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Based on ground-based Atmospheric Emitted Radiance Interferometer (AERI) observations in Shouxian, Anhui province, China, the authors retrieve the cloud base height (CBH) and effective cloud emissivity by using the minimum root-mean-square difference method. This method was originally developed for satellite remote sensing. The high-temporal-resolution retrieval results can depict the trivial variations of the zenith clouds continuously. The retrieval results are evaluated by comparing them with observations by the cloud radar. The comparison shows that the retrieval bias is smaller for the middle and low cloud, especially for the opaque cloud. When two layers of clouds exist, the retrieval results reflect the weighting radiative contribution of the multi-layer cloud. The retrieval accuracy is affected by uncertainties of the AERI radiances and sounding profiles, in which the role of uncertainty in the temperature profile is dominant.

The automatic all—sky imager developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, provides all—sky visible images in the red, green, and blue channels. This paper presents three major calibration experiments of the all—sky imager, geometric angular calibration, optical calibration, and radiometric calibration, and then infers an algorithm to retrieve relative radiance from the all—sky images. Field experiments show that the related coefficient between retrieved radiance and measured radiance is about 0.91. It is feasible to use the algorithm to retrieve radiance from images. The paper sets up a relationship between radiance and the image, which is useful for using the all—sky image in numerical-simulations that predict more meteorological parameters.

A new method applying an artificial neural network (ANN) to retrieve water vapor profiles in the troposphere is presented. In this paper, a fully-connected, three-layer network based on the back-propagation algorithm is constructed. Month, latitude, altitude and bending angle are chosen as the input vectors and water vapor pressure as the output vector. There are 130 groups of occultation measurements from June to November 2002 in the dataset. Seventy pairs of bending angles and water vapor pressure profiles are used to train the ANN, and the sixty remaining pairs of profiles are applied to the validation of the retrieval. By comparing the retrieved profiles with the corresponding ones from the Information System and Data Center of the Challenging Mini-Satellite Payload for Geoscientific Research and Application (CHAMP-ISDC), it can be concluded that the ANN is relatively convenient and accurate. Its results can be provided as the first guess for the iterative methods or the non-linear optimal estimation inverse method.

The Solar Backscatter Ultraviolet and Total Ozone Mapping Spectrometer (SBUV/TOMS) instrument has provided useful data for deducing ozone vertical profiles and total ozone amounts. Although, data processing techniques for solving the inverse problem have been discussed, the retrieval accuracy still needs to be improved by developing new methodology. In this paper the Newtonian non-linear iteration method was applied to the Ultraviolet radiative transfer equation to retrieve the atmospheric ozone vertical distribution. In order to reduce the number of unknowns for retrieval and make the solution procedure more stable, the atmospheric ozone profile is represented by its Empirical Orthogonal Functions (EOFs) which can be derived from a set of global ozone profiles such as TIGR. Ozone retrievals based on simulated SBUV albedo measurements from 6 model atmospheres show the potential application of this method in operational processing of SBUV real observed measurements which will fly on the satellite such as NOAA-K.