红外与毫米波学报, 2019, 38 (3): 0381, 网络出版: 2019-07-20
基于随机森林算法的FY-4A云底高度估计方法
Estimation of cloud base height for FY-4A satellite based on random forest algorithm
摘要
基于2017年8月至10月FY-4A的云顶高度、云光学性质等上游产品和A-Train系列卫星星载毫米波雷达和激光雷达主动探测的云底高度资料, 利用随机森林算法建模, 提出了FY-4A对最上层云云底高度的估计算法, 并用2017年11月独立样本对算法进行了检验与评估.结果表明, 该算法可以有效实现对最上层云云底高度的估计, 与星载主动探测结果相比, 平均绝对偏差为1.29 km, 相关系数为0.80.对单层云的估计结果相对较好, 而多层云存在时云底高度的估计结果一般偏小.
Abstract
Based on upstream products of FY-4A and A-Train satellites data during August and October, 2017, an estimation algorithm of cloud base height for FY-4A has been presented utilizing Random Forest model. The algorithm is evaluated in the comparison with CloudSat and CALIPSO. The results show that cloud base height for top layer cloud can be generated by using upstream products of FY-4A. Compared with CloudSat and CALIPSO, the mean absolute error is less than 1km and the relationship coefficient is bigger than 0.8. The presence of multi-layer clouds may result in underestimate of cloud base height.
谭仲辉, 马烁, 韩丁, 高顶, 严卫. 基于随机森林算法的FY-4A云底高度估计方法[J]. 红外与毫米波学报, 2019, 38(3): 0381. TAN Zhong-Hui, MA Shuo, HAN Ding, GAO Ding, YAN Wei. Estimation of cloud base height for FY-4A satellite based on random forest algorithm[J]. Journal of Infrared and Millimeter Waves, 2019, 38(3): 0381.
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