基于随机森林算法的FY-4A云底高度估计方法
投稿时间:2019-01-06  修订日期:2019-01-22  点此下载全文
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作者单位E-mail
谭仲辉 国防科技大学 tzh_go@126.com 
马烁 国防科技大学 mashuo0601 
韩丁 解放军96901部队  
高顶 解放军62275部队  
严卫 国防科技大学  
基金项目:国家自然科学基金面上项目“基于灯光源的微光云图定标技术研究”
中文摘要:基于FY-4A的云顶高度、云光学厚度、云有效粒子半径和云液态水路径等上游产品和A-Train系列卫星资料,利用随机森林算法,提出了FY-4A对最上层云云底高度的估计算法,并用星载的毫米波雷达和激光雷达主动探测的云底高度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,而多层云的存在会导致云底高度的结果偏小。云顶高度的误差会导致云层厚度和云底高度结果误差也增大。此外,随着云层厚度的增大,云底高度的误差呈增加的趋势。
中文关键词:FY-4A  云底高度  随机森林  A-Train
 
Estimation of Cloud Base Height for FY-4A Satellite based on Random Forest Algorithm
Abstract:Based on upstream products of FY-4A and A-Train satellites data, 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, the error in cloud top height may also introduce uncertainties in estimation of cloud thickness and cloud base height. In addition, error of the cloud base height tend to increase as the cloud thickness increasing.
keywords:FY-4A, Cloud base height, Random Forest, A-Train
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