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

( Doswell et al. 1990 ) for the presented results in Figs. 7 – 9 . We also compare the algorithm outputs with the precipitation phase products of the MRMS on a seasonal basis ( Figs. 10 , 11 ). Finally, some results are presented at a storm scale to demonstrate the detection capabilities of the algorithm for a few precipitation events that are coincidentally captured by the DPR and high-resolution ground-based radars ( Figs. 12 , 13 ) and simulated by the Weather Research and Forecasting (WRF) Model

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Linda Bogerd, Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet

1. Introduction Precipitation observations are required for environmental applications that are highly embedded in the contemporary society, such as crop yield and flash flood forecasting, water management, and drought monitoring. However, the global coverage of ground-based precipitation measurements is limited, especially over Africa, South America, parts of Asia, and regions that are difficult to access (e.g., oceans, mountainous areas, polar regions; Lorenz and Kunstmann 2012 ; Saltikoff

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Xinxuan Zhang and Emmanouil N. Anagnostou

study. c. Numerical weather simulations To simulate storm events in the different study areas, we used the numerical Weather Research and Forecasting (WRF) Model, version 3.7.1 ( Skamarock et al. 2008 ). The periods of our WRF storm simulations ranged from 1 to 5 days, with a 12-h spinup prior to each. We initialized and constrained the simulations at the model boundaries by NCEP Global Forecast System (GFS) final analysis fields of 0.5° or 1° ( http://nomads.ncdc.noaa.gov/data/gfsanl ), depending

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

high-resolution forecasts of long-lived convective precipitation in the central U.S . J. Adv. Model. Earth Syst. , 7 , 1248 – 1264 , https://doi.org/10.1002/2015MS000497 . 10.1002/2015MS000497 Cangialosi , J. P. , A. S. Latto , and R. Berg , 2018 : Hurricane Irma. National Hurricane Center, 111 pp. , https://www.nhc.noaa.gov/data/tcr/AL112017_Irma.pdf . Chen , M. , S. Nabih , N. S. Brauer , S. Gao , J. J. Gourley , Z. Hong , R. L. Kolar , and Y. Hong , 2020

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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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M. Petracca, L. P. D’Adderio, F. Porcù, G. Vulpiani, S. Sebastianelli, and S. Puca

Measurement precipitation processing system: File specification 2ADPR. NASA/JAXA Tech. Rep., 127 pp., https://storm.pps.eosdis.nasa.gov/storm/data/docs/filespec.GPM.V1.2ADPR.pdf . Nash , J. E. , and J. V. Sutcliff , 1970 : River flow forecasting through conceptual models 1: A discussion of principles . J. Hydrol. , 10 , 282 – 290 , https://doi.org/10.1016/0022-1694(70)90255-6 . 10.1016/0022-1694(70)90255-6 Neeck , S. P. , R. K. Kakar , A. A. Azarbarzin , and A. Y. Hou , 2014 : Global

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

requirements. The Early Run, available at a 6-h delay for real-time applications such as hazard predictions, is limited to rainfall morphing only forward in time. The Late Run, with an 18-h delay for purposes such as crop forecasting, employs morphing both forward and backward in time. The Final Run is at a 4-month delay for research applications. Both the Early and Late Runs have climatological gauge adjustment while the Final Run uses monthly gauge adjustments to reduce bias. Moreover, runs with longer

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Jackson Tan, Walter A. Petersen, and Ali Tokay

.1° every half hour covering up to ±60° latitudes. It has three runs to accommodate the different user requirements for latency and accuracy. The Early run, available at a 6-h delay for real-time applications such as in the prediction of flash floods, is limited in rainfall morphing to propagation only forward in time. The Late run, with an 18-h delay for purposes such as crop forecasting, employs forward and backward morphing in time. Both the Early and Final runs have climatological gauge calibrations

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

: New numerical analysis and forecast system (in Japanese). Japan Meteorological Agency Annual Rep. 33, 143 pp. Joyce , R. , J. Janowiak , P. Arkin , and P. Xie , 2004 : CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution . J. Hydrometeor. , 5 , 487 – 503 , doi: 10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2 . 10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2 Kidd , C. , T. Matsui , J. Chern , K

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