1. Rutherford Appleton Laboratory 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 Wavelength μm Noise Sun-normalised rad. Homogeneity noise % Co-registration noise % 0.640.000270.752 0.810.000380.752 1.60.00911.5 Bright.Temp. /K 3.90.250.50.15 6.30.05 7.40.06 8.70.15 9.70.11 110.21 120.23 130.35

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

13. Two-layer; OCA channels Correlate High+Low PC+RE

14. Two-layer; OCA channels Correlate High+Low ZC+RE

15. Two-layer; OCA channels + 3.9µm Correlate High+Low ZC+RE

16. Two-layer; all channels Correlate High+Low ZC+RE

17. Two-layer; OCA channels Correlate only high ZC+RE

18. Two-layer; OCA + 3.9 µm channels Correlate only high ZC+RE

19. Two-layer; all channels Correlate only high ZC+RE

20. 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...