TC STRUCTURE GUIDANCE UPDATES

Transcription

1 TC STRUCTURE GUIDANCE UPDATES FROM NESDIS (CO)/CIRA Status and update for the multi-platform tropical cyclone wind analysis (MTCSWA) New microwave-sounder-based intensity and structure estimates New method to provide wind radii estimates to accompany Dvorak intensity fixes 18 February 2016 IWSATC-II, Honolulu Exploring the idea of an objective best track 1 Prepared by J. Knaff, NOAA/NESDIS

2 MTCSWA UPDATES The operational MTCSWA is static The experimental/cira MTCSWA has been updated. Uses a new IR-based method to estimate winds within 400km INPUTS: TC location, TC motion, TC intensity, IR principle components Uses a more realistic flight-level-to-surface wind reduction (R) Uses new inflow angle (A) parameterization Both R and A depend on distance from the rmwand azimuth with respect to motion. Details of each is provided in Knaff et al. (2015) Prepared by J. Knaff, NOAA/NESDIS 2 18 February 2016 IWSATC-II, Honolulu

3 EXAMPLE: TC BANSI (2015) OPERATIONAL EXPERIMENTAL Smaller/less symmetric core Smaller gales/ weaker outer winds More realistic 18 February 2016 IWSATC-II, Honolulu Prepared by J. Knaff, NOAA/NESDIS

4 IWSATC-II, Honolulu OPERATIONAL EXPERIMENTAL

5 NEW MICROWAVE INTENSITY AND STRUCTURE ESTIMATES(NESDIS) Description Uses the Microwave Integrated Retrieval System (MIRS) NOAA -18, -19, Metop-A,-B, ATMS Follows previous methodology (Demuth et al. 2004, 2006) Create radial/height profiles Calculate gradient winds Statistically bias correct to provide intensity and structure. Available from AMSU: ftp://satepsanone.nesdis.noaa.g ov/tcfp/amsutc/ ATMS: ftp://satepsanone.nesdis.noaa.g ov/tcfp/npptc/ Statistics (JTWC/NHC/CPHC best) ATMS AMSU 5 18 February 2016 IWSATC-II, Honolulu Prepared by J. Knaff, NOAA/NESDIS

6 PROVIDING WIND RADII ESTIMATES TO ROUTINE DVORAK INTENSITY FIXES Description Inputs: TC intensity *, TC location*, TC motion*, matching IR image TC size is estimated from the IR image (Knaff et al. 2014) TC size is related to average wind radii (Knaff et al. 2016) Asymmetries are based on climatology and the motion vector (Knaff et al. 2007) Fixes (text) will be generated at CIRA and JTWC and available from the fdecksand the CIRA TC-Realtime web page. R34 Performance given size and intensity 18 February 2016 IWSATC-II, Honolulu * Based on Dvorak fixes Prepared by J. Knaff, NOAA/NESDIS Errors largest for large, weak, fast and high-latitude systems 6

7 Example: Hurricane Gonzalo (2014) Dvorak-based (3-hr interpolated) AMSU-based GFS-tracker Best track 18 February 2016 IWSATC-II, Honolulu Prepared by J. Knaff, NOAA/NESDIS

8 CONCEPTUAL OBJECTIVE STRUCTURE GUIDANCE There are now enough estimates to form a simple consensus This consensus forms the basis for initial wind radii estimates and the best track NRLMRY is working on the operational wind radii button for JTWC 18 February 2016 IWSATC-II, Honolulu Prepared by J. Knaff, NOAA/NESDIS

9 CURRENT AND FUTURE EFFORTS/TOPICS Ongoing projects JTWC s wind radii button Statistical Dynamical wind radii prediction Objective/Automated IR Eye detection (yes/no) Objective Eye anticipation/forecast (0-24h) What do small ice particles at cloud top tell us? 3-D bogus from satellite data. Future problems Radius of maximum winds all intensities Modification of Dvorak wind radii based on shear Objective best tracks 9 18 February 2016 IWSATC-II, Honolulu Prepared by J. Knaff, NOAA/NESDIS

10 REFERENCES USED IN THIS PRESENTATION Demuth, J.L.,M. DeMaria, J.A. Knaff, and T.H. VonderHaar, 2004: Evaluation of advanced microwave sounder unit (AMSU) tropical cyclone intensity and size estimation algorithm, J. App. Met., 43, Demuth, J., M. DeMaria, andj.a. Knaff, 2006: Improvement of Advanced Microwave Sounding Unit Tropical Cyclone Intensity and Size Estimation Algorithms, Journal of Applied Meteorology and Climatology, 45:11, Knaff, J.A., C. R. Sampson,M. DeMaria, T. P. Marchok, J. M. Gross, and C. J. McAdie, 2007: Statistical Tropical Cyclone Wind Radii Prediction Using Climatology and Persistence, Wea. Forecasting, 22:4, Knaff, J.A., S.P. Longmore, R.T DeMaria, D.A. Molenar, 2015: Improved tropical cyclone flight-level wind estimates using routine infrared satellite reconnaissance. J. App. Meteor. Climate. 54, Knaff, J. A., S. P. Longmore, and D. A. Molenar, 2014: An objective satellitebased tropical cyclone size climatology. J. Climate, 27, Knaff, J.A., C.J. Slocum, K.D. Musgrave, C.R. Sampson, and B. Strahl: 2016: Using routinely available information to estimate tropical cyclone wind structure. Mon. Wea. Rev., in press. doi: Available at Or available upon request from John.Knaffat noaa.gov February 2016 IWSATC-II, Honolulu

11 TC Structure Parameters from IR imagery Deviation Angle Variance (DAV) Technique for Wind Structure Based on a parameter that captures the departure from axisymmetry of the IR cloud pattern

12 Hurricane Lab Techniques based on the DAV Rita Intensity Estimation 18/0815 UTC Surface Wind Field 19/1415 UTC 21/1415 UTC 0015 UTC 21 October 2005 Genesis Detection/ Probabilistic Disturbance Tracking

13 Combine the DAV with environmental parameters and best track information into a multiple linear regression model to estimate the radius of 34-, 50-, and 64-kt wind radii 0015 UTC 21 October 2005 Symmetric and Asymmetric models are derived

14 R34 R50 R64 SYM NE SE SW NW

15 DATA 21 Tropical Cyclones = 4,708 ½ hr GOES-12 IR images Recon within 3 hr of BT 48 hr semi-continuous monitoring Restricts cases to west of 55 deg longitude METHODOLOGY Use a past running mean to smooth DAV Calculate azimuthally averaged DAV (full circle and by quadrant) Use multiple linear regression Both symmetric and asymmetric models are derived Use DAV with lowest RMSE for each wind radii Forward selection: Test each predictor for strength of linear relationship to peach Wind Radii (BT and SHIPS) Latitude SSTs Vmax TC age (since TS) Vertical Wind Shear RH in the lower, mid, and upper troposphere,

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17 Example: Ike (2008) Dolling et al. (2014; 2015)

18 R34 EBT DAV R50 R64

19 Errors for dataset of 21 TCs from R34 R50 R64 R34 R50 R64 SYM NE SE SW NW Mean DAV radii

20 Advantages: TC wind parameters can be extracted at up to half hourly intervals For current surface wind estimation R34/R50/R64 Full 2-D surface wind field (with addition of Vmax, RMW, motion) for as long as IR imagery has been available (1970spresent) Can be utilized in basins where aircraft recon does not exist Automated and Objective techniques Disadvantages: Validation in basins without reconnaissance utilize scatterometry/buoy observations/etc. where possible

21 Combine the DAV with environmental parameters and best track information into a multiple linear regression model to estimate the radius of 34-, 50-, and 64-kt wind radii 0015 UTC 21 October 2005 Symmetric and Asymmetric models are derived

22 Thank you

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25 18 February 2016 IWSATC-II, Honolulu