Abstract
Abstract. In order to study the on-board processing technology of meteorological satellites, a decision tree cloud detection algorithm is proposed by taking FY-4A satellite data as an example. According to the channel setting of the Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A satellite, the 0.65 μm, 1.375 μm, 3.75 μm, and 10.7 μm bands are selected as the cloud detection channels, and the reflectance, brightness temperature or bright temperature difference of the four channels are used as the cloud detection indicators, the thresholds of the four cloud detection indicators are obtained through statistics. On this basis, the decision tree cloud detection model is constructed and validated using FY-4A satellite data. The results show that the algorithm is simple, convenient and efficient, and the overall effect of cloud detection is good. It is an effective way for meteorological satellite cloud detection on-board processing technology.
Highlights
Cloud detection is a focus of research on atmospheric science
For selecting the proper channels to detect cloud, it is necessary to compare the channels of Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A with the channels of MODIS used for detecting cloud and the channels of AVHRR used for detecting cloud, firstly
The three cases shows that the results of the decision tree cloud detection model in research area are consistent with the cloud mask products (CLM) products of FY-4A on the overall trend
Summary
Cloud detection is a focus of research on atmospheric science. The precise result of cloud detection is helpful to grasp the development of weather situation, and plays an important role of analysing the change of climate. M., 1997; Xie et al, 2018; Shi et al, 2009; Sheng et al, 2005; Tian et al, 2008; Keith et al, 2011; Steven et al, 1998; Takashi et al, 2011; Keith et al, 2010), ClAVR algorithm(Stowe et al, 1995) and APOLLO algorithm based on AVHRR(Kriebel et al, 2003), algorithm based on NWC/GEO developed by French Meteorological Agency, algorithm based on TRMM VIRS(Liu et al, 2010), algorithm based on AHI of Himawari-8(Shang et al, 2017), and algorithms based on FY-3C(Zhang et al, 2017), FY-3D(Jing, 2018), FY-2C(Liu et al, 2009), FY-2G(Fu et al, 2019) in China The essence of these algorithms is using the thresholds of reflectance and bright temperature on visible light, infrared channels to detect cloud. This article carries out cloud detection on-board processing technology research taking the data of FY-4A as an example, according to the theory of decision tree
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