data. The study used the NESDIS Microwave Land Surface Emissivity Model ( Weng et al. 2001

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Md. Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, and Emmanouil N. Anagnostou

other precipitation datasets ( Beck et al. 2019 ). In addition, to obtain the best possible precipitation estimates at global scale, MSWEP accounted a gauge-correction scheme that minimizes timing mismatches when applying the daily gauge corrections ( Beck et al. 2019 ). Bhuiyan et al. (2017) developed a machine learning–based multisource data blending technique and have used it to evaluate the impact of land surface conditions (e.g., vegetation cover and soil moisture) on passive microwave

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Daniel Watters, Alessandro Battaglia, Kamil Mroz, and Frédéric Tridon

produce the DPR-only product and the combined DPR and GMI product, which are stored as level-2A DPR and level-2B CMB data files, respectively, freely available from NASA (2017) . The most recent version, version 5 (V05) released in May 2017, is used in this work. Level-2 data provide the precipitation rate at the surface plus additional parameters and flags, such as freezing-level altitude, range bin for the clutter-free bottom, and land surface type (ocean, land, coast, inland water; Skofronick

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Jackson Tan, Walter A. Petersen, Pierre-Emmanuel Kirstetter, and Yudong Tian

September to October 2014, the use of gauge adjustment should minimize, if not eliminate, artifacts for estimates over land ( Bolvin and Huffman 2015 ). c. Reference The MRMS system (formerly National Mosaic and Multi-Sensor QPE) is a gridded product by NOAA/NSSL based primarily on the U.S. WSR-88D network ( Zhang et al. 2011b ). Reflectivity data are mosaicked onto a 3D grid over the United States with quality control for beam blockages and bright band. From the reflectivity structure and environmental

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Jackson Tan, Walter A. Petersen, and Ali Tokay

satellites, including Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS; Hong et al. 2004 ), the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ; Joyce and Xie 2011 ), Global Satellite Mapping of Precipitation (GSMaP; Kubota et al. 2007 ; Kachi et al. 2014 ), the Naval Research Laboratory blended-satellite technique (NRL blended; Turk and Miller 2005 ), and TRMM Multisatellite

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Jiaying Zhang, Liao-Fan Lin, and Rafael L. Bras

; Villarini and Krajewski 2007 ; Sapiano and Arkin 2009 ; Baxter et al. 2014 ; Yong et al. 2016 ). Nevertheless, such seasonal and latitudinal variations could be captured by air temperature ( Peel et al. 2007 ; Behrangi et al. 2015 ; Smalley et al. 2014 ). Seasonal and latitudinal variations of the quality of satellite-based precipitation products have been widely investigated. Maggioni et al. (2016) reviewed previous work that evaluates commonly used satellite-based precipitation products across

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Xinxuan Zhang and Emmanouil N. Anagnostou

-based PMW sensors ( Joyce et al. 2004 ). This study used the CMORPH V1.0 near-real-time and gauge-adjusted products with 0.073°/30-min resolution. The gauge-adjusted product is corrected by two widely used long-term datasets, the CPC unified gauge analysis over land and the pentad Global Precipitation Climatology Project (GPCP) over the ocean. CMORPH has a newer version named V0.x, which employed more advanced algorithms. However, CMORPH V0.x does not provide a gauge-adjusted product, and it is not

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