1 / 42

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

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

elwyn
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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 Now at EUMETSAT Oxford University

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

  3. ATSR2/AATSR 0.55um 0.67um 0.87um 1.6um 3.7um 11um 12um ATSR Channels • 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 • Auxillary information • ECMWF T and q profiles • MODIS surface albedo

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

  5. 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. Why use Optimal Estimation?

  6. Forward Model

  7. Plates Columns Rosettes Aggregates 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.

  8. Size distribution Water clouds: spherical drops Mie theory: solution of electromagnetic equations on dielectric sphere 10 mm drop, 0.87 mm wavelength 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.

  9. Tac(e.g. MODTRAN) Tbc Rs Cloud + Atmosphere/surface • Separate solar and ‘thermal’ models • Both embed cloud with precalculated radiative properties (LUTs) in clear atmosphere Solar model tre pc (f)

  10. Atmosphere emitted Reflected Cloud emitted Transmitted Tac Rup Rdown B(T(pc))e Thermal model tre pc (f) Rbc From e.g. RTTOV

  11. Guess a priori xo xb Calculate measurements y(xn) Compare J = [ym-y(xn)] Sy-1 [ym-y(xn)]T + [xn-xb] Sx-1 [xn-xb]T dx = - J’/J’’ (Newton’s Method) Adjust (minimise J) dJ < 0.1 or n>10 Stop! Inversion: Optimal estimation = 1D-Variational analysis. Same principles > 3D, 4D Var (assimilation)

  12. 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.

  13. Guess a priori xo xb Calculate measurements y(xn) Compare J = [ym-y(xn)] Sy-1 [ym-y(xn)]T + [xn-xb] Sx-1 [xn-xb]T dx = - J’/J’’ (Newton’s Method) Adjust (minimise J) dJ < 0.1 or n>10 Stop! Inversion: Optimal estimation = 1D-Variational analysis. Same principles > 3D, 4D Var (assimilation)

  14. xsolution Minimising J: optically thick cloud -No a priori, -0.55, 1.6mmchannels - t, Re only xo

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

  16. Error Analysis and Quality Control False colour Error Cloud top pressure Cost Ssolution = J’’ solution = (Sx-1 + KT.Sy-1K)-1

  17. Validation Activities

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

  19. Validation at SGP 20th Oct. 1997 Microwave radiometer AATSR overpass17:26 SGP ARM data courtesy of Roger Marchand.

  20. Case study 20th October 1997 LWP Effective radius Optical Depth

  21. SGP validation Mean: -0.08 Stdev: 1.21 Optical depth calculated using Han et al J. Atmos Sci.,1995. Errors shown are the standard deviation of the matches used. 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.

  22. Validation of CTH Chilbolton 94GHz Galileo Radar

  23. Comparison with ISCCP data ATSR-2 May 1999 Optical depth ISCCP Optical depth May 1999

  24. Level 3 products

  25. Cloud top pressure

  26. Cloud optical depth

  27. Cloud effective radius

  28. Cloud fraction

  29. 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

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

  31. 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 QC: Summary

  32. “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 Retrieval (inversion): required steps Stop!

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

  34. Monthly Averaged Results May 1999 log10Optical depth May 1999 effective radius

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

  36. Cloud top pressure

More Related