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Reliability information of satellite precipitation products is required for various applications. This study describes and evaluates a reliability flag of the Global Satellite Mapping of Precipitation Near-Real-Time precipitation product (GSMaP_NRT). This flag was developed to characterize the reliability of GSMaP_NRT data simply and qualitatively by considering its algorithm characteristics. The reliability at each pixel is represented by any one of ten levels (10 being the best and 1 the worst) by considering three major factors: 1) "surface type reliability"--which takes into account that estimation of rainfall using passive microwave sensors is better over the oceans than over land and coastal areas; 2) "low-temperature reliability"--which takes into account the lower reliability due to surface snow cover in low-temperature conditions; and 3) "Moving Vector with Kalman Filter (MVK) propagation reliability"--which means that the reliability gets worse with the increase in time since the last overpass of the passive microwave sensor. To evaluate the utility of the reliability flag, statistical indices are calculated for each reliability level using gauge-calibrated ground radar data around Japan. It is found that the reliability flag represents the differences in GSMaP accuracy: the accuracy worsens as the reliability decreases. The GSMaP errors exhibit seasonal changes that are well represented by the ten levels of the reliability flag, indicating that the reliability flag can be used to catch seasonal variations in GSMaP accuracy due to changes in environmental factors. This study also raises the possibility of improving the reliability flag by using information related to heavy orographic rainfall. It is shown how the error features of heavy orographic rainfall differ from those of the total rainfall, and it is suggested that heavy orographic rainfall information can be utilized to further improve the reliability flag. [ABSTRACT FROM AUTHOR]

Observations of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) over 16 yr yielded hundreds of large precipitation systems (⩾100 km) for each 0.1° grid over major rainy regions. More than 90% of the rainfall was attributed to large systems over certain midlatitude regions such as La Plata basin and the East China Sea. The accumulation of high-impact snapshots reduced the significant spatial fluctuation of the rain fraction arising from large systems and allowed the obtaining of sharp images of the geographic rainfall pattern. Widespread systems were undetected over low-rainfall areas such as regions off Peru. Conversely, infrequent large systems brought a significant percentage of rainfall over semiarid tropics such as the Sahel. This demonstrated an increased need for regional sampling of extreme phenomena. Differences in data collected over a period of 16 yr were used to examine sampling adequacy. The results indicated that more than 10% of the 0.1°-scale sampling error accounted for half of the TRMM domain even for a 10-yr data accumulation period. Rainfall at the 0.1° scale was negatively biased in the first few years for over more than half of the areas because of a lack of high-impact samples. The areal fraction of the 0.1°-scale climatology with a 50% accuracy exceeded 95% in the ninth year and in the fifth year for those areas with rainfall >2 mm day-1. A monotonic increase in the degree of similarity of finescale rainfall to the best estimate with an accuracy of 10% illustrated the need for further sampling. [ABSTRACT FROM AUTHOR]