A Multiple Linear Regression Model for Tropical Cyclone Intensity Estimation from Satellite Infrared Images
AbstractAn objectively trained model for tropical cyclone intensity estimation from routine satellite infrared images over the Northwestern Pacific Ocean is presented in this paper. The intensity is correlated to some critical signals extracted from the satellite infrared images, by training the 325 tropical cyclone cases from 1996 to 2007 typhoon seasons. To begin with, deviation angles and radial profiles of infrared images are calculated to extract as much potential predicators for intensity as possible. These predicators are examined strictly and included into (or excluded from) the initial predicator pool for regression manually. Then, the “thinned” potential predicators are regressed to the intensity by performing a stepwise regression procedure, according to their accumulated variance contribution rates to the model. Finally, the regressed model is verified using 52 cases from 2008 to 2009 typhoon seasons. The R2 and Root Mean Square Error are 0.77 and 12.01 knot in the independent validation tests, respectively. Analysis results demonstrate that this model performs well for strong typhoons, but produces relatively large errors for weak tropical cyclones. View Full-Text
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Zhao, Y.; Zhao, C.; Sun, R.; Wang, Z. A Multiple Linear Regression Model for Tropical Cyclone Intensity Estimation from Satellite Infrared Images. Atmosphere 2016, 7, 40.
Zhao Y, Zhao C, Sun R, Wang Z. A Multiple Linear Regression Model for Tropical Cyclone Intensity Estimation from Satellite Infrared Images. Atmosphere. 2016; 7(3):40.Chicago/Turabian Style
Zhao, Yong; Zhao, Chaofang; Sun, Ruyao; Wang, Zhixiong. 2016. "A Multiple Linear Regression Model for Tropical Cyclone Intensity Estimation from Satellite Infrared Images." Atmosphere 7, no. 3: 40.
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