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Satellite-Derived Atmospheric Motion Vectors

Tracking motions in sequences of satellite images to determine atmospheric motions and relate them to flow patterns in the atmosphere. Provides essential input to numerical weather prediction models.

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Satellite-Derived Atmospheric Motion Vectors

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  1. Satellite Derived Atmospheric Motion Vectors AOS 745 30 Nov 2009 Paul MenzelUW/CIMSS/AOS

  2. Tracking motions in sequences of images

  3. “the clouds moved - not the satellite” Verner Suomi In 1966, ATS-1's geostationary spin scan cloud camera provided full disk visible images of the earth and its cloud cover every 20 minutes

  4. Determination of atmospheric motions from sequences of • satellite images involves • image rectification • navigation • tracer identification • height assignment • displacement determination • vector validation • The ability to track clouds and relate them to flow patterns in the atmosphere has become an integral part of operations at the national forecast centers and an essential input to numerical weather prediction (NWP) models • Assimilation in NWP requires indication of AMV quality.

  5. ATS-1 pictures at 1417, 1440, 1503, 1526, 1549, and 1612 Hawaii Standard Time.

  6. A loop projector constructed by SMRP for use in cinegrammetric study of ATS cloud motions

  7. Cloud motion winds in various parts of a FL thunderstorm reveal that leading edge of drifting anvil cloud moves faster than central region of the cloud

  8. Meteosat introduced water vapor loops revealing atmospheric motion

  9. In 1979, three GOES, one Meteosat, and GMS were used in the First Global Atmospheric Research Program (GARP) Experiment (FGGE) to define atmospheric circulations. 15 Nov 1979

  10. An example wind set produced on McIDAS during FGGE

  11. Water vapor tracking provided winds where there were no clouds

  12. Water vapor winds derived from a sequence of half hourly Meteosat 6.7 micron images; from a data set developed by Verner Suomi

  13. NWP improved (from 82 to 92) appreciably; AMVs need rms errors less than 8 m/s to contribute 16 m/s 14 12 10 8 6 SH NH 82 84 86 88 90 92

  14. within 10 m/s of raobs within 8 m/s of raobs 7% slow bias of upper winds Flow diagram of evolved AMV algorithm Heights within 50 hPa 10 to 33% rejected after acceleration and nearest neighbor checks within 5 to 7 m/s of raobs

  15. Penalty Function Adjustments

  16. Cloud drift winds improved with GOES-8 Before After Velden, CIMSS

  17. GOS – Geo Atmospheric Motion Vectors Five geos are providing global coverage for winds in tropics and mid-lats

  18. Polar winds from MODIS

  19. Every 100 min MODIS covers polar regions Image loops enable feature tracking

  20. Leo coverage of poles every 100 minutes

  21. Tracking Polar Atmospheric Motion from Leo Obs

  22. NWP Considerations

  23. Satellite winds on GFDL Forecasts GOES-8 1km visible Soden et al. Former National Hurricane Center director Robert Sheets once said that if he had only one tool to do his forecasting job, it would be the geostationary weather satellite. http://www.usatoday.com/weather/news/2000/w330satann.htm

  24. Images of hurricanes help with intensity and track forecasts Wade, ORA & CIMSS

  25. Positive Impact NH Tropics SH in all regions From Kelly (1997)

  26. How well are AMVs meeting NWP requirements

  27. Excerpts from Global NWP SOG • Ongoing need for operational measurements from at least 2 polar orbiting • and 5 geostationary platforms. • NWP requires v(p) (especially in tropics) and T(p) and Q(p) with raob type accuracy over land and ocean. • NWP showed positive impact from recent addition of AMSUs • (adding stratospheric skill and cloudy sky soundings). • Increased coverage of aircraft data providing benefit, particularly from ascent/descent. • NWP awaiting high spectral resolution measurements from AIRS, IASI, & CrIS (for enhanced vertical resolution clear sky soundings). • Measurement of wind profiles most challenging • (remote sensing lidar systems offer promise, but need opportunity to mature). • NWP needs include surface pressure, snow equivalent water content, precipitation, and soil moisture. • Variational data assimilation techniques offer potential for improved exploitation of observations with high temporal frequency • (geo IR interferometer and microwave).

  28. Other AMV remote sensing

  29. Hurricane Dora QuikSCAT -- 10 August 1999 Global backscatter and ocean vector winds < 12 hour coverage M. H. Freilich COAS/OSU W. L. Jones UCF

  30. Lidar Wind Measurements:The Atmospheric Dynamics Mission (ADM-Aeolus)

  31. GIFTS Simulation of Hurricane BonnieWinds from Water Vapor Retrieval Tracking

  32. Inferring AMVs from Soundings

  33. Atmospheric Motions

  34. Geostrophic Flow

  35. Gradient Winds

  36. Thermal Wind Relation

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