Accurate precipitation observations are crucial for water resources management and as inputs for a gamut of hydrometeorological applications. Precipitation data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) have recently been widely used to complement traditional rain gauge systems. However, the satellite precipitation data needs to be validated before being widely used in the applications and this is still missing over the Indonesian maritime continent (IMC). We conducted a validation of the IMERG product version 6 for this region. The evaluation was carried out using gauge data in the period from 2016 to 2020 for three types of IMERG: Early (E), Late (L), and Final (F) from annual, monthly, daily and hourly data. In general, the annual and monthly data from IMERG showed a good correlation with the rain gauge, with the mean correlation coefficient (CC) approximately 0.54–0.78 and 0.62–0.79, respectively. About 80% of stations in the IMC area showed a very good correlation between gauge data and IMERG-F estimates (CC = 0.7–0.9). For the daily assessment, the CC value was in the range of 0.39 to 0.44 and about 40% of stations had a correlation of 0.5–0.7. IMERG had a fairly good ability to detect daily rain in which the average probability of detection (POD) for all stations was above 0.8. However, the false alarm ratio (FAR) value is quite high (<0.5). For hourly data, IMERG’s performance was still poor with CC around 0.03–0.28. For all assessments, IMERG generally overestimated rainfall in comparison with rain gauge. The accuracy of the three types of IMERG in IMC was also influenced by season and topography. The highest and lowest CC values were observed for June–July–August and December–January–February, respectively. However, categorical statistics (POD, FAR and critical success index) did not show any clear seasonal variation. The CC value decreased with higher altitude, but with slight difference for each IMERG type. For all assessments conducted, IMERG-F generally showed the best rainfall observations in IMC, but with slightly difference from IMERG-E and IMERG-L. Thus, IMERG-E and IMERG-L data that had a faster latency than IMERG-F show potential to be used in rainfall observations in IMC.

Integrated Multi-satellite Retrievals for GPM (IMERG) data have been widely used to analyze extreme precipitation, but the data have never been validated for the Indonesian Maritime Continent (IMC). This study evaluated the capability of IMERG Early (E), Late (L), and Final (F) data to observe extreme rain in the IMC using the rain gauge data within five years (2016–2020). The capability of IMERG in the observation of the extreme rain index was evaluated using Kling–Gupta efficiency (KGE) matrices. The IMERG well captured climatologic characteristics of the index of annual total precipitation (PRCPTOT), number of wet days (R85p), number of very wet days (R95p), number of rainy days (R1mm), number of heavy rain days (R10mm), number of very heavy rain days (R20mm), consecutive dry days (CDD), and max 5-day precipitation (RX5day), indicated by KGE value >0.4. Moderate performance (KGE = 0–0.4) was shown in the index of the amount of very extremely wet days (R99p), the number of extremely heavy precipitation days (R50mm), max 1-day precipitation (RX1day), and Simple Daily Intensity Index (SDII). Furthermore, low performance of IMERG (KGE < 0) was observed in the consecutive wet days (CWDs) index. Of the 13 extreme rain indices evaluated, IMERG underestimated and overestimated precipitation of nine and four indexes, respectively. IMERG tends to overestimate precipitation of indexes related to low rainfall intensity (e.g., R1mm). The highest overestimation was observed in the CWD index, related to the overestimation of light rainfall and the high false alarm ratio (FAR) from the daily data. For all indices of extreme rain, IMERG showed good capability to observe extreme rain variability in the IMC. Overall, IMERG-L showed a better capability than IMERG-E and -F but with an insignificant difference. Thus, the data of IMERG-E and IMERG-L, with a more rapid latency than IMERG-F, have great potential to be used for extreme rain observation and flood modeling in the IMC.

This study is a preliminary assessment of the latest version of the Global Satellite Measurement of Precipitation (GSMaP version 08) data, which were released in December 2021, for the Indonesian Maritime Continent (IMC), using rain gauge (RG) observations from December 2021 to June 2022. Assessments were carried out with 586 rain gauge (RG) stations using a point-to-pixel approach through continuous statistical and contingency table metrics. It was found that the coefficient correlation (CC) of GSMaP version 08 products against RG observations varied between low (CC = 0.14–0.29), moderate (CC = 0.33–0.45), and good correlation (CC = 0.72–0.75), for the hourly, daily, and monthly scales with a tendency to overestimate, indicated by a positive relative bias (RB). Even though the correlation of hourly data is still low, GSMaP can still capture diurnal patterns in the IMC, as indicated by the compatibility of the estimated peak times for the precipitation amount and frequency. GSMaP data also manage to observe heavy rainfall, as indicated by the good of detection (POD) values for daily data ranging from probability 0.71 to 0.81. Such a good POD value of daily data is followed by a relatively low false alarm ratio (FAR) (FAR < 0.5). However, the GSMaP overestimates light rainfall (R < 1 mm/day); as a consequence, it overestimates the consecutive wet days (CWD) and number of days with rainfall ≥ 1 mm (R1mm) indices, and underestimates the consecutive dry days (CDD) extreme rain index. GSMaP daily data accuracy depends on IMC’s topographic conditions, especially for GSMaP real-time data. Of all GSMaP version 08 products evaluated, outperformed post-real-time non-gauge-calibrated (GSMaP_MVK), and followed by post-real-time gauge-calibrated (GSMaP_Gauge), near-real-time gauge-calibrated (GSMaP_NRT_G), near-real-time non-gauge-calibrated (GSMaP_NRT), real-time gauge-calibrated (GSMaP_Now_G), and real-time non-gauge-calibrated (GSMaP_Now). Thus, GSMaP near-real-time data have the potential for observing rainfall in IMC with faster latency.

Flash flooding is an important issue as it has a devastating impact over a short time and in a limited area. However, predicting flash floods is challenging because they are connected to convection systems that rapidly evolve and require a high-resolution forecasting system. In addition, modeling a case study of a mesoscale convective complex (MCC) is the key to improving our understanding of the heavy rainfall systems that trigger flash floods. In this study, we aim at improving modeling skills to simulate a heavy rainfall system related to flash-flood-producing MCCs. We simulated a heavy rainfall event related to a severe flash flood in Luwu, Sulawesi, Indonesia, on 13 July 2020. This flood was preceded by persistent heavy rainfall from 11 to 13 July 2020. In this case, we investigated the role of sea surface temperature (SST) in producing the persistent heavy rainfall over the region. Therefore, we explore the physical and dynamic processes that caused the heavy rainfall using a convection-permitting model with 1 km resolution and an experiment comparing the situation with and without updated SST. The results show that the heavy rainfall was modulated by the development of a pair of MCCs during the night. The pair of MCCs was triggered by a meso-low-pressure system with an anti-cyclonic circulation anomaly over the Makassar Strait and was maintained by the warm front passing between the sea and land over central Sulawesi. This front was characterized by moist–warm and cold–dry low-level air, which may have helped extend the lifetime of the MCCs. The north-westward propagation of the MCCs was due to the interaction between predominantly a south-easterly monsoon and SST anomalies. This study suggested that the long-lived (>10 h) MCCs (>80,000 km² cloud shield) and persistent precipitation are reproduced well in the updated SST scenario in the WRF model. This relatively simple technique in the running model provides a new strategy for improving flash flood forecasting by better predicting rainfall as an input in the hydrological model. Our findings also indicated a long-lived MCC maintained by back-building mechanisms from night to morning inland as an exceptional MCC, which does not correspond to a previous study.