Acta Optica Sinica, Volume. 44, Issue 12, 1201010(2024)
Optimization Algorithm for Recognizing Phase States of Cloud Particles Based on Fuzzy Logic
Figures & Tables(9)
Fig. 2. Distribution of input parameters in the vertical direction. (a) Reflectivity factor detected by millimeter wave cloud radar; (b) radial velocity; (c) spectral width; (d) temperature
Fig. 3. Observation results of millimeter wave cloud radar and microwave radiometer on 6 February 2022. (a) Corrected reflectivity factor; (b) spectral width; (c) radial velocity; (d) temperature
Fig. 5. Results of identifying cloud phase on 6 February 2022 using T-function coefficients showed in Table 2
Fig. 6. Phase state of precipitation particles at different time recorded by ground precipitation phenomenon meter on 6 February 2022 (the position of blue box corresponds to different particle phase states, and the solid line represents ground temperature)
Fig. 7. Distribution of precipitation particles raindrop spectra at different time periods on 6 February 2022. (a) Snow at 05:30—12:44; (b) snow at 15:30—20:00
Table 1. Classification of cloud particle phase state [2]
View tableTable 1. Classification of cloud particle phase state [2]
Class Description Snow Only snow particles （defined based on a reflectivity threshold which is related to particle size） Ice crystal Only cloud ice particles Mixed phase Cloud liquid droplets and cloud ice particles in the same volume Supercooled water Liquid particles at temperature <0 ℃ （using reflectivity factor， radial velocity， and spectral width threshold） Warm cloud droplet Liquid particles at temperature >0 ℃ （using reflectivity factor， radial velocity， and spectral width threshold） Drizzle Only drizzle drops （defined based on a reflectivity threshold which is related to particle size） Rain Only rain drops （defined based on the thresholds of reflectivity and radial velocity which are related to particle size）
Table 2. Optimized T-function coefficients corresponding to different phase particles
View tableTable 2. Optimized T-function coefficients corresponding to different phase particles
Function Parameter Snow Ice crystal Mixed phase Supercooled water Warm cloud droplet Drizzle Rain P（Z） X1 /dBZ -5 -40 -25 -31 -35 -25 -15 X2 /dBZ 0 -30 -15 -26 -25 -17 -10 X3 /dBZ 15 -10 -5 -17 -20 0 20 X4 /dBZ 20 0 5 -12 -15 5 25 P（V） X1 /（m·s-1） -2.5 -1.5 -2 -0.8 -1 -4 -13 X2 /（m·s-1） -1 -0.5 -1.5 -0.7 -0.5 -3 -8 X3 /（m·s-1） 0.2 1 0.5 0 0.5 -1.5 -2.5 X4 /（m·s-1） 0.5 2 1 0.5 1 -0.5 -1.5 P（σv） X1 /（m·s-1） 0 0 0.2 0 0 0 0 X2 /（m·s-1） 0.4 0 0.6 0.3 0.4 0.4 2 X3 /（m·s-1） 2 0.1 4 0.7 0.8 2 4 X4 /（m·s-1） 4 0.4 4 1 1.2 4 4 P（T） X1 /℃ -40 -50 -40 -30 0 0 0 X2 /℃ -30 -50 -20 -28 0 0 0 X3 /℃ 0 -20 0 -2 50 50 50 X4 /℃ 0 -10 5 0 50 50 50
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Yun Yuan, Huige Di, Yuxing Gao, Mei Cao, Dengxin Hua. Optimization Algorithm for Recognizing Phase States of Cloud Particles Based on Fuzzy Logic[J]. Acta Optica Sinica, 2024, 44(12): 1201010
Paper Information
Category: Atmospheric Optics and Oceanic Optics
Received: Sep. 25, 2023
Accepted: Oct. 21, 2023
Published Online: Apr. 18, 2024
The Author Email: Di Huige (dihuige@xaut.edu.cn)
CSTR:32393.14.AOS231598
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