California Institute of Technology Jet Propulsion Laboratory tpagano@jpl.nasa May 1, 2008

1. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California CLARREO Pre-Phase A StudyJPL Study ActivitiesTom Pagano, Eric Fetzer, Kevin Bowman, Alex Ruzmaikin, Hartmut Aumann, Samantha Infeld California Institute of Technology Jet Propulsion Laboratory tpagano@jpl.nasa.gov May 1, 2008 AIRS Water Vapor Isosurfaces

2. JPL Study Team • Hartmut Aumann: AIRS Climate Science • Kevin Bowman: TES Climate Science • Mous Chahine: AIRS Science • Eric Fetzer: AIRS Water Vapor • Samantha Infeld: Mission Systems Engineering • Tony Mannucci: GPS Science • Tom Pagano: Instrument Calibration • Alex Ruzmaikin: Climate Science • Joao Teixiera: Climate Model Validation • Duane Waliser: Climate Model Assessment

3. JPL Agenda • JPL Perspective Pagano • Radiance Tasks • Simulated CLARREO IR Data SetsUsing AIRS and IASI • Instrument Error Analysis • Cross-calibration Sensitivity • Science Tasks Fetzer • Science Questions • Current Capabilities • Future needs from CLARREO • Modeling Tasks Bowman • Relation of OSSE’s to Measurement Requirements Also present from JPL: Samantha Infeld and Alex Ruzmaikin

4. JPL CLARREO Team Perspective • CLARREO is a great idea. • JPL manages Aqua AIRS and Aura TES experiments • Both hyperspectral infrared instruments • AIRS and TES now used for climate science • Model Validation, Process Studies, Trending • AIRS radiances are the only climate data record from AIRS today. Geophysical products not far behind (2 years) • AIRS, CrIS and IASI Calibration Exceptional and Meet Majority of CLARREO MW/LW Measurement requirements with better coverage and resolution. • Plan to continue climate observations using CrIS & IASI • NASA NPP Sci Team / PEATE to continue work with CrIS & IASI • Hyperspectral sounders will be around “in perpetuity” • Low risk of NOAA discontinuing them because they provide real forecast impact and NOAA has a high interest in climate observations • Additional Hardware Cost to NASA: Free • CLARREO is a great idea • CLARREO must address the key uncertainties in climate science • What are the CLARREO science questions (not measurement requirements)? • The question is not “What can we do with CLARREO”, but “What can CLARREO do for us”? • Can we phrase a question that if answered will improve model representation of cloud and water vapor feedbacks? See Fetzer “Testable Hypotheses”

5. CO Atmospheric Temperature Cloud Properties Atmospheric Water Vapor Dust Ozone CO2 Methane Methane Emissivity SO2 AIRS Climate Data Products Global: Day & Night, Pole to Pole, Land & Oceans, Cloudy & Clear, Daily

6. AIRS Products Validation Status *Necessary Products are required to retrieve accurate temperature profiles (1K/km) in all condition **Product not yet available in AIRS Level 2 Files. Products will be available in Version 6

7. 2022 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Sounder Constellation in Place Continuously from 2002 onward IASI 10:30 AM Orbit 12 km GSD ±49° Swath MetOp 2007 AIRS 1:30 PM Orbit 14 km GSD ±49.5° Swath Aqua: 2002 CrIS 1:30 PM Orbit 14 km GSD ±48.3° Swath NPP: 2010 C1: 2013 C3: 2020 NPOESS 5:30 AM C2: 2016 CrIS De-Manifested C3 NPP C1 C2 TES on Aura 1:30 PM Orbit Sounder NEdT Comparison Performance Comparable for AIRS, CrIS and IASI

8. AIRS/CrIS and IASI Provide 4 Points in Diurnal Cycle Diurnal Cycle of SST AIRS: Aqua: 13:30, 1:30TES: Aura: 13:30, 1:30 CrIS: NPOESS C1 and C3: 13:30, 1:30 IASI: MetOp, 9:30, 21:30 CrIS: NPOESS C2 (Demanifested): 5:30, 17:30 A C A C C I I C I C A Recommendation made to “NRC Panel on Options to Ensure the Climate Record from the NPOESS and GOES-R Spacecraft” that loss of CrIS on C2 will impact ability to study diurnal cycle (June, 2007) C

9. AIRS/CrIS show high accuracy(Based on pre-flight test and estimates) CrIS Accuracy < 0.2K 3s, 287K AIRS Accuracy < 0.2K 3s, 250K NIST Traceable Requirement Requirement CLARREO Requirement Predicted With LinearityCorrection CLARREO Requirement T. Pagano ITT Estimates converted to T. Edge effects not shown. Pagano, IEEE TGRS, 2007 Pagano, Proc ITOVS, 2003 Pagano, AGU 2007 Pagano, SPIE 2008 (TBD)

10. Radiometric Accuracy and Stability Validation match Observational Accuracy Scanning HIS Validates Rad Accy to <0.2K – Tobin, Revercomb (UW) RTGSST Validates Radiometric Stability <10mK/Y – H. Aumann (JPL) AIRS Given perfect Accuracy stability is ESSENTIAL to Climate observations to differentiate instrument variability from scene variability Validated against NIST Traceable Buoys Reference: JGR, VOL. 111, April 2006

11. Cross-Calibration of AIRS and IASI made with tropical clear data sets 3 Million AIRS Total Obs/Day Clear AIRS 74,841 Clear IASI 40,407 Dx < 10 km Night 719 Dt < 5 hrs 309

12. AIRS and IASI Warm Radiometry Agree in Window Regions to less than 50 mK Longwave Window Shortwave Window Warm (~290K), Uniform, Clear No Spectral Correction, No PC Filtering AIRS-IASI (K) AIRS-IASI (K) Mean Difference: 0.0356 K Standard Deviation: 0.1319 K Mean Difference: -0.0063 K Standard Deviation: 0.1961 K Cross-calibration of Sounder Radiometry Not Required by CLARREO. We will test how well CLARREO cross-calibrate using this technique.

13. Fractional Clear Drops Fast with Spatial Resolution Fraction Clear vs Area For 1K Cloud Contamination1 100 km, 2% 1 km, 32% 15 km, 12% 1J. Krijger et. al, The effect of sensor resolution on the number of cloud-free observations from space, Atmos. Chem. Phys. Discuss., 6, 4465-4499, 2006, www.atmos-chem-phys-discuss.net/6/4465/2006

14. Cross-Calibration and Comparison Successful with Imagers and Sounders AIRS-MODIS/HIRS Trend in Radiometric Calibration Dome Concordia Must Calibrate Temperature Dependence Shift in MODIS Calibration Algorithm V4 to V5 MODIS Bias ~ 1K HIRS Stable S. Broberg, Evaluation of AIRS, MODIS, and HIRS 11 micron brightness temperature difference changes from 2002 through 2006, SPIE 6296-22, August 2006

15. AIRS Radiance Trending Analysis • Difficulty with Trends: Nonlinear • Linear fits and averaging could be misleading • Signed forcing: anthropogenic (+), volcanic (-), solar (), ... • Interlock with natural variability -- 2-order fingerprint may be insufficient • Data must have high time cadence (1 day) and spatial resolution (≤ 10 km) to allow successful data analysis over mission life (< 10 yrs) using advanced nonlinear methods Linear fit: -30  42 mK/y Nonlinear trend insignificant for monthly data, significant for 8-day data A. Ruzmaikin

16. Spatial or Temporal Averaging Will Reduce Sensitivity to Clouds Without time average Without time average High clouds got washed out! 10-day average 10-day average King-Fai, Duane Waliser, Yuk Yung, CalTech

17. Cloud variability (scene noise) masks calibration errors in high contrast scenes Spatial Response Function Must be well known Channel 774, FP 70 AIRS-MODIS Non-uniform Scene Uncorrected 4.2K Simple Correction 2.26K Corrected 0.57K D. Eliott, et. al, “The Impact Of the AIRS Spatial Response On Channel-To-Channel and Multi-Instrument Data Analyses”, Proc. SPIE, 6296-01 (2006) • Higher Spatial Resolution CLARREO (<5 km) will • Improve characterization of biases in high contrast scenes • Will improve SNR of cross-comparisons • Will improve accuracy of validation (more samples per aircraft overpass)

18. CLARREO Measurement Ideas • CLARREO is needed to advance science and improve cross-calibration • CLARREO must provide an advancement in observational performance beyond the current sounders • Low spatial resolution reduces sensitivity to phenomenon affecting climate signatures; cloud, water vapor and temperature variability (correlations) • Narrow swath reduces global coverage and sampling • Both of these will limit CLARREO’s ability to do climate science and cross-calibration • Going to great expense to achieve diurnal cycle and sampling • Multiple satellites will greatly increase program costs • Sounders cover 4 points in diurnal cycle • Multiple instruments will not reduce systematic errors (especially if built the same and calibrated by the same equipment) • Cost of higher performance need not be high • Use of modern technologies (e.g. Large Format Array HgCdTe) • Use of technology developed in industry on GOES HES and NASA IIP • Overall program would be more robust if we can • Start with a single satellite. Eventually we will move to constellations. • Improve spatial resolution (better than sounders) and wide swath • Single instruments rather than redundant.

19. AIRS Water VaporWide Swath gives Daily Global Coverage This one feature has made AIRS data invaluable for cross-calibration and climate science products (Level 3 Products)

20. Task Summary: Generate and Analyze CLARREO simulated radiance data sets • Generate Simulated CLARREO Climate Data Product • AIRS and IASI Data • ~6 years, ~14 km • Nadir Only • Aggregate to 152 x 90, 602 x 90, 902 x 90 km. • Repeat/Combine with IASI Data • ~12 Months, ~ 12 km • Nadir Only • Aggregate to 152 x 90, 602 x 90, 902 x 90 km • Generate Clear Data Subset of Aggregates • Science Assessment • Is spatial/temporal coverage sufficient to trend stability? • Extrapolate to full-up CLARREO suite by analysis • Verify ability to cross-calibrate with IASI and MODIS • What resolution is best suited for cross-calibration? • Trend Assessment