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CLARREO Visible-NIR Science Studies. Greg Kopp, Peter Pilewskie, and Ryan Woolley CU/LASP. LASP Visible/NIR Study Summary. Measurement Requirement Determination Methodology. Estimate long-term climate variability and trends desired to be detected Include spectral signatures

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clarreo visible nir science studies

CLARREO Visible-NIR Science Studies

Greg Kopp, Peter Pilewskie, and Ryan Woolley

CU/LASP

measurement requirement determination methodology
Measurement Requirement Determination Methodology
  • Estimate long-term climate variability and trends desired to be detected
    • Include spectral signatures
    • Estimates based on climate models
    • Consider statistical variations
      • Discern seasonal and random variability (HYPERION, AVIRIS images)
      • Global / regional dependence of variability (MODIS coverage)
  • Compare time regimes in which absolute accuracy and stability are most useful
    • Determine absolute accuracy needed to create “benchmark” measurements
    • Determine stability and measurement duration needed to detect trends

IPCC AR4 2007

measurement requirement determination methodology1
Measurement Requirement Determination Methodology
  • Estimate long-term climate variability and trends desired to be detected
    • Include spectral signatures
    • Estimates based on climate models
    • Consider statistical variations
      • Discern seasonal and random variability (HYPERION, AVIRIS images)
      • Global / regional dependence of variability (MODIS coverage)
  • Estimate long-term climate variability and trends desired to be detected
    • Include spectral signatures
    • Estimates based on climate models
    • Consider statistical variations
      • Discern seasonal and random variability (HYPERION, AVIRIS images)
      • Global / regional dependence of variability (MODIS coverage)

IPCC AR4 2007

measurement requirement determination methodology2
Measurement Requirement Determination Methodology
  • Estimate long-term climate variability and trends desired to be detected
    • Include spectral signatures
    • Estimates based on climate models
    • Consider statistical variations
      • Avoid seasonal and random variability (HYPERION, AVIRIS images)
      • Global / regional dependence of variability (MODIS coverage)
  • Compare time regimes in which absolute accuracy and stability are most useful
    • Determine absolute accuracy needed to create “benchmark” measurements
    • Determine stability and measurement duration needed to detect trends

IPCC AR4 2007

what is the trend to be detected tsi example

0.1%

What Is the Trend to Be Detected? TSI Example
  • 0.1-0.3% over a few days
    • Short duration causes negligible climate effect
  • 0.1% over 11-year solar cycle
    • Small but detectable effect on climate
  • 0.1-0.3% over centuries (unknown)
    • Direct effect on climate (Maunder Minimum and Europe’s Little Ice Age)
long term change detection via instrument stability
Long-Term Change – Detection Via Instrument Stability

Current TSI instrument stabilities are comparable to desired long-term solar variability detection desired.

“Any plan based on non-interruption is broken.”

Jim Anderson, ASIC3 2006

long term change detection via instrument accuracy
Long-Term Change – Detection Via Instrument Accuracy

The better the absolute accuracy, the less time required for trend detection.

Good absolute accuracy frees data record from reliance on continuity.

“With absolute accuracy, time is on your side.”

Jim Anderson, ASIC3 2006

Times required for detection

Improved instrument accuracies speed trend detection

“Absolute accuracy provides perpetuity.”

Greg Kopp, CLARREO Mtg 2008

lasp science studies1
LASP Science Studies
  • Linked to IIP activities: determine requirements for CLARREO reflected-shortwave instruments.
  • Climate Benchmarking
    • Accuracy (needed to detect climate change signal)
      • Radiometric
      • Spectral
    • Stability
    • Spatial coverage and resolution
    • Spectral coverage and resolution
  • Cross-Calibration
    • Current and future instruments
    • Orbital requirements
climate fingerprinting water vapor feedback
Climate Fingerprinting: Water Vapor Feedback

0.4 kg/m2 per decade

Santer et al., PNAS, 2007.

spectral radiance water vapor retrieval

Sample CLARREO Slit Functions

Spectral Radiance: Water Vapor Retrieval

Define accuracy/stability requirements needed to detect trend in water vapor ~ 0.4 kg/m2 per decade.

examples of conjunction
Examples of conjunction

Linear model

Uniform prior

Resulting posterior is Gaussian

Vukicevic and Posselt, 2008, JAS

examples of conjunction1
Examples of conjunction

Exponential model

Resulting posterior is approximately Log-Normal

spectral decomposition information content
Spectral Decomposition: Information Content

http://www.silvereng.com/PDF/NEMO.pdf

slide20

SHEBA Downwelling Irradiance

Information Content

300 wavelengths ≠ 300 pieces of information

98% of the variance explained by first 6 components

  • 6 components ≠ 6 spectral bands (wavelengths)
  • Cloud absorption/ near-infrared ice reflectance (82%)
  • Molecular and aerosol scattering (8%)
  • Water vapor absorption (4%)
  • Spectral contributions not known a priori.

Rabbette and Pilewskie, JGR, 2002

sciamachy scanning imaging absorption spectrometer for atmospheric chartography
SCIAMACHY SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY
  • Radiometric Accuracy: <4%
  • Spatial Resolution
    • Limb vertical 3 x 132km
    • Nadir horizontal 32 x 215km
  • Swath Width (limb and nadir mode): 1000km (max)
  • Wavebands (UV-SWIR): 240-314, 309-3405, 394-620, 604-805, 785-1050, 1000-1750, 1940-2040 and 2265-2380nm
hyperspectral image projector for advanced sensor characterization
Hyperspectral Image Projector for Advanced Sensor Characterization

Spectrally Tunable Source

Digital Light Processing (DLP) projector

Brown et al., SPIE, 2006

radiometric accuracy and stability
Radiometric accuracy and stability
  • Study objective
    • Determine required instrument radiometric accuracy and stability levels for CLARREO in solar spectrum (Earth-reflected).
  • Approach
    • Using trend in water vapor feedback as “benchmark”, determine error in accuracy required for detection.
    • Examine other suitable variables.
spectral range and resolution
Spectral range and resolution
  • Study objective
    • Determine information content in hyperspectral (reflected solar) imagery.
  • Approach
    • Use Hyperion (and other candidate data sources such as AVIRIS, SCIAMACHY, etc.) to derive independent spectral modes of variability using PCA, SVD, EOF, etc.
    • Determine influence of spectral resolution and range on derived components.
spatial range and resolution
Spatial range and resolution
  • Study objective
    • Determine information content in hyperspectral (reflected solar) imagery.
  • Approach
    • Use Hyperion and SCIAMACHY to derive independent modes of variability at varying spatial scales.
    • Using broad-swath imagers (SCIAMACHY, potentially MODIS) to examine scales over which spectral variance is conserved.
cross calibrations with other instruments
Cross-Calibrations With Other Instruments
  • Improve calibrations of other instruments
    • Dependent on knowledge of instrument profile, including spectral and spatial sensitivity, polarization dependence, and stray light rejection
    • Instruments having greatest benefit are those with well-known instrument profiles and poor radiometric accuracy
  • Extend CLARREO spatial and temporal coverage via coverage from other instruments
    • These can improve CLARREO science requirements in RTM
    • Prioritize missions, particularly those with climate instruments in A-Train, able to extend CLARREO coverage
    • Instrument cross-calibration selection may influence CLARREO orbit latitude and altitude
      • Low-latitude orbits give more frequent but shorter duration cross-overs
      • Mis-matched altitudes give greatest cross-over observing times
orbit selection
Orbit Selection
  • Cross-overs with other instruments
  • Desired global spatial coverage
    • Polar orbit gives frequent polar coverage at expense of tropics
  • Desired repeat views of regions (repeat ground tracks) to determine temporal (seasonal) variations
    • Desired: an orbit such that nadir objects are viewed with the same solar angle (same LMT for ascending node crossing) (±10°) at least every season (~90 days)
    • Preliminary study includes effects of Earth’s oblateness, nodal precession
orbit phenomena
Orbit Phenomena
  • Sun Cycle (CS)
    • Orbit plane precesses as Earth revolves around the Sun
  • Terrestrial Cycle (CT)
    • Ground track shifts westwards as Earth rotates beneath and orbit precesses
analemma limits exact solar repeat angles
Analemma Limits Exact Solar Repeat Angles

6/21

2/11

~30’ (7.5°)

11/5

12/21

slide37

CS ~90 Days

CS ~45 Days

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5

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5

15

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9

15

9

6

2

3

2

7006 unique solutions likely represent >99% of all possible solutions in plotted range

credit: Ryan Woolley, LASP

orbit study summary

CS ~90 Days

1

5

4

5

15

3

4

6

9

15

9

6

2

3

2

CS ~45 Days

Orbit Study Summary
  • There are finite number of possible orbits giving repeat ground tracks and solar angles
    • Solutions linked to global coverage and overlap with other instruments