AIRS (Atmospheric Infra-Red Sounder) as NASA's first high spectral resolution sounding instrument provides both new and improved measurements of clouds, atmosphere, and land and oceans, with higher accuracy and higher resolution required by future weather and climate models. It will largely improve the deficiencies of the inability of current sounders (e.g. HIRS-3) to obtain high vertical resolution of retrieved atmosphere profiles. In this paper, temperature profiles with 1km vertical resolution at 100 pressure layers, from surface up to 0.005 hPa, were retrieved on different spectral bands and on different types of terrain in the middle latitude area by using a three-layered feed-forward neural networks with back-propagation algorithm. Results show that temperature profiles with accuracy of less than 1K in 1 km thick tropospheric layers can be achieved by using AIRS data and neural networks method. And the Qinghai-Tibet Plateau has a measurably impact on the retrieval accuracy which is corresponding to the spectral bands used in performing retrievals. A promising approach to the elimination of this effect is to apply additional predictors which are non-satellite observed (e.g. surface altitude).
With a fast yet accurate infrared radiation transfer model KCARTA, transmittance, radiance and brightness temperature spectra of top of atmosphere (TOA) over thermal infrared region (605-2805cm-1) was simulated. In simulation, the effects of different spectral resolution, response function shape, spectral calibration accuracy, propagation path and surface emissivity were taken into account. The results from forward calculations show: 1) Improvement of spectral resolution changes the probability of present brightness temperature so that more brightness temperature can be observed. Increased observed brightness temperature guarantee atmospheric sounding with better vertical resolution. 2) A small change in response function, spectral calibration, propagation path or surface emissivity will lead in much larger difference on observed brightness temperature for hyperspectral sounding than for low spectral resolution sounding. Therefore, hyperspectral sensor requires more sensitive SNR. Otherwise, the improvement of sounding will be limited. Results here can be taken as a reference in designing future hyperspectral IR sounder and retrieval algorithm.
Sensitivity studies of atmospheric temperature and humidity profile retrieval from EOS AQUA/AIRS measurements , that involve spectral coverage sensitivity , channel coverage sensitivity , additional predictors effect , are performed via empirical orthogonal function (eigenvectors of covariance ) expansion , leading to the revealment of new features of high-resolution infrared sounding . Simulation studies on atmospheric temperature profile retrieval based on the channel characteristics and spectral response function of IRAS had also be done In order to investigate the performance of InfRared Atmospheric Sounder (IRAS) which will be onboard the FY-3A satellite.
With incremental form of three-dimensional variational (3D-Var) method as data assimilation method and the MM5 mesoscale model as both assimilation and prediction model, NOAMSUA experiment, where only radiosonde observations were assimilated, was in comparison with AMSUA experiment, where radiosonde observations and AMSU-A channel brightness temperatures were assimilated simultaneously. The comparison results show that direct assimilating AMSU-A data has a positive impact on model prediction as a whole.
Conference Committee Involvement (3)
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications
13 November 2006 | Goa, India
Multispectral and Hyperspectral Remote Sensing Instruments and Applications II
9 November 2004 | Honolulu, Hawai'i, United States