Afternoon deep convection over the Maritime Continent islands propagates offshore in the evening to early morning hours, leading to a nocturnal rainfall maximum over the nearby ocean. This work investigates the formation of the seaward precipitation migration off western Sumatra and its intraseasonal and seasonal characteristics using BMKG C-band radar observations from Padang and ERA5 reanalysis. A total of 117 nocturnal offshore rainfall events were identified in 2018, with an average propagation speed of 4.5 m s−1 within 180 km of Sumatra. Most offshore propagation events occur when the Madden–Julian oscillation (MJO) is either weak (real-time multivariate MJO index < 1) or active over the Indian Ocean (phases 1–3), whereas very few occur when the MJO is active over the Maritime Continent and western Pacific Ocean (phases 4–6). The occurrence of offshore rainfall events also varies on the basis of the seasonal evolution of the large-scale circulation associated with the Asian–Australian monsoons, with fewer events during the monsoon seasons of December–February and June–August and more during the transition seasons of March–May and September–November. Low-level convergence, resulting from the interaction of the land breeze and background low-level westerlies, is found to be the primary driver for producing offshore convective rain propagation from the west coast of Sumatra. Stratiform rain propagation speeds are further increased by upper-level easterlies, which explains the faster migration speed of high reflective clouds observed by satellite. However, temperature anomalies associated with daytime convective latent heating over Sumatra indicate that gravity waves may also modulate the offshore environment to be conducive to seaward convection migration.

This work investigates impacts of the Madden‐Julian Oscillation (MJO) on the daily and diurnal rainfall over the Maritime Continent, with emphasis on the influences of topography over Sumatra, Borneo and New Guinea. Eighty‐nine MJO events were identified during 2001–2019 using IMERG satellite data. Daily and diurnal rainfall maxima on the east side of topography lag the west side as the MJO passes. In addition, the island vanguard (pre‐MJO) rain is more convective, while the island rearguard (behind the main MJO body) rain is more stratiform. The timing and magnitude of diurnal rainfall is defined using the maximum hourly rain rate instead of the first diurnal harmonic to avoid smoothing. While a single sharp peak is observed over the mountains around 19 LT, a much broader delayed peak occurs over land to the west of topography and two peaks are observed over land to the east of topography at 15 and 2 LT. Rain peaks offshore from 3 to 7 LT. Cluster analysis shows that the afternoon and nighttime rainfall peaks are highest before the MJO arrives, then gradually decrease and become earlier on the west side of the topography, whereas the afternoon (nighttime) peak east of the topography is enhanced before (after) the MJO arrives. The contrasting east‐west features can be attributed to topographic influences on the moisture flux convergence of the mean column moisture by MJO‐induced winds. The MJO wind modulation of diurnal rainfall over most of the open ocean areas is insignificant.

A nocturnal Amazonian low-level jet (ALLJ) was recently diagnosed using reanalysis data. This work assesses the ability of CESM1.2.2 to reproduce the jet and explores the mechanisms by which the ALLJ influences convection in the Amazon. The coupled CESM simulates the nocturnal ALLJ realistically, while CAM5 does not. A low-level cold air temperature bias in the eastern Amazon exists in CAM5, thus the ALLJ is weaker than observed. However, a cold SST bias over the equatorial North Atlantic in the coupled model offsets the cold air temperature bias, producing a realistic ALLJ. Climate models significantly underestimate March-April-May (MAM) precipitation over the eastern Amazon. We ran two sensitivity experiments using the coupled CESM by adding bottom-heavy diabatic heating at noon and midnight for 2.5 hours along the coastal Amazon during MAM to mimic the occurrence of shallow precipitating convection. When heating is added during the early afternoon, coastal convection deepens and the ALLJ transports moisture inland from the ocean, preconditioning the environment for deep convective development during the ensuing hours. The increased convection over the eastern Amazon also moderately alleviates the equatorial Atlantic westerly wind bias, leading to deepening of the east Atlantic thermocline in the following months and partially improving the simulated June-July-August (JJA) Atlantic cold tongue in the coupled model. When heating is added at night, coastal convection does not strengthen as much and the ALLJ transports less moisture. Improvements in the simulated Atlantic winds and SST are negligible. Therefore, diurnal circulations matter to the organization of convection and rain across the Amazon, with impacts over the equatorial Atlantic.

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolution. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the sub-grid scale. This work adapts Quickbeam to the dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate-to-heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land/ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP sub-grid algorithm by applying a more realistic, non-deterministic approach to assigning GCM grid box convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated sub-grid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud cover assumption.

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