The Maritime Continent is characterized by intense, deep atmospheric convection dominated by the diurnal cycle. Understanding the deep convective clouds and organized mesoscale convective systems (MCS) requires the use of high temporal and spatial resolution data, such as remote sensing meteorological data (RSMD), conventional observations, and numerical simulations. The inhomogeneous RSMD coverage represents a limitation for analyzing the development and evolution of MCS and a challenge when it is posed in a storm tracking of tropical convection system. Since becoming operational in 2015, the next-generation geostationary meteorological satellite, Himawari-8, offers unique opportunities to investigate rapidly evolving tropical convection and associated cloud properties, thanks to its revolutionary high spatial and temporal resolutions.

We present the analysis of an initial case study, where a clear offshore land/sea breeze and squall line event occurred off the southwest coast of Sumatra. We explore the propagating precipitation from Indonesia toward the Indian Ocean for this event, using Himawari-8 observations, together with Global Satellite Mapping of Precipitation (GSMaP), satellite rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) TMPA 3B42, the Climate Prediction Center morphing technique (CMORPH). Cloud and precipitation vertical structures are examined using CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), along with the Global Precipitation Measurement (GPM). Observations are compared with simulations from the Weather Research and Forecasting (WRF) model with a horizontal resolution of 4 km. Although some of the comparisons are limited by satellite overpassing times which cluster around two times of day, a good consistency is found between the simulated and satellite-estimated surface rainfall and cloud macro-physical properties such as cloud morphology and cloud-top temperature. The simulated cloud vertical structure and microphysical properties are also assessed using the radar-lidar observations from CloudSat-CALIPSO and GPM satellites.