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Alan J. Geer, Peter Bauer, and Christopher W. O’Dell

is done in the context of the observation minus forecast statistics, which are a main output of an operational numerical weather prediction (NWP) system, along with analyses and forecasts. 2. Method a. ECMWF assimilation system ECMWF produces routine global analyses and 10-day forecasts from an assimilation system based on an atmospheric model with a semi-Lagrangian, spectral formulation. The model has 91 levels from the surface to an altitude of 80 km and a T799 horizontal resolution

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Jonathan J. Gourley, Yang Hong, Zachary L. Flamig, Li Li, and Jiahu Wang

domain very rarely (B. Lawrence, Arkansas–Red Basin RFC, 2009, personal communication); thus the Stage IV product is built with radar rainfall estimates and rain gauge measurements, with careful quality control performed manually by operational forecasters. c. Satellite rainfall products In recent years, satellite-based precipitation estimates have been developed on subdaily time resolution over the globe by combining information from microwave (MW) and infrared (IR) observations ( Hsu et al. 1997

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Frank S. Marzano, Domenico Cimini, Tommaso Rossi, Daniele Mortari, Sabatino Di Michele, and Peter Bauer

single atmospheric state. In the case of profiles over land, wind speed is replaced by emissivity parameters. Profiles of temperature, humidity, surface temperature, and additional variables, obtained from the short-range ECMWF model forecasts, are extracted from the global database described in section 3b . The covariance 𝗕 ε represents the uncertainty associated with each of the components of x . In 1D-VarP, the first guess covariance statistics from the operational forecasts are used as the

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Ali Behrangi, Koulin Hsu, Bisher Imam, and Soroosh Sorooshian

products that use VIS bands are not necessarily adequate for the development of long-term precipitation climatology. Operational hydrologists and flood forecasters, on the other hand, are very interested in improving the accuracy of real-time rain-rate estimates and the ability to accurately identify the areal extent of precipitation at any time. As such, it is likely that they will welcome any improvement, whether it is at daytime, nighttime, or both. 6. Summary and conclusions In this paper, two of

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J. J. Shi, W-K. Tao, T. Matsui, R. Cifelli, A. Hou, S. Lang, A. Tokay, N-Y. Wang, C. Peters-Lidard, G. Skofronick-Jackson, S. Rutledge, and W. Petersen

simulations are compared with in situ and satellite observations, including the Environment Canada King City operational dual-polarimetric radar located about 35 km southeast of the CARE site and CloudSat -observed reflectivities. In addition, mean cloud hydrometeor profiles from the simulations are examined. The summary and conclusions are given in section 6 . 2. Brief review of WRF, the Goddard physical packages, and the satellite simulators WRF is a next-generation mesoscale forecast model and

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Meike Kühnlein, Boris Thies, Thomas Nauß, and Jörg Bendix

Rainfall Measuring Mission (TRMM) precipitation radar (PR), the reader is referred to Iguchi et al. (2000) and Ferreira et al. (2001) . The following overview is arranged by the complexity of the algorithms according to Barrett and Martin (1981) . Cloud index methods use thresholds for IR cloud-top temperature to detect rain areas, to which a rainfall rate is assigned. The most popular cloud index method is the Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI

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Feyera A. Hirpa, Mekonnen Gebremichael, and Thomas Hopson

1. Introduction The availability of high-resolution satellite precipitation products has made them very attractive for hydrological applications in regions that have less-dense and less-consistent ground-based measurements. Some of these products are available in (near) real time, making them suitable for flood-forecasting applications. The concept behind these high-resolution satellite precipitation algorithms is to combine information from the more accurate (but infrequent) microwave (MW

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Tufa Dinku, Pietro Ceccato, Keith Cressman, and Stephen J. Connor

consistent high-resolution daily rainfall time series for climatological applications over Africa, whereas RFE is an operational product. At this time, ARC data are available starting from 1995. CPC is currently working to extend the time series back to 1982. The main differences between ARC and RFE are that 1) RFE uses half-hourly TIR observations whereas ARC uses 3-hourly observations and that 2) RFE incorporates passive microwave (PM) rainfall estimates ( Xie et al. 2002 ). The CMORPH algorithm

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Cristian Mitrescu, Tristan L’Ecuyer, John Haynes, Steven Miller, and Joseph Turk

numerical model runs (produced operationally at the European Centre for Medium-Range Weather Forecasts) are also used to flag the precipitation phase. To keep consistency between the present profiling algorithm and that developed by Haynes et al. (2009) , we adopt all the tests and thresholds defined within. More details about these two precipitation techniques can be downloaded from the Data Processing Center at Colorado State University ( http://CloudSat.cira.colostate.edu/dataSpecs.php ). Given the

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Hilawe Semunegus, Wesley Berg, John J. Bates, Kenneth R. Knapp, and Christian Kummerow

(vertically and horizontally polarized). Detailed specifications for the spacecraft and instrument are given by Colton and Poe (1999) and Raytheon (2000) . The SSM/I was replaced by the Special Sensor Microwave Imager/Sounder (SSM/IS) in November 2005, although SSM/I is still operating. Eventually, the record of passive microwave instruments is planned to continue under the National Polar-Orbiting Operational Environmental Satellite System (NPOESS). SSM/I data are publicly available at National Oceanic

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