# Presentation: 369412

## 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

## NHC Forecasts For Hurricane Isidore 2002

**Typical Area**
**Of Hurricane Winds**

**NHC Forecasts For Hurricane Isidore 2002**

## 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

## Sample TPC Probability Products

- 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**

## 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)

## 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

## 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

## 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(t+12) = 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)

## Climatological Radii Model(Provides R(Vmax) for MC Realizations)Vmax = 50, 75, 100, 125 kt  moving 270/15 kt)

- 50 kt

- 75 kt

- 100 kt

- 125 kt

## Example: Hurricane Michelle 11/02/01  00 UTC(Track, Intensity Error Distributions  from 1997-2002)  

**First 100**
**MC Track Realizations**
**(120 hr)**

## Probability of 64 kt Winds  Michelle 11/02/01  00 UTCIntensity Forecast (hr:kt): 0:55,   24:75,   48:90,  72:95 ,  96:70,  120:60

**0-24 hr                                        24-48 hr                           48-72 hr**

**CI=5%                                        CI=2%   **

**72-96 hr                                   96-120 hr                              0-120 hr**

**CI=10%**

**CI=10%                             CI=10%                             CI=10%**

## Probability of 34, 50, and 64 kt Winds 0-120 hr Michelle 11/02/01  00 UTC

**34 kt                                           50 kt                                    64 kt**

## Probability of 64 kt Winds 0-120 hrMichelle 11/02/01 00 UTCSensitivity to Intensity Uncertainty

_**With**__ _Intensity Uncertainty         _**Without **_Intensity Uncertainty

-  _**t      V**__**f**_** ****   **
**  ****0     55     **
** ****24    75**
** ****48    90**
** ****72    95**
** ****96    70**
**120   60**

## 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