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W.-K. Tao, T. Iguchi, and S. Lang

mainly from evaporation prevails beneath the melting level. Therefore, Tao et al. (1993) proposed a LH algorithm known as the CSH algorithm. It used a simple LUT consisting of rain-normalized Q 1 profiles for the convective and stratiform region composited for land and ocean from sounding budgets and a few GCE simulations. The CSH algorithm’s performance was tested through self-consistency checking using GCE-simulated cloud heating data as “truth” ( Tao et al. 2000 ), and the algorithm was used to

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Dalia B. Kirschbaum, George J. Huffman, Robert F. Adler, Scott Braun, Kevin Garrett, Erin Jones, Amy McNally, Gail Skofronick-Jackson, Erich Stocker, Huan Wu, and Benjamin F. Zaitchik

). This system is also in the process of testing IMERG precipitation estimates. GFMS couples the Variable Infiltration Capacity (VIC) land surface model ( Liang et al. 1994 ) and the Dominant River Tracing Routing (DRTR) model to form the Dominant River routing Integrated with VIC Environment (DRIVE) modeling system. To establish percentile thresholds for flood detection within the GFMS system, the DRIVE model was run retrospectively for 15 years using the TMPA record to provide a history of water

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Gail Skofronick-Jackson, Mark Kulie, Lisa Milani, Stephen J. Munchak, Norman B. Wood, and Vincenzo Levizzani

-ocean retrievals are much more susceptible than those over land because of classification errors in the radar-based methods that do not use information about the temperature structure near the surface. This does not necessarily mean that the GMI GPROF and CloudSat 2CSP approach is always better than the DPR approach since it relies upon a model analysis, which may be in error, particularly near sharp temperature gradients and complex terrain, whereas the DPR approach uses the radar measurements more directly

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Catherine M. Naud, James F. Booth, Matthew Lebsock, and Mircea Grecu

latitudinal coverage, observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E; Kawanishi et al. 2003 ) are also used over the oceans ( Kummerow et al. 2011 ), but this instrument has some sensitivity issues in the midlatitudes ( Stephens et al. 2010 ; Behrangi et al. 2012 ). The availability of gridded combined products such as the Global Precipitation Climatology Project (GPCP; Adler et al. 2003 ) helps to overcome the coverage issue, but these are typically

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Rachael Kroodsma, Stephen Bilanow, and Darren McKague

will generally be different for the forward look of the instrument than for the backward look. There is a large contrast between land and ocean TAs at microwave imager frequencies, so small offsets in geolocation cause coastlines to appear highlighted when taking the difference between the forward- and backward-looking maps of observed TAs. This method has been used with great success for various sun-synchronous orbiters by taking the difference between ascending and descending passes (e.g., Berg

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Xiang Ni, Chuntao Liu, Daniel J. Cecil, and Qinghong Zhang

stronger scattering at 37 GHz. Fig . 11. Median reflectivity profiles of TRMM PFs that have minimum 37-GHz PCT of less than 230 K over land from 1998 to 2013, as shown in Fig. 10 . 4. Summary and discussion On the basis of previous studies about global distributions of hailstorms using passive microwave observations, we examine the performance of spaceborne precipitation radar reflectivity and passive microwave brightness temperature for the detection of hailstorms with large-size hail (>19 mm

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

1. Introduction The National Aeronautics and Space Administration (NASA), as a research-driven engineering and science innovator, has always included the reprocessing of mission data products as part of all mission phases. Within NASA missions, reprocessing allows for all the data collected during a mission to be reprocessed using the latest algorithms, ensuring that consistent data products are generated for the entire mission. During the course of its more than 17 yr of operations, the

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Kenneth D. Leppert II and Daniel J. Cecil

of simulations where all hydrometeor types were included suggest little sensitivity of BTs at any frequency to changing any PSD parameter of rain, big drops, or snow. The emission signal of liquid hydrometeor types can be important over ocean (radiometrically cold background) at lower frequencies (e.g., Wilheit et al. 1991 ). However, the radiometrically warm background of land (used here) provides relatively little distinction from the emission from liquid in a cloud. In addition, ice above

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