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TU4.T10.4.pptx Presentation Transcript

  • 1. Spaceborne synthetic aperture radar (SAR) applications
    in marine meteorology
    Xiaofeng Li
    NOAA/NESDIS
    Acknowledgment
    Zheng Weizhong and William Pichel, NOAA/NESDIS
    Xiaofeng Yang, Institute of Remote Sensing Application, CAS
    NOAA Gilmore Creek Ground Station, Alaska
  • 2. Outline
    Motivation
    SAR Observation and WRF model simulation of
    Marine Atmospheric Boundary Layer (MABL) phenomena
    Atmospheric Gravity Waves
    Atmospheric Vortex Streets
    Katabatic Winds
    Storm pattern
    Summary
  • 3. Motivation
    SAR Provides high resolution (tens to hundreds m) all day/night synoptic observation of ocean surface.
    Community weather model now provides time series of sub-km actual atmospheric circulation fields.
    Synergy of the two to understand the actual atmospheric conditions that lead
    To the generation and evolution of these phenomena in the MABL.
  • 4. SAR applications in Marine meteorology
    atmospheric lee waves
    atmospheric disturbances
    atmospheric boundary layer rolls
    atmospheric fronts
    katabatic winds, gap winds
    Hurricanes
    island wakes
    cold air outbreaks
    Storms
    atmospheric vortex streets
    Land breeze
    Cloud line

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