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Olivier Hautecoeur and Régis Borde

1. Introduction Atmospheric motion vectors (AMVs) are derived from satellites by tracking clouds or water vapor features in consecutive satellite images. Because they constitute the only upper-level wind observations with good global coverage for the tropics, midlatitudes, and polar areas, especially over the large oceanic areas, the AMVs are continuously assimilated into numerical weather prediction (NWP) models to improve the forecast score. AMVs are extracted routinely by a number of

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Mircea Grecu, William S. Olson, Stephen Joseph Munchak, Sarah Ringerud, Liang Liao, Ziad Haddad, Bartie L. Kelley, and Steven F. McLaughlin

emissivities of the earth’s surface, are required to simulate the remaining satellite radar and radiometer observations that are included in of Eq. (1) . The vertical distributions of water vapor and cloud water in each radar profile are described using low-order representations based on an empirical orthogonal function (EOF) decomposition. The EOFs are derived from Weather Research and Forecasting (WRF) Model ( Michalakes et al. 2001 ) simulations representing diverse meteorological situations

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Veljko Petković, Marko Orescanin, Pierre Kirstetter, Christian Kummerow, and Ralph Ferraro

. Combining both direct (gauges) and remote (radar/radiometer) measurement techniques, using ground and in-orbit observations complemented by the state-of-the-art atmosphere simulations, the GPM constellation offers full global coverage of rain and snow every 30 min at a resolution of only 0.1° and a latency of only a few hours. Freely available precipitation products are implemented across a spectrum of decision-making scientific tools, ranging from hydrology to world health. To ensure user demands for

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Takuji Kubota, Shinta Seto, Masaki Satoh, Tomoe Nasuno, Toshio Iguchi, Takeshi Masaki, John M. Kwiatkowski, and Riko Oki

atmospheric simulations and observational data have been utilized in previous works. The 2A25 algorithm for the TRMM PR assumed the attenuation by CLWC based on the result of a numerical simulation of storms with a cloud-system-resolving model (CRM) ( Iguchi et al. 2009 ). The vertical distributions of cloud liquid water in each radar profile were described using Weather Research and Forecasting (WRF) Model simulations in the GPM combined algorithm, which provides precipitation estimates using both the

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Christian D. Kummerow, David L. Randel, Mark Kulie, Nai-Yu Wang, Ralph Ferraro, S. Joseph Munchak, and Veljko Petkovic

. , Kozu T. , Meneghini R. , Awaka J. , and Okamoto K. , 2000 : Rain-profiling algorithm for the TRMM precipitation radar . J. Appl. Meteor. , 39 , 2038 – 2052 , doi: 10.1175/1520-0450(2001)040<2038:RPAFTT>2.0.CO;2 . JMA , 2000 : New numerical analysis and forecast system (in Japanese). Japan Meteorological Agency Annual Rep. 33, 143 pp . Kummerow, C. D. , and Giglio L. , 1994 : A passive microwave technique for estimating rainfall and vertical structure information from space. Part

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Tomoaki Mega and Shoichi Shige

1. Introduction Global precipitation information is critical for understanding the global energy and water cycle. Since the 1970s, scientists have been developing techniques to estimate precipitation from satellite radiometric observations, which can cover most of the globe. The first techniques used visible or infrared (IR) data to infer precipitation intensity based on cloud reflectivities or cloud-top temperature ( Barrett 1970 ). The IR technique performs poorly in estimation of warm rain

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F. Joseph Turk, Z. S. Haddad, and Y. You

constrained to weight a priori candidate profiles with the same classification index, and similar surface temperature ( T sfc ) and total column water vapor (TWV) as the observation ( Kummerow et al. 2015 ), although more recent studies have suggested the use of the 2-m air temperature ( T 2m ) ( Sims and Liu 2015 ). Differences between forecast models can arise between the formulation (gridpoint spacing, or wave resolution in spectral models) and its temporal resolution. Furthermore, the surface

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Eun-Kyoung Seo, Sung-Dae Yang, Mircea Grecu, Geun-Hyeok Ryu, Guosheng Liu, Svetla Hristova-Veleva, Yoo-Jeong Noh, Ziad Haddad, and Jinho Shin

observations were corrected for attenuation using the Hitschfeld–Bordan formulation and an alpha-adjustment technique to incorporate information from the surface reference method into the correction ( Iguchi and Meneghini 1994 ; Iguchi et al. 2000 ). Radar-observed rain profiles are classified into three types: stratiform, convective, and others ( Awaka et al. 1998 ). For every TMI pixel, the collocated PR pixels are found over ocean only. In the collocation process, we consider only the subset of TMI

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