Caroline Poulsen ATSR-2 Group Cloud parameters estimated by variational analysis of visible and infrared measurements from ATSR-2 Caroline Poulsen, Richard Siddans, Barry Latter and Brian Kerridge, Chris Mutlow, Sam Dean 2, Don Grainger 2, Gareth Thomas 2, Graham Ewen 2 and Phil Watts 1 Space Science and Technology Department Rutherford Appleton Laboratory UK 1.Now at EUMETSAT 2.Oxford University

Outline Why use ATSR? Why Variational Analysis? Forward Model Examples Validation Level 3 products Future

ATSR Channels ATSR2/AATSR 0.55um 0.67um 0.87um 1.6um 3.7um 11um 12um Cloud Parameters Retrieved Cloud top pressure/height Cloud fraction Cloud optical depth Cloud effective radius Cloud phase Auxillary information ECMWF T and q profiles MODIS surface albedo Aerosol Parameters Retrieved Aerosol optical depth Aerosol effective radius

Comparing measurements with calculations: Ice, water and mixed phase water ice

Why use Optimal Estimation? Basic principle is to maximise the accuracy the retrieved cloud parameters based on the measurements and any ‘apriori’ Allows us to characterise the error in each cloud parameter under the assumption of a reasonably plane parallel cloud model It’s a very flexible approach that enables us to utilise any prior information, for example on cloud fraction. All the clear sky atmospheric effects can be derived from NWP profiles. Allows us to utilise ALL the information in the measurements for each channel contributes to a greater or lesser extent to the retrieval of individual cloud parameters.

Forward Model

Ice clouds: complex particles Currently uses a combination of geometric optics (ray tracing); for large ice crystals and a T- matrix (ray tracing); method for small crystals. Plates Columns Rosettes Aggregates

Water clouds: spherical drops Mie theory: solution of electromagnetic equations on dielectric sphere Size distribution 10  m drop, 0.87  m wavelength Since real time calculations of cloud radiative properties are too slow calculations are made once DISORT (plane-parallel) model and incorporating rayleigh scattering and stored in easily accessible Look up Tables. Look up Tables

T bc T ac (e.g. MODTRAN) Cloud + Atmosphere/surface Separate solar and ‘thermal’ models Both embed cloud with precalculated radiative properties (LUTs) in clear atmosphere  r e p c (f) Solar model RsRs

T ac From e.g. RTTOV  r e p c (f) Thermal model Transmitted R bc Cloud emitted B(T(p c ))  Reflected R down Atmosphere emitted R up

Inversion: Optimal estimation Guessxoxo Calculate measurementsy(x n ) Adjust (minimise J)  x = - J’/J’’ (Newton’s Method) Stop!  J 10 CompareJ = [y m -y(x n )] S y -1 [y m -y(x n )] T a priorixbxb + [x n -x b ] S x -1 [x n -x b ] T = 1D-Variational analysis. Same principles > 3D, 4D Var (assimilation)

Cost Function CompareJ = [y m -y(x n )] S y -1 [y m -y(x n )] T + [x n -x b ] S x -1 [x n -x b ] T J = [y m -y(x n )] S y -1 [y m -y(x n )] T Where y m are the radiances, S y the measurement error covariance and y(x n ) the cloud parameters modelled into radiance space. + [x n -x b ] S x -1 [x n -x b ] T Where X b is the apriori and S x the apriori covariance.

Inversion: Optimal estimation Guessxoxo Calculate measurementsy(x n ) Adjust (minimise J)  x = - J’/J’’ (Newton’s Method) Stop!  J 10 CompareJ = [y m -y(x n )] S y -1 [y m -y(x n )] T a priorixbxb + [x n -x b ] S x -1 [x n -x b ] T = 1D-Variational analysis. Same principles > 3D, 4D Var (assimilation)

Minimising J: optically thick cloud xoxo x solution -No a priori, , 1.6  m channels - , R e only

Retrieved Cloud Parameters Optical depth Effective radius Fraction Cloud top pressure False colour

Error Analysis and Quality Control Cost S solution = J’’ solution = (S x -1 + K T.S y -1 K) -1 Error Cloud top pressure False colour

Validation Activities

R e validation against MRF FSSP probe Optical depth (scaled to fit) Effective radius Hercules - ERS-2 Coincidence FSSP ATSR

Validation at SGP 20 th Oct AATSR overpass17:26 Microwave radiometer SGP ARM data courtesy of Roger Marchand.

Case study 20 th October 1997 ParameterATSR-2SGP Optical depth Effective radius Liquid water path Effective radius LWP Optical Depth

SGP validation Mean: Stdev: 1.21 Liquid water path is calculated using the technique of Frisch et al, J. Atmos Sci. 1995, the technique is only valid for non- raining, water clouds. Optical depth calculated using Han et al J. Atmos Sci.,1995. Errors shown are the standard deviation of the matches used.

Validation of CTH Chilbolton 94GHz Galileo Radar

Comparison with ISCCP data ATSR-2 May 1999 Optical depthISCCP Optical depth May 1999

Level 3 products

Cloud top pressure

Cloud optical depth

Cloud effective radius

Cloud fraction

Summary and plans 6 years of ATSR-2 data processed at 3x3km resolution and a variety of level 3 products Version 2 to begin soon with many improvements Potential is there to use information from other satellites Dual view tomographic cloud retrieval Extension to AATSR- long time series More validation, comparison with met. Office models

The ATSR cloud and aerosol algorithm was developed under funding from the following projects The end

QC: Summary Model adequate (J<1) –Expected errors, S parameter dependent state dependent Information for assimilation (Discussed today Not discussed) Model inadequate (J>1) –A priori out of range rogue values –Measurements out of range calibration errors rogue values –Model out of range multi-layer cloud shadows incorrect ice crystals incorrect surface reflectance incorrect statistical constraints

Retrieval (inversion): required steps “Forward modelling”: –Optical properties of average particle in ‘single scattering’ event –Optical properties of a cloud of particles: multiple scattering –Interaction of cloud radiative processes with atmosphere and surface –y = y(x) “Inverse modelling”: –x = ? (y) –Guess cloud conditions (x) –Calculate radiances y(x) –Compare to measurements –Change cloud conditions Stop!

R e validation against MRF FSSP probe Optical depth (scaled to fit) Effective radius Hercules - ERS-2 Coincidence FSSP ATSR

Monthly Averaged Results May 1999 log 10 Optical depthMay 1999 effective radius

Water clouds: spherical drops Single particle Mie theory: solution of electromagnetic equations on dielectric sphere Size distribution 10  m drop, 0.87  m wavelength

Cloud top pressure