Business process for radar reflectivity inversion based on geostationary meteorological satellites
In China,there exists an imbalance and inadequacy in the distribution of meteorological radar,with partic-ularly significant gaps in coverage in the southwestern region and over the sea.While geostationary meteorological satellites possess a broader range of observation and high temporal and spatial resolution multispectral observational results,utilizing satellite data for radar reflectivity inversion can to some degree compensate for deficiencies in radar networks.However,the current inversion process faces challenges in effectively extracting the complex,multichan-nel characteristics of satellite data in both the channel and spatial dimensions during data processing.In light of these issues,the business process of radar reflectivity inversion utilizing geostationary satellites was explored.The high-level semantic features of satellite data was extracted through ResNet,and then spatial and channel attention mod-ules was utilized to aggregate features pertinent to radar echoes in both spatial and channel dimensions and eliminate interference features from non-precipitation clouds.Through a comparison experiment utilizing multiple indicators,the research in this paper had been verified to have a notable improvement in the precision of echo inversion across various sizes,highlighting the algorithm's superiority.
business processradar echoesremote sensingdeep learningHamawari-8
NETL
NSTL
万方数据
