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A rain-gauge-adjusted algorithm for global satellite mapping of precipitation (GSMaP) that estimates the surface precipitation rate with resolutions of 0.1° and 1 h over the globe is described herein. Precipitation is one of the most important parameters of the earth’s system, and its global distribution and changes are essential data for modeling the water cycle, maintaining ecosystems, increasing agricultural production, improving weather forecasting precision, and implementing flood warning systems. In the Global Precipitation Measurement project, integrated products of high-resolution mapping of precipitation, obtained from microwave measurements made by a constellation satellite and infrared radiometers in geostationary orbit, are developed and supplied to the public. However, these high-resolution products, such as GSMaP_MVK, sometimes underestimate surface precipitation, introducing large errors into hydrological modeling. This paper combines the global gauge data set with GSMaP_MVK, using a new algorithm [gauge-adjusted GSMaP (GSMaP_Gauge)], described and evaluated herein using local radar and rain-gauge data sets. This algorithm outperforms other GSMaP products in all validation tests. [ABSTRACT FROM AUTHOR]

The authors improve the high-resolution Global Satellite Mapping of Precipitation (GSMaP) product for Typhoon Morakot (2009) over Taiwan by using an orographic/nonorographic rainfall classification scheme. For the estimation of the orographically forced upward motion used in the orographic/nonorographic rainfall classification scheme, the optimal horizontal length scale for averaging the elevation data is examined and found to be about 50 km. It is inferred that as the air ascends en masse on the horizontal scale, it becomes unstable and convection develops. The orographic/nonorographic rainfall classification scheme is extended to the GSMaP algorithm for all passive microwave radiometers in orbit, including not just microwave imagers but also microwave sounders. The retrieved rainfall rates, together with infrared images, are used for the high-resolution rainfall products, which leads to much better agreement with rain gauge observations. [ABSTRACT FROM AUTHOR]

The adequacy of hourly rainfall sampling was examined in terms of the detection of diurnal variations using 8 yr (1998–2005) of data observed by the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM) satellite. It was found that the monthly and hourly rain samples for each 0.2° grid point over the 8-yr period are composed of multiple precipitation systems. In this study, a “3-h-significant diurnal peak” was defined as the time of maximum rainfall with consecutive positive anomalies for more than 3 h. The fraction of the analyzed area with a 3-h-significant diurnal peak increased annually and accounted for 43% of the total global tropics at 0.2° resolution over the 8-yr period. The diurnal signature over Tibet and the Amazon showed a high degree of spatial uniformity (at >10° scale). The degree of similarity and locations of the regional diurnal characteristics are described in terms of seasonal variations and at multiple resolutions based on spatial uniformity. For example, uniform early-afternoon peaks generally appear over the coastal land and areas of high relief, whereas a seasonally invariant early-afternoon peak over the low-lying Amazon basin is recognized as a regional characteristic. In areas of coastal ocean, early-morning peaks appear in certain regions such as the area surrounding the so-called Maritime Continent and the area off the west coast of Mexico. These peaks are distinct from the global characteristics of late-morning rainfall maxima recorded over most coastal oceans and early-morning peaks recorded over open ocean. The results are also compared with those derived from TRMM Microwave Imager (TMI) data. In addition to obtaining a coherent signal, regional differences in the timing of maximum rainfall over the Tibetan Plateau were addressed; this discrepancy is attributed to limitations of the scattering algorithm used for TMI data in terms of detecting shallow convection and screening cold surfaces. [ABSTRACT FROM AUTHOR]