Two impact studies of airborne DWL data on tropical cyclone ...

Two impact studies of airborne DWL data on tropical cyclone ...

Two impact studies of airborne DWL data on tropical cyclone track and intensity forecasts G. D. Emmitt, K. Godwin and S. Greco Simpson Weather Associates Z. Pu University of Utah

WGSBLW, Miami,2011 Acknowledgements Funding Ron Ferek(ONR) Operators

CDR Dan Eleuterio (NAVY) Dan Carre (Simpson Weather Associates) Michael Riemer (NPS) Brian Tang (MIT) Key support (software and hardware) Chris OHandley (SWA)

Kevin Godwin (SWA/UVA) LMCT Outline

P3DWL tropical cyclone observations (general) Nuri case study Hagupit case study Plans for next impact studies

Mission Plan Study of tropical cyclogenesis, intensification, transition and weakening Based out of Guam (P3) Other aircraft include USAF C130 and DLR Falcon P3: dropsondes, ELDORA and P3DWL C130: dropsondes

Falcon: dropsondes, DWL, DIAL Use ferry flights to collect long curtains of wind soundings to test data impact on NWP. Pax River to West Coast West Coast to Hawaii Hawaii to Guam

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)

Basic LOS Signal 50 meter range resolution (Gaussian pulse ~ 70 meters FWHM) 10 cm beam diameter Spectral domain (Heterodyne detection) Frequency (LOS speed) Spectral feature width (turbulence within the

illuminated volume) Frequency discrimination of multiple LOS motions (ground, air, precipitation signals all in same spectrum) The Data Profiles of u,v, and w above and below flight level following ~ 3km of scanning.

Information on aerosol structures, precipitation, and sea state Challenges to signal processing and interpretation Aircraft extreme motions (mainly rolling) Chaotic marine boundary layer Sprays, waves and foam Clouds (partial obscuration)

Precipitation Comparisons with dropsondes provides basis for developing smarter algorithms Dropsonde Comparisons The following series of comparisons are

selected from a much larger set of DWL profiles obtained near Typhoon Hugapit. 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. Comparisons with dropsondes 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

DWL vs. Dropsonde (high wind speed near eye) Profiling in a turn 267 CW 205 (62 degrees) Profiling in a turn 327 CW 25 (57 degrees)

Profiling in rain and through clouds while turning 114 CW 75 (39 degrees) Data Impact Studies Initial impact study on typhoon Nuri

conducted at University of Utah and published Zhaoxia Pu, Lei Zhang, and G. David Emmitt Impact of airborne Doppler wind lidar profiles on numerical simulations of a tropical cyclone, GRL, VOL. 37, 2010 Working on additional impact evaluation for other typhoons in various segments of their

life cycles. Typhoon Nuri Courtesy G. D. Emmitt Example of P3DWL data display in Google Earth

Winds are displayed in 50 meter vertical resolution wind flags; Panel between wind profiles contains aerosol loading as function of height Flight level winds from P3 A G denote location of dropsondes

P3DWL winds at 2100 M P3DWL winds at 1500 M P3DWL winds at 500 M

P3DWL winds at 200 M DWL vs. Dropsonde Quality of the data Correlation of wind speed is nearly 98%

Impact of Airborne Doppler Wind Lidar obtained near Typhoon Nuri (2008) Model: Mesoscale community Weather Research and Forecasting (WRF) model Data: Doppler wind Lidar (DWL) profiles during T-PARC for the period of 0000UTC 0200 UTC 17 August 2008 Forecast Period: 48-h forecast from 0000UTC 17 August 2008 to 0000UTC 19 August 2008

Control: without DWL data assimilated into the WRF model. Data Assimilation: With DWL data assimilated into the WRF model Assimilation of DWL profiles eliminated the northern bias of the simulated storm track .

Assimilation of DWL profiles resulted in a stronger storm that is more close to the observed intensity of the storm. P3DWL flight near Typhoon Hagupit (September 21, 2008) Impact of DWL wind profiles on numerical simulations of Typhoon

Hagupits rapid intensification Zhaoxia Pu and Lei Zhang, Department of Atmospheric Sciences, University of Utah G. David Emmitt, Simpson Weather Associates, Charlottesville, VA Data: Doppler wind Lidar (DWL) profiles during 0000UTC 0430 UTC 22 September 2008 Forecast Period: 48-h forecast; 0000UTC 22 September 2008 --- 0000UTC 24 September 2008

SLPminError Observations Assimilation of conventional and dropwindsonde data (CTRL) shows a positive impact on Hagupits track and

intensity forecasts. Including the DWL wind observations in the model initialization (3DVAR) resulted in significant

improvements for both the track and intensity forecasts Vmax Error Track Error Current research using P3DWL data

Zhaoxia Pu (University of Utah) Nuri Sinlaku Hagupit Jangmi Foster (University of Washington)

Organized Large Eddies Emmitt (Simpson Weather Associates) Dropsonde comparisons Layer Adjacent to the Surface (LAS) investigations Summary

More than 10,000 profiles of the three dimensional Doppler lidar winds in the vicinity of tropical cyclones have been processed and are now available for use. FTP site set up for P3 processed data Questions regarding data access and utility: [email protected]

Recently Viewed Presentations

  • Presentation Outline - Housing Diversity Network

    Presentation Outline - Housing Diversity Network

    Go through topics that might be covered in classes. Go through the topics highlighted in forms as popular. * * * * * * * 1. Employers duty of care to reduce the incidents of stress at work 2. The...
  • Electrode Placement for Chest Leads, V1 to V6

    Electrode Placement for Chest Leads, V1 to V6

    C H A P T E R 3 Elements of Dance Chapter ??
  • CAO/CNO Recertification Training - Fort Sill

    CAO/CNO Recertification Training - Fort Sill

    Type "SIMM" into the course number search box. Enroll into all three . courses (SIMM01, SIMM02, SIMM03). You must score 80% or more in all


    Current rank-and-file awareness of HF principles and their plant's HF program varied widely. Team members (for PHAs,II, MOOC, etc.) need specialized training in HF principles and using the Latent Conditions Checklist, as well as a leader with strong facilitation skills....
  • Fossils - Callanan Science

    Fossils - Callanan Science

    Minerals can recrystallize (change their formula) The fossil can be petrified and turned to stone! Other routes to fossils . Amber: Trees create soft, sticky sap . Insects, frogs and lizards can get caught in the sap. The sap hardens-preserving...
  • 4.10 Relative Strengths of Acids and Bases

    4.10 Relative Strengths of Acids and Bases

    4.10 Relative Strengths of Acids and Bases. Chemistry 12. 4.10. In this lesson we will look at the relative strengths of some weak acids and use their Ka values to explain how they would behave when they are combined. ......
  • Probabilistic Inference

    Probabilistic Inference

    Hidden state variables. Observed variables. P(O. t |X t) called the observation model (or sensor model) Filtering. Name comes from signal processing. Goal: Compute the probability distribution over . current state given observations up to this point. X. 0 X....
  • Marketing Essentials

    Marketing Essentials

    Preliminary Activities Feature-Benefit Selling Section 13.1 Customer Referrals Cold Canvassing Employer Sales Leads Prospecting Techniques referral A recommendation of another person who might buy the product being sold. endless-chain method endless-chain method When salespeople ask previous customers for names of...