The Principal Investigator (PI) of this proposal is currently a member of the AIRS science team. This proposal will enable us to continue to provide the necessary and mandatory scientific support required to maintain the quality and accuracy of the AIRS/AMSU products, and also to provide support for improving product accuracy. The PI is responsible for maintaining the quality and accuracy of the eigenvector regression retrieval used as the initial condition (i.e. first guess) for the AIRS physical retrieval algorithm and to provide AIRS/AMSU products to the numerical weather prediction (NWP) community, which include NCEP, the Goddard GMAO and foreign institutions such as the ECMWF, UKMO, CMC and JMA. The distribution of AIRS products to the NWP community is now a critical component of the objectives of the AIRS science mission. Our proposal will allow the continuation of these activities - not only distribution, but the iterative cycle of validation, improvements, testing and implementation. The real-time products and our real-time validation collocated datasets (such as AIRS collocation to radiosondes and DOE ARM site data) are also provided to the AIRS Science Team facility at JPL to support science team activities.
The proposed work includes: 1) Continued distribution of high-quality AIRS products to the NWP community, 2) Monitoring of AIRS instrument performance, a task more important now than earlier in the mission, 3) Monitoring the quality of the regression coefficients and the eigenvectors, 4) Monitoring and validation of the quality of the retrieval products, including the microwave-only product, the initial AIRS retrieval and the final AIRS retrieval, 5) Coefficient updates whenever necessary (changes in algorithm, radiance characteristics, etc.), 6) Improve regression retrieval of atmospheric profiles and surface properties, 7) Algorithm improvements as a result of unexpected algorithm deficiencies and/or discovery of new science applications.
In addition to the above activities, we propose the following two very important developments with respect to scientific innovation: 1) Improve the quality control and accuracy of cloud cleared radiances by the integration of high spatial resolution MODIS sensor data, and 2) Develop techniques for the generation of a high quality, limb adjusted spectrally resolved radiance dataset for climate studies.
The proposed work is essential to provide current and evolving products to the NWP community, the AIRS science team and the collaborators of other NASA ROSE06 proposals. In summary, the proposal will continue the generation of near real time products for NWP and the quality assurance of the AIRS/AMSU products. The proposal includes adding MODIS to the algorithm processing chain to provide improved cloud detection, cloud-cleared radiances and products, and the generation of a radiance climatology.
