Typhoon Maria Precipitation Retrieval and Evolution Based on the Infrared Brightness Temperature of the Feng-Yun 4A/Advanced Geosynchronous Radiation Imager

Abstract

Recognizing the importance and challenges inherent in the remote sensing of precipitation in typhoon monitoring, a study of the Advanced Geosynchronous Radiation Imager (AGRI) data from Feng-Yun 4A on typhoon precipitation was conducted. First, Typhoon Maria was selected to statistically analyze the AGRI infrared brightness temperature in the “precipitation” and “nonprecipitation” channels of the field of view. When there was precipitation, the brightness temperature of the AGRI channel changed significantly. Second, the shrunken locally linear embedding algorithm (SLLE) was adopted to carry out the retrieval of precipitation based on the brightness temperatures of AGRI infrared channels 8–14. The contribution rate of the brightness temperature at different channels to the objective function of precipitation retrieval model was obtained by the Bayesian model averaging (BMA). Based on the preliminary experimental “quantification” evaluation index, we concluded that the method adopted in this paper can be used to retrieve precipitation in infrared data and to retrieve the spiral cloud rain bands of a typhoon. Finally, based on the AGRI channel brightness temperature of a 10.8-micron window channel, we applied the membership degree information of a typhoon’s dominant cloud system from the fuzzy c-means (FCM) clustering method to modify precipitation retrieval results. The results were used to obtain the main morphological structure of typhoon precipitation. By further analyzing the temporal variation of dominant cloud system development using the FCM method, we concluded that the brightness temperature gradient can assist in the analysis of the variation of a typhoon’s intensity. This method can be applied to the continuous retrieval of large-scale precipitation. Precipitation retrieval via the AGRI can yield indicators for typhoon precipitation warnings and forecasts, thus providing a reliable reference tool for disaster prevention and mitigation.

Precipitation retrieval by the L1‐norm regularization: Typhoon Hagibis case

“…In order to describe this inverse problem, we denote the observed brightness temperatures (simplified as: BTs) of n c channels to be and the corresponding precipitation (simplified as: r s ) to be at a field of view (FOV) (also known as satellite footprint or pixel) (Wang and Zhang, 2014). In the current study, we simply set n r = 1 representing a single layer of precipitation and consider the following problem (Ebtehaj et al ., 2015; Wang et al ., 2020): where H : x → y denotes the “mapping” between precipitation and the brightness temperature, and represents the observation error. In an ideal case, one may approximate Equation (1) as follows: and assume an invertible H , then we could derive …”

“…When constructing the dictionary, we referred to the research of Wang et al . (2020), and used the “nearest neighbour” method to interpolate the brightness temperature of each channel of the H8/AHI to the GPM FOVs as the data source and the AHI infrared brightness temperature precipitation retrieval in the current study.…”

“…During the development and occurrence of catastrophic weather events such as typhoons, heavy rain or strong convection, the monitoring and forecasting of precipitation play essential roles. Typhoon disasters are mainly caused by wind, heavy rain and storm surge, where typhoon rainfall is more catastrophic compared with typhoon winds (Wang et al ., 2020). The typhoon Hagibis , for example, made landfall in Japan in 2019 and caused serious losses in agriculture, forestry, fishery etc.…”

“…Differences and complimentary aspects exist among these methods in terms of observation principle, algorithm accuracy, product space–time resolution, and coverage (Beck et al ., 2017). Due to the insufficient density of surface rain‐gauges and ground‐based radar observation networks, these common approaches are hard to monitor and track heavy rain and other severe weather phenomena, and there is insufficient data resolution to grasp the spatial distribution and intensity changes of typhoon precipitation (Wang et al ., 2020). Meteorological satellites, on the other hand, can effectively obtain the brightness temperature and other data of unmonitored areas such as oceans, mountains and deserts.…”

“…Wang et al . (2020) improved the SLLE method and retrieved the precipitation by the typhoon Maria via the infrared brightness temperature data from the FY‐4A/AGRI, where the contribution rates of the brightness temperatures from different channels to the objective functional of the precipitation retrieval model were measured using Bayesian model averaging (BMA). In order to fit the global precipitation measurement (GPM) precipitation data in local areas, Sharifi et al .…”