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
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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,
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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
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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.
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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• MSLPReal-time cases available at http://rammb.cira.colostate.edu/products/tc_realtime/
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http://www.ssd.noaa.gov/PS/TROP/mtcswa.html
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Real-World ExampleNorthern Hemisphere/Sheared TC
HurricaneKyle2008
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Verification (2008-2009, Atlantic)Are These Any Good?
Ground Truth
1. H*Wind Analyses
2. NHC best track of wind radii (when aircraft reconnaissance ± 2 hours)
3. NHC best track of central pressure (when aircraft reconnaissance ± 2 hours)
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Vs. H*Wind (all cases)
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Vs. H*Wind (> 64 kt cases)
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Vs. H*Wind (≤ 64 kt)
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Do the size extimates correlate with the observations?
Answer: Yes
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Pressure Estimation
MTCSWA Climatology
(Dvorak 1975)
Bias 0.5 2.4
MAE 6.8 7.0
RMSE 9.5 9.2
R2 [%] 84 82
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Interpreting the Verification
Strengths• 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.
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
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Review of the purposeDevelop 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.
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Questions?
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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. Vonder Haar: 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. Vonder Haar, 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/