Opportunities for Students
Master Thesis: AI/ML spatially infilled precipitation recordings of weather station by training with radar data over Germany
Modern precipitation recordings contain a lot of high end hardware. Still, to get a valid spatial field from weather stations, statistical or numerical methods or models need to be applied. Radars can bridge information between station regarding reflectivity of precitable (rain/snow/mix) drops. However, these are totally different measurement techniques. AI/ML methods are designed to connect related fields in space and time. To go even beyond that, AI/ML methods can be used to infill missing information of related climate information (see Kadow et al. 2020). The Master thesis will investigate the possibility to build scientificly valid spatial precipitation fields combining station and radar data by these AI/ML techniques.
This thesis will be in cooperation with Laurens Bouwer from the German Climate Service Center (GERICS)
Interested? Please contact Dr. Christopher Kadow at [Email protection active, please enable JavaScript.]
More to come...
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