Infrared Temperature and Water Vapor Sounding Presented by
Infrared Temperature and Water Vapor Sounding Presented by Chris Barnet Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000
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). Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 2
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 Cr. IS 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. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 3
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. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 4
NESDIS’ Operational Hyper-spectral products • STAR led the development and implementation of new and improved geophysical products from hyperspectral 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. Product AIRS IASI Temperature NASA DAAC NCDC/ CLASS Water vapor NASA DAAC NCDC/ CLASS Ozone NASA DAAC NCDC/ CLASS Carbon monoxide NASA DAAC NCDC/ CLASS Methane NASA DAAC NCDC/ CLASS Carbon dioxide NOAA NESDIS (experimental) NCDC/ CLASS Volcanic Sulfur Dioxide Real time flag From NOAA/NESDIS Nitric Acid NOAA NESDIS (experimental) NCDC/ CLASS Nitrous Oxide NOAA NESDIS (experimental) NCDC/ CLASS Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 5
Recent experiments show potential value of geophysical products • 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) Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 6
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 Cr. IS 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. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 7
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