The Study On The Algorithm Of Retrieving The Temperature And Moisture Profile From Satellite Infrared Measurements

Some conclusions can be gained from the generalized linear inverse theory. When the atmospheric parameters (temperature, moisture) were retrieved from the remote radiation measurements, there is some invalid information in the data which is ineffective to the retrieval. The invalid information is the important reason for the ill-conditioning of the retrieval equation. On the other hand, some vertical variety characteristics of the temperature and moisture can't be retrieved from the satellite data, namely the irretrievable mode exists. It's the important source of the retrieval error as well as the basic reason of the high sensitivity of the retrieval to the observation error. So when we solve the retrieval problem, the parameter space should be separated into the retrievable mode and the irretrievable mode, and the data space should be separated into the effective information space and the ineffective information space. That's the first problem should be solved for getting a good retrieval. Based on the cognition, a method is developed to assess the retrievability of temperature and the distribution of the global retrievability with HIRS/3 and AIRS data in Jan and Jul are firstly given by using the NCEP reanalysis data. The results show that the retrievability of temperature is low in the upper and lower atmosphere, and high between 400 hPa and 850 hPa. In geographical distribution, the retrievabilities are low in the low latitude marine regions and in some regions in Antarctica, and relatively high in mid-high latitude regions and continental regions. This partly represents the relationship between retrievability and the variability of temperature. In comparing the retrievabilities obtained with the AIRS and HIRS/3 data, the former are 0.15-0.4 higher than the latter and the retrievabilities obtained with the AIRS data are improved more evidently in the low latitude regions.Secondly, we modify the traditional one-dimensional variational (1DVAR) retrieval method by the SVD and EOF technique. The basic method is that utilize the SVD to get rid of the ineffective information and use the EOF to extract the vertical structure of the atmospheric parameters. The tests by the ideal data show the retrieval precision of the temperature and moisture profiles can be improved and the dependingon the background profiles can be reduced. In comparing the retrieval results obtained with the AIRS and HIRS/3 data, the revised method can improve more on the retrieval with HIRS/3 data.A new statistical-physical method for retrieving the atmospheric temperature and moisture profiles is given too in the paper. In the method, we use the coupling SVD technique to decompose the atmospheric parameters and the observation data synchronously. The basic function not only can denote the primary structure characteristics of the parameters and the observation, but also can indicate the relation between them. In virtue of this relation, the retrieval can be implemented. Because the method has more statistical character, the calculation is comparatively simple and convenient. The tests by the model data show that the retrieved temperature is more precise than the background profile and the retrieval by 1DVAR at most height;and the retrieved moisture profile can be improved at mid-high troposphere and the surface layer. The tests by practice data also illuminate that the method can improve the retrieval of temperature at some altitudes and the retrieval precision of moisture profile can be improved more, and the depending on the background profile is relatively small.