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Rémy Roca, Philippe Chambon, Isabelle Jobard, Pierre-Emmanuel Kirstetter, Marielle Gosset, and Jean Claude Bergès

2007 ), a subset is selected that relies on the direct—or indirect through the fitting technique—use of the individual errors (for another selection, see Kelly 2007 ). Hence, the following indices are kept for the analysis: the mean coefficient of correlation R and the mean slope and mean intercept of the regression line a and b. The distributions of the coefficients a , b , and R , which are available from Kelly’s technique, were analyzed and the significance of the mean values of the

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Long S. Chiu and Roongroj Chokngamwong

, Fig. 5 shows the spatial pattern of the trend from linear regression analysis for both V4 and V6. The slope of the linear regression, in units of millimeters per day per month, shows the trend. The linear trend pattern for both V4 and V6 is very similar. Significant increasing trends mostly occur in the ITCZ, South Pacific convergence zone, and Bay of Bengal. However, small regions of decrease are found near northeastern coastal Australia. Analyses of zonal mean rain rates show a significant

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B. J. Sohn, Hyo-Jin Han, and Eun-Kyoung Seo

). More recently, new merged data with higher spatial and temporal resolutions (0.25° and 3-h scales, respectively) have become available [e.g., Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN; Sorooshian et al. 2000 ), Climate Prediction Center Morphing Method (CMORPH; Joyce et al. 2004 ), TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ), and the National Research Laboratory blended precipitation dataset (NRL

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

(e.g., Wertz and Larson 1999 ). Moreover, the design of the FLORAD multifrequency compact radiometer was supported by a reduced-entropy analysis of the MMW information content obtaining an overall set of 10 channels between 89 and 229 GHz. The comparison among the various FLORAD radiometer configurations was carried out by using two statistical inversion schemes, namely a multiple regression (MR) and a maximum-likelihood (ML) algorithm, for retrieving temperature, humidity, and hydrometeor

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

precipitation estimation algorithms have been introduced and made operational during the past few years. Although some of these products depend on GEO single infrared channel to track cloud motions or fill the gap of PMW rain estimate [the Climate Prediction Center morphing method (CMORPH; Joyce et al. 2004 ) and the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 )], others use infrared data as a main rain estimator after being adjusted by PMW estimate

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

. Experiment setup Using the framework of the 1D + 4D-Var rain- and cloud-affected assimilation, SSM/I and SSMIS radiances were simulated using FG forecasts as input. The FG is a short-range (up to 12 h) forecast, initialized from the previous analysis, and it is used to provide the background state for data assimilation. FG forecasts were generated for a period of 20 days, from 1 to 20 August 2007, based on a modified version of cycle 32r3 of the ECMWF assimilation system and using a slightly reduced

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

power-law regression algorithm. Coppola et al. (2006) presented a neural network approach, which combines numerical weather model information with the IR satellite imagery to derive daily rainfall values. Feature-based methods rely on the assumption that the relationship between the satellite cloud-top brightness temperature and surface rainfall rate is non unique for most pixel-based rainfall estimation algorithms. Wu et al. (1985) proposed a visible (VIS)/IR pattern recognition technique to

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