#### Transcript pres_meris_aatsr2005_v2

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 Dean2, Don Grainger2, Gareth Thomas2, Graham Ewen2 and Phil Watts1 Space Science and Technology Department Rutherford Appleton Laboratory UK 1. Now at EUMETSAT 2. Oxford University Caroline Poulsen ATSR-2 Group 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 Auxillary information • ECMWF T and q profiles • MODIS surface albedo Cloud Parameters Retrieved • Cloud top pressure/height • Cloud fraction • Cloud optical depth • Cloud effective radius • Cloud phase Aerosol Parameters Retrieved • Aerosol optical depth • Aerosol effective radius Comparing measurements with calculations: Ice, water and mixed phase ice water 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 Tmatrix (ray tracing); method for small crystals. Plates Columns Rosettes Aggregates Water clouds: spherical drops Mie theory: solution of electromagnetic equations on dielectric sphere 10 mm drop, 0.87 mm wavelength Size distribution Look up Tables 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. Cloud + Atmosphere/surface • Separate solar and ‘thermal’ models • Both embed cloud with precalculated radiative properties (LUTs) in clear atmosphere Tac(e.g. MODTRAN) t re pc (f) Solar model Rs Tbc Cloud emitted From e.g. RTTOV Transmitted Reflected Atmosphere emitted Rup Tac Rdown B(T(pc))e t re pc (f) Thermal model Rbc Inversion: Optimal estimation Guess a priori Calculate measurements Compare xo xb y(xn) J = [ym-y(xn)] Sy-1 [ym-y(xn)]T + [xn-xb] Sx-1 [xn-xb]T Adjust (minimise J) dx = - J’/J’’ Stop! dJ < 0.1 or (Newton’s Method) n>10 = 1D-Variational analysis. Same principles > 3D, 4D Var (assimilation) Cost Function Compare J = [ym-y(xn)] Sy-1 [ym-y(xn)]T + [xn-xb] Sx-1 [xn-xb]T J = [ym-y(xn)] Sy-1 [ym-y(xn)]T Where ym are the radiances, Sy the measurement error covariance and y(xn) the cloud parameters modelled into radiance space. + [xn-xb] Sx-1 [xn-xb]T Where Xb is the apriori and Sx the apriori covariance. Inversion: Optimal estimation Guess a priori Calculate measurements Compare xo xb y(xn) J = [ym-y(xn)] Sy-1 [ym-y(xn)]T + [xn-xb] Sx-1 [xn-xb]T Adjust (minimise J) dx = - J’/J’’ Stop! dJ < 0.1 or (Newton’s Method) n>10 = 1D-Variational analysis. Same principles > 3D, 4D Var (assimilation) Minimising J: optically thick cloud -No a priori, -0.55, 1.6 mm channels - t, Re only xo xsolution Retrieved Cloud Parameters Effective radius Fraction False colour Cloud top pressure Optical depth Error Analysis and Quality Control False colour Error Cloud top pressure Cost Ssolution = J’’ solution = (Sx-1 + KT.Sy-1K)-1 Validation Activities Re validation against MRF FSSP probe Optical depth (scaled to fit) Effective radius ATSR FSSP Hercules - ERS-2 Coincidence Validation at SGP 20th Oct. 1997 Microwave radiometer SGP ARM data courtesy of Roger Marchand. AATSR overpass17:26 Case study 20th October 1997 Effective radius LWP Optical Depth Parameter ATSR-2 SGP Optical depth 37.3 35.8 Effective radius 8.8 8.9 Liquid water path 244.0 209.8 SGP validation Mean: -0.08 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 nonraining, 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 depth ISCCP 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 end The ATSR cloud and aerosol algorithm was developed under funding from the following projects 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! Re validation against MRF FSSP probe Optical depth (scaled to fit) Effective radius ATSR FSSP Hercules - ERS-2 Coincidence Monthly Averaged Results May 1999 log10Optical depth May 1999 effective radius Water clouds: spherical drops Mie theory: solution of electromagnetic equations on dielectric sphere 10 mm drop, 0.87 mm wavelength Single particle Size distribution Cloud top pressure