Using Cloud Microphysical Properties and Multi-Channel Observation for Satellite Quantitative Precipitation Estimation (2)( I )

Project Details


There is an increasing trend of extreme precipitation event occurrence frequency in Taiwan, in particular under the warming scenario. Therefore, for the purpose of disaster mitigation and risk reduction, it is critical to have the information of precipitation, in terms of its intensity, duration and etc. Although we have some meteorological satellite precipitation product from global scale, we need high temporal and spatial resolutions in Taiwan for many applications. Therefore, we anticipate to establish the capacity for meteorological satellite quantitative precipitation estimation (MSQPE). Currently, Japanese geostationary satellite, Himawarui-8/-9, are taking observation routinely, at each 10 minutes and 2 km spatial resolution in infrared channels. We propose to take the observation from IR, and polar orbiting satellite GPM observation to conduct the traditional QPE. We also propose to generate the MSQPE from the use of cloud-top microphysical properties.The major challenge of MSQPE is how to describe the nonlinear relationship between cloud properties and rainfall rate. The machine learning models have been proved to be powerful tools in solving the nonlinear problems. This research proposes a machine learning method to estimate the rainfall rate in Taiwan area during February to December 2017. The precipitation events used for training and validation are obtained from the Central Weather Bureau (CWB) rain gauges. The preliminary results shows that the satellite cloud retrieval products have the capabilities to estimate the precipitation, with mean error (ME) of -0.4 mm/hour and root-mean-squared error of 0.76 mm/hour. In the future, this automatic MSQPE system could be utilized for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters.
Effective start/end date1/08/2031/07/21

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 11 - Sustainable Cities and Communities
  • SDG 13 - Climate Action
  • SDG 17 - Partnerships for the Goals


  • Meteorological Satellite Quantitative Precipitation Estimation (MSQPE)
  • Cloud Microphysical Properties


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