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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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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

Microwave Sounder API Application programming interface BB Bright band DNN Deep neural network DPR Dual-frequency precipitation radar ECMWF European Centre for Medium-Range Weather Forecasts FOV Field of view GANAL Global analysis GLM Geostationary Lightning Mapper GMI GPM Microwave Imager GPM Global Precipitation Measurement GPROF Goddard profiling algorithm GV-MRMS Ground Validation–Multi Radar/Multi Sensor HSS Heidke skill score IR Infrared JMA Japan Meteorological Agency MHS Microwave Humidity

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

2014 by using TPW from reanalyses [the Japanese Global Analysis (GANAL) ( JMA 2000 ) for near-real-time operations, and GPM standard products and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) ( Dee et al. 2011 ) for all other products]. The second aspect of the algorithm that was not fully parametric was the channel uncertainties assigned to sensors. For TMI, these uncertainties were determined by examining the residual differences between computed

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

representative of summer rainfall around the Korean Peninsula and in East Asia. A 24-h forecast was produced for each case. Initial and boundary conditions were derived from the National Centers for Environmental Prediction Final Analysis data ( Kanamitsu et al. 2002 ). All experiments involved one-way interactive triple-nested domains with a Lambert conformal map projection. The finest grid domain had a resolution of 2 km and was nested in a 6-km-resolution domain, which in turn was nested in an 18-km

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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

. , Iguchi T. , and Oki T. , 2008 : Advanced rain/no-rain classification methods for microwave radiometer observations over land . J. Appl. Meteor. Climatol. , 47 , 3016 – 3029 , doi: 10.1175/2008JAMC1895.1 . Schaefer, J. T. , 1990 : The critical success index as an indicator of warning skill . Wea. Forecasting , 5 , 570 – 575 , doi: 10.1175/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2 . Shige, S. , Kida S. , Ashiwake H. , Kubota T. , and Aonashi K. , 2013 : Improvement of TMI rain

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Atsushi Hamada and Yukari N. Takayabu

Pacific at 0904 UTC 4 Jun 2014. Colors show reflectivity at the near-surface level (dB Z ). The number of precipitating pixels by setting the minimum detectable reflectivity to 12 and 18 dB Z is indicated in the right-top corner. (b) Horizontal winds at 850 hPa (black) and 500 hPa (red) at 1200 UTC, derived from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) dataset ( Dee et al. 2011 ). (c) Along-track cross section of reflectivity at the 25th angle bin

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