Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation

Dear Colleagues,

Data and data products are the basis of meteorological scientific research and weather forecasting. With the development of technology, a wide range of meteorological remote sensing equipment has been developed, such as polarimetric radar, phased-array radar, millimeter-wave cloud radar, Doppler wind lidar, as well as various meteorological satellites and GPM. These devices provide us with huge amounts of data, and it is critical to process these data to obtain physical information about clouds and precipitation. The retrieval of meteorological information from observational data usually relies on physics-based analysis or data statistics, or a combination of both. The physical principles involve the scattering of radar electromagnetic waves, atmospheric radiative transfer, physical characteristics of clouds and precipitation particles, and so on. According to these, theoretical retrieval algorithms can be obtained, and empirical formulas can be obtained through data statistics, which can also help in obtaining the product. The recent development of machine learning has been playing an increasingly important role in the processing of meteorological data. The product is ultimately obtained on the basis of extracting the feature information of massive data to establish the corresponding relationship between observation and product. In fact, different types of algorithms have their distinct advantages. With the continuous upgrading and improvement of equipment, the exploration of various retrieval algorithms for clouds and precipitation should be encouraged to obtain more accurate products that reflect the characteristics of clouds and precipitation.

This Special Issue focuses on recent advances in radar and satellite remote sensing algorithms for clouds and precipitation. These algorithms may include, but are not limited to, meteorological data quality control, the retrieval of clouds and precipitation properties, quantitative precipitation estimation or forecasting, and the assimilation of radar or satellite data in numerical weather prediction. Research may address the improvement of traditional algorithms and the development of new algorithms. Current machine-learning-related algorithms are also very welcome.

Dr. Yang Zhang
Dr. Zhiqun Hu
Dr. Yabin Gou
Dr. Jiafeng Zheng
Dr. Hao Hu
Dr. Yi-Ning Shi
Guest Editors

Manuscript Submission Information

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