Retrieval of thermal infrared cooling rates from EOS instruments

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Retrieval of thermal infrared cooling rates from EOS instruments

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Retrieval of thermal infrared cooling rates from EOS instruments

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Retrieval of thermal infrared cooling rates from EOS instruments

Daniel Feldman

Thursday IR meeting

January 13, 2005

- Introduction
- Methodology
- Clear vs. Scattering
- Instrumentation questions
- Representative scenarios

- State vector components are frequently retrieved to derive standard products
- We intend to explore in detail infrared cooling rate retrievals in clear and scattering atmospheres using EOS instruments:
- AIRS
- TES
- MODIS/MISR

- Closure of infrared radiation balance for input to regional-scale models
- Evaluate the direct forcing of mineral dust in the infrared via direct measurement.
- Ultimately improve parameterizations of treatment of radiation in regional-scale models.

- Cooling rate retrieval:
- Liou and Xue (1988)
- Liou (2002)

- AIRS dust:
- X. Huang (JGR 2004)
- Thomas (AGU)
- Pierangelo (ACP 2004)

- Analytic expression derives spectral and band radiance as a Fredholm integral of cooling rate profile and kernel transmittance function.

- Utilize either Goody random model or correlated-k
- Transmittance function assumes constant form over spectral and band regions
- Planck function for band equals Planck function for spectral channel.

- Clear-sky calculations only, transmission function takes simple form

Project Flow Chart

- Heating/cooling rate profile retrieval methods show distinct differences compared to standard retrievals
- Standard retrieval performs an inversion of the forward model mapping state vector to radiances.
- Given full radiance field, heating rate calculation is trivial
- Challenge of heating/cooling rate retrieval involves determining spectral and channel information to perform forward model heating/cooling rate calculation.

- Utilize LBRTM with RADSUM
- For faster calculations, use Modtran 5
- Develop framework for cooling rate retrieval
- Test cooling rate retrieval algorithm for H2O (800-960) using AIRS scan pattern

- Perform retrieval test by first deriving a state vector and then deriving the cooling rate.

- Included Volz description of dust indices of refraction and tri-model log-normal distribution of aerosols per Seinfeld and Pandis (AOD ~ 1)

- Doubling-adding module on top of LBLRTM called CHARTS
- User-supplied spectral functions for Modtran 5
- Derivation by Liou and Xue no longer valid because source function is not Planck function.
- What are valid assumptions that can be made about source function?

- Composition
- Sokolik et al.

- Phase function/sphericity
- Spatial/height distribution
- Pierangelo et al.
- Mahowald

- Particle Size Distribution
- MODIS/MISR products

- AERONET validation
- Thomas

- Use Modtran 5 to develop a cooling rate retrieval program similar to that described by Liou.
- Need validation with AIRS spectra
- Use of DISORT option
- Problems with sertran parameters

- Test out program sensitivity to dust layer using range of dust fields provided by Mahowald.

- Create cooling rate jacobians with respect to standard state vector
- Look at variation in band radiance with respect to view angle
- Explore band radiance variations with respect to state components
- Effect of uncertainty in measurements and state components (chain rule)

I = radiance

x = state vector

T = heating/cooling (h/c) rate

z = height coordinate

k = state vector component index

j = channel index

n = matrix index for h/c rate designation

- AIRS vs. TES
- TES has coverage over bright surfaces
- AIRS radiances are better validated

- Surface emissivity
- MODIS 5km land emissivity map?

- Role of AERONET for validation