摘要
目前多数资料同化系统中对卫星的观测值都是采用晴空模拟,然而用晴空辐射传输模式模拟云区卫星微波通道的辐射值会造成与观测较大的偏差,导致大量云区卫星资料被直接抛弃无法进入同化系统,因而有必要改进云区卫星辐射亮温的模拟能力,进而提高同化系统中云区卫星资料利用率。以2010年台风"圆规"、"凡比亚"和"鲇鱼"为例,基于先进的微波扫描辐射计AMSR-E观测应用一维变分算法反演台风区域的云宏观参数,包括云液水含量廓线、云冰水含量廓线和雨水含量廓线;然后,以大气温度、湿度廓线及这些反演的云参数作为快速辐射传输模式CRTM的输入参数,模拟AMSR-E各通道的辐射亮温。通过对比晴空、有云两种情况下模拟亮度温度与实际观测亮度温度间的偏差,发现增加云参数作为辅助参数、启动辐射传输的散射模块,可以有效地改进台风外围云区卫星辐射亮温的模拟效果,大幅减少模拟亮温与观测亮温间的偏差,增加了同化进数值预报系统的卫星观测数据量。
A limitation of most of present-day global data assimilation systems is that they are only for the assimilation of satellite data in clear-sky regions, which will cause large deviations of the simulations from the satellite observations over cloudy areas, and huge amount of satellite data cannot be directly assimilated to numerical models in cloudy cases. Thus it is necessary to improve the ability of simulating the cloudy-area satellite radiations to enhance their utilization in global data assimilation systems. Based on AMSR-E observations using the 1D-Var method, three typhoons in 2010, Kompasu, Fanapi and Megi, were used as example here to retrieve cloud macro parameters over typhoon areas, including the profiles of cloud liquid water, ice water, and rain water. Then the profiles of temperature and moisture and retrieved cloud parameters were used together as the input parameters of the Community Radiative Transfer Model（CRTM） to simulate the satellite brightness temperatures of all the channels of AMSR-E. By comparing the biases between the simulations and the satellite observations in the clear-sky and cloudy cases, we found that providing the cloud parameters as auxiliary parameters and activating the scattering module of radiative transfer model can effectively improve the simulations over the typhoon periphery system, greatly reduce the deviations between the simulations and the observations, and increase the amount of satellite data which can be assimilated into the numerical weather prediction system.
出处
《热带气象学报》
CSCD
北大核心
2015年第5期664-672,共9页
Journal of Tropical Meteorology
基金
国家自然科学基金项目(41175034)
江苏省高校自然科学研究重大项目(13KJA170003)共同资助
