Evaluation of Global Precipitation Measurement‐Integrated Multi‐satellitE Retrieval for GPM (GPM‐IMERG) final precipitation product is performed over Japan, Nepal, and Philippines regions against further improved APHRODITE‐2 V1801R1 product. The evolution is carried out for nearly two consecutive years 2014–2015. Various qualitative and quantitative statistical indices such as mean bias, root‐mean‐square error, correlation coefficient, false alarming ratio, and probability of detection are considered to evaluate GPM‐IMERG precipitation estimates with APHRODITE‐2. Intraseasonal variability of two products is shown to explore the seasonal dependency of GPM‐IMERG performance. The performance of GPM‐IMERG research product with respect to rainfall intensity is shown by the cumulative probability distribution of target and reference data sets. Percentile‐based statistics is implemented for evaluating the advantages of GPM‐IMERG over Tropical Rainfall Measuring Mission‐3B42 while detecting the light and heavy rainfall events during wet/dry seasons. The overall performance of GPM‐IMERG seems to be good over Japan followed by Philippines and Nepal regions. This feature is clearly evidenced in terms of mean bias, root‐mean‐square error, and correlation magnitudes over three regions. GPM‐IMERG shows ability to follow the intraseasonal variability as shown by APHRODITE‐2 product with minor differences observed in precipitation maximum values during rainy season. Good agreement is seen between GPM‐IMERG and APHRODITE‐2 at different rainfall intensities except underestimation during heavy rainfall events. GPM‐IMERG seems to be improved in detecting light/heavy rainfall event magnitude than TRM‐3B42. However, the performance of both data sets encountered clear dependency on seasons.

The northern Japan facing the Japan Sea is known for its heavy snowfall. As snowfall is of great importance as a water resource, accurate measurements are required. We develop an optimal method for adjustment of wind‐induced precipitation undercatch for rain‐gauge‐based daily gridded precipitation data in Japan. In Japan Meteorological Agency's surface observation network, wind speed data are not measured at all precipitation stations; therefore, we tested three approaches for applying wind correction using four years' observational data (2009–2012). First, we interpolate corrected precipitation by using only stations, which measure wind; second, we define gridded correction parameters, interpolate uncorrected precipitation of all stations, and adjust gridded precipitation; third, we interpolate corrected precipitation by using wind data of Japan Meteorological Agency's surface network and a reanalysis at the stations where wind are not measured. Water budget analysis over the mountainous regions (dam's catchments) indicated that the best result among the three was obtained by compensating wind speed using high‐resolution meteorological reanalysis data, followed by a method that applies daily correction parameters to daily gridded precipitation data. The best method increased the winter (December–February) precipitation amount by 12.7% in northern Japan, and the bias in the annual hydrological balance was reduced from 33% to 26% over the mountainous terrain. Proper consideration of the rain gauge meta information, such as the existence/absence of a wind shield, is critically important for quantitative assessment of solid precipitation, especially in regions of heavy snowfall such as northern Japan.

This study developed a rain-gauge-based hourly precipitation dataset to analyze the heavy precipitation event of July 2018 in Japan (H2018). We modified the APHRODITE algorithm to treat hourly precipitation data, and we detected orographically induced heavy precipitation patterns in western Japan. We compared the heavy precipitation pattern along with moisture transport with that of another disastrous precipitation event in 2014 over Hiroshima (H2014). It is evident that heavy precipitation occurred over a much wider area in Chugoku district during H2018 than in H2014, and extreme precipitation which exceeds 10mm/hr appeared three times in H2018 while at one time in H2014. Atmospheric rivers (ARs) were detected during two distinct episodes of heavy precipitation over Hiroshima, i.e., 19 August 2014 and 6 July 2018. Of the two events, the precipitation amount and the depth (height) of the AR were much greater in the latter. In the mid-troposphere, abundant moisture and high equivalent potential temperatures along the Meiyu frontal system can produce a large area of continuous heavy precipitation. The intensive hourly rainfall dataset developed in this study will be useful for investigations of AR and meso-scale system that affect heavy precipitation and validation of numerical models.

Several versions of Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Extreme Events (APHRODITE-2) have been released for analyzing rain-gauge-based daily precipitation. APHRODITE-2 constitutes an improvement compared with the previous versions for evaluating extreme precipitation. One advantage of APHRODITE-2 products (versions 1801R1 and 1901) over APHRODITE-1 products is the ability to ensure uniformity in the daily accumulation period (end of the day, EOD) used in a specific domain. To create these EOD segregated or EOD adjusted products, we applied an EOD judgment scheme using multi-satellite merged precipitation products CMORPH V1.0 and ERA-Interim reanalysis. The novelty of the current methodology was tested against rain-gauge datasets with known EOD information. Despite the difference in horizontal resolution, ERA-Interim shows similar EOD detection performance to CMORPH. However, CMORPH showed better performance over India than ERA-Interim. The current method has potential to judge EOD of rain-gauge station data with an unknown observation time. Having prior EOD information as metadata is very important for gridded datasets as well as station data. Here, we also present deterministic/estimated EOD that we used for V1801R1 over Monsoon Asia.