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Abstract. 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.

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Floods are a major contributor to natural disasters in Sumatra. However, atmospheric conditions leading to floods are not well understood due, among other factors, to the lack of a complete record of floods. Here, the 5 year flood record for Sumatra derived from governmental reports, as well as from crowd-sourcing data, based on Twitter messages and local newspapers’ reports, is created and used to analyze atmospheric phenomena responsible for floods. It is shown, that for the majority of analyzed floods, convectively coupled Kelvin waves, large scale precipitation systems propagating at ∼12 m/s along the equator, play the critical role. While seasonal and intraseasonal variability can also create conditions favorable for flooding, the enhanced precipitation related to Kelvin waves was found in over 90% of flood events. In 30% of these events precipitation anomalies were attributed to Kelvin waves only. These results indicate the potential for increased predictability of flood risk.

Intraseasonal variation of raindrop size distribution (DSD) in response to Madden Julian Oscillation (MJO) is studied using a 2D video disdrometer (2DVD), a boundary layer radar (BLR) and the Equatorial Atmosphere Radar, operated at Koto Tabang, west Sumatra, as well as GOES‐9 infra‐red brightness temperature. As a parameter of DSD, ΔZMP, which is defined as the difference between a measured radar reflectivity in dB and that from the Marshall‐Palmer (MP) radar reflectivity (Z) ‐ rain rate (R) relationship, Z = 200 R1.6, is used. It is found that in non‐active phase of MJO, 2DVD‐derived ΔZMPs are generally positive, indicating that DSDs are broad, while they decrease toward negative values as the phase of MJO shifts to active ones. Rain‐top height derived from the BLR indicates that the convective processes are more intense in the non‐active MJO phase than in the active phase, which would cause the difference in DSDs.

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.