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

. Proc. IEEE , 98 , 666 – 687 , . 10.1109/JPROC.2010.2043032 Koizumi , K. , Y. Ishikawa , and T. Tsuyuki , 2005 : Assimilation of precipitation data to the JMA mesoscale model with a four-dimensional variational method and its impact on precipitation forecasts . SOLA , 1 , 45 – 48 , . 10.2151/sola.2005-013 Koster , R. D. , and Coauthors , 2004 : Regions of strong coupling between soil moisture and

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Zhaoxia Pu, Chaulam Yu, Vijay Tallapragada, Jianjun Jin, and Will McCarty

, intensity, and structure ( Gopalakrishnan et al. 2011 ). It uses the Nonhydrostatic Mesoscale Model (NMM) core of the Weather Research and Forecasting (WRF) system as its dynamic solver. Since 2011, HWRF has adopted a triply nested domain configuration. Specifically, HWRF, version 3.7 [see details in Tallapragada et al. (2015) ], is used in this study, in which the parent domain is configured with 18-km horizontal resolution, covering roughly 80° × 80° on a rotated latitude–longitude E-staggered grid

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

with satellite observations and physical constraints of the underlying processes, with fully realized dynamic interaction and feedback through explicit microphysics and mesoscale dynamics. Using an advanced ensemble data assimilation system developed for the NASA Unified Weather Research and Forecasting (NU-WRF; Peters-Lidard et al. 2015 ) Model, precipitation-sensitive microwave radiances are directly assimilated into a storm-scale NU-WRF simulation of the WAM. Assimilation of precipitation

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

with 5 day summer precipitation forecasting in the greater Beijing Area . Geophys. Res. Lett. , 42 , 579 – 587 , . 10.1002/2014GL061623 Dudhia , J. , 1989 : Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model . J. Atmos. Sci. , 46 , 3077 – 3107 ,<3077:NSOCOD>2.0.CO;2 . 10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2 Ebert , E. E. , U. Damrath

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Robert A. Houze Jr., Lynn A. McMurdie, Walter A. Petersen, Mathew R. Schwaller, William Baccus, Jessica D. Lundquist, Clifford F. Mass, Bart Nijssen, Steven A. Rutledge, David R. Hudak, Simone Tanelli, Gerald G. Mace, Michael R. Poellot, Dennis P. Lettenmaier, Joseph P. Zagrodnik, Angela K. Rowe, Jennifer C. DeHart, Luke E. Madaus, Hannah C. Barnes, and V. Chandrasekar

determine the snow depth. The February flights occurred at a midseason time before maximum snow cover. The late March flights took place when winter snow cover was near maximum. SUCCESSFUL PROJECT COORDINATION. OLYMPEX operations required careful coordination of forecasting, decision-making, and scheduling of aircraft, radars, and soundings. The success of OLYMPEX operations is perhaps best illustrated in Fig. 5 showing how all three aircraft were positioned in the center of a GPM overpass at 1522 UTC

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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° ( ), depending

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

the time-dependent characteristics of storms in the datasets. Cui et al. (2020) investigated the mesoscale convective systems (MCSs) characteristics in the central and eastern United States during a 3-yr study period by implementing an object-based approach over IMERG V06B and NCEP Stage IV datasets. The findings indicated that IMERG agrees reasonably well with Stage IV; however, IMERG tended to overestimate the total precipitation and underestimate the hourly mean precipitation. They found

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Stephen E. Lang and Wei-Kuo Tao

observations and the thermal energy equation. More recently, Ahmed et al. (2016) built an algorithm to retrieve LH based on the sizes of convective and stratiform areas as well as their echo-top heights from a multiweek Weather Research and Forecasting (WRF) Model simulation using data from the Dynamics of the MJO (DYNAMO) field campaign in the Indian Ocean. In addition, the original TRMM-related algorithms have and will need to continue to evolve, especially with the expansion of TRMM’s successor, the

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

(a) ground radar and (b) rain gauge data. 4. Sensitivity to rain intensity, seasonal cycle, and altitude The physiography and geographical setting of the Italian Peninsula makes it a key region to represent the Mediterranean climate, with dry summers and wet winters. Cold months are dominated by cyclonic development and frontal structures, while in summer, which is generally dry, the occurrence of isolated and mesoscale organized convection is possible. Mesoscale convection is particularly severe

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