AGU Highlights Vijay Natraj

CO 2 Retrieval Simulation from GOSAT Thermal IR Spectra 15 um CO 2 band; 0.2 cm -1 res, ~ 300 S/N 110 layers for forward model, reduced grid for retrievals MAP retrieval If T known perfectly, excellent agreement above 700 mbar If T had random errors, results with ~ 1.5% precision if channels selected on the basis of CO 2 IC or CO 2 IC – T IC as appropriate

Impact of Aerosols on CO 2 Retrievals using NIR GOSAT Data 1.6 um CO 2 band Large CO 2 errors for aerosols at high latitudes even for low aerosol od (>~ 0.05) CO 2 errors also large when surface albedo is large Simultaneous retrieval of aerosol, CO 2 and surface albedo reduces bias

Cirrus Cloud Characteristics from GLAS Observations Geoscience Laser Altimeter System Cirrus clouds located at ~ 13 km in tropics and 8 km in mid-latitudes, with ~ 2 km thickness everywhere Optical thickness less than 0.2 in UT and approx. constant at 0.25 in mid and lower trop in the tropics Mean value of optical thickness increases with latitude In the tropics, 56% of cirrus cloud events occur above other cloud layers!

Ozone Profile Retrieval from OMI Data um 18-layer atmosphere; 6-8 km vertical res DOAS technique with optimal estimation 6-stream LIDORT+polarization correction LUT+RRS Results good for levels <~ 50 mbar

Accounting for Non-uniform Spatial IC of Remotely Sensed Data Spatial characteristics of observations different from those of assimilation model Typically use point-based interpolation techniques such as bilinear interpolation Such techniques ignore footprint characteristics of observations; hence uncertainty inherent in resampling Geostatistical Inverse Modeling (GIM) incorporates spatial scale of observations and models uncertainty inherent to making estimates at different spatial scales Essentially, GIM is a bayesian approach similar to traditional inverse modeling Treats each pixel as an non-uniform integration of footprint depending on sensor’s point-spread function and viewing geometry, and not as a point or rectangle with uniform information Inverse modeling used to estimate value for center pixel using information from both center and surrounding measurements