As a part of the Joint Polar Satellite System (JPSS, formerly the NPOESS afternoon orbit), the instruments Cross-track
Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) make up the Cross-track Infrared and
Microwave Sounder Suite (CrIMSS). CrIMSS will primarily provide global temperature, moisture, and pressure
profiles and calibrated radiances . In preparation for the NPP launch in 2011, we have ported and tested the
operational CrIMSS Environmental Data Record (EDR) algorithms using both synthetic and proxy data generated from
the IASI, AMSU, MHS data from Metop-A satellite.
The CrIS and ATMS instruments on NPOESS will provide high quality temperature and moisture profiles greatly surpassing the capabilities of current operational satellite sounders. However, performance of these systems continues to be a challenge in cloudy scenes. The VIIRS sensor on NPOESS will provide much higher spatial resolution data than that of CrIS, with some overlap in spectral coverage. This sub-pixel information can be used to enhance the retrieval performance in a number of ways. This paper presents a potential technique to improve performance of the CrIS temperature profile retrievals by incorporating data from VIIRS. Improvements include more accurate retrievals in partly cloudy situations, better effective spatial resolution and more robust quality control diagnostics. We provide an overview of our approach and show examples utilizing data from the EOS-Aqua AIRS, AMSU and MODIS sensors.
The AER algorithm testbed has been applied to instruments measuring in spectra from the ultraviolet to the microwave. The sensor simulation component starts with environmental data from numerical weather prediction models, surface property and terrain databases, and imagers, and simulates the detailed sensor spatial and spectral sampling processes, radiative transfer, polarization, and detector characteristics. This simulation is integrated with algorithm execution, to provide end-to-end capability. The tools allow for simulation of specific sensor errors, and tracing of their impact through the algorithm process to the quality of the retrieved environmental products. A critical component of the testbed is its radiative transfer models, which employ state-of-the-science programs for line-by-line optical properties, for radiative transfer in scattering atmospheres, and for a variety of surfaces. Fast and accurate computational methods are incorporated, such as Optimal Spectral Sampling (OSS). Application examples are shown, including characterization of AMSU sensor data errors and retrieval performance evaluation, in which retrievals from the microwave sounder data and infrared image data contribute to the interpretation of remote sensing phenomena in cloudy environments.
The Cross-track Infrared and Microwave Sounder Suite (CrIMSS) consists of a microwave radiometer and an infrared interferometer and is scheduled to fly on the NPP and NPOESS satellites. The sensors are designed for the accurate measurement of atmospheric pressure, temperature and moisture profiles. This paper presents an overview of the CrIMSS sensors and the retrieval algorithm. Validation of the algorithm with current satellite sounder data will also be presented.
As more and more high-resolution FTIR instruments planned for future satellite remote sensing, robust and fast physical retrieval algorithms are needed to invert the measured radiances to geophysical parameters. One of the important components of a physical retrieval algorithm is the fast forward model. For interferometer-based sounders, the Sensor Response function (SRF) may not be localized. A fast and accurate forward model, Optimal Spectral Sampling (OSS), is used to calculate upwelling radiances in our physical retrieval algorithm. It's capable of modeling radiance spectra with either a localized or a non-localized SRF with negative side lobes. Derivatives with respect to atmospheric and surface properties can be calculated analytically and efficiently. The OSS fast RT model is idea for atmospheric sounding or atmospheric compensation applications. The inversion method is based on an optimal estimation algorithm. Empirical Orthogonal Functions (EOF) are used to transform the atmospheric profiles into more compact form for fast and stable inversions. The non-linearity of the radiative transfer function is taken into account in the algorithm so the inversion is very robust. The algorithm has been used to simulate the Environmental Data Record (EDR) retrieval performance for the Cross-track Infrared Sounder (CrIS). We extended the method to model cloud directly. The cloud parameters are retrieved simultaneously with the atmospheric and surface parameters. This algorithm has been successfully applied to the NPOESS Aircraft Sounder Testbed (NAST-I) measured radiances.