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Uncertainty Analysis of Tropospheric Carbon Monoxide Data Records Using AIRS and IASI from a Uniform Algorithm

Project Description
The research objectives of this proposal are: 1) to quantify the errors and uncertainties in the tropospheric Carbon Monoxide (CO) products from recent and upcoming satellite missions, and 2) to provide a uniform algorithm that generates tropospheric CO retrievals from the Atmospheric Infrared Sounder (AIRS)/EOS/Aqua (Aumann, et al., 2003) and the Infrared Atmospheric Sounding Interferometer (IASI)/MetOp (Coheur et al., 2009) to address known issues. This product with known quality will provide twice daily global coverage of tropospheric CO for a period from 2002 through the lifetime of AIRS and continue with IASI instruments for the current and planned missions of 15 years started in late 2006 to obtain consistent CO climate records. Under previous funding by ROSES, we developed an alternative algorithm for AIRS CO products using the Optimal Estimation (OE) method, described by Rodgers (2000), which is different from AIRS operational method (Warner et al., 2010). We use AIRS operational L2 meteorological and ozone profiles and the cloud-cleared radiances, which are provided by NASA/GES DISC (http://disc.gsfc.nasa.gov/AIRS/index.html) as input. The output from the new retrieval system not only includes global CO profiles as does by AIRS operational products, but also provides the Averaging Kernels (AKs), the error covariance matrices, and the degrees of freedom for signals (DOFS) that are computed using a similar formulation as in the other CO sensors such as MOPITT and TES. The new CO products have undergone validations against in situ measurements and have been inter-compared with MOPITT and TES CO. IASI on MetOp has similar temporal and spatial coverage as AIRS, and they are both hyperspectral thermal sensors. Therefore, the CO data products from both sensors can be retrieved very similarly. These products, with outputs that the user communities are more accustomed to, will improve community understanding of the data records and increase data usability. Project PI: Juying Warner/University of Maryland, Baltimore County 5523 Research Park Dr., Suite 320, Baltimore, MD 21228 Phone: (410) 455-3320 Fax: (410) 455-1291 Email:  juying@umbc.edu http://asl.umbc.edu/~warner/
Project Administrator(s):
Cristina Milesi

Members

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Cristina Milesi