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The article presents an analysis of averaging kernels to evaluate the sensitivity of the Aqua Atmospheric Infrared Sounder (AIRS) and Aura Microwave Limb Sounder (MLS) to water vapor. Particular focus is given to the implementation of a method in order to join the AIRS and MILS profiles into an emerged set of water vapor profiles through averaging kernel information. Further, it quantifies the sensitivity of the AIRS to hydrogen dioxide in the upper trophosphere/lower stratosphere.

The combination of multiple satellite instruments on a pixel-by-pixel basis is a difficult task, even for instruments collocated in space and time, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) on board the Earth Observing System (EOS) Aqua. Toward the goal of an improved collocation methodology, the channel- and scan angle–dependent spatial response functions of AIRS that were obtained from prelaunch measurements and calculated impacts from scan geometry are shown within the context of radiance comparisons. The AIRS spatial response functions are used to improve the averaging of MODIS radiances to the AIRS footprint, and the variability of brightness temperature differences (Δ Tb) between MODIS and AIRS are quantified on a channel-by-channel basis. To test possible connections between Δ Tb and the derived level 2 (L2) datasets, cloud characteristics derived from MODIS are used to highlight correlations between these quantities and Δ Tb, especially for ice clouds in H2O and CO2 bands. Furthermore, correlations are quantified for temperature lapse rate ( dT/ dp) and the magnitude of water vapor mixing ratio ( q) obtained from AIRS L2 retrievals. Larger values of dT/ dp and q correlate well to larger values of Δ Tb in the H2O and CO2 bands. These correlations were largely eliminated or reduced after the MODIS spectral response functions were shifted by recommended values. While this investigation shows that the AIRS spatial response functions are necessary to reduce the variability and skewness of Δ Tb within heterogeneous scenes, improved knowledge about MODIS spectral response functions is necessary to reduce biases in Δ Tb. [ABSTRACT FROM AUTHOR]

With its high accuracy, stability, and worldwide coverage GPS radio occultation offers an attractive means of independently validating and calibrating the world's premier weather and climate sensors. These include such instruments as AIRS, AMSU, and MODIS on NASA's EOS platforms, and similar systems on operational weather satellites. GPSRO also offers a valuable comparison standard for global weather analyses, such as those produced by NOAA's National Center for Environmental Predictions (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF). We have studied the performance of GPSRO temperature profiles through comparisons of coincident data from CHAMP and SAC-C, as well as from COSMIC. We have also compared GPSRO temperature profiles with nearby profiles from AIRS (Atmospheric Infrared Sounder), carried on NASA's Aqua platform, and with the ECMWF analyses. Our principal findings are: • AIRS and ECMWF temperature profiles depart in systematic ways from GPSRO profiles. These departures are highly repeatable and vary by geographical region. • There is significant correlation between the AIRS and ECMWF departures from GPSRO, not explainable by GPSRO error. This may arise because AIRS retrievals are initialized with estimates derived from ECMWF training samples. • ECMWF single-profile RMS temperature deviations range between 0.6 and 1.8 K and are at a maximum near the tropopause. Biases are typically below 0.5 K. • AIRS single-profile RMS temperature deviations range between 0.9 and 2.2 K and are also at a maximum near the tropopause. Biases are typically below 0.5 K but reach 1 K near the tropopause in the Antarctic. [ABSTRACT FROM AUTHOR]