Search

Objectives: Traditional remote sensing-based air temperature estimation method usually used the global models, which ignored the effects of spatiotemporal heterogeneity, especially for the researches in large regional areas. Methods: Taking the Yangtze River Economic Zone as a typical research area, this paper introduces the geographically and temporally neural networks for high-precision temperature estimation. The influence of spatiotemporal heterogeneity was considered by establishing the local models in the generalized regression neural network. Remote sensing data, assimilation data and station data were fused to obtain the spatially continuous near-surface temperature. The model performance was evaluated by the site-based ten-fold cross-validation method. Results: The results show that the geographically and temporally weighted neural network had effectively improved the estimation accuracy with the root mean square error(RMSE) =1.899 ℃, mean absolute error (MAE) =1.310 ℃ and correlation coefficient (R) =0.976. Compared with the multiple linear regression method and the traditional global neural network, the MAE value decreased by 1.112 ℃ and 0.378 ℃, respectively. Conclusions: The temperature mapping results indicated that the model used in this paper can well reflect the spatial distribution differences, which means that this study is possible to provide a new way for temperature estimation with high precision. [ABSTRACT FROM AUTHOR]

Objectives: Atmospheric temperature and humidity are important parameters to describe the dynamic and thermal state of the atmosphere. We analyzed the diurnal and seasonal variations of temperature and humidity profiles, atmospheric boundary layer height (ABLH), and liquid water path (LWP) profiles measured by microwave radiometer in Wuhan.Methods: The detection results of radio sounding and RPG-HATPRO microwave radiometer were compared in the absence and presence of clouds. Then, the diurnal and seasonal variations of meteorological elements from 2011 to 2015 were analyzed to realize the thermodynamic characterization of the tropospheric atmosphere in Wuhan.Results: The temperature and humidity observation accuracy obtained by the microwave radiometer inversion would vary due to different weather conditions. When using products of microwave radiometer, the influence of cloud should be considered. ABLH began to increase in the morning, reached its highest value at 15:00, then decreased and remained stable throughout the night. The content of liquid water path is higher at night than that during the day, and higher in winter than in summer. Relative humidity (RH) rose with the increase of altitude at 0−1 km, and then decreased. Conclusions: The diurnal and seasonal variations of meteorological parameters in Wuhan are observed and analyzed, which provides reliable information for meteorological exploration, air pollution research, air quality monitoring, and weather forecast. [ABSTRACT FROM AUTHOR]