Evolution mechanisms modeling of cloud-ice, cloud-water and rain from microwave remote sensing

The precipitation evolution plays a key role in the refined precipitation forecasting. At present, the description on the evolution of hydrometer particles is insufficient. There are still deviations in the quantification of cloud, ice, and rain microphysical parameters in the cloud model, and the evolution mechanism is not refined enough, which affects the accurate prediction and decision-making of precipitation. In order to solve this problem, this project makes full use of the advantages of 89GHz for cloud ice identification and inversion. With the existing ground-based multi-frequency and multi-angle (10.65 similar to 89GHz) dual-polarization scanning microwave radiometer, micro rain radar MRR and Raindrop spectrometer, accurate model of cloud ice, cloud water and rain paths can be obtained. Profiles and spatial-temporal distribution of particle concentrations and average size are further retrieved using deep learning methods. The project aims at the physical evolution process of cloud ice, cloud water and rain in each development stage, trying to quantify their indicative role and develop a microwave remote sensing model according to the atmospheric microphysical information in the precipitation process. The project results will provide insight into the cloud rainfall efficiency in cloud models, and support important theoretical references for refined precipitation forecasts and weather modification.