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This paper introduces the Method of Microwave Rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) algorithm in its latest version (V05, algorithm version 8), released in December 2021. The method aims to mitigate the discrepancy of GSMaP rainfall estimates among passive microwave (PMW) imagers/sounders (MWIs/MWSs) due to differences in sensor specifications and retrieval algorithms. The basic idea of the MMN module is to calibrate target PMW sensors with reference sensors (the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)) using the cumulative distribution function (CDF) of the rain rate. Differences between the CDF and normalization table for MWSs are greater than MWIs due to different rain retrieval algorithms. More (less) MWS rainfall is detected over the ocean (land) than GMI rainfall. Matchup rainfall data between GMI and a target PMW sensor are compared to evaluate MMN performance. The monthly mean rainfall and mean bias error were improved for almost all PMW sensors. This study leaves open the possibility for further inter-calibration and improvement of rain detection and heavy rainfall retrievals. [ABSTRACT FROM AUTHOR]

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]

Heavy ice precipitation (HIP) flags in the products of dual‐frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) core satellite indicate the existence of large ice particles. In this study, we analyzed the distribution of a HIP band detected along the warm front of an oceanic extratropical cyclone. The HIP band was more than 125 km long and probably exceeded the 245‐km range of the Ku‐band precipitation radar. We found two zones with distinct microphysical processes in the HIP band. Ice particles near the echo top height showed little change in their median size with height, where frontogenetical updraft was maximum. This suggests that particles are generated and grown in this zone by the updrafts. In the zone below, the particle sizes increased as the height decreased, implying particles aggregate as they fall. Plain Language Summary: Bands of heavy snowfall are often reported in the presence of extratropical cyclones over land. However, observations of bands of heavy snowfall over the ocean are limited. In this study, we detected a band of heavy snowfall occurring over the ocean using satellite‐borne precipitation radar for the first time. This signal had a narrow width of 10–20 km and a length that was likely greater than the 245‐km swath of the Ku‐band radar. We examined the change in precipitation echoes with height for the snowfall to understand how ice particles grow and subsequently result in heavy snowfall. The analysis suggested that in the upper layer of the heavy snowfall band, ice particles are produced and grown by an upward flow, and that in the middle layer, they collide with other ice crystals, resulting in larger crystals, which subsequently fall. Further statistical analysis of similar signals could reveal the structure and driving mechanisms for heavy snowfall over the land and ocean in future studies. Key Points: We detected a heavy ice precipitation (HIP) band along the warm front of an oceanic cyclone observed by GPM/DPRIce particles are grown by updraft near the echo top height of the HIP band and then aggregate in the zone below [ABSTRACT FROM AUTHOR]

This study examined the vertical gradient of radar reflectivity below the detected bright band in stratiform regions from the Tropics to the extratropical latitudes using data from the Ku‐band (13.6 GHz) precipitation radar on board the Global Precipitation Measurement (GPM) Core Observatory. Stratiform precipitation profiles with reflectivity decreasing (increasing) from the melting level toward the surface occur frequently in the tropical oceans (mid‐ and high‐latitude oceans). High fractions of downward increasing stratiform pixels are found over the North Pacific Ocean throughout the year and over East Asia except for winter. In contrast, the North American continent and the adjacent North Atlantic Ocean are characterized by low fractions of downward‐increasing pixels during summer. The difference is consistent with the dominant type of convection over East Asia (warm‐type clouds) and over the North American continent (cold‐type clouds). Even in the tropical oceans such as the Atlantic and eastern Pacific intertropical convergence zones, there are some areas with moderate fractions of downward‐increasing stratiform pixels where the warm rain process dominates. The downward reduction of reflectivity in the stratiform region of MCSs is obviously due to evaporation, which is a function of lower‐tropospheric relative humidity. The downward increase of reflectivity in stratiform regions over the midlatitude oceans appears the result of raindrop growth. This is achieved via the collection of cloud droplets while falling through low‐level clouds produced by large‐scale vertical motion in the lower troposphere due to large‐scale convergence associated with synoptic‐scale systems. This study examined the vertical structure of radar reflectivity below the detected bright band in stratiform regions using data from the Ku‐band precipitation radar on board the Global Precipitation Measurement (GPM) Core Observatory. Reflectivity decreases toward the surface in the stratiform region of mesoscale convection [(a) and (c)], indicating a downward reduction of rainwater content. In stratiform regions of synoptic‐scale weather systems, such as extratropical cyclones [(b) and (d)], reflectivity increases toward the surface, indicating a downward increase of rainwater content. [ABSTRACT FROM AUTHOR]

This letter identifies causes of the deterioration of the Global Satellite Mapping of Precipitation rain detection over the Tibetan Plateau during the summer monsoon season. Using the rain/no-rain classification (RNC) method over the Plateau, observed brightness temperature (Tb) at 21 (23) GHz from the Tropical Rainfall Measuring Mission Microwave Imager (the Defense Meteorological Satellite Program Special Sensor Microwave Imager) [Tb(21 V) and Tb(23 V)], and surface emissivity ( $\varepsilon )$ was substituted for surface temperatures (Ts) to exclude areas of low Ts as snow cover because it is difficult to distinguish between the scattering signals of precipitation and snow cover. A case study demonstrates that rain systems are excluded because Ts is often below the threshold for snow cover due to the use of an inadequate value for $\varepsilon $ (constant value throughout the year), even though a rain scattering signal at high-frequency channels under no snow cover is evident. After $\varepsilon $ is replaced with values from a monthly mean satellite observation-based land surface emissivity database, rainfall detection is improved. In addition, it is suggested that a database of RNCs should also consider diurnal variations in Tb(21 V) and Tb(23 V) due to large diurnal differences over the Plateau. [ABSTRACT FROM PUBLISHER]

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]

We incorporate an orographic/nonorographic rainfall classification scheme into the Global Satellite Mapping of Precipitation algorithm for passive microwave radiometers. It improves rainfall estimation over the entire Asian region. However, low verification scores over the United States and Mexico result because vertical profiles of rainfall over the Sierra Madre Mountains are high even for orographic rainfall conditions. In this region, lightning activity is vigorous, with large amounts of solid particles such as graupels, occurring with strong convections. Hence, the orographic/nonorographic rainfall classification scheme is switched off for regions where strong lightning activity occurs in the rainfall type database. The revised zonal mean rainfall amounts obtained from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager are now in better agreement with those from the version 7 of the TRMM Precipitation Radar over the United States and Mexico, as well as Asia. [ABSTRACT FROM AUTHOR]