Advanced Applications of the Monte Carlo Wind Probability Model: A Year 2 Joint Hurricane Testbed Project Update Mark DeMaria 1 , Robert DeMaria 2 , Andrea Schumacher 2 , Daniel Brown 3 , Michael Brennan 3 , Richard Knabb 4 , Pablo Santos 5 , David Sharp 6 , John Knaff 1 and Stan Kidder 2 1 NOAA/NESDIS, Fort Collins, CO 2 CIRA, Colorado State University, Fort Collins, CO 3 NCEP National Hurricane Center, Miami, FL 4 The Weather Channel, Atlanta, GA 5 NOAA/National Weather Service, Miami, FL 6 NOAA/National Weather Service, Melbourne, FL Interdepartmental Hurricane Conference March 2011
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Mark DeMaria 1 , Robert DeMaria 2 , Andrea Schumacher 2 ,
Advanced Applications of the Monte Carlo Wind Probability Model: A Year 2 Joint Hurricane Testbed Project Update. Mark DeMaria 1 , Robert DeMaria 2 , Andrea Schumacher 2 , Daniel Brown 3 , Michael Brennan 3 , Richard Knabb 4 , Pablo Santos 5 , - PowerPoint PPT Presentation
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Advanced Applications of the Monte Carlo Wind Probability Model:
A Year 2 Joint Hurricane Testbed Project Update
Mark DeMaria1, Robert DeMaria2, Andrea Schumacher2, Daniel Brown3, Michael Brennan3, Richard Knabb4, Pablo Santos5,
David Sharp6, John Knaff1 and Stan Kidder2
1NOAA/NESDIS, Fort Collins, CO2CIRA, Colorado State University, Fort Collins, CO
3NCEP National Hurricane Center, Miami, FL4The Weather Channel, Atlanta, GA
• Divide NHC/JTWC track errors into three groups based on GPCE values– Low, Medium and High– Reduces or increases probabilities ~10%
• Evaluation in 2009 showed improved skill in all basins
• GPCE version implemented in 2010
Current JHT Project Tasks• Model Improvements
1. Adjust time step for small/fast storms2. Improve azimuthal interpolation of wind radii3. Improve spatial interpolation for text/grid product
consistency4. Evaluate wind radii model
• Advanced Applications1. Application to WFO local products 2. Landfall timing and intensity distributions3. Probabilities integrated over coastal segments4. Automated guidance for watch/warnings
M1. Time Step AdjustmentCompleted and Implemented in 2010
Example: Hurricane Gordon, 19 Sept 2006 18 UTC
R64 ~ 25 nmi, c = 28 kt
∆t = 2 hr ∆t = 1 hr
M2. Improve Azimuthal Interpolation
• Special conditions– Slow moving, large storm– Max winds near 50 or 65 kt– Initial wind radii = 0 in some quadrants
• Azimuthal interpolation of wind threshold radii can be smaller than next lower threshold
0-120 hr CumulativeProbabilities for TS Fay18 UTC 20 Aug 2008
M2. Improve Azimuthal Interpolation
• Solution: Impose radius of max wind as lower bound on outer wind radii interpolation
• Ready for implementation in 2011
t=0 hr probabilities for TS Fay 18 UTC 20 Aug 200834 kt 50 kt 50 kt (corrected)