# Presentation: 084117

## An Improved Wind Probability Program:

**A Joint Hurricane Testbed Project Update**** **

**Mark DeMaria and John Knaff, NOAA/NESDIS, Fort Collins, CO**
**Stan Kidder, CIRA/CSU, Fort Collins, CO**
**Patrick Harr, Naval Postgraduate School, Monterey, CA**
**Chris Lauer, NCEP/TPC, Miami, FL**

- Presented at the
- Interdepartmental Hurricane Conference
- March 5, 2008

## Monte Carlo Wind Probability Model

- 1000 track realizations from random sampling NHC *track error* distributions
- Serial correlation and bias of errors accounted for
- Intensity of realizations from random sampling NHC *intensity error* distributions
- Serial correlation and bias of errors accounted for
- Special treatment near land
- Wind radii of realizations from radii CLIPER model and its *radii error* distributions
- Serial correlation included
- Probability at a point from counting number of realizations passing within the wind radii of interest

## MC Probability Example

**1000 Track Realizations                   64 kt 0-120 h Cumulative Probabilities**

**MC Probability Example**
**Hurricane Dean 17 Aug 2007 18 UTC**

-  **Major Hurricane**
** ****Non-major Hurricane**
** ****Tropical Storm**
** ****Depression**

## Project Tasks

- Improved Monte Carlo wind probability program by using situation-depending track error distributions
- Track error depends on Goerss Predicted Consensus Error (GPCE)
- Improve timeliness by optimization of MC code
- Update NHC wind speed probability table product
- Extend from 3 to 5 days
- Update probability distributions (currently based on 1988-1997)

## Code Optimization

- Code profiling showed ~85% of CPU in distance calculation routine
- Automated procedure added to test for regular grid
- If yes, rectangular mask added at each time step to reduce number of distance calculations
- Speed up of ~600% for large grid
- 25 to 50% expected
- Implemented before 2007 season

## Code Optimization

**Text Product Grid                              Graphical Product Grid**

## Wind Speed Probability Table

## Wind Speed Probability Table

- Developed by E. Rappaport and M. DeMaria as part of original NHC graphical products
- Limitations addressed by JHT project
- Based on 1988-1997 NHC error statistics
- Extends only to 3 days
- Other limitations
- Does not directly account for land interaction
- Inconsistent with other probability products from MC model
- Rick Knabb and Dan Brown suggestion*:
- Use output from MC model as table input
- Addresses all of the above limitations
- Will automatically update when MC model updates

**via Dave Thomas*

## Wind Speed Probability Table Evaluation Procedure

- Examine MC model intensity probability distributions for idealized storms
- Compare MC intensity probabilities with WSPT values for real forecasts
- Frances   29 Aug 2004 12 UTC
- Katrina     24 Aug 2005 18 UTC
- Katrina     27 Aug 2005 18 UTC
- Ernesto    29 Aug 2006 06 UTC
- Ernesto    29 Aug 2006 18 UTC
- Humberto 12 Sep 2007 12 UTC
- Humberto 12 Sep 2007 18 UTC
- Ingrid        13 Sep 2007 00 UTC

**Straight west track**
**far from land**
**Three cases:**
**Constant max wind of**
**30, 90 and 150 kt     **

**Wind Speed Probability Table **
**Idealized Storm Cases**

**Straight north track**
**close to land**
**Three cases:**
**Constant max wind of**
**30, 90 and 150 kt     **

## MC Intensity Distributions  Far from Land

**30 kt fcst                       90 kt fcst                    150 kt fcst**

## MC Intensity Distributions150 kt fcst far from and near land

- Far from land                                              Near land

## MC and Wind Speed Table Probability Comparison 

**Hurricane France Example                          Nine Forecast Totals**

## Forecast-Dependent Probabilities

*Track plots courtesy of J. Vigh, CSU*

-   Operational MC model uses basin-wide track error distributions
-   Can situation-dependent track distributions be utilized?

## Goerss Predicted Consensus Error (GPCE)

- Predicts error of CONU track forecast
- Consensus of GFDI, AVNI, NGPI, UKMI, GFNI
- GPCE Input
- Spread of CONU member track forecasts
- Initial latitude
- Initial and forecasted intensity
- Explains 15-50% of CONU track error variance
- GPCE estimates radius that contains ~70% of CONU verifying positions at each time

## Use of GPCE in the MC Model

- 2002-2006 database of GPCE values created by NRL*
- Are GPCE radii correlated with NHC and JTWC track errors?
- GPCE designed to predict CONU error
- How can GPCE values be used in the MC model?
- MC model uses along/cross track error distributions

**Buck: Domestic or Imported?*

## 72 hr Atlantic NHC Along Track Error Distributions Stratified by GPCE(2002-2006)

## NHC Along and Cross Track Error Standard Deviations Stratified by GPCE(2002-2006 Atlantic Sample)

## MC Model with Track Errors from Upper and Lower GPCE Terciles 

- Lower Tercile Distributions                        Upper Tercile Distributions

- Hurricane Frances 2004  01 Sept 00 UTC Example
- 120 hr Cumulative Probabilities for 64 kt

## Remaining Questions

- How to include GPCE in MC model
- Method 1: Sample from appropriate tercile
- Method 2: Include GPCE input in serial correlation correction
- Behavior in other basins
- Does GPCE correction improve probability verification?
- Real time tests beginning Aug 2008

## Summary

- Code optimization is complete
- Factor of 6 speed up
- Wind speed table product input from MC model is a reasonable approach
- Implementation in 2008
- GPCE-dependent MC model is promising
- Further evaluation needed
- Real time parallel runs in Aug 2008?