nasa airs project highlights ( jpl/gsfc)

NASA, CGMS-41, July 2013

Coordination Group for Meteorological Satellites - CGMSCoordination Group for Meteorological Satellites - CGMS

Atmospheric Sounding Product Development and Cal/Val Activities at NASA using

AIRS/AMSU on Aqua, CrIMSS on SNPP, and NAST-I on NASA High-altitude Aircraft Platforms

Presented to CGMS-41 Working Group II, WGII/4

T. Pagano1, A. Larar2, E. Fetzer1, B. Lambrigtsen1, J. Teixeira1, Steve Friedman1, Daniel Zhou2, Xu Liu2, Joel Susskind3

1NASA Jet Propulsion Laboratory2NASA Langley Research Center

3NASA Goddard Spaceflight Center


Coordination Group for Meteorological Satellites - CGMS

Operations: AIRS fully operational and expected to last at least another 10 years. Recovered 50 channels

Calibration: AIRS, CrIS and IASI radiometric calibration agree to within 50 mK under clear tropical ocean conditions.

Product Development: Version 6 Products released in February 2013 at the GES/DISC. Reprocessing almost complete. Improved bias, yield and reduced trend. Demonstrated retrievals using CrIS/ATMS data

Product Test and Validation: Released V6 test report complete.

Science Usage: Over 100 peer reviewed publications using AIRS data released in 2012

2

NASA AIRS Project Highlights (JPL/GSFC)



3

Near Real TimeAssimilation

6 Hrs on 5 Day Improvement onOperational Forecast

NCEP, ECMWF, and UKMetOperational Forecasts

Direct Broadcast

AIRS Direct Broadcast

Water Vapor

GES/DISC

Near Real TimeProduct Generation

NASAGroundstations

AIRS Spectra

Scientific Community and

Operational Agencies

(Volcanic SO2)

ImprovedPrecip

Forecasts

AIRS/AMSU on Aqua

AIRS/AMSU is Part of the International Operational Weather Forecast System



Third Highest Impact to Forecasts AMSU has 4 Instruments IASI is also an IR Sounder Cardinali and Healy 2012

4

JCSDA

NOAANESDIS/NCEP

JPL/GSFC

IR Sounders

IR Sounders Rank High on Operational Forecast Impact

http://www.ecmwf.int/



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Pre-Launch: 1856 Channels “Good”180 were “Poor”: High Noise but Usable68 were “Bad”: Non-Gaussian Noise, Un-usable

50 Channels Recovered Using Redundant Detectors

Example impact to noise due to radiation damageNEdT’s Pre-launch Match Current

AIRS NEdT’s stable for most detectors. Some impacted by radiation



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AIRS Product ProductAccuracy

(V5)Val Status

(V5)

Core: Radiances

AIRS IR Radiance L1B-AIRS <0.2K Stage 3

AIRS VIS/NIR Radiance L1B-VIS 15-20% Stage 1

AMSU Radiance L1B-AMSU 1-3 K Stage 3

HSB Radiance L1B-HSB 1-3 K Stage 3

Core: Geophysical

Cloud Cleared IR Radiance L2 1.0 K Stage 2

Sea Surface Temperature L2 1.0 K Stage 3

Land Surface Temperature L2 2-3 K Stage 2

Temperature Profile L2 1 K / km Stage 3

Water Vapor Profile L2 15% / 2km Stage 3

Total Precipitable Water L2 5% Stage 3

Fractional Cloud Cover L2 20% Stage 3

Cloud Top Height L2 1 km Stage 3

Cloud Top Temperature L2 2.0 K Stage 2

Carbon Monoxide L2 15% Stage 2

Carbon Dioxide Post-Proc 1-2 ppm Stage 1

Total Ozone Column L2 5% Stage 2

Ozone Profile L2 20% Stage 2

Land Surface Emissivity L2 10% Stage 2

IR Dust L1B-Flag 0.5 K Stage 1

Research Products

Methane L2 2% Stage 2

OLR L2-Support 5 W/m2 Stage 3

Sulfur Dioxide L1B-Flag 1 DU Stage 1

Stage 1: Validation Product accuracy has been estimated using a small number of independent measurements obtained from selected locations and time periods and ground-truth/f ield program effort.Stage 2: Validation Product accuracy has been assessed over a w idely distributed set of locations and time periods via several ground-truth and validation efforts.

Stage 3: Validation Product accuracy has been assessed, and the uncertainties in the product w ell-established via independent measurements made in a systematic and statistically robust w ay that represents global conditions.

*Necessary Products are required to retrieve accurate temperature profiles (1K/km) in all conditions

Validation Status Definitions (Common to all Aqua Instruments)

Temperature Water Vapor

Ozone Carbon Monoxide Methane

Cloud Properties

Carbon Dioxide

AIRS/AMSU Data Products Validation Status



NAST-I Activities Benefit Atmospheric Sounding NAST-I satellite under-flights improve SDRs, EDRs, and CDRs, and enable inter-

platform cross-validation Retrieval algorithm enhancements improve EDRs & CDRs Fast RT modeling enables handling data volume to come from future sensors

and more accurate / efficient data assimilation



Slide: 8

NAST-I satellite under-flights Validation activities verify / improve SDRs, EDRs, and CDRs Under-flying multiple satellites enables cross-validation (e.g. IASI vs

AIRS vs CrIS), critical for climate measurement continuity (CDRs)

Retrieval algorithms Algorithm verification / enhancement improves EDRs & CDRs (CrIMSS

and LaRC-unique algorithms)

Radiative transfer modeling Fast RT model development (PCRTM) enables handling data volume to

come from next generation sensors and more accurate / efficient data assimilation (e.g. NWP)

NAST-I Team Sounding-related Contributions



NASA / NPOESS Airborne Sounder Testbed - Interferometer (NAST-I) Overview

IR Michelson Interferometer

Validation tool

AQUA/SNPP/JPSS risk mitigation

Airborne science

Engineering testbed

(a)

C O

NAST-I / S-HIS

NAST has flown ~ 175 mission sorties accumulating ~ 950 hours of flight data in

19 field experiments [e.g., CAMEX, C-IOP, WV-IOP, TRACE-P, IHOP,

CRYSTAL-FACE, INTEX, EAQUATE, JAIVEx, SNPP]

Spectral range: 3.5 - 16 m (630 – 2700 cm-1)Spectral res.: 0.25 cm-1 (/ > 2000 )Spatial res.: 130m/km flight alt.A/C platforms: ER-2, Proteus, WB-57

Radiometric Measurement Capability Radiances <0.5 K absolute accuracy, ~ 0.1 K precision

“Upwelling” IR Radiance Spectrum: Earth + Atmosphere



Modern hyperspectral sensors have high information content, but handling data volume is problematic Two orders of magnitude more spectral channels and increased

dimensionality (i.e. imaging) than traditional sensors [e.g., GOES (19), MODIS (36), AIRS (2378), IASI (8461), NAST-I (8632), TES (> 15000), HES (> 30000), …]

Only a few hundred channels are typically used in physical retrievals and data assimilation, due to computational expense, yielding sub-optimal results

Developed a super fast/accurate RT model in Empirical Orthogonal Function (EOF) domain—Principal Component Radiative Transfer Model (PCRTM) Enables operational processing alternative, and independent FM,

radiance, and geophysical product validation Order of magnitude faster than current channel-by-channel based RT

models Validated using clear scenes from NAST-I, AIRS, & IASI, and accuracy

compares favorably with international community standards (e.g. ITWG RTA inter-comparison)

Implemented within a physical retrieval using all channels Implemented for other sensors and spectral regions (e.g. CrIS,

CLARREO, & O2 A-band at OCO / SCIAMACHY)

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Challenge

LaRC approach

IASI CO retrieved using PCRTM

Mean spectra

Comparison of IASI instrument measured and PCRTM model calculated

IASI noiseOBS - CALC

Without CO

With CO

CO (ppmv)CLARREO (IR) PCRTM

Simulated spectra: LBL vs PCRTM

Fast Radiative Transfer Model Evolves from NAST-I Program

nasa airs project highlights ( jpl/gsfc)

Documents

airs data

multiple satellites

working group

iasi vs airs vs cris

high noise

atmospheric sounding

handling data volume

ir soundercardinali