Extreme weather events, such as typhoons, have occurred more frequently in the last few decades in the Philippines. The heavy precipitation caused by typhoons is difficult to measure with traditional instruments, such as rain gauges and ground‐based radar because these instruments have an uneven distribution in remote areas. Satellite precipitation data sets (SPDs) provide integrated spatial coverage of rainfall measurements, even for remote areas. However, the speed and direction of the wind has the interaction with terrain, which leads the uncertainty of the SPDs. This study performed sub‐daily assessments of near‐real‐time and high resolution SPDs (i.e., IMERG, GSMaP, and PERSIANN data sets) during five typhoon‐related heavy precipitation events in the Philippines, with the analysis under the impact due to wind and terrain effect. The aforementioned assessments were performed through a point‐to‐grid comparison by using continuous and volumetric statistical validation indices for the 34‐knot wind radii of the typhoons, rainfall intensity, terrain, and wind velocity effects. The results revealed that the IMERG exhibited good agreement with rain gauge measurements and exhibited high performance in detecting rainfall. The GSMaP data set overestimated the gauge observations during peak rainfall, while the IMERG and PERSIANN data sets considerably underestimated rainfall. The GSMaP exhibited the best performance for detecting heavy rainfall at high elevations, whereas IMERG exhibited the best performance for rainfall detection at low elevations. The IMERG exhibited a strong ability to detect heavy rainfall under various wind speeds.

Understanding the mechanisms that control tropical cyclone (TC) intensity changes is crucial for weather forecasters to predict intensification, especially rapid intensification (RI). TC intensification is considered to occur under the following favorable large-scale environmental conditions: warm sea surface temperature (SST), weak vertical wind shear (VWS), high low-to-mid troposphere humidity, and high upper oceanic heat content (Chang

Extreme weather events, such as typhoons, have occurred more frequently in the last few decades in the Philippines. The heavy precipitation caused by typhoons is difficult to measure with traditional instruments, such as rain gauges and ground-based radar, because these instruments have an uneven distribution in remote areas. Satellite precipitation datasets (SPDs) provide integrated spatial coverage of rainfall measurements, even for remote areas. This study performed subdaily (3-hour) assessments of SPDs (i.e., the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [IMERG], Global Satellite Mapping of Precipitation [GSMaP], and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks datasets) during five typhoon-related heavy precipitation events in the Philippines between 2016 and 2018. The aforementioned assessments were performed through a point-to-grid comparison by using continuous and volumetric statistical validation indices for the 34-knot wind radii of the typhoons, rainfall intensity, the terrain, and wind velocity effects. The results revealed that the IMERG exhibited good agreement with rain gauge measurements and exhibited high performance in detecting rainfall during five typhoon events, whereas the GSMaP exhibited high agreement during peak rainfall. All the SPDs tended to overestimate rainfall during light to moderate rainfall events and underestimate rainfall during heavy to extreme events. The IMERG exhibited a strong ability to detect moderate rainfall events (5–15 mm/3 hours), whereas the GSMaP exhibited superior performance in detecting heavy to extreme rainfall events (15–25, 25–50, and >50 mm/3 hours). The GSMaP exhibited the best performance for detecting heavy rainfall at high elevations, whereas the IMERG exhibited the best performance for rainfall detection at low elevations. The IMERG exhibited a strong ability to detect heavy rainfall under various wind speeds. A strong ability to detect heavy rainfall events for different wind speeds in the western and eastern parts of the mountainous region of Luzon were found for the GSMap and IMERG, respectively. This study demonstrated that the IMERG and GSMaP datasets exhibit promising performance in detecting heavy precipitation caused by typhoon events.