Estimation of Cloud Properties in Three-dimension (3D) from Cloud Resolving Data Assimilation

该项目将提供中观尺度上依赖于空间和时间的云特性的准确估计。基于区域大气建模系统 (RAMS) 的四维变分 (4DVAR) 数据同化将用于将高分辨率卫星辐射、卫星探测检索和 DOE-ARM 南部大平原测量结果同化为云解析模型。空间/时间云属性的准确性将使用三种方法进行量化：1）通过建模空间中的统计误差分析，2）观测空间中与同化观测相关的统计误差分析，以及c）根据 DOE-ARM 档案中的独立观测进行验证。这项工作的更广泛影响将在于在业务数值天气预报模型中同化卫星辐射测量值方面取得的进展以及随后在定量降水预报方面的预期改进。

This project will provide accurate estimates of space and time dependent cloud properties on the meso-scale. Four-dimensional variational (4DVAR) data assimilation, based on the Regional Atmospheric Modeling System (RAMS), will be used to assimilate high-resolution satellite radiances, satellite sounding retrievals, and DOE-ARM Southern Great Plains measurements into a cloud-resolving model. The accuracy of the space/time cloud properties will be quantified using three methods: 1) by statistical error analysis in the modeled space, 2) statistical error analysis in the observation space with respect to the observations that are assimilated, and c) verification against independent observations from the DOE-ARM archive.The broader impacts of this work will be in the progress made for the assimilation of satellite radiance measurements in operational numerical weather prediction models and the subsequent expected improvement in quantitative precipitation forecasts.