1. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Infrared Temperature and Water Vapor Sounding Presented by Chris Barnet Presented by Chris Barnet

2. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 2 Requirement, Science, and Benefit Requirement/Objective Weather & Water: –Increase lead time and accuracy for weather and water warnings and forecasts. –Increase development, application, and transition of advanced science and technology to operations and services Climate –Reduce uncertainty in climate projections through timely information on the forcing and feedbacks contributing to changes in the Earth’s climate. –Increase number and use of climate products and services to enhance public and private sector decision making. Science How can hyper-spectral information be exploited to improve accuracy and reduce uncertainty in satellite-derived temperature, moisture and trace gases? Benefit National Weather Service forecasters and their customers: –Fully exploit temperature and moisture information content from hyper-spectral instruments. –Increase utilization of hyper-spectral information in difficult sounding domains (e.g., cloudy scenes) Climate applications –Long-term temperature and moisture trends and their interaction (water vapor feedback) –Long-term monitoring of greenhouse gases (carbon dioxide, methane, carbon monoxide).

3. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 3 Challenges and Path Forward Science challenges –“First principles” algorithm still has regional and time dependent biases as result of “null space” errors between temperature, water vapor, and carbon dioxide. Next steps –Migration of the AIRS/IASI algorithm to the CrIS instrument on NPP –Mitigation of biases through algorithm improvements. –Collaboration with modeling centers to develop efficient communication of retrieval vertical correlation (averaging functions) and error covariance. Transition Path –For IASI the transition path is through SPSRB and products available via OSDPD. –For AIRS the transition path is through the NASA AIRS Science team and products are available at the NASA data archive.

4. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 4 Satellite Hyper-spectral Infrared Sounding Research Research description –Hyper-spectral instruments have 1000’s of channels covering the near and far IR –State-of-the-art forward models used to compute instrument radiances. –Cloud clearing approach removes cloud effects. –Simultaneous solution of trace gases improves temperature and moisture products –Geophysical products derived from cloud cleared radiances capture more of the relevant weather information and significantly reduces data volume. Recent science accomplishments –NOAA develops algorithms as part of the Atmospheric Infrared Sounder (AIRS) Science Team and delivers these to NASA for implementation. –AIRS algorithm has been migrated to the operational Infrared Atmospheric Sounding Interferometer (IASI) processing system. –Common algorithm and spectroscopy provides consistent products in the am and pm orbits. IASI spectrum above comprised of 8460 channels that sample molecular absorption from carbon dioxide, water, ozone, & trace gases. Sounding algorithms use specific (i.e., select best) channels to retrieve temperature and other products.

5. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 5 NESDIS’ Operational Hyper-spectral products STAR led the development and implementation of new and improved geophysical products from hyper- spectral instruments. Led development of cloud clearing approaches. Led development of deriving surface emissivity. Knowledge of trace gases improves temperature and moisture accuracy in difficult sounding regions. –ozone absorption affects the temperature retrieval. Trace gas products are useful in air-quality and climate applications –carbon monoxide is a precursor to tropospheric ozone which is both pollutant and greenhouse gas. Detection of sulfur dioxide from volcanic events has enabled hazard condition alerts for air travel. Atmospheric carbon products (carbon dioxide, methane, and carbon monoxide) are valuable for monitoring and understanding the carbon cycle and climate. ProductAIRSIASI Temperature NASA DAACNCDC/ CLASS Water vapor NASA DAACNCDC/ CLASS Ozone NASA DAACNCDC/ CLASS Carbon monoxide NASA DAACNCDC/ CLASS Methane NASA DAACNCDC/ CLASS Carbon dioxide NOAA NESDIS (experimental) NCDC/ CLASS Volcanic Sulfur Dioxide Real time flag From NOAA/NESDIS Real time flag From NOAA/NESDIS Nitric Acid NOAA NESDIS (experimental) NCDC/ CLASS Nitrous Oxide NOAA NESDIS (experimental) NCDC/ CLASS

6. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 6 STAR has participated in experiments to demonstrating impact of using sounding products in operational forecast. Joint Center for Satellite Data Assimilation experiments show that cloud cleared radiances have positive impact on the global forecast. –Use of cloud clear radiances (red) improves 6 day forecast by ≈ 4 hours relative to assimilation with AIRS clear scenes (blue). Univ. Maryland, College Park has used experimental temperature and moisture products (w/ covariance) in their Kalman Ensemble model –AIRS T(p) and q(p) profiles improve zonal and meridional winds (blue regions) NASA/Global Modeling and Assimilation Office has evaluated AIRS operational products. –Use of AIRS T(p) and q(p) profiles with QC improves forecast (red vs. black lines) more so than assimilating radiances (green) Recent experiments show potential value of geophysical products

7. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 7 Challenges and Path Forward Science challenges –“First principles” algorithm still has regional and time dependent biases as result of “null space” errors between temperature, water vapor, and carbon dioxide. Next steps –Migration of the AIRS/IASI algorithm to the CrIS instrument on NPP –Mitigation of biases through algorithm improvements. –Collaboration with modeling centers to develop efficient communication of retrieval vertical correlation (averaging functions) and error covariance. Transition Path –For IASI the transition path is through SPSRB and products available via OSDPD. –For AIRS the transition path is through the NASA AIRS Science team and products are available at the NASA data archive.