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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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Yagmur Derin, Pierre-Emmanuel Kirstetter, and Jonathan J. Gourley


As a fundamental water flux, quantitative understanding of precipitation is important to understand and manage water systems under a changing climate, especially in transition regions such as the coastal interface between land and ocean. This work aims to assess the uncertainty in precipitation detection over the land-coast-ocean continuum in the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B. It is examined over three coastal regions of the U.S., i.e. the West Coast, the Gulf of Mexico, and the East Coast, each of which are characterized by different topographies and precipitation climatologies. Detection capabilities are contrasted over different surfaces (land, coast, ocean). A novel and integrated approach traces the IMERG detection performance back to its components (passive microwave, infrared, and morphing-based estimates). The analysis is performed by using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference. The best detection performances are reported with PMW estimates (hit rates in the range of [25-39]%), followed by morphing ([20-34]%), morphing+IR ([17-27]%) and IR ([11-16]%) estimates. Precipitation formation mechanisms play an important role, especially in the West Coast where orographic processes challenge detection. Further, precipitation typology is shown to be a strong driver of IMERG detection. Over the ocean, IMERG detection is generally better but suffers from false alarms ([10-53]%). Overall, IMERG displays nonhomogeneous precipitation detection capabilities tracing back to its components. Results point toward a similar behavior across various land-coast-ocean continuum regions of the CONUS, which suggests that results can be potentially transferred to other coastal regions of the world.

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Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

available only over CONUS, all the results based on this dataset are valid at a regional scale. While all sun synchronous GMI orbits over CONUS have been considered, only ATMS ascending orbits (between 0600 and 1300 UTC), closest in time to the SNODAS reference time (0600 UTC), have been selected. The dataset has been built following the same procedures used for the development and validation datasets, obtaining a snow-cover occurrence index, a land fraction index (since SNODAS provides information only

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

’s AutoSnow product ( Romanov et al. 2000 ). The GMI GPROF version 05 product ( Iguchi and Meneghini 2016c ) tested in this study, considers different sources of precipitation retrievals to build its a priori database depending on the surface type detected in the GMI IFOV. The Ku-band and the DPR combined version 04 algorithms (Ku-V04, CORRA-V04) are used to build the database over “land” (i.e., vegetated surfaces, inland waters, and coastlines) and “ocean” (i.e., oceans, sea ice, and sea ice

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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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