首页|New Machine Learning Findings from Colorado State University Published (Airborne Radar Quality Control with Machine Learning)
New Machine Learning Findings from Colorado State University Published (Airborne Radar Quality Control with Machine Learning)
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New research on artificial intelligence is the subject of a new report. According to news reporting originating from Fort Collins, Colorado, by NewsRx correspondents, research stated, “Airborne Doppler radar provides detailed and targeted observations of winds and precipitation in weather systems over remote or difficult-to-access regions that can help to improve scientific understanding and weather forecasts. Quality control (QC) is necessary to remove nonweather echoes from raw radar data for subsequent analysis.” Funders for this research include National Science Foundation; Noaa Research; Office of Naval Research. Our news correspondents obtained a quote from the research from Colorado State University: “The complex decision-making ability of the machine learning random-forest technique is employed to create a generalized QC method for airborne radar data in convective weather systems. A manually QCed dataset was used to train the model containing data from the Electra Doppler Radar (ELDORA) in mature and developing tropical cyclones, a tornadic supercell, and a bow echo. Successful classification of 96% and 93% of weather and nonweather radar gates, respectively, in withheld testing data indicate the generalizability of the method. Dual-Doppler analysis from the genesis phase of Hurricane Ophelia (2005) using data not previously seen by the model produced a comparable wind field to that from manual QC. The framework demonstrates a proof of concept that can be applied to newer airborne Doppler radars. Significance Statement: Airborne Doppler radar is an invaluable tool for making detailed measurements of wind and precipitation in weather systems over remote or difficult to access regions, such as hurricanes over the ocean. Using the collected radar data depends strongly on quality control (QC) procedures to classify weather and nonweather radar echoes and to then remove the latter before subsequent analysis or assimilation into numerical weather prediction models. Prior QC techniques require interactive editing and subjective classification by trained researchers and can demand considerable time for even small amounts of data.”
Colorado State UniversityFort CollinsColoradoUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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