Resilient dataset of rain clusters with life cycle evolution during April to June 2016–2020 over eastern Asia based on observations from the GPM DPR and Himawari-8 AHI

Abstract

Abstract. Our knowledge of the properties of precipitation and clouds over their life cycles has progressed with the rapid development of satellite observations. However, previous studies have focused on the life cycle evolution of the macroscale features of precipitation and clouds, whereas the evolution of the microphysical properties of precipitation and clouds over their life cycles is yet to be determined. One of the reasons for this lack of knowledge is the fact that there is no single dataset providing both the three-dimensional structure of precipitation and the relevant life cycle properties. We identified initial rain clusters (RCs) from the Global Precipitation Measurement (GPM) 2ADPR dataset and mesoscale convective systems (MCSs) from the Himawari-8 Advanced Himawari Image (AHI) gridded product. Based on the contours of the initial RCs and MCSs, we then carried out a series of resilient processes, including filtration, segmentation, and consolidation, to obtain the final RCs. The final RCs had a one-to-one correspondence with the relevant MCS. We extracted the RC area, central location, average radar reflectivity profile, average droplet size distribution profile, and other precipitation information from the contours of the final RCs and GPM 2ADPR dataset. The life cycle evolution of the MCS area, location, and cloud-top brightness temperature were retrieved from the corresponding MCSs and their tracks from Himawari-8 observations. The final dataset provides both three-dimensional precipitation information and life cycle information of precipitating clouds during April to June 2016–2020 over eastern Asia. This dataset facilitates studies of the life cycle evolution of precipitation and provides a good foundation for convection parameterizations in precipitation simulations. The dataset used in this paper is freely available at https://doi.org/10.5281/zenodo.6198716 (Zhang et al., 2022).