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.
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.
作者
谭仲辉
马烁
韩丁
高顶
严卫
TAN Zhong-Hui;MA Shuo;HAN Ding;GAO Ding;YAN Wei(National University of Defense Technology,College of Meteorology and Oceanography,Nanjing 210000,China;PLA 96901,Beijing 100000,China;PLA 61175,Nanjing 210000,China)
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2019年第3期381-388,共8页
Journal of Infrared and Millimeter Waves
基金
国家自然科学基金(41705007,41575028)~~
关键词
卫星遥感
云底高度
随机森林
FY-4A
satellite remote sensing
cloud base height
Random Forest
FY-4A
