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Yonghe Liu, Jinming Feng, Zongliang Yang, Yonghong Hu, and Jianlin Li

technique to infer local information ( Sunyer et al. 2015a ) by statistically relating local variables to the GCM large-scale variables (LSV). Another technique is dynamical downscaling (DD) ( Bao et al. 2015 ; Castro et al. 2005 ), which is computationally expensive. Usually, DD can produce gridded simulations covering a region with visually realistic spatial patterns that imply spatial dependence or spatial autocorrelation of precipitation, whereas SD mostly produces outputs at gauge sites and not

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Thomas Stanley, Dalia B. Kirschbaum, George J. Huffman, and Robert F. Adler

. Satellite precipitation data are used in many applications such as flood monitoring, crop forecasting, numerical weather prediction, and disease tracking ( Kucera et al. 2013 ; Kirschbaum et al. 2017 ). These user communities have relied upon TMPA data, and several workshops have highlighted the need for long precipitation records ( Ward et al. 2015 ; Ward and Kirschbaum 2014 ). While the GPM mission plans to create a consistent record of precipitation available from 1998 to the present using TRMM

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

precipitation intensity and modulates the propagation of cloud precipitation systems associated with AEJ. Using an ensemble-based assimilation technique that allows state-dependent forecast error covariance among dynamical and microphysical variables, the assimilation of radiances over the WAM rainband provides better hydrometeor distributions for storm initiation with characteristics of continental tropical convection, although fundamental model biases associated with insufficient model resolution remains

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

). Another precipitation data source available at the global scale is from atmospheric reanalyses produced by different national and international organizations, including the National Centers for Environmental Prediction (NCEP; Kalnay et al. 1996 ), the European Centre for Medium-Range Weather Forecasts (ECMWF; Uppala et al. 2005 ; Bosilovich et al. 2008 ), and NASA’s Goddard Space Flight Center (GSFC; Rodell et al. 2004 ). These products are affected by irregularly distributed observation stations

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Xinxuan Zhang and Emmanouil N. Anagnostou

corrections of satellite precipitation products. Future research should investigate the feasibility of using real-time weather forecasts to correct near-real-time high-resolution satellite precipitation products (e.g., CMORPH, GSMaP, PERSIANN-CCS, and IMERG) for heavy precipitation events over complex terrain areas and evaluate hydrologic impacts in terms of flood forecasts. Furthermore, a future study should focus on demonstrating the technique on recently released versions of CMORPH and GSMaP and the

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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

remains largely unknown. To this end, this paper develops a framework that allows simultaneous assimilation of satellite soil moisture and precipitation data into a coupled land–atmosphere model. Direct assimilation of precipitation has received a lot of attention in the past years. The most common technique used for assimilation of accumulated precipitation is the four-dimensional variational data assimilation (4D-Var). Examples of global weather prediction systems capable of precipitation data

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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

planned PMM activities, specifically focusing on the GPM suite of data products relevant for an applications-focused audience. We then provide case studies of how TRMM and GPM data have been applied across four thematic areas: tropical cyclone track forecasting, flood modeling, agricultural monitoring, and disease tracking. Table 1. Examples of applications’ thematic areas and topics where satellite precipitation estimates are being used for situational awareness and decision-making. More information

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Wesley Berg, Stephen Bilanow, Ruiyao Chen, Saswati Datta, David Draper, Hamideh Ebrahimi, Spencer Farrar, W. Linwood Jones, Rachael Kroodsma, Darren McKague, Vivienne Payne, James Wang, Thomas Wilheit, and John Xun Yang

analyses. Michigan and UCF use model analysis data from the Global Data Assimilation System (GDAS), which uses NOAA’s Global Forecast System (GFS) model ( NOAA/NCEP 2000 ). CSU uses the interim reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ( Dee et al. 2011 ). Further details on the implementation of the double-difference techniques for each of these groups is given in Yang and McKague (2016) for the Michigan approach, in Biswas et al. (2013) for the UCF

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

al. (2016) revealed that the IMERG product has more skill in representing daily precipitation than the post-real-time Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA-3B42) and the ERA-Interim product from the European Centre for Medium-Range Weather Forecasts (ECMWF) in Iran from March 2014 to February 2015. For the midlatitude region of the Ganjiang River basin in southeast China, Tang et al. (2016b) showed that the detection skill of the Day-1 IMERG

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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

availability; 3) improving climate modeling and prediction capabilities; 4) improving weather forecasting and four-dimensional (4D) reanalysis; and 5) improving hydrological modeling and prediction. More details about these scientific objectives can be found in Hou et al. (2014) . GPM CO ’s well-calibrated instruments allow for scientifically advanced observations of precipitation in the midlatitudes, where a majority of Earth’s population lives. The middle panel in Fig. 1 shows the coverage of the GPM

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