Hurricane Forecast Improvement Project (HFIP): Where do we stand after 3 years? Bob Gall – HFIP Development Manager Fred Toepfer—HFIP Project manager Frank.

Hurricane Forecast Improvement Project (HFIP):Where do we stand after 3 years?

Bob Gall – HFIP Development Manager

Fred Toepfer—HFIP Project manager

Frank Marks – HFIP Research Lead

Ed Rappaport – HFIP Operations Lead

March 6, 2013

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The HFIP Project – Vision/Goals

• Visiono Organize the hurricane community to dramatically

improve numerical forecast guidance to NHC in 5-10 years

• Goalso Reduce numerical forecast errors in track and

intensity by 20% in 5 years, 50% in 10 yearso Extend forecasts to 7 dayso Increase probability of detecting rapid intensification

at day 1 to 90% and 60% at day 5

HFIP Baselines and Goals:Track

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HFIP Baselines and Goals:Intensity

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HFIP Overall Strategy

• Use global models at as high a resolution as possible to forecast track out to 7 days

• Use regional models at 1-3 km resolution to predict inner core structure to meet intensity goals out to 5 days including rapid intensification

• Hybrid DA for both regional and global using as much satellite and aircraft data as possible

• Both regional and global models run as ensemblesBoth regional and global models run as ensembles

• Statistical post processing of model output to further increase forecast skill

Track Error of Models (2010-2011)(% Improvement over HFIP baseline)

Impact of Aircraft Data(% improvement over D-SHIFOR)

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How are we doing?

• The HFIP goals are for model products delivered from NCEP to NHC.– The delivery date for these goals is hurricane

season 2014

• The following show the operational models (Global and Regional) performance for hurricane track and intensity in the Atlantic for latest hurricane season (2012)

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Baseline skill

5-year skill goal

GFS

HWRFGFDL

Comparison of 2012 NCEP Operational Models to the 5 Year HFIP

Goal: Track

Baseline skill

GFS

HWRF

Comparison of 2012 NCEP Operational Models to the 5 Year HFIP

Goal: Intensity

GFDL

HFIP 5 year Goal

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Stream 1.5 Results for 2012

AHW

HWRF

FSU

FIMGFS

NOGAPS

TVCA

GFDL

ECMWF

UKMET

Canadian Model

AHW

HWRF

FSU

Intensity Consensus

Wisconsin

GFDL

DSHPLGEM

SPC3

TC-COAMPS

• The upgrade to the 3km triple-nested HWRF is a result of multi-agency efforts under HFIP support

– EMC - Computational tuning to speed up the model, nest motion algorithm, physics improvements, 3km initialization and pre-implementation T&E

– HRD/AOML - multi-moving nest, nest motion algorithm, PBL upgrades, interpolation routines for initialization and others.

– DTC - code management and maintain subversion repository

– ESRL - Physics sensitivity tests and idealized capability– NHC - Diagnose the HWRF pre-implementation results– URI - 1D ocean coupling in Eastern Pacific basin

2012 HWRF Upgrades

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2012 3km HWRF Operational Upgrade Summary

HOPS: oper. HWRF

H212: 2012 HWRF

ATL Tracks• Significant Improvements of H212

– Track/intensity forecast skills for 2011/2010 seasons on Atlantic basin 20-25% improvement against HOPS

– Track forecast skills of H212 of Eastern Pacific basin maximum 25% over the HOPS in 2011 season, but little degradation at day 4 and 5 in 2010 season mainly due to Hurricane Frank

– Intensity of 2011 EP basin with over 40% to HOPS. Significant improvements in intensity bias is noted for both Atlantic and Eastern Pacific, for both 2010-2011 seasons.

– The storm structure in terms of storm size and PBL height significantly improved

– Much improved wind-pressure relationship in high wind speed regime

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ATL IntensityHOPS: oper. HWRF

H212: 2012 HWRF

20-25% improvement

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Impact of Radar Data

Impact of TDR data assimilation to hurricane intensity forecast

2.2.2 (EMC)TDR assimilation

OPR HWRF

HWRF TDR

Cross section at initial time

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With TDR

Impact of TDR Data In Operational HWRF

Without TDR

Without TDR

With TDR

Track Error Intensity Error

• Questions?

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Extra Slides

Comparison of 2012 NCEP Operational Models to the 5 Year HFIP

Goal: Track

Comparison of 2012 NCEP Operational Models to the 5 Year HFIP

Goal: Intensity

Statistical Post Processing

• Statistical Post Processing can add skill to dynamical forecasts.

• There are a number of techniques based on ensembles or individual models.

• One method is shown in the following figure

– From the FSU Multi-Model Ensemble (MMEN) which forms a weighted mean of the many global and regional models run both operationally and by HFIP in real time.

2012 all storms

Genesis

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Verification of model genesis for operational global models

• All models have a bias towards over-prediction, caused by both false alarms as well as genesis occurring in the forecast long (>>48h) before observed genesis.

• 4-ensemble consensus close to reliable up through 50-60%.26

NHC Hurricane Genesis Statistics

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