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Daniel Vila, Ralph Ferraro, and Hilawe Semunegus

. 2003 ). The GPCC uses a method similar to SPHEREMAP ( Willmott et al. 1985 ) to interpolate the data to regular grids and to produce a 2.5° product. This product undergoes extensive quality control. The errors in the GPCC product vary as a function of terrain type and number of stations in the grid. Matchups between the SSM/I dual-satellite rainfall retrieval and GPCC monthly estimates were generated for the period January 1992–December 2007. In this case, the dual-satellite product has been used

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

and Grody, 1998 ; Jackson et al. 2002 ; Zhang et al. 2006 ). To improve geophysical parameters such as global rainfall estimation in support of the National Aeronautics and Space Administration (NASA) Global Precipitation Mission (GPM), Berg and Kummerow (2006) developed SSM/I quality control procedures that have been shown to significantly remove spurious geolocation, radiance, and climatologically anomalous data. Vila et al. (2010) have demonstrated the effectiveness of these statistically

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

given time t represents a 3-h window from t − 1.5 to t + 1.5 h for TMPA and from t to t + 3 h for others (CMORPH, PERSIANN, and NRL-blended), AWS data are processed in time to be compatible with each HRPP. 3. Geographic rain distribution Before statistically analyzing the quality of HRPP products, we examine how the summer mean rainfall climatologies of four HRPPs and TMI rain rate compare with AWS distributions. Figure 2 presents the mean rain rates (mm h −1 ) averaged over four summer

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Zhong Liu, Hualan Rui, William Teng, Long Chiu, Gregory Leptoukh, and Steven Kempler

-real-time precipitation products, are widely used in various research and applications ( Hsu et al. 1999 ; Sorooshian et al. 2000 ; Kidd et al. 2003 ; Joyce et al. 2004 ; Huffman et al. 2007 ; Turk and Mehta 2007 ). However, the lack of support for user-defined areas or points of interest poses a major obstacle to quickly gaining knowledge of product quality and behavior on a local or regional scale. The National Aeronautics and Space Administration (NASA) Goddard Earth Science Data and Information Services

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Mark S. Kulie and Ralf Bennartz

, all near-surface CPR observations exceeding 20 dB Z e were replaced with data from the eighth data bin above the actual surface instead of the sixth bin. As shown in Fig. 6b , this rudimentary quality-control method completely removed the large increase in average snowfall rate above the 10 mm h −1 snow-rate bin. Table 2 also indicates corrected values in boldface print for the various percentages with most of the clutter eliminated. The total accumulation percentages detected by a DPR

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

products The gauge-based product used here is the 4-km U.S. gridded gage-only hourly precipitation analysis developed for operational use by the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC; available online at http://data.eol.ucar.edu/codiac/dss/id=21.088 ). The product is derived from automated rain gauge reports from several networks with different reporting intervals, maintenance requirements, gauge types, wind screening, and quality control procedures

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

) provides a decision support system required for monitoring desert locusts ( Ceccato et al. 2007 ). The information produced by DLIS is used by about 30 countries in the affected region to plan survey-and-control operations. This information may also be used by the international donor community to target assistance, especially during emergencies. Rainfall data are one of the inputs into the DLIS decision support system. Rainfall is very important in determining the extent and intensity of desert locust

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Tufa Dinku, Franklyn Ruiz, Stephen J. Connor, and Pietro Ceccato

density of the stations is very good over the mountainous region. It is very sparse over the southern and eastern plains, but the spatial variability of rainfall is also low over those regions ( Fig. 2 ). Data from 2003 to 2005 were used. The quality-controlled rain gauge measurements were interpolated into regular grids of 0.05° latitude/longitude using kriging and an approach similar to Barancourt et al. (1992) . The interpolated values were then averaged to 0.25° spatial resolution for comparison

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Song Yang, Fuzhong Weng, Banghua Yan, Ninghai Sun, and Mitch Goldberg

SDRs, EDRs, and CDRs. We do not explicitly correct any possible error due to different sensor incidence and azimuth angles, although this bias should be very small in this study due to the SCO pair selection procedure to be discussed in section 3c . The data quality control of the SCO pixels described in section 3c is one of the key procedures in the calibration process. A double-difference technique (DDT) will be applied for SSM/I datasets if there is no direct interception between F-13 and

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M. Tugrul Yilmaz, Paul Houser, Roshan Shrestha, and Valentine G. Anantharaj

from the radar, spurious echoes resulting from anomalous propagation of the radar beam, brightband contamination, and scatter from ground-clutter targets. Precipitation forecasts by numerical models are not observed data, although they may assimilate observations such as radiosonde profiles, cloudiness, satellite temperatures, and so on. Numerical models may produce high-quality precipitation distributions in their analyses and short-range forecasts but less-skillful simulations over tropical areas

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