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Second portion of Part1

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  1. Second portion of Part1

  2. Processing to near the water surface • Previous slides show the improvement of air motion retrievals when accounting for the strong and coexistent surface returns. • Here the return from the water is somewhat weaker and depends strongly on the sea state and angle of incidence of the laser beam making the identification and removal more challenging.

  3. LAS Water LOS spectrum for a single range gate associated with data taken at 33 degrees off nadir and just before (~ 200m ) intercepting the water surface.

  4. Examples of the LAS processing algorithm developed with TODWL data and applied to the P3DWL data from TCS-08 are shown later

  5. From TODWL to P3DWL • While most of the processing algorithms developed with the TODWL data applied directly to the P3DWL data, there were some exceptions: • The combination of LOS’s during sharp turns by the P3DWL had to be significantly modified to obtain reliable 3D wind profiles. • Accounting for hydrometeor contributions to the signal spectra had to be developed since TODWL was usually flown in fair weather situations.

  6. P3DWL for TPARC/TCS-08 1.6 um coherent WTX (ARL/LMCT) 10 cm bi-axis scanner (NASA) P3 and other parts (NRL) Analyses software (SWA/CIRPAS)

  7. P3DWL flight into Typhoon Haugapit Each yellow square represents 5 -8 individual profiles

  8. Dropsonde Comparisons • The following series of comparisons are selected from a much larger set of DWL profiles obtained near Typhoon Haugapit. • It is expected that the curtain of DWL profiles will be used for independent diagnostics on typhoon dynamics and to assign situational observation errors to the dropsondes (mass field information) for assimilation into forecasting models. • While compulsory, comparisons between dropsondes and airborne DWL profiles must be done considering: • The dropsonde takes ~ 3 minutes from flight level (3km) to the surface; a DWL profile takes ~ 20 -30 seconds to construct. • With a 10 m/s wind, the dropsonde would drift 1.8 km; at 120 m/s ground speed, the P3DWL data for a single profile would cover 2.4 – 3.6 km. Separation of the near surface returns from the nearest dropsonde measurement could be ~ 5 -6 km

  9. DWL vs. Dropsonde (high wind speed near eye)

  10. DWL vs. Dropsondes (low wind speed)

  11. Cloud free situations with turning

  12. Profiling in rain and through cloudswhile turning 114 CW 75 (39 degrees)

  13. The “w” shown is for the hydrometeor fall velocity contribution to the spectrum

  14. Precipitation Effects • Hydrometeors are detected by the wind lidar and their fall speeds (projected on the LOS) are measured. In the spectral domain, both the air and the hydrometeor motions are frequently distinguishable. • An algorithm that objectively identifies the two spectral features and estimates a rain rate is currently be evaluated.

  15. MBL evolution and organization • The following are examples of nadir (vertical velocity only) sampling between Hawaii and the West Coast on 8 October, 2008. • The top panel shows the vertical velocity as a function of height (m) and horizontal distance (1 unit = xxxm) • The bottom panel shows the signal return strength

  16. Height (m) 14 km Height (m)

  17. Processing the range gate identified as bracketing the surface during nadir stares • The first slide is for a case near Typhoon Haugapit but in a relatively calm (flight level turbulence) area. • The horizontal resolution is ~ 5 meters • Vertical aircraft motion component has been removed from the LOS sensed velocity • Signals are smoothed with a five point running mean • The second slide is for a region where the horizontal wind speeds at 200 meters were ~ 30 m/s.

  18. Horizontal scale in meters

  19. Horizontal scale in meters

  20. Current research using P3DWL data • ZhaoxiaPu (University of Utah) • Nuri • Sinlaku • Haugapit • Foster (University of Washington) • Haugapit (OLEs) • Emmitt (Simpson Weather Associates) • Dropsonde comparisons • Layer Adjacent to the Surface (LAS) investigations

  21. Related publications and presentations • Pu, Z., L. Zhang and G.D. Emmitt, 2010: Impact of airborne Doppler wind lidar profiles on numerical simulations of a tropical cyclone, Geophys, Res. Letters, Vol. 37, L05081, 5pp • Foster, R., G.D. Emmitt, D. Carre, S. Greco, S. Wood, D. Eleuterio, B. Tang and M. Riemer, 2009: Preliminary Look at Boundary Layer Doppler Wind Lidar Wind Profiles From the Tropical Cyclone Structure, 2008 (TCS-08) Experiment, 16th Conference on Air-Sea Interaction . Proc. of the Annual Amer. Met. Soc. Conference, Phoenix, AZ • Emmitt, G.D. and S. Greco, 2010: Airborne Doppler Wind Lidar: investigating tropical cyclones with curtains of wind profiles, Proc. of the Annual Amer. Met. Soc. Conference, 15th Symposium on Meteorological Observation and Instrumentation, Atlanta, GA • Emmitt, G.D., Z. Pu, K. Godwin and S. Greco, 2011: Airborne Doppler Wind Lidar data impacts on tropical cyclone track and intensity forecasting: the data processing, interpretation and assimilation, Proc. of the Annual Amer. Met. Soc. Conference, 15th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans and Land Surface (IOAS-AOLS), Seattle, WA • Emmitt, G. D., K. Godwin and S. Greco, 2010: Advanced Signal Processing for Airborne DWL Data in Severe Weather, Working Group meeting for wind lidar, Destin, Fl., 2-4 February, 2010. Note: Kevin Godwin is a graduate student at the University of Virginia.