No abstract

Modern infrared satellite sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-Track Infrared Sounder (CrIS), the Tropospheric Emission Spectrometer (TES), the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS), and the Infrared Atmospheric Sounding Interferometer (IASI) are capable of providing high spatial and spectral resolution infrared spectra. To fully exploit the vast amount of spectral information from these instruments, superfast radiative transfer models are needed. We present a novel radiative transfer model based on principal component analysis. Instead of predicting channel radiance or transmittance spectra directly, the principal component-based radiative transfer model (PCRTM) predicts the principal component (PC) scores of these quantities. This prediction ability leads to significant savings in computational time. The parameterization of the PCRTM model is derived from the properties of PC scores and instrument line-shape functions. The PCRTM is accurate and flexible. Because of its high speed and compressed spectral information format, it has great potential for superfast one-dimensional physical retrieval and for numerical weather prediction large volume radiance data assimilation applications. The model has been successfully developed for the NAST-I and AIRS instruments. The PCRTM model performs monochromatic radiative transfer calculations and is able to include multiple scattering calculations to account for clouds and aerosols.

The National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed (NAST) consists of two passive collocated cross-track scanning instruments, an infrared interferometer (NAST-I) and a microwave radiometer (NAST-M), that fly onboard high-altitude aircraft such as the NASA ER-2 at an altitude near 20 km. NAST-I provides relatively high spectral resolution (0.25-cm(-1)) measurements in the 645-2700-cm(-1) spectral region with moderate spatial resolution (a linear resolution equal to 13% of the aircraft altitude at nadir) cross-track scanning. We report the methodology for retrieval of atmospheric temperature and composition profiles from NAST-I radiance spectra. The profiles were determined by use of a statistical eigenvector regression algorithm and improved, as needed, by use of a nonlinear physical retrieval algorithm. Several field campaigns conducted under varied meteorological conditions have provided the data needed to verify the accuracy of the spectral radiance, the retrieval algorithm, and the scanning capabilities of this instrumentation. Retrieval examples are presented to demonstrate the ability to reveal fine-scale horizontal features with relatively high vertical resolution.

A physical inversion scheme has been developed dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1D) variational multivariable inversion solution is used to improve an iterative background state defined by an eigenvector-regression retrieval. The solution is iterated in order to account for nonlinearity in the 1D variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud-top level are obtained. For both optically thin and thick cloud situations, the cloud-top height can be retrieved with relatively high accuracy (i.e., error Ͻ1