aerosol retrieval algorithm for meteosat second generation

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

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Aerosol Retrieval Algorithm for Meteosat Second Generation. Sam Dean, Steven Marsh and Don Grainger. Overview. Introduction Defining aerosol properties Optimal estimation The forward model Results Summary. Introduction. - PowerPoint PPT Presentation

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Page 1: Aerosol Retrieval Algorithm for Meteosat Second Generation

University of Oxford EUMETSAT Satellite Conference 2004

Aerosol Retrieval Algorithm for Meteosat Second Generation

Sam Dean, Steven Marsh and Don Grainger

Page 2: Aerosol Retrieval Algorithm for Meteosat Second Generation

University of Oxford EUMETSAT Satellite Conference 2004

Overview

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

Page 3: Aerosol Retrieval Algorithm for Meteosat Second Generation

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.

Page 4: Aerosol Retrieval Algorithm for Meteosat Second Generation

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

Page 5: Aerosol Retrieval Algorithm for Meteosat Second Generation

University of Oxford EUMETSAT Satellite Conference 2004

Aerosol Physical Properties

Aerosols distributions are characterised by:

• Concentration (N)

• Size distribution (ref and )

• Shape (spherical)

• Chemical composition (m = mr + imi)

• Vertical profile

Page 6: Aerosol Retrieval Algorithm for Meteosat Second Generation

University of Oxford EUMETSAT Satellite Conference 2004

Aerosol Optical Properties

0 0

( ) ( ) ( )( )e s adz dz

( )( ) ,

( )s

e

With knowledge of these characteristics, required optical properties may be computed by applying Mie theory:

cos ( , ) (cos )

( ), (cos )

P dg

P d

Main retrieved parameter

Page 7: Aerosol Retrieval Algorithm for Meteosat Second Generation

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

+ H2O

Aerosol Types

Assumed vertical locations ~ 0-3 km

Page 8: Aerosol Retrieval Algorithm for Meteosat Second Generation

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

Page 9: Aerosol Retrieval Algorithm for Meteosat Second Generation

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]

Page 10: Aerosol Retrieval Algorithm for Meteosat Second Generation

University of Oxford EUMETSAT Satellite Conference 2004

1

1

( ) ( ( ) ) ( ( ) )

( ) ( )

Tm y m

Tb x b

J x y x y S y x y

x x S x x

Measurement Measurement errorState mapped into measurement space

The Cost Function

State A priori A priori error

The forward model maps the state into measurement space – i.e. calculates y(x)

Page 11: Aerosol Retrieval Algorithm for Meteosat Second Generation

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)

Rs

T =1

T=1

0 km

100 km

RT Calculations Forward Model

Page 12: Aerosol Retrieval Algorithm for Meteosat Second Generation

University of Oxford EUMETSAT Satellite Conference 2004

The Aerosol Forward Model

00

( , ) ( , )( ) ( , , , )

1 ( )B D v s

BD vs FD

T x T x Ry x R x

R R x

Rs

RBD

TB

TBRsTDTBR2

STDRFD

TBRsRFD

TOA reflectance given by infinite sum which can be expressed for state x and viewing geometry (θ0, θν, Ф) as:

F=1

Page 13: Aerosol Retrieval Algorithm for Meteosat Second Generation

University of Oxford EUMETSAT Satellite Conference 2004

Retrieval of a Scene

Page 14: Aerosol Retrieval Algorithm for Meteosat Second Generation

University of Oxford EUMETSAT Satellite Conference 2004

Aerosol Optical Depth Feb 1998

Page 15: Aerosol Retrieval Algorithm for Meteosat Second Generation

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