1. GIFTS/IOMI Cloudy/Aerosol Forward Model Development
Steve Ackerman, Dave Tobin, Leslie Moy, host of others….
University of Wisconsin - Madison
May , 2002
Review of overall goals
The need to couple cloud/aerosol model to fast model transmittances
Status of ongoing activities
Detailed model
Retrieval model

2. GIFTS/IOMI Aerosol Laden Forward Model Development
Relationship to MURI Objectives:
The primary objective of this processing algorithm is to detect the presence of suspended matter such as dust, sand, volcanic ash, or smoke, especially near the surface and in the boundary layer. Detection of suspended matter is based on the spectral variations of these aerosol radiative properties; this approach has been moderately successful using radiometric measurements with low spectral resolution in the infrared wavelengths, and will significantly be improved with high-spectral resolution observations.
Modeling is important for understanding the physics behind the algorithms, but observations are required to truly test and verify the techniques.
Brief Scientific Description of Work:
Identify specific radiative signatures of aerosols in high-spectral resolution observations. The basic tasks include 1) perform a series of trade studies designed to determine the accuracy of the existing fast models and to determine ways to improve the existing model, 2) perform detailed forward modeling to interpret the aerosol signatures spectra; 3) development of detection and aerosol retrieval algorithms for use with high-spectral observations
Plans as they pertain to MURI Goals:
An accurate and fast cloud/aerosol laden sky forward model is required for producing simulated GIFTS/IOMI data and for several of the retrieval algorithms under development. Coordination with the clear-sky modeling is required to assure consistency.

3. Generic Fast Model Production Flowchart:
Profile
Database
Fixed Gas
Amounts
Spectral line
parameters
Lineshapes
& Continua
Layering, l
Compute monochromatic
layer-to-space
transmittances
Reduce to sensor’s
spectral resolution
Effective Layer
Optical Depths, keff
Convolved
Layer-to-Space
Transmittances, tz (l)
Fast Model
Predictors, Qi
Fast Model
Regressions
Fast Model
Coefficients, ci
R = ( esB(Ts) + rs ) tz(L) + Sl=1:L B(T(l)) (tz (l -1) - tz (l) )
keff = -ln (teff ) = Si=1:N ci Qi teff (l) = tz (l) / tz (l -1)
Cloud Optical
Depths ???

4. Reflectance (R), Transmittance (T ),
Observed Radiances
Retrieval Models
Reflectance (R), Transmittance (T ),
Absorptance (A )[Emittance( )]
Temperature and Gas
Incident Fluxes
Single Scattering
Properties
  , º , P(, ´)
Macrophysical properties
Cloud or aerosol Microphysics
n(r), n(h), m=mr-imi
Forward Models
The retrieval cycle

5. Current GIFTS/IOMI Cloud/Aerosol RTE Model Activities:
Super-window correlated-K DISORT calculations: Used in retrieval of cloud/aerosol properties.
Fast model with cloud effective emissivity.
Doubling/Adding model that uses LBLRTM layer optical depths, CPU intensive but needed for error studies.
Another approach?
Single Scattering Properties

8. GIFTS/IOMI Data Cube Simulations

10. MODIS Example

11. Simulations

12. Simulations

13. Example from cloud application (Antonelli, Nasiri, Baum, Yang,…

14. Current On-going Activities:
Examine differences in full-blown multiple scattering model (doubling/adding with LBLRTM monochromatic optical) with fast-model approximate methods.
Quantify spectral errors in the fast-model approaches with respect to expected errors in retrieval objectives and as a function of aerosol micro-phyiscal and macro-physical properties
Select appropriate radiative transfer model for aerosol detection and property retrievals (effective emissivity, super-window, some combination).
Coordinate activities with the ‘clear-sky folks’ to get appropriate profile databases in the fast model production.