california institute of technology jet propulsion laboratory [email protected] may 1, 2008

National Aeronautics and Space Administration

Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadena, California

1

CLARREO Pre-Phase A Study

JPL Study Activities

Tom Pagano, Eric Fetzer, Kevin Bowman, Alex Ruzmaikin, Hartmut Aumann, Samantha Infeld

California Institute of TechnologyJet Propulsion Laboratory

[email protected]

May 1, 2008



AIRS Water VaporIsosurfaces



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

AIRS Climate Data Products

Atmospheric Temperature

Atmospheric Water VaporOzone

Cloud Properties

Methane SO2

Dust

CO

EmissivityMethane CO2

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

AIRS Product Uncertainty Estimate (Version 5)

Val Status (Version 5) Source

RadiancesAIRS IR Radiance <0.2% Stage 3 ProjectAIRS VIS/NIR Radiance 15-20% Stage 1 ProjectAMSU Radiance 1-3 K Stage 3 ProjectHSB Radiance 1-3 K Stage 3 ProjectCore ProductsCloud Cleared IR Radiance 1.0 K Stage 2 ProjectSea Surface Temperature 1.0 K Stage 2 ProjectLand Surface Temperature 2-3 K Stage 1 ProjectTemperature Profile 1 K / km Stage 2 ProjectWater Vapor Profile 15% / 2km Stage 2 ProjectTotal Precipitable Water 5% Stage 2 ProjectFractional Cloud Cover 20% Stage 2 ProjectCloud Top Height 1 km Stage 2 ProjectCloud Top Temperature 2.0 K Stage 2 ProjectNeccesary Products* Total Ozone Column 5% Stage 2 ProjectOzone Profile 20% Stage 2 ProjectLand Surface Emissivity 10% Stage 1 ProjectIR Dust** 0.5 K Stage 1 ProjectResearch ProductsCarbon Monoxide 15% Stage 2 NOAA/UMBCMethane 2% Stage 1 NOAACarbon Dioxide** 1-2 ppm Stage 1 NASA/NOAAOLR 5 W/m2 Stage 1 GSFCHNO3** 0.2 DU Stage 1 NOAA/UMBCSulfur Dioxide** 1 DU Stage 1 NOAA/UMBC*Necessary Products are required to retrieve accurate temperature profiles (1K/km) in all conditions



7

Sounder Constellation in Place Continuously from 2002 onward

AIRS1:30 PM Orbit

14 km GSD±49.5° SwathAqua: 2002

IASI10:30 AM Orbit

12 km GSD±49° SwathMetOp 2007

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022TES on Aura1:30 PM Orbit

NPP C1 C3C2

CrIS1:30 PM Orbit

14 km GSD±48.3° Swath

NPP: 2010C1: 2013 C3: 2020

NPOESS5:30 AMC2: 2016CrIS De-Manifested

Sounder NEdT Comparison

PerformanceComparable forAIRS, CrIS and IASI



8

AIRS/CrIS and IASI Provide 4 Points in Diurnal Cycle

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 CA

CA

I

I

C

C

A

C

C

I

Diurnal Cycle of SST

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)



9

AIRS/CrIS show high accuracy(Based on pre-flight test and estimates)

AIRS Accuracy < 0.2K 3, 250K CrIS Accuracy < 0.2K 3, 287K

RequirementRequirement

With LinearityCorrection

Predicted

T. PaganoITT Estimates converted to T. Edge effects not shown.

CLARREO Requirement

CLARREO Requirement

Pagano, IEEE TGRS, 2007Pagano, Proc ITOVS, 2003Pagano, AGU 2007Pagano, SPIE 2008 (TBD)

NIST Traceable



10

(AIR

Sob

s-A

IRS

calc

)-(S

HIS

obs-

SH

ISca

lc) (

K)

Final “Comparison 2” (21 November 2002)Excluding channels strongly affected by atmosphere above ER2

Scanning HIS Validates Rad Accy to <0.2K – Tobin, Revercomb (UW)

H. H. Aumann

AIRS IR Radiometry Extremely Stable

Instrument Stability Fundamental to Weather and Climate Quality Observations

SST2616 compared to RTG.SST at night

-0.57K bias observed-0.37K bias expected

First principles using NIST traceable calibration

Stability better than 8 mK/Year

Bias: Slope = 5mK/year

Aumann et al 2004 Aumann et al 2004 ““Evaluation of AIRS Data for Climate ApplicationsEvaluation of AIRS Data for Climate Applications””SPIE 5570b Las Palmas September 2004 SPIE 5570b Las Palmas September 2004

difference between observedand expected bias due to cloud contamination

RTGSST Validates Radiometric Stability <10mK/Y – H. Aumann (JPL)

Reference: JGR, VOL. 111, April 2006

Radiometric Accuracy and Stability Validation match Observational Accuracy

AIRS

Given perfect Accuracy stability is ESSENTIAL to Climate observationsto differentiate instrument variability from scene variabilityValidated against NIST Traceable Buoys



11

Cross-Calibration of AIRS and IASI made with tropical clear data sets

Clear AIRS74,841

Clear IASI40,407

x < 10 kmNight719

t < 5 hrs309

3 Million AIRSTotal Obs/Day



12

AIRS and IASI Warm Radiometry Agree in Window Regions to less than 50 mK

Mean Difference: 0.0356 KStandard Deviation: 0.1319 K

Mean Difference: -0.0063 KStandard Deviation: 0.1961 K

Longwave Window Shortwave Window

Warm (~290K), Uniform, ClearNo Spectral Correction, No PC Filtering

AIR

S-IA

SI (K

)

AIR

S-IA

SI (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

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

1 km, 32% 100 km, 2%15 km, 12%

Fraction Clear vs AreaFor 1K Cloud Contamination1

http://www.atmos-chem-phys-discuss.net/6/4465/2006



14

Cross-Calibration and Comparison Successful with Imagers and Sounders

Trend of 200 K offset over Antarctica

-0.05

0.15

0.35

0.55

0.75

0.95

1.15

1.35

9/1/2002 1/29/2003 6/28/2003 11/25/2003 4/23/2004 9/20/2004 2/17/2005 7/17/2005 12/14/2005 5/13/2006Date

200

K off

set

MODIS 31 - AIRS equivalent HIRS8 - AIRS equivalent

AntarcticData>>200 K

Antarctic Data>>200 K



AIRS-MODIS/HIRS Trend in Radiometric

Calibration Dome Concordia

MODIS Bias ~ 1K

Shift in MODIS Calibration Algorithm V4 to V5

HIRS StableS. Broberg, Evaluation of AIRS, MODIS, and HIRS 11 micron brightness temperature difference changes from 2002 through 2006, SPIE 6296-22, August 2006

Must CalibrateTemperature Dependence



15

Linear fit: -30 42 mK/y

Nonlinear trend insignificant for monthly data, significant for 8-day data

A. Ruzmaikin

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



16

Spatial or Temporal Averaging Will Reduce Sensitivity to Clouds

10-day average

Without time average

10-day average

Without time average

High clouds got washed out!

King-Fai, Duane Waliser, Yuk Yung, CalTech



17

Cloud variability (scene noise) masks calibration errors in high contrast scenes

Spatial Response FunctionMust be well knownChannel 774, FP 70

AIRS-MODISNon-uniform Scene

Corrected0.57K

Uncorrected4.2K

SimpleCorrection

2.26K

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)

D. Eliott, et. al, “The Impact Of the AIRS Spatial Response On Channel-To-Channeland Multi-Instrument Data Analyses”, Proc. SPIE, 6296-01 (2006)



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



21

california institute of technology jet propulsion laboratory [email protected] may 1, 2008

Documents