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Cloud Model for operational Retrievals from MSG SEVIRI PM2, RAL, 17 Feb 2009

Cloud Model for operational Retrievals from MSG SEVIRI PM2, RAL, 17 Feb 2009 Non-linear Simulations. Overview of Non-linear scheme. Interfaces to both the fast LUT & Ref. FM

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Cloud Model for operational Retrievals from MSG SEVIRI PM2, RAL, 17 Feb 2009

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  1. Cloud Model for operational Retrievals from MSG SEVIRI PM2, RAL, 17 Feb 2009 Non-linear Simulations

  2. Overview of Non-linear scheme • Interfaces to both the fast LUT & Ref. FM • Similar measurement errors to the OCA scheme, including the same specification of homogeneity and corregistration errors (solar) • Allows channels to be optionally used “passively” in the retrieval i.e. all channels analysed but some may have infinite measurement error. • Clear-sky / surface state from ECMWF / MODIS • Marquadt-Levenberg iteration. Two convergence tests • |cost change| < 1 applying M-L. • |cost change| < 1 after a further Gauss-Newton step • If not attempt to continue with less M-L damping

  3. Measurement errors

  4. Overview of Non-linear scheme • Single layer mode: • State variables are • log10 cloud optical thickness (LCOT) • effective radius (RE), • cloud pressure (PC) • surface temperature (TC). • Cloud fraction is not retrieved and is currenlty always assumed to be 1. • Retrieval runs once assuming liquid cloud and once assuming ice cloud. • The case with lowest total cost is taken to represent the scene. • Initially, First guess = a priori. • If a retrieval converges successfully then it is used as first guess for the next pixel (for the same phase). A priori value ± error -1 ± 1000 50 ± 1000 μm 500 ± 1000 hPa ECMWF ± 1 K

  5. Overview of Non-linear scheme • Single layer mode: • State variables are • log10 cloud optical thickness (LCOT) • effective radius (RE), • cloud pressure (PC) • surface temperature (TC). • Cloud fraction is not retrieved and is currenlty always assumed to be 1. • Retrieval runs once assuming liquid cloud and once assuming ice cloud. • The case with lowest total cost is taken to represent the scene. • Initially, First guess = a priori. • If a retrieval converges successfully then it is used as first guess for the next pixel (for the same phase). A priori value ± error -1 ± 1000 50 ± 1000 μm 500 ± 1000 hPa ECMWF ± 1 K

  6. Retrieval experiments (so far) • “Three-pixel” cases similar to the linearisation points used for the linear retrieval simulations: • 3 pixels are retrieved simultaneously • (state vector of 7×3, measurement vector 11×3 elements). • One pixel contains two-layer cloud and the other two pixels contain either the upper or lower cloud layer only • Strong a priori correlation between variables in all 3 pixels is assumed • Options • single layer or two-layer • Channels: OCA standard ; OCA+3.9μm ; All. • PC or ZS retrieved • 2. Radiances simulated by SHDOM • 3. Real L1B data co-located with an A-train overpass

  7. Single-layer; standard channels

  8. Single-layer; standard + 3.9 µm channels

  9. Single-layer; all channels

  10. Two-layer; OCA channelsCorrelate High+Low PC+RE

  11. Two-layer; OCA channelsCorrelate High+Low ZC+RE

  12. Two-layer; OCA channels + 3.9µmCorrelate High+Low ZC+RE

  13. Two-layer; all channelsCorrelate High+Low ZC+RE

  14. Two-layer; OCA channelsCorrelate only high ZC+RE

  15. Two-layer; OCA + 3.9 µm channelsCorrelate only high ZC+RE

  16. Two-layer; all channelsCorrelate only high ZC+RE

  17. Conclusions • Non-linear single layer scheme functioning as expected • Two-layer scheme confirms potential for extracting info on two layers under certain circumstances • Further investigation of cases of improper convergence required before applying to more difficult scenes • Appears that • Retrieval of cloud altitude better behaved than pressure • Only correlating upper cloud height + RE is sufficient for extracting useful information on 2 layers • Use of 3.9 and / or water vapour channels is extremely helpful • But these will be difficult to exploit in practise...

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