University of Oxford EUMETSAT Satellite Conference 2004 Aerosol Retrieval Algorithm for Meteosat Second Generation Sam Dean, Steven Marsh and Don Grainger

University of Oxford EUMETSAT Satellite Conference 2004 Overview Introduction Defining aerosol properties Optimal estimation The forward model Results Summary

University of Oxford EUMETSAT Satellite Conference 2004 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.

University of Oxford EUMETSAT Satellite Conference 2004 Introduction MSG SEVIRI: –0.6  m –0.8  m –1.6  m ATSR-2: –0.67  m –0.87  m –1.6  m ATSR-2 is a good test dataset as the channels are comparable

University of Oxford EUMETSAT Satellite Conference 2004 Aerosol Physical Properties Aerosols distributions are characterised by: Concentration (N) Size distribution (r ef and  ) Shape (spherical) Chemical composition (m = m r + im i ) Vertical profile

University of Oxford EUMETSAT Satellite Conference 2004 Aerosol Optical Properties With knowledge of these characteristics, required optical properties may be computed by applying Mie theory: Main retrieved parameter

University of Oxford EUMETSAT Satellite Conference 2004 Aerosol Model Aerosol Components Water-Soluble, Dust-Like, Soot, Sea Salt, Sulphate, Oceanic, Mineral Clean or Average Continental, Urban, Clean Maritime, Maritime/Polluted, Desert + H 2 O Aerosol Types Assumed vertical locations ~ 0-3 km

University of Oxford EUMETSAT Satellite Conference 2004 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

University of Oxford EUMETSAT Satellite Conference 2004 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]

University of Oxford EUMETSAT Satellite Conference 2004 MeasurementMeasurement errorState mapped into measurement space The Cost Function StateA prioriA priori error The forward model maps the state into measurement space – i.e. calculates y ( x )

University of Oxford EUMETSAT Satellite Conference 2004 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) RsRs T =1 0 km 100 km RT Calculations Forward Model

University of Oxford EUMETSAT Satellite Conference 2004 The Aerosol Forward Model RsRs R BD TBTB TBRsTDTBRsTD T B R 2 S T D R FD T B R s R FD TOA reflectance given by infinite sum which can be expressed for state x and viewing geometry (θ 0, θ ν, Ф) as: F=1

University of Oxford EUMETSAT Satellite Conference 2004 Retrieval of a Scene

University of Oxford EUMETSAT Satellite Conference 2004 Aerosol Optical Depth Feb 1998

University of Oxford EUMETSAT Satellite Conference 2004 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