Abstract
Microwave radiometer(MWR)demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the train-ing dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sound-ing data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an al-ternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF)model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML)algorithms includ-ing extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN)are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE)of 2.05 K for temperat-ure,0.67 g m-3 for water vapor density(WVD),and 13.98％for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD)algorithm indicate that our al-gorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for deve-loping MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observa-tions in global locations.
基金项目
National Natural Science Foundation of China(42175144)
国家科技期刊平台
NSTL
万方数据