Aerosol Retrieval Algorithm for Meteosat Second Generation

1. Aerosol Retrieval Algorithm for Meteosat Second Generation Sam Dean, Steven Marsh and Don Grainger

2. Overview • Introduction • Defining aerosol properties • Optimal estimation • The forward model • Results • Summary

3. Introduction The University of Oxford has implemented modifications to the Enhanced Cloud Products (ECP) processor which facilitate the retrievals of aerosol optical depth and effective radius. A surface albedo perturbation is also retrieved. This code is intended for use on MSG SEVIRI data (Phil Watts) This talk will discuss the testing of the algorithm on data from ATSR-2 Knowledge of aerosol optical thickness is not only important for climate physics. Operational applications include health warnings and aircraft routing.

4. Introduction ATSR-2 is a good test dataset as the channels are comparable • MSG SEVIRI: • 0.6 mm • 0.8 mm • 1.6 mm • ATSR-2: • 0.67 mm • 0.87 mm • 1.6 mm

5. Aerosol Physical Properties • Aerosols distributions are characterised by: • Concentration (N) • Size distribution(refand s) • Shape (spherical) • Chemical composition (m = mr + imi) • Vertical profile

6. Aerosol Optical Properties With knowledge of these characteristics, required optical properties may be computed by applying Mie theory: Main retrieved parameter

7. Aerosol Model Aerosol Components Water-Soluble, Dust-Like, Soot, Sea Salt, Sulphate, Oceanic, Mineral + H2O Aerosol Types Clean or Average Continental, Urban, Clean Maritime, Maritime/Polluted, Desert Assumed vertical locations ~ 0-3 km

8. Aerosol Types The following nine aerosol types have been defined from the OPAC report: • Continental Clean • Continental Average • Continental Polluted / Biomass Burning • Desert • Mineral Transported • Maritime Clean • Maritime Polluted • Arctic • Antarctic

9. Optimal Estimation The retrieval method used is Optimal Estimation (OE). The basic principle of OE is to maximize the probability of the retrieved sate (x) conditional on the value of the measurement and any a priori information. OE is an iterative process which determines the most likely solution; this is equivalent to determining the state with the minimum value of cost, J(x). x= [Optical depth (0.55 μm), Effective radius, Surface albedo]

10. The Cost Function State mapped into measurement space Measurement Measurement error State A priori A priori error The forward model maps the state into measurement space – i.e. calculates y(x)

11. The Aerosol Forward Model • 32 DISORT layers are used to describe radiative transfer (MODTRAN provides gaseous absorption contribution. Rayleigh scattering included) • These layers extend from surface to 100 km in height (US Std Atmos) 100 km T=1 T=1 Rs 0 km Forward Model RT Calculations

12. The Aerosol Forward Model TBR2STDRFD RBD TBRsTD F=1 TB TBRsRFD Rs TOA reflectance given by infinite sum which can be expressed for state x and viewing geometry (θ0,θν,Ф) as:

13. Retrieval of a Scene

14. Aerosol Optical Depth Feb 1998

15. Summary • The ATSR-2 instrument has channels comparable to that of MSG SEVIRI • A retrieval scheme that retrieves aerosol properties from MSG SEVIRI data has been tested on ATSR-2 • A 32 layer radiative transfer model is used to estimate TOA reflectance for an atmosphere containing aerosol • Some results for February 1998 have been presented