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

NOAA Satellite Science Week

March 21, 2013

Fred Toepfer—HFIP Project Manager

HFIP OVERVIEW

10-year program with ambitious forecast improvement goals – to reduce evacuations costs

Designed and run by NOAAwith non-NOAA collaborators

Began in fiscal year 2009

Focus on improving numerical weather prediction model forecast guidance provided to the National Hurricane Center

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Track forecasting today

National Hurricane Forecast System

•50% improvements to hurricane track and 50% improvements to hurricane track and intensity forecasts out to 7 daysintensity forecasts out to 7 days

•Reduce cone of uncertaintyReduce cone of uncertainty

Track forecasting after HFIP Improvements

50% 50% reduced reduced forecastforecasterrorserrors

50% 50% reduced reduced forecastforecasterrorserrors

Goals

Drivers for an HFIP Program

• Lives: More than 50% of U.S. population lives within 50 miles of coast; Number of people at risk increasing along coast and inland; 180 million people visit the coast annually

• Property: Value of coastal infrastructure and economy rising… now > $3 trillion; annual U.S.tropical-cyclone-related damage lossesaveraged about $10 billion circa 2008;averaged losses double about every ten years

• Forecasts: Hurricane track forecasts have improved greatly; intensity forecasts have not

• Research: Tropical cyclone research has been under-resourced and not well-coordinated within the meteorological community

Courtesy: Ed Rappaport

There is a great need and potential for substantial improvements above and beyond current research efforts in hurricane forecasting.

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Bolivar peninsula after Ike (2008)

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The HFIP Project – Vision/Goals

• Vision• Organize the hurricane community to dramatically

improve numerical forecast guidance to NHC in 5-10 years

• Goals• Reduce numerical forecast errors in track and

intensity by 20% in 5 years, 50% in 10 years• Extend forecasts to 7 days• 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 Charter and Leadership

• NOAA-wide HFIP Charter signed August 1, 2007

• Hurricane Executive Oversight Board

– Jointly chaired by AA for National Weather Services and AA for Oceanic and Atmospheric Research

– Cross-NOAA Membership

• Current HFIP Management– Project Manager: Fred Toepfer, NWS/OST– Development Manager: Robert Gall, UCAR– Research Lead: Frank Marks, OAR/AOML/HRD– Operations Lead: Ed Rappaport, NWS/NHC

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Technical Team Structure2013

FY 2013 Teams FY 2013 Team Leads

1. HFIP Model Strategy Vijay Tallapragada, Stan Benjamin

2. Model Physics Brad Ferrier, Jian-Wen Bao

3. Data Assimilation/Initialization John Derber, Xuguang.Wang

4. Ensemble Development Jeff Whitaker, Jiayi Peng

5. Post Processing and Verification Development Team Mark DeMaria, David Zelinski, Tim Marchok

6. Societal Impacts Jennifer Sprague, Rick Knabb

FY 2013 Teams Strategic Team FY2013 Team Leads

1. Web Page Design 5 Paula McCaslin, Laurie Carson

2. 3 KM Physics Package 2 Joe Cione, Chan Kieu

3. Regional Hybrid DA System 3 John Derber, Jeff Whitaker

4 Use of Satellite Data in Hurricane Initialization

3 Tomi Vukicevic, John Knaff, Emily Liu

5. Stream 1.5 and Demo System Implementation 1 James Franklin, Barb Brown

6. Recon Data Impact Tiger Team. 1 James Franklin (NHC),

Vijay Tallapragada (EMC)

FY 2013 Tiger Teams

FY 2013 Strategic Planning Teams

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

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

Experimental Model Resultsfor 2012

• Operational HWRF (Stream 1.0)• Real-time delivery to NHC of Experimental

Models (Stream 1.5)• Experimental Research models (Stream 2.0)

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• 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 (2011)

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 (2011)

H212: 2012 HWRF

20-25% improvement

AHW

HWRF

FSU

FIMGFS

NOGAPS

TVCA

GFDL

ECMWF

UKMET

Canadian Model

AHW

HWRF

FSU

Intensity Consensus

Wisconsin

GFDL

DSHPLGEM

SPC3

TC-COAMPS

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Impact of Radar Data

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

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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

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Better Use of Satellite Data

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Use of Satellite Data in Hurricane Initialization

• Develop a system using high resolution satellite data near the vicinity of the hurricane core (regional scale)

• Improve the capability for assimilation of satellite observations using hybrid DA for basin scale HWRF

• Operational Regional DA system under development

• Develop and test use of various satellite data sets for initialization

• Cloudy radiances assimilation – Development underway for Global Data Assimilation System

• Upper Level Outflow Environment• Atmospheric Motion Vectors (AMV) (GOES winds)• Best combination of satellite data

• Initial Joint HFIP/JCSDA Workshop held in 2010 – Recommended focus on Global Cloudy Radiance Assimilation

• 10th JCSDA Workshop on Satellite Data Assimilation – October 2012 Recommends:• Research on better use of rapid-scan AMVs at the storm

canopy level; continued to investigate if cloudy radiance assimilation can help in the thinner outflow cirrus regimes.

• Investigate the expansion of the TC vitals (output from imagery analysis or derived products) to provide information to the assimilation i.e. eyewall structure/strength/radius, rainbands/asymmetries/shear, system depth.

• Development of a tool so forecasters can articulate their perception of the current state of the storm and to translate into something objective data assimilation can use.

JCSDA Workshop on Satellite Data Assimilation

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• Questions?

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Backup 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%.33

NHC Hurricane Genesis Statistics

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HFIP Appropriation History(2009-2013)

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FY09 FY10 FY11 FY12 FY13

WCOSS PAC 6.000M 3.000M 3.000M 4.000M

(OMB increase)

2.000M*(OMB 1yr reduction)

NWS ORF 15.040M 14.040M 14.040M 14.040M($6.5M NWS reduction)

13.640M* (OMB restored

less $400K)

OAR ORF 6.100M 6.100M 6.100M 6.000M 6.000M

TOTAL

$27.140M $23.140M $23.144M $24.040M $21.640M

*Anticipate Restoration in FY14

*Senate proposed full restoration of $400K

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