分类: 地球科学 >> 空间物理学 提交时间: 2017-03-10
摘要: An one-dimensional variational retrieval system was developed to retrieve the clear sky atmospheric temperature and humidity profiles over land using the measurements of microwave humidity-temperature sounder (MWHTS) on Chinese FY-3C satellite. The system parameters are configured by analyzing the MWHTS channel properties and the climate condition over land. The retrieval results are evaluated by root mean square error (rmse) with respect to European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The validated results show that the maximum root mean square error of temperature and humidity are 2.59K and 11.87%, respectively. The retrieval results, compared with National Centers for Environmental Prediction (NCEP) 6 hour forecast profiles, show that the background profiles can affect the accuracy of retrieval profiles and FY-3C/MWHTS measurements can improve forecast precision of humidity. �2016 IEEE.
分类: 地球科学 >> 空间物理学 提交时间: 2017-03-10
摘要: For Microwave Humidity and Temperature sounder (MWHTS) measurements over ocean, a cloud filtering method is presented to filter out cloud- and precipitation-affected observations by analyzing the sensitivity of the simulated brightness temperatures of MWHTS to cloud liquid water, and using the root mean square error (RMSE) between observation and simulation in clear sky as a reference standard. The atmospheric temperature and humidity profiles are retrieved using MWHTS measurements with and without filtering by multiple linear regression (MLR), artificial neural networks (ANN) and one-dimensional variational (1DVAR) retrieval methods, respectively, and the effects of the filtering method on the retrieval accuracies are analyzed. The numerical results show that the filtering method can improve the retrieval accuracies of the MLR and the 1DVAR retrieval methods, but have little influence on that of the ANN. In addition, the dependencies of the retrieval methods upon the testing samples of brightness temperature are studied, and the results show that the 1DVAR retrieval method has great stability due to that the testing samples have great impact on the retrieval accuracies of the MLR and the ANN, but have little impact on that of the 1DVAR.