1 / 44

Progress on cloud parameters from SEVIRI: i ) Multi-layer clouds ii) Use of HRV channel

Progress on cloud parameters from SEVIRI: i ) Multi-layer clouds ii) Use of HRV channel. Phil Watts EUMETSAT. Acknowledgements to Ralf Bennartz , Andy Walther and Frank Fell robust and user-friendly SEVIRI – ATrain software. Richard Siddans, Caroline Poulsen, Elisa Carboni

gyula
Download Presentation

Progress on cloud parameters from SEVIRI: i ) Multi-layer clouds ii) Use of HRV channel

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. Progress on cloud parameters from SEVIRI: i) Multi-layer cloudsii) Use of HRV channel Phil Watts EUMETSAT Acknowledgements to Ralf Bennartz, Andy Walther and Frank Fell robust and user-friendly SEVIRI – ATrain software Richard Siddans, Caroline Poulsen, Elisa Carboni Cloud Model Study for 2 Layer model Bryan Baum, Anthony Baran, ice scattering models Eva Borbas surface emissivity maps METEOSAT Workshop May 10-11 IfT Leipzig

  2. Talk Outline • The OE cloud retrieval scheme • Solution Cost Jm • Jm for single and multi-layer cloud • 2-layer retrievals and validation • Use of HRV • Detection • Use in OCA METEOSAT Workshop May 10-11 IfT Leipzig

  3. EUMETSAT OE cloud retrievals 0.6, 0.8, 1.6, 3.9, 6.3, 7.2 8.7, 9.6, 12, 13.4 mm Estimate: P, t0.55, re, f, Ts P, t0.55, re, f, Ts Prior: P, t0.55, re, f, Ts ECMWF Ts HRV f METEOSAT Workshop May 10-11 IfT Leipzig

  4. y X2 a ‘Ny > Nx over-constrained‘ ‘Ny = Nx evenly-constrained‘ y X2 a Advantage:When radiances do not fit.. Quality Control Disadvantage:When radiances do not fit.. No result! METEOSAT Workshop May 10-11 IfT Leipzig

  5. ym 1. Relate measurements, ym, to a ‘realistic’ model of the cloud, x, with a radiativetransfer model, y(x) Tac(e.g. LOWTRAN) x = t re pc f Tbc Rs EUMETSAT OE cloud retrievals 2. Find statexthat maximisesP(x|y) METEOSAT Workshop May 10-11 IfT Leipzig

  6. t, re, pc, f x = Tac Rac Rdown Bce tre pc f Rbc y(x) = tre pc f Scattering model DISORT - LUTs Rs Solar RT model Thermal RT model Tac [0.6, 0.8, 1.6, 3.9, 6.2, 7.3, 8.7, 9.7, 10.8, 12., 13.4 ] y = Scattering Properties Tbc y(x) – forward model METEOSAT Workshop May 10-11 IfT Leipzig

  7. ym 1. Relate measurements, ym, to a ‘realistic’ model of the cloud, x, with a radiative transfer model, y(x) Tac(e.g. LOWTRAN) x = t re pc f Minimise w.r.t.x: Tbc Rs in its simplest form: no prior and uncorrelated errors: EUMETSAT OE cloud retrievals 2. Find statexthat maximisesP(x|y) Use Bayes Theorem: P(x|y) ~ P(y|x).P(x) Radiance information Prior information METEOSAT Workshop May 10-11 IfT Leipzig

  8. large residuals And when this is the result, then the cloud model might be appropriate small residuals High Jm When this is the result, then something is wrong Low Jm OE is minimisation of this: METEOSAT Workshop May 10-11 IfT Leipzig

  9. Error Analysis: simple graphical example Thick cloud.. -No prior, -0.55, 1.6mmchannels - t, Re only METEOSAT Workshop May 10-11 IfT Leipzig

  10. Error Analysis: simple graphical example Thin cloud.. -No prior, -0.55, 1.6mmchannels - t, Re only METEOSAT Workshop May 10-11 IfT Leipzig

  11. Applies to all products in a pixel. Use first. Most important for cloud scenes. “Measurement cost” Applies to products individually. Less important for cloud scenes. “Expected error” METEOSAT Workshop May 10-11 IfT Leipzig

  12. Solution cost and QC METEOSAT Workshop May 10-11 IfT Leipzig

  13. SEVIRI and ancillary data • Coincident with Atrain overpass (bracketing times) • Detailed studies: individual overpasses • Full sample: 17 overpasses, Aug 2006 • 9 SEVIRI channels • 0.6, 0.8, 1.6, 6.2, 7.3, 8.7, 10.8, 12.0, 13.4 mm • ECMWF temperature, humidity and ozone profiles, 0.25o / 3 hourly • +RTTOV as IR channel RTM; +Bennartz SW channel RTM • CIMSS land surface emissivity (monthly) • EUMETSAT operational cloud masks • EUMETSAT operational clear sky reflectance maps METEOSAT Workshop May 10-11 IfT Leipzig

  14. A Train Validation tool: AVACS • A Train active instruments, vertical cloud profiles, comprehensive co-location with SEVIRI, large instrument suite. METEOSAT Workshop May 10-11 IfT Leipzig

  15. Solution cost as quality control METEOSAT Workshop May 10-11 IfT Leipzig

  16. Solution cost as quality control METEOSAT Workshop May 10-11 IfT Leipzig

  17. Jm < 90 No QC Solution cost as quality controlUse of a) Jmb) Sx(pc) Jm < 90 & Sx(Pc) < 5hPa METEOSAT Workshop May 10-11 IfT Leipzig

  18. 2.27 Km - no QC > 0.8 Km - no QC > Solution cost as quality control 1.31 Km - no QC > 0.43 Km - no QC > METEOSAT Workshop May 10-11 IfT Leipzig

  19. Multi-layer cloud METEOSAT Workshop May 10-11 IfT Leipzig

  20. Multi-layer Cloud Radar (CPR) …..OCA ice…… OCA water …… OCA Reject (upper plot) METEOSAT Workshop May 10-11 IfT Leipzig

  21. IR-only more sensitive for thin upper layer IR-only less sensitive in for medium upper layer Multi-layer Cloud Radar (CPR) … Radar (CPR)… MODIS…..OCA ice…… OCA water …. OCA Jm reject METEOSAT Workshop May 10-11 IfT Leipzig

  22. V.Low underlying water cloud: IR-only:- upper Mid-L underlying water cloud: IR-only:- intermediate VIS IR combined e.g. OCA Single layer ice cloud: IR/VIS:- upper Single layer ice cloud: IR-only:- upper V.Low underlying IR/VIS:- intermediate Mid-L underlying IR/VIS:- intermediate Multi-layer Cloud IR only e.g. MODIS METEOSAT Workshop May 10-11 IfT Leipzig

  23. Multi-layer Jm ~600 :- approx 100 per channel:- S/N of 10 left to unravel 2-layer system RAL/Oxford Study Multi-layer Cloud METEOSAT Workshop May 10-11 IfT Leipzig

  24. VIS + IR Measurement residuals (9) y-y(x) Low solution J CTP 0.0 iteration Tskin Single layer cloud – single layer model Reality Model COT Reff METEOSAT Workshop May 10-11 IfT Leipzig

  25. VIS + IR Measurement residuals (9) High solution J CTP y-y(x) COT Reff 0.0 iteration Tskin Two layer cloud – single layer model Reality Model Now: don’t reject but Re-run using 2-layer model >> METEOSAT Workshop May 10-11 IfT Leipzig

  26. IR only Measurement residuals (9) y-y(x) Low solution J CTP 0.0 COT Reff iteration X = [ t, reff, pc, f, Tskin] Two layer cloud – Tskin as proxy lower CTT Reality Model Black lower boundary Tskin Tskin = proxy black cloud CTT_L METEOSAT Workshop May 10-11 IfT Leipzig

  27. CTP_L COT_L= (COT_VIS+IR COT_U) CTT_L Tskin = CTT_L adjust for transparency (COT_L) Two layer cloud – proxy two layer model COT_u Reff_u CTP_u Upper level (ice) properties Grey lower level cloud METEOSAT Workshop May 10-11 IfT Leipzig

  28. Channel noise for 2-Layer • 0.6 Nominal • 0.8 Nominal • 1.6 Nominal • 3.9(not currently used – but getting close?) • 6.2 – use @ noise = 0.4 K • 7.3 – use @ noise = 0.4 K • 8.7 Nominal • 9.6not used • 10.8 Nominal • 12.0 Nominal • 13.4 Nominal WV channels necessary! METEOSAT Workshop May 10-11 IfT Leipzig

  29. CloudSat CPR CPR CLDCLASS product SEVIRI True RGB Retrievals SEVIRI 2L upper MODIS +30 hPa QC (valid upper layer only) SEVIRI 2L Lower SEVIRI 1L (water) 2-Layer CTP Examples 1: CPR orbit 1415 METEOSAT Workshop May 10-11 IfT Leipzig

  30. CloudSat CPR SEVIRI True RGB Retrievals 2-Layer CTP Examples 1: CPR orbit 1415 SEVIRI Lower COT SEVIRI Upper COT METEOSAT Workshop May 10-11 IfT Leipzig

  31. CALIOP SEVIRI Cost 2-Layer CTP Examples 2: CPR orbit 1705 METEOSAT Workshop May 10-11 IfT Leipzig

  32. 2-Layer CTP Examples 3: CPR orbit 11318 METEOSAT Workshop May 10-11 IfT Leipzig

  33. 2-Layer CTP validation – 2nd layer analysis METEOSAT Workshop May 10-11 IfT Leipzig

  34. All cases +Re-assigned 2-Layer CTP validation2L Upper layer only +Re-assigned +Jm50 +Spc30 +Re-assigned +Jm50 METEOSAT Workshop May 10-11 IfT Leipzig

  35. All cases +Jm50 2-Layer CTP 2L Lower layer – Re-assigned pixels +Jm50+Spc30 METEOSAT Workshop May 10-11 IfT Leipzig

  36. All cases +Jm50 2-Layer CTP validation2L Lower layer – CPR 2nd layer heights +Jm50+Spc30 METEOSAT Workshop May 10-11 IfT Leipzig

  37. When Cost =/= ML e.g. (1) Single layer OCA Scene east of Madagascar > High view angle Aerosol? 2 layer OCA METEOSAT Workshop May 10-11 IfT Leipzig

  38. When Cost =/= ML e.g. (2) Central section not strictly multi-layer, but highly diffuse upper cloud > high Jm 2 Layer models the diffuse aspects Lower layer properties open to interpretation METEOSAT Workshop May 10-11 IfT Leipzig

  39. Multi-layer cloud: Summary • Solution cost in scene Quality Control • Flags most Multi-Layer situations • Flags also diffuse top situations • Flags “other” situations tbd..& td reduced by improved RT? • Recovery of 2 layer information in ML cloud • Recovers accurate upper layer and plausible lower CTP • Improves diffuse top situations • Todo: Implement proper scheme! Re-introduce VIS in 2Layer.. • Todo: Validate 2L COTs METEOSAT Workshop May 10-11 IfT Leipzig

  40. SEVIRI 0.6,0.8,1.6 HRV CFR .. by counting .. by ratio HRV for fraction F.Guess. HRV cloud detection + Time constrained METEOSAT Workshop May 10-11 IfT Leipzig

  41. HRV for fraction F.Guess. OCA Cost – control Mean 61 OCA Cost – HRV CFR by Counting Mean 48 OCA Cost – HRV CFR by Ratio Mean 29 METEOSAT Workshop May 10-11 IfT Leipzig

  42. HRV for fraction: Validation with A-Train CPR METEOSAT Workshop May 10-11 IfT Leipzig

  43. HRV for fraction: Validation with A-Train CPR f=0 f=HRV METEOSAT Workshop May 10-11 IfT Leipzig

  44. Thank you for your attention! DLR Oberpfaffenhofen 4 Mar 2011

More Related