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A Multi-platform ( i.e , Satellite) Tropical Cyclone Surface Wind Analysis

A Multi-platform ( i.e , Satellite) Tropical Cyclone Surface Wind Analysis. John Knaff, NOAA/NESDIS/ StAR , RAMMB, Fort Collins, CO, USA Mark DeMaria , NOAA/NESDIS/ StAR , RAMMB, Fort Collins, CO, USA Debra Molenar , NOAA/NESDIS/ StAR , RAMMB, Fort Collins, CO, USA

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A Multi-platform ( i.e , Satellite) Tropical Cyclone Surface Wind Analysis

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  1. A Multi-platform (i.e, Satellite) Tropical Cyclone Surface Wind Analysis John Knaff, NOAA/NESDIS/StAR, RAMMB, Fort Collins, CO, USA Mark DeMaria, NOAA/NESDIS/StAR, RAMMB, Fort Collins, CO, USA Debra Molenar, NOAA/NESDIS/StAR, RAMMB, Fort Collins, CO, USA Buck Sampson, Naval Research Laboratory, Monterey, CA, USA Matthew Seybold, NOAA/NESDIS/OSDPD, Suitland, MD, USA Graciously Presented by Andrew Burton ,Australian BoM, Perth, WA, Australia

  2. Need • Estimates of tropical cyclone (TC) surface wind structure is a routinely analyzed and forecast quantity. • However, there are few tools to estimate tropical cyclone wind structure in the absence of aircraft reconnaissance • Cloud drift winds • Scatterometer wind vectors • SSM/I wind speeds • AMSU • Etc… • and the existing tools fail to provide a complete picture of the surface wind field, particularly near the center of strong TCs. WMO International Workshop on Satellite Analysis of Tropical Cyclones

  3. Solution • Global product that combine satellite-based winds or Multi-platform Tropical Cyclone -Surface Wind Analysis (MTC-SWA) • Storm relative winds (12-h window) • Account for the shortcomings • Quality control • Variational data analysis at flight-level • Data weights • Previous analysis as first guess • Cylindrical analysis grid • Adjust flight-level winds to the surface • Simple rules • Account for land/sea differences • Produce diagnostics every 6 hours & globally • Wind radii • MSLP Real-time cases available at http://rammb.cira.colostate.edu/products/tc_realtime/ and http://www.ssd.noaa.gov/PS/TROP/mtcswa.html WMO International Workshop on Satellite Analysis of Tropical Cyclones

  4. Input Data • AMSU – derived balanced winds • Scatterometry • Cloud and feature track winds • IR – based analogs of flight-level (850-700 hPa) winds (i.e., aircraft-based wind analogs) WMO International Workshop on Satellite Analysis of Tropical Cyclones

  5. Input: AMSU-Based Balanced WindsBessho et al. (2006) • These are created as part of a NCEP operational tropical cyclone intensity and structure products • AMSU antenna temperatures are used to estimate temperature retrievals and cloud liquid water (Goldenberg 1999) • Cloud liquid water and horizontal temperature anomalies are used to correct temperature retrievals (Demuth et al. 2004, 2006) • The corrected temperatures are then analyzed on standard pressure levels (using GFS boundary conditions). • Using the resulting height field the non-linear balance equation is solved to estimate the 2-dimensional wind field (Bessho et al. 2006) • Because of the resolution of AMSU, the winds in the core of TCs are not resolved using this method. WMO International Workshop on Satellite Analysis of Tropical Cyclones

  6. Input: AMSU-Based Balanced WindsBessho et al. (2006) Advanced Microwave Sounding Unit (AMSU) – Based, by-product of an operational intensity estimation algorithm • Polar orbit (NOAA-15, 16 & 18) • Analysis of temperature retrievals provide a height field • Non-linear balance approximation provides wind estimates at flight-level (700 hPa) Shortcomings • Resolution, too weak near the center • Too asymmetric 2 km resolution Hurricane Paloma 7 Nov 2008 2225 UTC WMO International Workshop on Satellite Analysis of Tropical Cyclones

  7. Input: Surface Scatterometry • Active radar method (k-band, c-band) • Accurate low level winds • Attenuates in high winds (i.e., > ~50 kt) • Is adversely affected by heavy precipitation WMO International Workshop on Satellite Analysis of Tropical Cyclones

  8. Input: Surface ScatterometryA-SCAT on MetOp Surface wind vectors from ASCAT and QuikSCATscatterometers • Polar orbit • 10-m wind vectors • ASCAT is c-band • 25km resolution • Less effected by precipitation • QuikSCAT is k-band • N/A Shortcomings • Saturation in high winds • Attenuation/contamination in heavy rain 2 km resolution Hurricane Paloma 8 Nov 2008 0545 UTC WMO International Workshop on Satellite Analysis of Tropical Cyclones

  9. Input: Cloud/Feature Tracked Winds • Routinely available • Accurate • But low-level winds are often not available near the core of TCs WMO International Workshop on Satellite Analysis of Tropical Cyclones

  10. Input: Cloud/Feature Track windsvarious methods from operational centers GOES Cloud/Feature Track Winds – Operational Product at NESDIS, JMA, EUMETSAT • Track clouds or water vapor features • Assign a pressure level • Available 3 hourly Shortcoming • Coverage near the center 4 km resolution Hurricane Paloma 8 Nov 2008 0545 UTC WMO International Workshop on Satellite Analysis of Tropical Cyclones

  11. Input: IR Flight-Level Analog WindsMueller et al. (2006) • Relatively new development • Provides representative winds near the core of the TC • Makes a surface analysis possible WMO International Workshop on Satellite Analysis of Tropical Cyclones

  12. Input: IR Flight-Level Analog WindsMueller et al. (2006) • IR imagery (typically 3) • Analysis of the azimuthal mean brightness temperatures • Scales TC size • Intensity estimate (advisories) • Latitude (advisories) • Storm motion (advisories) Output • 2-D flight-level (700 hPa) wind estimate Shortcomings • Too symmetric • Cases of small radius of maximum winds or multiple wind maxima 1 km resolution Hurricane Paloma 8 Nov 2008 0600 UTC WMO International Workshop on Satellite Analysis of Tropical Cyclones

  13. Output: Quality Controlled Inputs (Hurricane Paloma 8 Nov 06UTC) Scatterometry Cloud / Feature winds WMO International Workshop on Satellite Analysis of Tropical Cyclones

  14. Output: Quality Controlled Inputs (Hurricane Paloma 8 Nov 00UTC) AMSU Balanced Winds IR flight-level analog WMO International Workshop on Satellite Analysis of Tropical Cyclones

  15. Sample Analysis of Hurricane Paloma 8 Nov 2008 06UTC Analysis: R34 75 70 60 65 R50 55 55 55 55 R64 45 45 40 40 RMW 16 MSLP 950 hPa NHC Best track: R34 120 80 60 80 R50 60 45 35 45 R64 25 25 25 25 RMW 10 MSLP 951 hPa WMO International Workshop on Satellite Analysis of Tropical Cyclones

  16. Real-Time Products Graphical Products Text Products (ftp) Input: Input assumptions Raw Input data (ascii) 600km environmental pressure Products Fix file (ATCF formatted) Surface Winds (ascii) Polar grid Azimuthal average Analysis level Winds (ascii) GrADS binaries & .ctl files Kinetic Energy Vmax and CP • Surface Wind Analysis • 10 degree • 4 degree • Inputs reduced/turned to the surface • AMSU • SCAT • CDFT • IRWD • Time series of Vmax & Central pressure (CP) • Kinetic Energy (ftp) • IR image WMO International Workshop on Satellite Analysis of Tropical Cyclones

  17. http://www.ssd.noaa.gov/PS/TROP/mtcswa.html WMO International Workshop on Satellite Analysis of Tropical Cyclones

  18. WMO International Workshop on Satellite Analysis of Tropical Cyclones

  19. WMO International Workshop on Satellite Analysis of Tropical Cyclones

  20. Real-World ExampleNorthern Hemisphere/Sheared TC Hurricane Kyle 2008 WMO International Workshop on Satellite Analysis of Tropical Cyclones

  21. WMO International Workshop on Satellite Analysis of Tropical Cyclones

  22. Verification (2008-2009, Atlantic)Are These Any Good? Ground Truth • H*Wind Analyses • NHC best track of wind radii (when aircraft reconnaissance ± 2 hours) • NHC best track of central pressure (when aircraft reconnaissance ± 2 hours) WMO International Workshop on Satellite Analysis of Tropical Cyclones

  23. Vs. H*Wind (all cases) WMO International Workshop on Satellite Analysis of Tropical Cyclones

  24. Vs. H*Wind (> 64 kt cases) WMO International Workshop on Satellite Analysis of Tropical Cyclones

  25. Vs. H*Wind (≤ 64 kt) WMO International Workshop on Satellite Analysis of Tropical Cyclones

  26. WMO International Workshop on Satellite Analysis of Tropical Cyclones

  27. Do the size estimates correlate with the observations? Answer: Yes WMO International Workshop on Satellite Analysis of Tropical Cyclones

  28. Pressure Estimation WMO International Workshop on Satellite Analysis of Tropical Cyclones

  29. Interpreting the Verification Strengths Weaknesses 64-kt winds too large, which causes central pressure estimates to be too low for the most intense systems. 34-kt winds a little too small Negative biases in SE (NE) quadrant in the N. Hemisphere (Southern Hemisphere) Most of the inner core errors are associated with poorly estimating the radii of maximum winds • Always available • Global • Available every 6 hours • Wind radii well correlated with storm radii • Errors are generally lower than climatology (Knaff et al. 2007), except in the SE quadrant. • Central pressure estimates, particularly for the Vmax < 100 kt. WMO International Workshop on Satellite Analysis of Tropical Cyclones

  30. Review of the purpose Develop a product that uses existing TC surface and near-surface wind information to construct an analysis of the 2-dimensional structure of the surface wind around TC. Uses existing satellite inputs Combines their strengths Produces and analysis with lower errors than any of the inputs. WMO International Workshop on Satellite Analysis of Tropical Cyclones

  31. Questions? WMO International Workshop on Satellite Analysis of Tropical Cyclones

  32. Additional information/reading Knaff, J. A., M. DeMaria, D. A. Molenar, C. R. Sampson and M. G. Seybold, 2011: An automated, objective, multi-satellite platform tropical cyclone surface wind analysis. Submitted to J. Appl. Meteorol. 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, 781–791. Mueller, K.J., M. DeMaria, J.A. Knaff, J.P. Kossin, T.H. VonderHaar: 2006: Objective Estimation of Tropical Cyclone Wind Structure from Infrared Satellite Data. Wea. Forecasting, 21:6, 990–1005. Bessho, K., M. DeMaria, J.A. Knaff , 2006:  Tropical Cyclone Wind Retrievals from the Advanced Microwave Sounder Unit (AMSU): Application to Surface Wind Analysis.  J. of Applied Meteorology. 45:3, 399 - 415. Demuth, J., M. DeMaria, and J.A. Knaff, 2006:  Improvement of Advanced Microwave Sounding Unit Tropical Cyclone Intensity and Size Estimation Algorithms, J. Appl. Meteor. Clim., 45:11, 1573–1581. Demuth, J. L., M. DeMaria, J. A. Knaff, and T. H. VonderHaar, 2004: Validation of an advanced microwave sounder unit (AMSU) tropical cyclone intensity and size estimation algorithm, J. App. Met., 43, 282-296. Real-time cases available at http://rammb.cira.colostate.edu/products/tc_realtime/ and http://www.ssd.noaa.gov/PS/TROP/mtcswa.html WMO International Workshop on Satellite Analysis of Tropical Cyclones

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