12 th International Winds Workshop Copenhagen, 16-20 June 2014 Manuel Carranza Régis Borde Marie Doutriaux-Boucher Recent Changes in the Derivation of.

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Presentation transcript:

12 th International Winds Workshop Copenhagen, June 2014 Manuel Carranza Régis Borde Marie Doutriaux-Boucher Recent Changes in the Derivation of Geostationary AMVs at EUMETSAT

12 th International Winds Workshop Copenhagen, June 2014 Summary Introduction to EUMETSAT’s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work

12 th International Winds Workshop Copenhagen, June 2014 Meteosat-10: launched on 5 July 2012 as MSG-3, located at 0° East. It supports the Meteosat Prime Service (Full Earth Scanning, FES). Meteosat-9: launched on 21 December 2005 as MSG-2, located at 9.5° East. It supports the Rapid Scanning Service (RSS). Meteosat-8: launched on 28 August 2002 as MSG-1, located at 3.5° East. It is a backup for Meteosat-10 and, with lower priority, Meteosat-9. Meteosat-7: launched on 2 September 1997, located at 57.5° East. It supports the Indian Ocean Data Coverage (IODC) service. Introduction to EUMETSAT’s geostationary AMVs Meteosat satellites currently in operation

12 th International Winds Workshop Copenhagen, June 2014 Introduction to EUMETSAT’s geostationary AMVs MSG SEVIRI channels

12 th International Winds Workshop Copenhagen, June Tracking 2. Height assignment 3. Quality control Introduction to EUMETSAT’s geostationary AMVs AMV derivation process Target area, 24x24 pixels (32x32 HRVIS) TT + 15 min Search area, 80x80 pixels (96x96 HRVIS) ?

12 th International Winds Workshop Copenhagen, June 2014 Summary Introduction to EUMETSAT’s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work

12 th International Winds Workshop Copenhagen, June 2014 Recent changes Latest MSG MPEF releases Release 1.5.3, September 2012 Introduction of CCC method for the AMV height assignment  Statistics of AMVs improved at high and mid levels, degraded at low levels Patch for low-level winds, February 2013  Statistics of AMVs slightly better at low levels Release 1.5.4, September 2013 AMVs extracted at low levels in WV channels set to a poor QI Introduction of the best-fit pressure calculation Introduction of OCA product (2 layers, hourly), not yet used for AMVs

12 th International Winds Workshop Copenhagen, June 2014 Summary Introduction to EUMETSAT’s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Mathematical formulation

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Histograms of height frequency per channel IR10.8 WV6.2 WV7.3 VIS0.8 Old method used for AMV height assignment CCC method used for AMV height assignment

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Low-level cloud problem (hPa) (hPa) AMV height (pre-CCC) AMV height (pre-CCC) AMV height (CCC) AMV height (CCC) Normalised RMS difference between CCC and control experiment for 48- hour wind forecast at 850 hPa Negative impact

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Speed bias per channel for June 2012 RegionAlgorithm IR10.8VIS0.8WV6.2WV7.3 ALLHGHMIDLOW HGH MID GLO OPE CCC NH OPE CCC SH OPE CCC TRO OPE CCC

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Forecast profile comparison

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Low-level cloud solution: re-introduction of an inversion correction algorithm 1.Each pixel has a height provided by the CLA/CTH product 2.CCC computes an average height for each target area (based on the pixels that contribute the most to the tracking) 3.In case of temperature inversion, the height is set to 1/3 of the inversion strength above the inversion layer bottom 4.If the EBBT pressure is larger than the inversion corrected pressure, then the EBBT pressure is used instead

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Average CCC height (left) and height difference (right), Feb (QI > 85) IR10.8 VIS0.8

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Impact on QI of VIS0.8 low-level winds, Feb (QI > 85)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Long-term statistics (I) Number of AMVs, channel 02 (VIS 0.8 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Long-term statistics (II) Spatial vector consistency, channel 02 (VIS 0.8 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Long-term statistics (III) Forecast consistency, channel 02 (VIS 0.8 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Long-term statistics (IV) Number of AMVs, channel 05 (WV 6.2 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Long-term statistics (V) Number of AMVs, channel 06 (WV 7.3 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Long-term statistics (VI) Number of AMVs, channel 09 (IR 10.8 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Long-term statistics (VII) QI including forecast consistency, channel 09 (IR 10.8 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Long-term statistics (VIII) Number of AMVs, channel 12 (HRVIS)

12 th International Winds Workshop Copenhagen, June 2014 Summary Introduction to EUMETSAT’s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work

12 th International Winds Workshop Copenhagen, June 2014 Best-fit pressure Recommendation IWW Definition and computation (from MET Office) “The best-fit pressure is the height where the vector difference between the observed wind and the model forecast wind is the smallest.” 1.The U and V wind components are extracted from the forecast profile. 2.The minimum vector difference w.r.t. the model forecast is computed. 3.A parabolic fit is used in order to find the best-fit pressure. 4.The best-fit U and V components are computed by linear interpolation. 5.The best-fit pressure is valid only if: The minimum vector difference is smaller than 4 m/s. The vector difference is larger than the minimum plus 2 m/s outside the band +/- 100 hPa from the best-fit pressure.

12 th International Winds Workshop Copenhagen, June 2014 Best-fit pressure CCC – best-fit height, 5 September 2013 (I) Pressure (hPa) Best-fit bias (hPa) Number of AMVs VIS0.8 Wind too lowWind too high Wind too lowWind too high

12 th International Winds Workshop Copenhagen, June 2014 Best-fit pressure CCC – best-fit height, 5 September 2013 (II) Pressure (hPa) Best-fit bias (hPa) Number of AMVs WV6.2 Wind too lowWind too high Wind too lowWind too high

12 th International Winds Workshop Copenhagen, June 2014 Best-fit pressure CCC – best-fit height, 5 September 2013 (III) Pressure (hPa) Best-fit bias (hPa) Number of AMVs WV7.3 Wind too lowWind too high Wind too lowWind too high

12 th International Winds Workshop Copenhagen, June 2014 Best-fit pressure CCC – best-fit height, 5 September 2013 (IV) Pressure (hPa) Best-fit bias (hPa) Number of AMVs IR10.8 Wind too lowWind too high Wind too lowWind too high

12 th International Winds Workshop Copenhagen, June 2014 Summary Introduction to EUMETSAT’s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work

12 th International Winds Workshop Copenhagen, June 2014 Optimal Cloud Analysis (OCA) Introduction to OCA -OCA: Optimal Cloud Analysis algorithm developed at EUMETSAT. -Latest version uses two layers. Cloud-top height is computed for both. -Microphysics parameters are computed: cloud phase, cloud effective radius, cloud optical thickness, etc. CLA/CTH OCA/CTH

12 th International Winds Workshop Copenhagen, June 2014 Optimal Cloud Analysis (OCA) Cloud phase: CLA vs. OCA, 26 November 2013

12 th International Winds Workshop Copenhagen, June 2014 Optimal Cloud Analysis (OCA) Cloud-top pressure: CLA vs. OCA, 26 November 2013

12 th International Winds Workshop Copenhagen, June 2014 Optimal Cloud Analysis (OCA) AMV Intermediate Product: CLA vs. OCA, 26 November 2013

12 th International Winds Workshop Copenhagen, June 2014 Optimal Cloud Analysis (OCA) AMV statistics: CLA vs. OCA, 26 November Winds with vs. without applied inversion (whole globe) CLAOCADIFF Mean overall QI (inversion points) Mean forecast QI (inversion points) Mean overall QI (other points) Mean forecast QI (other points) – Central inversion area (lat. [-35º,5º], lon. [-20º,20º]) CLAOCADIFF Mean overall QI Mean forecast QI Jet area (lat. [5º,30º], lon. [-10º,30º]) CLAOCADIFF Mean overall QI Mean forecast QI

12 th International Winds Workshop Copenhagen, June 2014 Optimal Cloud Analysis (OCA) Use of microphysics parameters to set the AMV height The cloud geometrical depth can be estimated from OCA parameters: −Meerkötter and Bugliaro: Alternatively: −Liquid water path: −CCC weighted average optical thickness, ∆τ Cloud depth

12 th International Winds Workshop Copenhagen, June 2014 Summary Introduction to EUMETSAT’s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments: Optimal Cloud Analysis (OCA) Nested tracking Future work

12 th International Winds Workshop Copenhagen, June 2014 Nested tracking Algorithm principles (image courtesy of Bresky et al.) Small box nested within larger boxVector displacements

12 th International Winds Workshop Copenhagen, June 2014 Nested tracking Preliminary results: CCC vs. Nested Tracking (tracking case)

12 th International Winds Workshop Copenhagen, June 2014 Nested tracking Preliminary results: CCC vs. Nested Tracking (nominal case)

12 th International Winds Workshop Copenhagen, June 2014 Nested tracking Comparison between NESDIS and EUMETSAT winds, 1 to 7 November 2013 VIS10.8 All winds NESDIS (Nested tracking) 23x23 box (HH, HH+15, HH+30) No accel. / gross error checks EUMETSAT (CCC) 24x24 box (HH, HH+15, HH+30, HH+45) Vector RMSE (m/s) Standard Deviation (m/s) Speed Bias (m/s) Speed (m/s) Average pressure (hPa) Sample3323 VIS10.8 All winds NESDIS (Nested tracking) 19x19 box (HH-15, HH, HH +15) EUMETSAT (CCC) 24x24 box (HH, HH+15, HH+30, HH+45) Vector RMSE (m/s) Standard Deviation (m/s) Speed Bias (m/s) Speed (m/s) Average pressure (hPa) Sample4261

12 th International Winds Workshop Copenhagen, June 2014 Summary Introduction to EUMETSAT’s geostationary AMVs Recent changes: Cross-Correlation Contribution (CCC) method Best-fit pressure Current developments Optimal Cloud Analysis (OCA) Nested tracking Future work

12 th International Winds Workshop Copenhagen, June 2014 Future work MFG Introduction of CCC method for the AMV height assignment (December 2015) Divergence product (December 2015) MSG Use OCA to set AMV height (dependant on OCA availability every 15 min.) Change WV AMV height assignment in clear-sky conditions (December 2015) Continue investigation on nested tracking scheme MTG MTG FCI: prototyping activities using proxy data MTG IRS: revisit the potential of optical flow methods applied to humidity fields (IASI data and/or proxy data). External study should start in 2014

12 th International Winds Workshop Copenhagen, June 2014 Thank you!

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Spatial vector consistency, channel 05 (WV 6.2 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Forecast consistency, channel 05 (WV 6.2 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Spatial vector consistency, channel 06 (WV 7.3 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Forecast consistency, channel 06 (WV 7.3 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Spatial vector consistency, channel 09 (IR 10.8 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Forecast consistency, channel 09 (IR 10.8 µm)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Spatial vector consistency, channel 12 (HRVIS)

12 th International Winds Workshop Copenhagen, June 2014 Cross-Correlation Contribution (CCC) method Forecast consistency, channel 12 (HRVIS)