GPM annual and daily precipitation data for real-time short-term nowcasting: A pilot study for a way forward in data assimilation

Kaiyang Wang, Lingrong Kong, Zixin Yang, Prateek Singh, Fangyu Guo, Yunqing Xu, Xiaonan Tang, Jianli Hao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


This study explores the quality of data produced by Global Precipitation Measurement (GPM) and the potential of GPM for real-time short-term nowcasting using MATLAB and the Short-Term Ensemble Prediction System (STEPS). Precipitation data obtained by rain gauges during the period 2015 to 2017 were used in this comparative analysis. The results show that the quality of GPM precipitation has different degrees efficacies at the national scale, which were revealed at the performance analysis stage of the study. After data quality checking, five representative precipitation events were selected for nowcasting evaluation. The GPM estimated precipitation compared to a 30 min forecast using STEPS precipitation nowcast results, showing that the GPM precipitation data performed well in nowcasting between 0 to 120 min. However, the accuracy and quality of nowcasting precipitation significantly reduced with increased lead time. A major finding from the study is that the quality of precipitation data can be improved through blending processes such as kriging with external drift and the double-kernel smoothing method, which enhances the quality of nowcast over longer lead times.

Original languageEnglish
Article number1422
JournalWater (Switzerland)
Issue number10
Publication statusPublished - 20 May 2021


  • GPM
  • Hydroclimatic changes
  • Mexico
  • Nowcasting
  • Rainfall
  • Satellite precipitation

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