Preprints
https://doi.org/10.5194/essd-2021-298
https://doi.org/10.5194/essd-2021-298

  04 Nov 2021

04 Nov 2021

Review status: this preprint is currently under review for the journal ESSD.

Resilient dataset of rain clusters with life cycle evolution based on observations from the GPM DPR and Himawari-8 AHI

Aoqi Zhang1, Chen Chen2, Yilun Chen1, Weibiao Li1, Shumin Chen1, and Yunfei Fu3 Aoqi Zhang et al.
  • 1School of Atmospheric Sciences, Sun Yat‐sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
  • 2School of Applied Economics, Renmin University of China, Beijing, 100872, China
  • 3School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China

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 2ADPR dataset and mesoscale convective systems (MCSs) from the Himawari-8 Advanced Himawari Image 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 final RCs and retrieved the life cycle evolution of the MCS area, location, and cloud-top brightness temperature from the corresponding MCSs and tracking algorithms. We provide both three-dimensional precipitation information and life cycle information in our resilient dataset. 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.5598418 (Zhang et al., 2021).

Aoqi Zhang et al.

Status: open (until 30 Dec 2021)

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Aoqi Zhang et al.

Data sets

An event-based precipitation dataset with life cycle evolution using resilient algorithms Aoqi Zhang;Chen Chen;Yilun Chen https://doi.org/10.5281/zenodo.5598418

Aoqi Zhang et al.

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Short summary
We constructed an event-based precipitation dataset with life cycle evolution based on coordinated application of observations from space-borne active precipitation radar and geostationary satellites. The dataset provides both three-dimensional structures of precipitation system and its corresponding life cycle evolution. The dataset greatly reduces the data size and avoids complex data processing algorithms for studying the life cycle evolution of precipitation microphysics.