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.

Hydrometeorological disasters due to extreme weather events represent a significant threat to the security of life in Jambi Province. In order to develop effective strategies for mitigating this threat, it is essential to gain a comprehensive understanding of the underlying dynamics that give rise to such disasters. Despite the high frequency of these events, more research is needed on the complex relationship between trends in extreme indices and the frequency of hydrometeorological disasters in this region. This study addresses this gap by utilizing rainfall data from 2008 to 2020 from the Integrated Multi-satellite Retrievals for GPM (IMERG) and hydrometeorological disaster data from the National Disaster Management Agency (BNPB). A range of extreme rainfall indices, including PRCPTOT, R85P, R95P, R99P, CWD, CDD, R1mm, R10mm, R20mm, R50mm, RX1Day, RX5Day, and SDII, were subjected to careful analysis concerning hydrometeorological disasters, including floods, landslides, tornadoes, droughts, and forest fires. Notable results indicate a significant increasing trend (p < 0.05) for the CWD index, while decreasing trends are observed for R85P, R95P, R99P, R50mm, RX1Day, RX5Day, and SDII. PRCPTOT and R20mm show decreasing trends, and CDD shows an increasing trend, although it is not statistically significant (p > 0.05). Subsequently, there was a significant increase in landslides and tornadoes, while forest fires and floods showed an insignificant increase (p > 0.05). Drought exhibited a significant decreasing trend in Jambi. Correlation analysis revealed the complex relationship between extreme weather indices and hydrometeorological disasters. The positive correlations observed between most extreme rainfall indices and floods and landslides, except for CDD, indicate that extreme rainfall is the primary cause of these disasters in Jambi. The correlation is particularly pronounced in areas with mountainous topography, where landslides are more prevalent. The positive correlations observed between CDD and droughts and forest fires suggest that periods of reduced rainfall and increased drought contribute to these disasters. This correlation is more robust in districts with extensive peatlands. The results provide valuable insights into the vulnerability of Jambi Province to hydrometeorological disasters and highlight the importance of understanding regional variations in extreme weather events. These findings improve our understanding of the interactions between climate indices and disasters and provide the basis for informed risk reduction and adaptation strategies in changing climatic conditions. Doi: 10.28991/ESJ-2024-08-05-012 Full Text: PDF

Regional variability of raindrop size distribution (DSD) along the Equator was investigated through a network of Parsivel disdrometers in Indonesia. The disdrometers were installed at Kototabang (KT; 100.32° E, 0.20° S), Pontianak (PT; 109.37° E, 0.00° S), Manado (MN; 124.92° E, 1.55° N) and Biak (BK; 136.10° E, 1.18° S). It was found that the DSD at PT has more large drops than at the other three sites. The DSDs at the four sites are influenced by both oceanic and continental systems, and majority of the data matched the maritime-like DSD that was reported in a previous study. Continental-like DSDs were somewhat dominant at PT and KT. Regional variability of DSD is closely related to the variability of topography, mesoscale convective system propagation and horizontal scale of landmass. Different DSDs at different sites led to different Z–R relationships in which the radar reflectivity at PT was much larger than at other sites, at the same rainfall rate.

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.

The measured rain rate, raindrop size distribution (DSD), and the ITU-R model over the frequency range from 1-100 GHz have been used to elucidate the cumulative rainfall rate and the variability of rain attenuation at Kototabang. Rain rate and DSD are recorded from ground-based optical rain gauge and disdrometer measurements, respectively. Considerable differences between the recorded data and the ITU-R model are observed at small time percentage. The specific rain attenuation obtained from the DSD measurement shows diurnal variation with the largest attenuation observed in the morning hours. This characteristic is due to the raindrop spectra of rain events in this period containing more small-sized drops (<2 mm) than at others as described by the largest contribution of these drops on the specific rain attenuation. The diurnal variation is serious for frequencies higher than 60 GHz especially in very extreme rain.

The characteristics of diurnal variation of precipitation in terms of amount (PA), frequency (PF), and intensity (PI) over Sumatra have been investigated using rain‐gauge data from 186 stations. Mean PA and PF show spatial variations significantly affected by the terrain elevation and stations' distance to the west coastline of Sumatra. A slightly larger PA and PF appear over the middle and western sections of the Barisan mountains, in which the mean PI is smaller in these regions. Most stations with large rainfall amount also have large rainfall frequencies, indicated by a strong correlation between PA and PF. The timing of the diurnal peak of PA, PF, and PI also has prominent regional characteristics. The cluster analysis reveals different grouping of stations with distinctive diurnal peaks. The prevailing afternoon and early‐evening peaks, that is, 1500–2000 LST, appear mostly over mountain ranges where amplitude of PA and PF tended to increase with elevation. Moreover, the amplitude of diurnal cycles tended to decrease as distance from the Sumatra's west coastline increases. On the western side of Barisan's mountain range, high amplitude of diurnal cycle prevails compared to the eastern side. We also found a strong relationship between rain event duration with diurnal cycle pattern. Generally, rain event with a long duration tended to have a peak occurring at a later time compared with those events of shorter duration. The diurnal variation of precipitation corresponds to the temporal evolution of the low‐level convergences and wind vectors, and cloud migration modulated by land‐sea breeze system.