首页|Study Results from Guangdong Ocean University in the Area of Machine Learning Pu blished (Enhancing Extreme Precipitation Forecasts through Machine Learning Qual ity Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative ...)
Study Results from Guangdong Ocean University in the Area of Machine Learning Pu blished (Enhancing Extreme Precipitation Forecasts through Machine Learning Qual ity Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news originating from Zhanjiang, People’s Republ ic of China, by NewsRx editors, the research stated, “Variational data assimilat ion theoretically assumes Gaussian-distributed observational errors, yet actual data often deviate from this assumption.” Funders for this research include National Natural Science Foundation of China. Our news reporters obtained a quote from the research from Guangdong Ocean Unive rsity: “Traditional quality control methods have limitations when dealing with n onlinear and non-Gaussian-distributed data. To address this issue, our study inn ovatively applies two advanced machine learning (ML)-based quality control (QC) methods, Minimum Covariance Determinant (MCD) and Isolation Forest, to process p recipitable water (PW) data derived from satellite FengYun-2E (FY2E). We assimil ated the ML QCprocessed TPW data using the Gridpoint Statistical Interpolation (GSI) system and evaluated its impact on heavy precipitation forecasts with the Weather Research and Forecasting (WRF) v4.2 model. Both methods notably enhanced data quality, leading to more Gaussian-like distributions and marked improvemen ts in the model’s simulation of precipitation intensity, spatial distribution, a nd large-scale circulation structures. During key precipitation phases, the Frac tion Skill Score (FSS) for moderate to heavy rainfall generally increased to abo ve 0.4. Quantitative analysis showed that both methods substantially reduced Roo t Mean Square Error (RMSE) and bias in precipitation forecasting, with the MCD m ethod achieving RMSE reductions of up to 58% in early forecast hou rs.”
Guangdong Ocean UniversityZhanjiangP eople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learnin g
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