GIFTS/IOMI Cloudy/Aerosol Forward Model Development Steve Ackerman, Dave Tobin, Leslie Moy, host of others…. University of Wisconsin - Madison May 14 - 15, 2002 Review of overall goals The need to couple cloud/aerosol model to fast model transmittances Status of ongoing activities Detailed model Retrieval model

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.

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 ???

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

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

GIFTS/IOMI Data Cube Simulations

MODIS Example

Simulations

Simulations

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

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.