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Satellite Data: Past, Present, and Future

Satellite Data: Past, Present, and Future. James G. Yoe NESDIS/ORA Sensor Physics Branch and Joint Center for Satellite Data Assimilation. Outline. Some Satellite Basics Past & Current Satellites/Sensors for AC Satellites/Sensors pipelined for next 10 yrs

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Satellite Data: Past, Present, and Future

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  1. Satellite Data:Past, Present, and Future James G. Yoe NESDIS/ORA Sensor Physics Branch and Joint Center for Satellite Data Assimilation

  2. Outline • Some Satellite Basics • Past & Current Satellites/Sensors for AC • Satellites/Sensors pipelined for next 10 yrs • Considerations for Data Assimilation & Observations System Planning • Summary

  3. NOAA’s SatellitesTwo polar (POES) and two geostationary (GOES) environmental satellites

  4. Contributions of GEOs and LEOs • GEOs (Geostationary satellites) Resolve diurnal cycle Observe at constant angles • LEOs (Low Earth Orbiters-polar orbiting satellites) Provide global coverage

  5. Atmosphere Temperature soundings Moisture soundings Winds Clouds Aerosols Earth Radiation Budget Precipitation Ozone Ocean Surface temperature Ice cover Surface winds Color Sea level Land Vegetation condition Snow pack characteristics Other land characteristics (e,g., albedo, skin temperature, soil wetness, insolation) Fire locations/Smoke Plumes NOAA Satellite Products Air Quality or Chemistry Related Products

  6. Ozone • Ozone is adjusted to NOAA-9 • Validated against Dobson Stations • Reprocessed when new algorithms are developed • Compared with models

  7. AVHRR vegetation, fires, smoke GOES-8 WF_ABBA fire product, August 13, 2002 Current Operational Satellite Products Useful for Air Quality GOES-12 aerosol optical depth, July 15-19, 2004 Currently used in NCEP’s air quality forecast efforts

  8. Continuity of Operational Satellite Programs NOAA Satellite Launches* Scheduled to Maintain Continuity 1997 1998 1999 1996 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 GOES 8 GOES 9 (removed from service) GOES 10 GOES 11 (stored in orbit) GOES 12 (stored in orbit) GOES N – Series GOES R-Series NOAA 12 NOAA 14 NOAA 15 NOAA 16 (pm) NOAA M (am) NOAA N (pm) NOAA N’ (pm) METOP ** European Coordination NPOESS Satellite is operational beyond design life * Actual launch dates are determined by the failure of on-orbit assets ** Assumes METOP will provide the morning orbit and NOAA-N’ will provide afternoon orbit instruments On-orbit GOES storage 3/25/02

  9. Spectral Coverage of AIRS, IASI, and CrIS

  10. AIRS is Sensitive to CO from Biomass Burning. Cloud Clearing yield ≈ 70%. On Sep. 9, 2002: low MODIS fire counts & low AIRS CO was measured. On Sep. 24, 2002: high MODIS fire counts & high AIRS CO was measured

  11. AIRS Global CO Movie (Wallace McMillan, UMBC)

  12. Hyperspectral Dust/Aerosol Modeling Negative Slope – The Dust Signature Green – Clear Spectrum 820 920 650 1000 Effect of Dust Layer Location Effect of Dust Particle Sizes Negative slope ( Sokolik, et al., 2002 - GRL) kCARTA+DISORT, spectral resolution = 0.0025 cm-1

  13. Larrabee Strow, UMBC

  14. Anatahan Volcano viewed with AIRS Abs Scat Tot Ash signal 1228-995 cm-1 SO2 signal 1284-1345 cm-1 Anatahan 10 May 2003 (1554 UT)

  15. Looking ahead: the next 10 years and beyond in the era of hyperspectral measurements • Exploit the current sensors • Demonstrate air quality applications of current satellite products relevant to AQ • Improve the quality of the current products (algorithms and calibration) • Develop new products (e.g., emissions from biomass burning) • Work with air quality forecast groups to utilize the satellite data in models (forecast verification or improvements via assimilation) • Prepare for the future sensors • Collect requirements • Invest in algorithm development work • Challenges: measuring trace gases/aerosols in PBL, tropospheric profiles, cloud screening • Exploit instrument synergy: multi-sensor and / or multi-platform approaches • Active participation in ground campaigns • Develop capabilities to process NASA’s air quality products near real time at NOAA

  16. Statistics of CO Retrieval from a simulation of a full day RMS BIAS Background Variability IASI has most skill in lower troposphere

  17. CALIPSO Mission Objectives • CALIPSO will fly as part of the Aqua constellation (A-train) to provide observations needed to improve: • The representation of aerosols and clouds in models • Improved climate predictions • Improved models of atmospheric chemistry • Our understanding of the role of aerosols and clouds in the processes that govern climate responses and feedbacks • – Direct and indirect aerosol effects • – Cloud forcing and feedbacks

  18. Aerosols: The Most Uncertain External Climate Forcing Agent In contrast to greenhouse gases, aerosols: - are shortlived, spatially inhomogenous, interact strongly with clouds - composition highly variable, heterogen. chemistry poorly understood (IPCC, 2001)

  19. CALIPSO CloudData from 12/08/03 Signal Average 14.5 to 24.4 km Signal Average 4.6 to 14.5 km

  20. CALIPSO Milestones • ASDC/DMS Launch Readiness Review (LRR) October 2004 • Flight Ops Review (FOR) December 2004 • Satellite ships to VAFB January 2005 • Launch 15 April 2005 • First lidar profiles 1 June 2005 • Prelim data release 1 Sept 2005

  21. Payload Integrated • Payload-platform mech. integration 1 Mar 2004 • Satellite Performance Verification Test completed 26 March • Conducted E-M Compatibility (EMC) completed 9 April • Satellite Sine-Vibe to finish today • Satellite T/V - August

  22. GOES-R AQ products at 15 minute refresh rate over the Americas • Aerosol optical depth • Aerosol size • Aerosol type • Biomass burning emissions • Carbon monoxide • Fire size and location • Height of aerosol layer • Methane • Ozone NPOESS and METOP will provide the same up to six times per day but with global coverage

  23. 5-Order Magnitude Increase in Satellite Data Over 10 Years Daily Upper Air Observation Count Satellite Instruments by Platform NPOESS METOP NOAA Windsat GOES DMSP 2003 Count 2002 Count (Millions) 1990 2000 2010 2010-250ch 1990 2010 Year Year Year

  24. Will Still Need In Situ Observations • Include advanced instruments: • Very high spectral resolution interferometers • Advanced microwave instruments • Cloud /aerosol lidars (surface & AC based) • Dropsondes and radiosondes • Objective – validate satellite radiances, improve radiative transfer models and retrieval algorithms in cloudy atmospheres, and to better understand physical processes

  25. Satellites can serve as transfers standards to monitor in situ observations VIZ B to Vaisala (RS80) at Chuuck Island

  26. Day/Night Bias versus Altitude (cm-1) Obs B(T) is proxy for altitude. Day/Night bias increases with altitude. Highly variable with site, so this result questionable. dB(T)/dQ = ~1K (for dQ = 10%) at high altitudes, so this represents ~10% water variability. Lower altitude global bias is about +-3-5% water.

  27. Figure 1(b). 500hPa Z Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, January 2004

  28. AMSU is a critical component of AIRS- provides retrievals in overcast conditions- drives cloud clearing AIRS acquires 2,916,000 spectra = 35 GByte/day

  29. MODIS Cloud-Clearing Strategies • MODIS 1 km resolution can be used to find clear holes • Clear MODIS channels can be compared with cloud-cleared AIRS convolved to MODIS spectral resolution for QC • Clear MODIS can be used to provide the clear estimate for cloud clearing

  30. Before Now Future Polar Satellites Geo Satellites Other Data Sources Goal - Transition Products from Individual Satellites to a “System of Systems” GOES R NPOESS Integrated System Other Data Sources MetOp Formulate and Integrate Environmental products using GOES-R series, NPOESS series, and MetOp satellites along with Other Structured Data Sources Environmental Products that are mostly generated from observations that are independent of one another Products are formulated and produced as one integrated system

  31. Steps to Successfully Achieving Our Goals Full Integrated System • Integrated Sensors: Initially need to simplify the complexity of building products between instruments on one platform • Integrated Calibration: Then we need to evaluate the calibration between multiple satellites at different orbits • Integrated Satellites: Finally we can utilize these cross-calibrated, collocated data sets to build enhanced products GOES-R Series Advanced Individual Capabilities NPOESS NPP MetOP Program NOAA-N / N’ GOES-N,O,P AQUA, NOAA-15/16/17/18, GOES-M,L 2004 2008 2012 2016 2020

  32. PARTNERS NOAA/NCEP Environmental Modeling Center NOAA/OAR Office of Weather and Air Quality NASA/Goddard Global Modeling & Assimilation Office US Navy NOAA/NESDIS Oceanographer of the Navy, Office of Naval Research (NRL) Office of Research & AF Director of Weather AF Weather Agency Applications US Air Force Joint Center for Satellite Data Assimilation

  33. JCSDA Mission and Vision • Mission: Accelerate and improve the quantitative use of research and operational satellite data in weather and climate analysis and prediction models • Near-term Vision: A weather and climate analysis and prediction community empowered to effectively assimilate increasing amounts of advanced satellite observations • Long-term Vision: An environmental analysis and prediction community empowered to effectively use the integrated observations of the GEOSS

  34. Goals – Short/Mid Term • Increase uses of current and future satellite data in Numerical Weather and Climate Analysis and Prediction models • Develop the hardware/software systems needed to assimilate data from the advanced satellite sensors • Advance the common NWP models and data assimilation infrastructure • Develop common fast radiative transfer system • Assess the impacts of data from advanced satellite sensors on weather and climate analysis and prediction • Reduce the average time for operational implementations of new satellite technology from two years to one

  35. JCSDA Goals – Longer Term • Provide the “bridge” for the integrated use of GEOSS data within numerical models • Develop the tools for effective integration of GEOSS observations into environmental models • Expand assimilation system to provide input to models of: • environmental hazards • air and water quality and resources • terrestrial, coastal, and marine ecosystems • climate variability and change • agricultural productivity • energy resources • human health • biodiversity

  36. Summary • Satellites have history of AC observations • Unrivaled coverage and temporal refresh • Current and future satellites adding more chemical species, and accuracy & volume • In situ observations remain critical for cal/val • Optimal design of future OS requires early assimilation, impact assessment • Integrated future OS: Integrated modeling and assimilation

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