| description abstract | This paper discusses the application of principal component analysis and stepwise regression in the retrieval of vertical profiles of temperature and humidity based on the measurements of a 35-channel microwave radiometer. It uses the radiosonde data of 6 yr from Hong Kong, China, and the monochromatic radiative transfer model (MonoRTM) to calculate the brightness temperatures of the 35 channels of the radiometer. The retrieval of the atmospheric profile is then established based on principal component analysis and stepwise regression. The accuracy of the retrieval method is also analyzed. Using an independent sample, the root-mean-square error of the retrieved temperature is less than 1.5 K, on average, with better retrieval results in summer than in winter. Likewise, the root-mean-square error of the retrieved water vapor density reaches a maximum value of 1.4 g m?3 between 0.5 and 2 km, and is less than 1 g m?3 for all other heights. The retrieval method is then applied to the actual measured brightness temperatures by the 35-channel microwave radiometer at a station in Nansha, China. It is shown that the statistical model as developed in this paper has better retrieval results than the profiles obtained from the neural network as supplied with the radiometer. From numerical analysis, the error with the water vapor density retrieval is found to arise from the treatment of cloud liquid water. Finally, the retrieved profiles from the radiometer are studied for two typical weather phenomena during the observation period, and the retrieved profiles using the method discussed in the present paper is found to capture the evolution of the atmospheric condition very well. | |
