Estimating Probabilities of Tropical Cyclone Surface Winds

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Estimating Probabilities of Tropical Cyclone
Surface Winds 
Mark DeMaria NOAA/NESDIS/ORA Regional and
Mesoscale Meteorology Team Fort Collins,
CO Presented At The IFPS Methodology
Workshop April 22, 2003 Boulder, CO
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NHC Forecasts For Hurricane Isidore 2002
Typical Area Of Hurricane Winds
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NHC Strike Probability Program

• Formally initiated by the NWS on 15 Aug 1983
• Sheets (1985)
• Motivated by the need to provide uncertainty
  information, remove focus from center point
• Provides quantitative measure of probability of a
  storm passing within 65 nmi of specified
  locations
• 0-24, 24-36, 36-48, 48-72, 0-72 hr
• Probabilities determined from error
  characteristics of NHC official track forecasts
• 2-D error distributions fitted to track errors

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ZCZC MIASPFAT5 ALL TTAA00 KNHC DDHHMM TROPICAL
STORM ISIDORE PROBABILITIES NUMBER 39 NATIONAL
WEATHER SERVICE MIAMI FL 4 PM CDT WED SEP 25
2002 PROBABILITIES FOR GUIDANCE IN HURRICANE
PROTECTION PLANNING BY GOVERNMENT AND DISASTER
OFFICIALS AT 4 PM CDT...2100Z...THE CENTER OF
ISIDORE WAS LOCATED NEAR LATITUDE 26.8
NORTH...LONGITUDE 90.5 WEST CHANCES OF CENTER
OF THE STORM PASSING WITHIN 65 NAUTICAL MILES OF
LISTED LOCATIONS THROUGH 1PM CDT SAT SEP 28
2002 LOCATION A B C D E
LOCATION A B C D E 30.5N 90.2W
49 4 X X 53 YARMOUTH NS X X X 7
7 35.2N 87.9W X 21 7 X 28 HALIFAX NS
X X X 6 6 39.3N 82.2W X X 12
6 18 SABLE ISLAND NS X X X 4 4 MYRTLE
BEACH SC X X 2 X 2 SYDNEY NS X
X X 6 6 WILMINGTON NC X X 2 X 2
EDDY POINT NS X X X 6 6 MOREHEAD CITY NC
X X 1 1 2 PTX BASQUES NFLD X X X 6
6 CAPE HATTERAS NC X X 1 1 2 BURGEO NFLD
X X X 5 5 NORFOLK VA X X 2 3
5 ILE ST PIERRE X X X 4 4 OCEAN CITY
MD X X 1 6 7 CAPE RACE NFLD X X
X 2 2 ATLANTIC CITY NJ X X X 9 9 PANAMA
CITY FL X 2 X X 2 NEW YORK CITY NY X X
X 9 9 PENSACOLA FL 5 12 1 X
18 MONTAUK POINT NY X X X 8 8 MOBILE AL
24 13 X X 37 PROVIDENCE RI X X X
8 8 GULFPORT MS 40 6 X X 46 NANTUCKET
MA X X X 7 7 BURAS LA 54 X
X X 54 HYANNIS MA X X X 7 7 NEW
ORLEANS LA 55 1 X X 56 BOSTON MA X
X X 9 9 NEW IBERIA LA 40 3 X X
43 PORTLAND ME X X X 9 9 PORT ARTHUR
TX 3 2 X X 5 BAR HARBOR ME X X X
8 8 GULF 29N 87W 2 2 X X 4 EASTPORT
ME X X X 8 8 GULF 28N 89W 36 1
X X 37 ST JOHN NB X X X 7 7 GULF
28N 91W 82 X X X 82 MONCTON NB X
X X 7 7 GULF 28N 93W 15 X X X 15
COLUMN DEFINITION PROBABILITIES IN PERCENT A
IS PROBABILITY FROM NOW TO 1PM THU FOLLOWING ARE
ADDITIONAL PROBABILITIES B FROM 1PM THU TO 1AM
FRI C FROM 1AM FRI TO 1PM FRI D FROM 1PM FRI
TO 1PM SAT E IS TOTAL PROBABILITY FROM NOW TO
1PM SAT X MEANS LESS THAN ONE PERCENT
FORECASTER BEVEN NNNN
Sample TPC Probability Products
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Limitations of Operational Probabilities

• Contains only one component of forecast
  uncertainty (track error)
• Intensity, wind radii forecasts also have
  uncertainty
• Uses idealized probability distribution of NHC
  track errors
• Method difficult to generalize method to higher
  dimensions
• P(x,y,t1-t2) F(xs,ys) F(xs,ys,Vs,
  Rs)

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Monte Carlo Method

• Random Walk technique originally developed to
  model interaction of atomic particles
• Requires known probability distributions of
  particle motion and energy
• Also used for visible scattering by clouds (MD
  1979)
• Basic idea
• Generate plausible set of paths and energies
  (intensities) by randomly sampling from know
  probability distributions
• Calculate statistics based upon set of paths,
  energies
• Brute force method where actual probability
  distributions used
• No need to fit assumed distributions
• Modification is flexible

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MC Application to Tropical Cyclone Surface Wind
Probabilities

• Calculate NHC track errors (along track and cross
  track) from multi-year sample
• Calculate NHC intensity errors from same sample
• Determine large set of tracks (realizations)
  centered around official forecast track by
  randomly sampling from track errors
• Determine intensity for each track realization by
  randomly sampling from intensity error
  distribution
• Estimate wind radii as function of intensity
• Calculate probabilities by number of times
  specified point comes with radii of specified
  wind speed relative to total number of
  realizations

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Special Problems for TC Applications

• NHC track and intensity forecasts are correlated
  in time
• Purely random sampling provides realizations that
  zig-zag about forecast track/intensity
• Solve by including serial correlation in errors
• E(t12) aE(t) Erandom
• Some track realizations are over land, when
  official forecast is not
• Adjust intensity using K-D inland decay model
• Radii and intensity are not independent
• Use climatological radii model to determine radii
  from Vmax for each realization
• Add random error of climo radii model (not incl.
  yet)

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Climatological Radii Model(Provides R(Vmax) for
MC Realizations)Vmax 50, 75, 100, 125 kt
moving 270/15 kt)
50 kt
75 kt
125 kt
100 kt
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Example Hurricane Michelle 11/02/01 00
UTC(Track, Intensity Error Distributions from
1997-2002)
First 100 MC Track Realizations (120 hr)
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Probability of 64 kt Winds Michelle 11/02/01
00 UTCIntensity Forecast (hrkt) 055, 2475,
4890, 7295 , 9670, 12060
CI10 CI10
CI10
0-24 hr
24-48 hr 48-72 hr
CI5
CI2
CI10
72-96 hr 96-120
hr 0-120 hr
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Probability of 34, 50, and 64 kt Winds 0-120 hr
Michelle 11/02/01 00 UTC
34 kt
50 kt 64 kt
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Probability of 64 kt Winds 0-120 hrMichelle
11/02/01 00 UTCSensitivity to Intensity
Uncertainty
t Vf 0 55 24 75 48
90 72 95 96 70 120 60
With Intensity Uncertainty Without
Intensity Uncertainty
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Future Plans

• Add option for additional wind radii
  perturbations
• Add error characteristics of parametric wind
  model
• Test in real-time during 2003 Atlantic season
• Compare with operational probabilities
• Use same lat/lon points as operational program
• Evaluate properties in large sample
• Develop versions for Atlantic, East/Central/West
  Pacific (JHT proposal)
• Watch/Warning Guidance tool
• Define probability/time threshold
• Identify coastal breakpoints within thresholds
• IFPS probability grid generation