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F. Joseph Turk, Ramon Padullés, Estel Cardellach, Chi O. Ao, Kuo-Nung Wang, David D. Morabito, Manuel de la Torre Juarez, Mayra Oyola, Svetla Hristova-Veleva, and J. David Neelin

ϕ = ϕ H − ϕ V and measurable with a dual orthogonal (H/V) RO receiver capability ( Fig. 1 ). The measurement is somewhat analogous to the cumulative propagation differential phase shift ( ϕ dp ) measured by the operational National Atmospheric and Oceanic Administration (NOAA) NEXRAD radar network, but only the forward propagation aspects (i.e., 1-way ϕ dp before backscatter) ( Bringi and Chandrasekar 2001 ). The value of Δ ϕ is derived separately from the RO processing of the excess

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

current operational algorithm is allowed to use radar-observed convective fraction information (bright blue) as an additional ancillary parameter (see section 4a ). As expected, a better match to the reference suggests that the information on convective fraction might be a key to mitigating PMW biases seen in Fig. 1 . However, radar observations of precipitation from space are sparse, typically limited to research missions (e.g., TRMM and GPM) and intended to serve as a reference rather than a

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

with any passive microwave sensor as long as the sensor characteristics and channel errors were properly specified. While it has taken a number of iterations, this paper describes GPROF 2014, the fully parametric algorithm, as well as the recent versions of the algorithm leading to it. The current impetus is provided by the Global Precipitation Measurement (GPM) ( Hou et al. 2014 ), which explicitly seeks to provide consistent 3-hourly rainfall products from a constellation of operational and

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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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Tomoaki Mega and Shoichi Shige

board low-Earth-orbit satellites is advantageous with respect to estimating daily and monthly rainfall. Adler et al. (1993) were the first to successfully combine the advantages of both types of instruments, by matching data from MWRs and IRs to tune the Geostationary Operational Environmental Satellite precipitation index algorithm ( Arkin and Meisner 1987 ). After the launch of the Tropical Rainfall Measurement Mission (TRMM) satellite ( Kummerow et al. 1998 ), the MWR rainfall algorithms

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

underlying atmospheric and surface conditions present at (or as close as possible to) the observation time. The environmental conditions at the satellite observation time are typically interpolated from operational global numerical weather prediction (NWP) models, such as the Japanese Meteorological Agency (JMA) global spectral model (GSM), for operational processing, and from lengthy model reanalysis, such as ERA-Interim ( Dee et al. 2011 ), for consistent reprocessing of both GPM and its predecessor

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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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