Abstract
In order to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probe (CIP) cannot be automatically recognized, this paper proposes an automatic recognition method of cloud and precipitation particle shape based on BP neural network. This method mainly uses a set of geometric parameters which can better describe the shape characteristics of cloud precipitation particles. Based on the cloud precipitation particle images measured by CIP in the precipitation stratiform clouds in northern China, a particle shape data training set and a testing set were constructed to train and verify the effect of the selected BP neural network model. The selected BP neural network model can classify the cloud particle image into tiny, column, needle, dendrite, aggregate, graupel, sphere, hexagonal and irregular. Utilizing the field campaign data measured by CIP, the habit identified results by the improved Holroyd method and by the selected BP neural network model were compared, which shows that the accuracy of BP neural network method is better than that of improved Holroyd method.
Highlights
The recognition of cloud precipitation particle shape is very important for many aspects of cloud microphysics research
Among the particle measuring instruments carried by the aircraft, the cloud precipitation particle imager based on the photodiode array is one of the most widely used airborne cloud microphysical measuring instruments
In order to test the actual performance of BP neural network, part of the flight segment data measured by the detection aircraft of Shanxi Artificial Rain and Hail Prevention Office in water-dropping layer cloud in Taiyuan area on April 20, 2010 was used to compare and verify the effect of the improved Holroyd method proposed by Huang Minsong (2020) and BP neural network
Summary
The recognition of cloud precipitation particle shape is very important for many aspects of cloud microphysics research. In the research of cloud precipitation particle shape recognition algorithm, Rahman et al (1981) used adaptive Kalman filter method and Bayesian decision theory to classify cloud particle images into 7 categories based on cloud particle images collected by 2DC probes [3, 4]. Huang Minsong et al (2020) proposed an improved Holroyd cloud particle shape recognition method, and cloud particle images can be divided into 8 categories [13]. The method of automatically identifying the shape of cloud precipitation particle images is used to assist China's research on cloud precipitation physics and artificial weather modification
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