TU4.T10.4.pptx

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TU4.T10.4.pptx

  1. 1. Spaceborne synthetic aperture radar (SAR) applications <br />in marine meteorology<br />Xiaofeng Li<br />NOAA/NESDIS<br />Acknowledgment<br />Zheng Weizhong and William Pichel, NOAA/NESDIS<br />Xiaofeng Yang, Institute of Remote Sensing Application, CAS<br />NOAA Gilmore Creek Ground Station, Alaska<br />
  2. 2. Outline<br />Motivation<br />SAR Observation and WRF model simulation of <br />Marine Atmospheric Boundary Layer (MABL) phenomena<br />Atmospheric Gravity Waves<br />Atmospheric Vortex Streets<br />Katabatic Winds <br />Storm pattern<br />Summary<br />
  3. 3. Motivation<br />SAR Provides high resolution (tens to hundreds m) all day/night synoptic observation of ocean surface.<br />Community weather model now provides time series of sub-km actual atmospheric circulation fields. <br />Synergy of the two to understand the actual atmospheric conditions that lead<br />To the generation and evolution of these phenomena in the MABL.<br />
  4. 4. SAR applications in Marine meteorology<br />atmospheric lee waves<br />atmospheric disturbances<br />atmospheric boundary layer rolls <br />atmospheric fronts<br />katabatic winds, gap winds<br />Hurricanes<br />island wakes<br />cold air outbreaks<br />Storms<br />atmospheric vortex streets<br />Land breeze<br />Cloud line <br />… <br />
  5. 5. 1. Atmospheric Gravity Wave Observations<br />Upstream Waves: Waves propagates against the mean flow <br />Lee wave has two types of wave patterns:<br />the transverse wave type where the wave crests are nearly perpendicular to the wind direction<br />(2) the diverging wave type where the wave crests are orientated outwards from the center of the wake. <br />
  6. 6. 1. Atmospheric Gravity Wave Observations<br />Upstream waves<br />
  7. 7. 1. Atmospheric Gravity Wave Observations<br />Upstream waves (2)<br />Wind variations are <br />between 8 (wave trough)<br />and 16 m/s (wave crest).<br />
  8. 8. 1. Atmospheric Gravity Wave Observations<br />Upstream waves 3<br />06/06/2001<br />@COPYRIGHT CSA<br />The number of wave crests increases from 3 to 8 in 4.5 hrs.<br />
  9. 9. 1. Atmospheric Gravity Wave Observations<br />Upstream AGW can be understood by solving the FeKdVModel with Froude number f=1.1<br />Li, et al. 2004, JGR<br />
  10. 10. 1. Atmospheric Gravity Wave Observations<br />Lee Wave (transverse waves)<br />A RADARSAT ScanSAR wide B SAR <br />Western Alaska<br />Image center (60N, 145W) <br />03:10:05 GMT, June 12, 2001. <br />(© Canadian Space Agency, 2001)<br />Wind Direction<br />
  11. 11. 1. Atmospheric Gravity Wave Observations<br />Wind Direction<br />Lee Wave (Diverging waves)<br />RADARSAT ScanSAR wide image shows the island lee wave pattern in the Gulf of Alaska. <br /><ul><li>Image center: (55N, 154W)
  12. 12. Time16:29:42 UTC, June 27, 1997. </li></ul>(© CSA, 1997)<br />
  13. 13. 1. Atmospheric Gravity Wave Observations<br />Lee Wave (diverging waves, Example 2)<br />Wind Direction<br />Constant lee wave phase lines<br />Chunchuzov, Vachon, and Li, <br />Remote Sensing of Environment, <br />74, 343-361, 2000.<br />
  14. 14. 1. Atmospheric Gravity Wave Observations<br />Lee Wave 3<br />
  15. 15. 1. Atmospheric Gravity Wave Observations<br />Lee wavesimulation with WRF<br />Weather Research and Forecasting<br />NCAR Community model<br />Two-way nested domains:<br />D1: 9km 280 x 251<br />D2: 3km 310 x 301<br />D3: 1km 271 x 262<br />Vertical levels: 27<br />Model parameterization: <br /><ul><li>Rapid RadiativeTransfer Model (RRTM) longwaveradiation
  16. 16. Dudhia shortwave radiation
  17. 17. YonseiUniversity (YSU) planetary boundary layer scheme
  18. 18. WRF Single Moment 3‐Class (WSM) cloudmicrophysics,
  19. 19. Kain‐Fritsch (new Eta) convective parameterization employed only in coarse and medium domains,
  20. 20. Monin‐Obukhov surface‐layer scheme
  21. 21. Noah land‐surface model.</li></ul>Boundary conditions are specified by linearly interpolating the NCEP 6 hourly final analyses (FNL) at a resolution of 1° × 1° degree.<br />The digital elevation of 30 s USGS topography data.<br />
  22. 22. 1. Atmospheric Gravity Wave Observations<br />WRF Simulation results at the imaging time<br />Vertical velocity<br />Horizontal velocity<br />
  23. 23. 1. Atmospheric Gravity Wave Observations<br />Lee Wave 3<br />Standing Waves<br />MODIS true color image acquired about 8.5 h prior to the <br />Envisat ASAR pass in Google Earth.<br />
  24. 24. 1. Atmospheric Gravity Wave Observations<br />Lee Wave 4Not trapped in the MABL <br />
  25. 25. 1. Atmospheric Gravity Wave Observations<br />Lee Wave 4<br />Comparison of WRF simulated surface wind speed with the independent SAR measurements<br />
  26. 26. 1. Atmospheric Gravity Wave Observations<br />AGW Summary<br />AGM patterns can be identified on SAR image.<br />Most of the waves generated by flow over bump observed by SAR are on the lee side of the bump, and has two types of wave patterns.<br />The terrain-forced stmospheric lee waves can be simulated using the WRF model. Model reveals evolution of the AGW.<br />Upstream waves happen when Froude number is close to 1. They are elevation waves. They are explained well with FeKDV model.<br />
  27. 27. 2. Atmospheric Vortex Streets (AVS)<br />Atmospheric vortex-street patterns on the lee side of a flow obstacle<br />double row of counter rotating vortex pairs shedding alternately near the edge of the obstacle<br />No observation in the atmosphere prior to the satellite age due to AVS scale (100-400 km): too small to be delineated by a synoptic observation network and too large to be observed by a single station<br /> h/a is the basic property of an atmospheric vortex streets<br />Uo is the wind speed, Ue is the vortex shedding velocity<br />
  28. 28. 2. Atmospheric Vortex Streets (AVS)<br />NASA space shuttle picture<br />cloud pattern (1)<br />NASA space shuttle picture<br />cloud pattern (2)<br />
  29. 29. 2. Atmospheric Vortex Streets (AVS)<br />Aircraft photo (Ferrier et al. Vol. 17, <br />No. 1, 1-8, International J. of Remote <br />Sensing, 1996)- sediment pattern<br />Landsat-7 (Defelice et al. <br />Vol. 81,No.5, 1047-1048, <br />BAMS, 2000)-cloud pattern<br />SAR Wind Pattern. IEEE/TGARSS<br />Feb. 2004<br />
  30. 30. ATMOSPHERIC VORTEX STREETS OBSERVED ON REMOTE SENSING IMAGES (3)<br />RADARSAT synthetic aperture radar (SAR) image (© CSA, 1999)<br />RADARSAT synthetic aperture radar (SAR) image (© CSA, 1999)<br />Li, et al. 2002, GRL<br />
  31. 31. 2. Atmospheric Vortex Streets (AVS)<br />SAR observation of AVS<br />Li, et al. 2008, Sensors<br />
  32. 32. 2. Atmospheric Vortex Streets (AVS)<br />WRF Simulation of AVS<br />Li, et al. 2008, Sensors<br />
  33. 33. 2. Atmospheric Vortex Streets (AVS)<br />WRF Simulation of AVS<br /><ul><li>Vortex shedding rate and propagation velocity
  34. 34. Reynolds Number (50-250)
  35. 35. Frude Number (0.15-0.4)
  36. 36. Vorticityand energy dissipation</li></li></ul><li>3. Katabatic Wind <br />katabatic wind is a gravitational air flow <br />that descends from a high-elevation <br />mountain down slope to lower elevation.<br />Happens night and early morning hours <br />in the winter season when the air mass <br />over the mountain top becomes colder <br />and heavier due to fast radiation cooling <br />and when the land-sea temperature <br />gradient is large.<br />
  37. 37. 3. Katabatic Wind <br />hourly coastal horizontal wind speed<br />
  38. 38. 3. Katabatic Wind <br />The black solid line is the topography; the red dash line represents the horizontal wind speed simulated by the model, the blue line represents the sea surface wind derived from SAR measurements, and the green dash line represents the near surface descending wind speed<br />
  39. 39. 3. Katabatic Wind <br /><ul><li>Summary:
  40. 40. The wind associated with the katabatic wind varies between 5 and 8 m/s
  41. 41. The katabatic wind finger extends up to 20 km offshore.
  42. 42. The 3-layer nested weather model simulation captures the finger-like katabatic wind pattern observed in the SAR image.
  43. 43. Katabatic wind is the strongest at 14:00 UTC when the land-sea temperature gradient is the largest.
  44. 44. The low-level model wind speed is very close to that derived from the SAR image.
  45. 45. The SAR image shows a more detailed finger-like katabatic wind structure than that shown in the
  46. 46. model simulation.</li></li></ul><li>4 Storm and Precipitation<br />
  47. 47. 4 Storm and Precipitation<br />
  48. 48. 4 Storm and Precipitation<br />
  49. 49. 4 Storm and Precipitation<br />Storm Summary:<br />WRF can capture wind and rain pattern in the storm <br />Heavy rain in the hurricanes damps NRCS. <br />Moderate rain has less effect in C-band NRCS than wind does <br />in the regular storm case<br />
  50. 50. 5 Summary<br />We have set up WRF model to simulate SAR observed MABL phenomena<br />WRF model captures the basic characteristics AGV, AVS, Katabatic and Storm<br />The 1-km resolution WRF model gives detailed structures of the phenomena <br />This leads to the understanding of generation and evolution of these phenomena<br />Synergy of SAR and WFR provides great opportunity for detailed study of MABL<br />Phenomena <br />

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