2010 HFIP R & D Activities Summary:

Accomplishments, Lessons Learned, and Challenges

R. Gall11, F. Toepfer11, S. Aberson1, J.W. Bao4, M. Bender6, S. Benjamin4, L. Bernardet4,14, M. 

Biswas5, B. Brown7, C. Davis6, M. DeMaria8, J. Doyle10, M. Fiorino4, J. Franklin9, I. Ginis13, J. 

Goerss10, S. Gopalakrishnan 1, T. Hamill4, H.S. Kim3, T. Krishnamurti 5, Y. Kwon 3, W. 

Lapenta3, S. Lord3, T. Marchok6, F. Marks1, L. Nance7, P. Kucera7, E. Rappaport9, C. 

Reynolds10, N. Surgi3, V.Tallapragada3, H. Tolman3, G. Vandenberghe3, Y. Weng12 J. 

Whittaker4, F. Zhang12, M. Zupanski2

30 March 2011

1Atlantic Oceanographic and Meteorological Laboratory OAR/NOAA

2Colorado State University

3Environmental Modeling Center NCEP/NOAA

4Earth System Research Laboratory OAR/NOAA 

5

Florida State University

6Geophysical Fluid Dynamics Laboratory OAR/NOAA 

7National Center for Atmospheric Research

8National Environmental Satellite Data Information Center NOAA

9National Hurricane Center NWS/NOAA

10Naval Research Laboratory, Monterey

11Office of Science and Technology, NWS/NOAA

12Pennsylvania State University 

13University of Rhode Island

13University of Colorado
1

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY 2

1.1. ENCOURAGING RESULTS 2

1.2. LESSONS LEARNED 2

1.3. CHALLENGES 3

2. BACKGROUND ON HFIP 4

3. THE HFIP MODEL SYSTEMS 6

3.1. THE GLOBAL MODELS: 6

3.2. THE REGIONAL MODELS: 7

3.3. INITIALIZATION SYSTEMS: 9

4. THE HFIP BASELINE 9

5. FY2010 DEMONSTRATION SYSTEM 11

6. ENCOURAGING RESULTS FROM FY10 12

7. LESSONS LEARNED FROM FY09 HFIP ACTIVITIES 25

8. CHALLENGES FOR HFIP BEYOND FY10 33

9. MEETING THE HFIP PERFORMANCE GOALS 33

10. LIST OF HFIP SUPPORTED PUBLICATIONS AND PRESENTATIONS 34

11. APPENDIX A: LIST OF HFIP TEAMS 35

12. APPENDIX B: ORGANIZATION ACRONYMS 38
2

2010 Hurricane Forecast Improvement Program (HFIP) R & D Activities 

Summary: Accomplishments, Lessons Learned, and Challenges

1. Executive Summary

This report describes the activities and results of the Hurricane Forecast Improvement Program in 

2010. It is organized around three themes, Encouraging results from 2010 HFIP testing, 

evaluation and development activities, lessons learned from negative results and the challenges

faced by the program to achieve its goals. The main topics from each of these three categories are:

1.1. Encouraging Results

The stream 1.5 testing and evaluation concept was successfully demonstrated. 

Four stream 1.5 candidate models were evaluated through a retrospective analysis 

study. Two of the models, AHW and GFDL, were accepted by NHC as part of 

stream 1.5 demonstration during the 2010 hurricane season.

1.2. Lessons Learned

The operational HWRF codes were fully synchronized with the community HWRF 

repositories, allowing operations and research to work from a common code.

The operational GFS model initialized with EnKF gave the best track forecasts in 

the Atlantic out to 5 days even beating the ECMWF. The improvement exceeded 

the 20% 5 year goal for HFIP on track demonstrating the potential to reach this

goal.

High resolution global ensembles (30 km, 20 members) can be run in real time on 

available computing resources meaning higher resolution is definitely possible. 

Using all aircraft data (including tail radar data) collected in 57 cases over 3 years, 

the Penn State regional model with an EnKF data assimilation scheme improved 

intensity forecasts beyond 24 hours by more than 20% relative to the HFIP baseline

demonstrating the potential to reach the HFIP 5 year intensity goal.

The multi-model regional ensemble using a “Correlation Based Correction” 

scheme substantially outperformed any of its components and showed a 40-50% 

gain over the 5-day Statistical Hurricane Intensity Forecast model. (SHF5).

When run as a seasonal prediction system, the Cubed Sphere model produced a 

remarkable prediction of the number of hurricanes and tropical storms for the 

period 2000-2010. This is powerful evidence of the ability of the global models to 

forecast genesis. 

For some 2010 storms, the possibility of using ensemble information to narrow the 

“cone of uncertainty” from the operational cone for track was demonstrated.
3

Global models consistently provide significantly better track forecasts than regional 

models.

1.3. Challenges

Operational computing not likely to be sufficient to meet HFIP goals within 3 

years.

In the following sections, we discuss each of the above lessons and challenges and our plans for 

addressing each.

The major problem with initialization of regional models identified last year 

remains. It is a particularly severe problem for the first 24 hours of the forecast, 

which results in a severe problem in forecasting rapid intensification.

Advanced data assimilation systems, particularly EnKF, are essential for improving 

both track and intensity forecasts.

Statistical post processing of ensemble output is likely to provide additional skill 

improvement.

The delivery of complete, consistent Tier 1 (ATCF files) and Tier 2 (gridded model) 

data is necessary to provide consistent evaluations of the model forecasts for the 

same forecast challenge using a common (operational) tracker.

Initialization systems for regional models need to be improved for forecasts in the 

first 24 hours.

HFIP needs to develop systems to make better use of satellite data in regional 

models.

Implementing the Hybrid data assimilation (DA) system in operations as soon as 

possible.

Development of appropriate statistical post processing systems for ensemble output.
4

2010 HFIP R & D Activities Summary:

Accomplishments, Lessons Learned, and Challenges

2. Background on HFIP

HFIP provides the basis for NOAA and other agencies to coordinate hurricane research needed 

to significantly improve guidance for hurricane track, intensity, and storm surge forecasts. It 

also engages and aligns the inter-agency and larger scientific community efforts towards 

addressing the challenges posed to improve hurricane forecasts. The goals of the HFIP are to 

improve the accuracy and reliability of hurricane forecasts; to extend lead time for hurricane 

forecasts with increased certainty; and to increase confidence in hurricane forecasts. These 

efforts will require major investments in enhanced observational strategies, improved data 

assimilation, numerical model systems, and expanded forecast applications based on the high 

resolution and ensemble-based numerical prediction systems. 

HFIP began in 2009 with specific goals to reduce the average errors of hurricane track and 

intensity forecasts by 20% within five years and 50% in ten years with a forecast period out to 7 

days. In addition, HFIP has a goal to increase the probability of detecting rapid intensification at 

day 1 to 90% and 60% at day 5. The benefits of HFIP will significantly improve NOAA’s 

forecast services through improved hurricane forecast science and technology. Forecasts of 

higher accuracy and greater reliability (i.e., user confidence) are expected to lead to improved 

public response, including savings of life and property. 

NOAA recognizes that addressing the broad scope of the research and technology challenges 

associated with improving hurricane forecasts requires interaction with, and support of, the larger 

research and academic community. It is hypothesized that these very ambitious goals of the 

HFIP can only be met using high-resolution (~5-15 km) global atmospheric forecasting 

numerical models run as an ensemble in combination with regional models at even higher 

resolution (~1-5 km). Demonstrating this is very expensive computationally and hence HFIP has 

been building up a computational system at the NOAA facilities in Boulder, Colorado, called tjet, where HFIP can demonstrate the techniques necessary to meet its goals. Only by 

demonstrating the value of high resolution is there any opportunity to obtain such a 

computational resource for operational hurricane forecasts. 

For FY10, the HFIP program consisted of about $23 M with $3 M dedicated to enhancing 

computer capacity available to the HFIP program. The funding for computing was used to 

enhance the t-jet system by the addition of processors. The t-jet system currently has 10,000 

processors available for HFIP computing. About $10M of the $23M is part of the base funding 

for the Atlantic Ocean and Meteorology Laboratory (AOML) in Miami and the Environmental 

Modeling Center (EMC) at NCEP for hurricane model development. The remaining $11M was 

distributed to various NOAA laboratories and centers (Earth System Research Lab (ESRL), 

Geophysical Fluid Dynamics Laboratory (GFDL), National Environment Satellite Data and 

Information Service (NESDIS), and National Hurricane Center (NHC). Funding was also 

provided to the National Center for Atmospheric Research (NCAR), Naval Research Laboratory 

in Monterey (NRL), and several universities: University of Wisconsin, The Pennsylvania State 

University, Colorado State University, University of Arizona, Florida State University, 

University of Wisconsin, University of Miami, and State University of New York-Albany, and 
5

University of Rhode Island. Finally, $1M was contributed for second year funding to the 

National Oceanographic Partnership Program (NOPP), Announcement of Opportunity for 

competed proposals related to improving understanding and prediction of hurricanes. The 

funding to NOPP from HFIP was matched by funding from the Office of Naval Research (ONR). 

Distribution of the $11M was accomplished through recommendations from 8 teams focused on 

various components of the hurricane forecast problem. Current teams are listed in Appendix A 

with the team leads shown in bold type. For reference, a list of acronyms for the various 

organizations is shown in Appendix B. These teams are made up of over 50 members drawn 

from the hurricane research, development, and operational community. 

HFIP is primarily focused on techniques to improve the numerical model guidance that is 

provided by NCEP operations to NHC as part of the hurricane forecast process. It is organized 

along two paths of development called Streams (see Table 1). Stream1 assumes that the 

computing power available for operational hurricane forecast guidance will not exceed what is 

already planned by NOAA. The development for this stream has been in planning for several 

years by EMC. HFIP activities at the NOAA labs and centers will help accelerate this 

development. 

Table 1. The HFIP Stream Strategy.

Stream Description

Stream 1 Development to directly improve the current operational global 

and regional hurricane models. Assumes that the computing that 

will be available for operations is that currently being planned.

Stream 2 Assumes that operational computing can be substantially increased 

above current plans. Seeks computing resources from major 

supercomputing centers for testing and evaluation. Emphasis is on 

high resolution global and regional models run as ensembles. It 

will include a demonstration system run in real time each summer 

to test and evaluate promising new technology.

Stream 1.5 This will be part of the summer demonstration system and will be 

forecaster defined. Components from Stream 2 that forecasters see 

as particularly promising in one year will be configured to run in 

real time the next year, with products made available to NHC.

HFIP Stream 2 does not put any restrictions on the increases in computer power available to 

NWS operations, and in fact, assumes that resources will be found to greatly increase available 

computer power in operations above that planned for the next 5 years. The purpose of Stream 2, 

therefore, is to demonstrate that the application of advanced science and technology developed 

under the auspices of HFIP along with increased computing will lead to the expected increase in 

accuracy and other aspects of forecast performance. Because the level of computing necessary 

to perform such a demonstration is so large, the Program is applying to resources outside NOAA

in addition to trying to increase internal computing for development. 

A major part of Stream 2 is a demonstration system, or Demo System, that is run in testing mode 

each hurricane season. The purpose of this system is to evaluate strengths and weaknesses of 

promising new technology. As a result of the Demo System testing, some components may be 
6

found to be of particular interest to the operational forecasters, and, if resources do not permit its 

implementation in the operational infrastructure, the Demo system for the following season will 

emphasize those components and will provide specific output that is made available to NHC 

forecasters for evaluation. We refer to this component of the Demo System as Stream 1.5.

Roughly half of the HFIP funding is going toward Stream 2 development activities. In Stream 2 

we are assuming that the best approach to improving the forecast hurricane track beyond 4 days 

is through the use of high resolution global models run as an ensemble. We describe below the 

logic behind this assumption. For improvements in forecast of hurricane intensity, especially in 

the 1 to 4 day time range, the best approach is likely to be high resolution regional models, also 

run as an ensemble. The global models are likely to be limited in resolution to about 10 km for 

at least the next 5 years, because of computer limitations, especially when they are run as an 

ensemble. Thus the only way to achieve the very high resolution of about 1 km necessary for 

resolving the inner core of the hurricane is with regional models. It is generally assumed that the 

inner core must be resolved before we can expect to see consistently accurate hurricane intensity 

forecasts.

To facilitate the transition of research to operations, HFIP has recognized the importance of 

having research and operations share the same code base, and HFIP has co-sponsored the 

Developmental Testbed Center (DTC) to make available and support HWRF to the community. 

This support started in February 2010 with the DTC/EMC/MMM Joint Hurricane Workshop and 

WRF for Hurricanes Tutorial. During FY10, EMC and DTC established a common version of 

the operational codes for HWRF in the community code repositories. This version has been 

thoroughly tested and shown to give comparable results to the operational codes. Thus with the 

repository in Boulder at the DTC, both the operations and research community will be drawing 

from the same central code repository facilitating the transitions of research results to operations.

3. The HFIP Model Systems

3.1. The Global Models:

FIM – Refers to the Flow-following finite-volume Icosahedral Model. The FIM is an 

experimental global model that can be run at various resolutions and uses initial conditions from 

a number of sources. It is currently using a fixed ocean underneath. It has been built by the 

NOAA Earth System Research Laboratory (ESRL). 

Cubed Sphere – Refers to a finite volume model developed by GFDL using a grid where the 

sphere is projected onto a cube. The governing finite difference equations are very similar to 

those used in the FIM

GFS – Refers to the Global Forecast System. There are two versions of this model currently 

running in the demonstration system. This includes a version of the current operational model 

run at the NOAA National Centers for Environmental Prediction (NCEP) and an experimental 

version of that model at ESRL. The main difference between the two versions is the 

initialization system; GSI for the operational model and EnKF for the ESRL version.

NOGAPS – Refers to Navy Operational Global Atmospheric Prediction System. Currently a 

semi-Lagrangian version of NOGAPS is being developed, which will allow for efficient highresolution forecasts.
7

A summary of some of the main attributes of the global models is shown in Table 2.

Table 2. Specifications of the HFIP Global Models.

Models

Horizontal 

resolution

Vertical 

levels

Cumulus 

Parameterization

Microphysics PBL Land Surface Radiation Initialization

FIM 20 km 64 From 2010GFS -

Simplified Arakawa 

Schubert

Ferrier GFS NonLocal PBLNoah LSM GFDL/RRTM ESRL EnKF

GFS 27 km ?? Simplified Arakawa 

Schubert

Ferrier GFS NonLocal PBLNoah LSM GFDL

scheme

ESRL EnKF

Cubed 

Sphere

25 km 32 Shallow only Modified Lin, 6-

class

Lock (AM2) GFDL LM3 GFDL nudging to NCEP 

analysis

NOGAPS41 km 42 Emanuel N/A NOGAPS NOGAPS Harshvardhan/ 

Fu-Liou

NAVDAS-AR

3.2. The Regional Models:

WRF – Refers to Weather Research and Forecasting model. This is actually a modeling system 

with options for the dynamic core (ARW—Advanced Research WRF built by NCAR and 

NMM—Non-hydrostatic Mesoscale Model, built by EMC) and several options for physics as 

well as initialization systems, post processing systems, and verification systems.

The NCEP Hurricane WRF (HWRF) is based on the Non-hydrostatic Mesoscale (NMM) 

dynamic core and has a movable, two-way nested grid capability for the 9 km inner nest. The 

coarse domain has 27-km grid spacing and covers a 75° x 75° region with 42 vertical layers. 

Advanced physics include atmosphere/ocean fluxes, coupling with the Princeton Ocean Model 

(POM) and the NCEP GFS boundary layer and deep convection.

Two configurations of the WRF ARW system were also run. The ARW system run by NCAR 

used a simplified one dimensional model of the ocean with two interactive nests within the outer 

regional model. FSU also ran a version of the ARW without an interactive ocean. 

The Penn State Regional Ensemble – This was another version of the WRF ARW system similar 

to the NCAR WRF ARW. It used a static interactive inner nest but no interactive ocean. It was 

run as a 30 member ensemble.

COAMPS-TC – It is a Navy model run by NRL Monterey. It is a version of their COAMPS 

regional prediction system that is being run operationally and has an interactive ocean. The 

model run for 2010 had horizontal resolution of 45, 15, and 5 km for the three nested grids.

The Multi-Model Ensemble – The multi-model ensemble is organized by Florida State University 

and was made up of a total of 7 models run by different organizations. The various models and 

their resolution are indicated in Table 3. Two of the members are the operational models, GFDL 

at 7.5 km and HWRF at 9 km. GFDL is the old operational model that is still being run in 

parallel with the current operational model HWRF. HWRF is constructed from the NMM core 

of the WRF (see above), and both GFDL and HWRF models are coupled to the Princeton Ocean 

Model (POM) in the Atlantic Basin. HWRF-x is an experimental version of the operational 

HWRF run by the Hurricane Research Division of OAR. It did not have an interactive ocean 

model associated with it but did have an interactive nest. GFDL Parallel, also referred to as 

GFD5 is a somewhat updated version of the GFDL operational model and run at the same 

resolution.
8

Table 3. Regional models run in the Demonstration System during 2010.

Models Nesting /

Horizontal 

Resolution (km)

Vertical 

Levels

Cumulus 

Parameterization

Microphysics PBL Land Surface Radiation Initial and 

Boundary 

Conditions

Atmospheric 

Initialization

HWRF 

(OPS)

2

27/9

42 Simplified Arakawa 

Schubert

Ferrier GFS Non-Local PBL GFDL Slab ModelGFDL

Scheme

GFS GSI 3DVAR with 

cycled vortex 

initialization

GFDL (OPS) 3

30/15/7.5

42 Arakawa Schubert Ferrier GFS Non-Local PBL Slab Model Schwarz-kopf-Fels Scheme GFS GFDL Synthetic 

Bogus Vortex

HWRF IC 2

27/9

42 Simplified Arakawa 

Schubert

Ferrier GFS Non-Local PBL GFDL Slab ModelGFDL

Scheme

GFS GSI 3DVAR with 

inner core data

HWRF -x 2

9/3

42 Simplified Arakawa 

Schubert

Ferrier GFS Non-Local PBL GFDL Slab ModelGFDL

Scheme

GFS HWRF (OPS)

HWRF 

HYCOM

2

27/9

42 Simplified Arakawa 

Schubert

Ferrier GFS Non-Local PBL GFDL Slab ModelGFDL

Scheme

GFS HWRF (OPS)

HWRF-HRD

EnKF DA

2

9/3

42 Simplified Arakawa 

Schubert

Ferrier GFS Non-Local PBL GFDL Slab ModelGFDL

Scheme

GFS EnKF with aircraft 

data

HWRF

NOAH LSM

2

27/9

42 Simplified Arakawa 

Schubert

Ferrier GFS Non-Local PBL NOAH LSM GFDL

Scheme

GFS HWRF (OPS)

GFDL 

Parallel

3

30/15/7.5

42 Arakawa Schubert Ferrier GFS Non-Local PBL Slab Model Schwarz-kopf-Fels Scheme GFS GFDL synthetic 

Bogus Vortex

WRF ARW

FSU

2

12/4

40 Simplified Arakawa 

Schubert

WSM5 YSU 5-Layer Thermal 

Diffusion soil 

Model

RRTM (longwave) / Dudhia 

(shortwave)

GFS (initial and 

boundary 

condition)

Initialized from GFS

WRF ARW 

(NCAR)

3

12/4/1.3

36 New Kain Fritsch (12 

km only)

WSM5 YSU 5-Layer Thermal 

Diffusion soil 

Model

RRTM (longwave) / Dudhia 

(shortwave)

GFS EnKF method in a 6-

hour cycling mode

WRF ARW

Utah

3

27/9/3

31 Betts-Miller Lin MYJ 5-Layer

Thermal Diffusion

Scheme

RRTM Dudhia WRF 3DVAR

COAMPSTC3

45/15/5

(15/5 km following 

the 

storm)

40 Kain Fritsch on 45 and 

15 km meshes

Explicit microphysics 

(5 class bulk scheme) 

Navy 1.5 Order Closure Slab with the 

NOAH LSM as an 

option

Harshvardhan NOGAPS and 

GFS

3D-Var data 

assimilation with 

synthetic 

observations

Wisconsin 

Model

UW NMS

(3D enstrophy/

entropy/KE

conserving 

dynamics core)

3-4 

90/45/9 

km

90/45/9/3 

km

52 Modified Emanuel Explicit bulk microphysics 

(cloud/rain/pristine/

aggregate/graupel)

1.5 Order Closure WRF vegetation/land 

surface/Andreas emulsion 

layer

RRTM GFS/GFDL

Penn State 

ARW

3

40.5/13.5/4.5 for 

ensemble forecast

1.5-km nest for 

control

35 Grell-Devenyi 

ensemble scheme

(40.5 km only)

WSM 6-class graupel 

scheme

YSU 5-layer thermal 

diffusion scheme

RRTM (longwave) / Dudhia 

(shortwave)

GFS EnKF with NOAA 

airborne radar
9

3.3. Initialization systems:

A number of approaches were used to create the initial state for the global and regional models in 

the experiments, which are described below. The choices include:

The initial state created for the current operational model (Global Forecast System or GFS) 

interpolated to the higher resolution grid. The GFS uses the Grid point Statistical 

Interpolation (GSI) initialization system that has run operationally for many years. It is a 

three-dimensional variational approach (3D-VAR). 

NRL Atmospheric Variational Data Assimilation System (NAVDAS). This is the system 

used to provide the initial conditions to the Navy global model. It has been a 3D-VAR 

system but starting late September 2009, it was upgraded to NAVDAS-AR (for accelerated 

representor), a four-dimensional variational approach (4D-VAR). The COAMPS-TC makes 

use of NAVDAS as well as synthetic observations to initialize the tropical cyclone structure 

and intensity. 

Ensemble Kalman Filter (EnKF). This is also an advanced data assimilation approach 

(somewhat like 4D-VAR) that uses an ensemble to create background error statistics for a

Kalman Filter. While this approach is still in the experimental stage in the U.S. (though 

operational in Canada), it has shown considerable promise.

Hybrid Variational-Ensemble Data Assimilation System (HVEDAS). This system combines 

aspects of the EnKF and 3D- or 4D-VAR for example, using the ensemble of forecasts to 

estimate the covariances at the start of a 4D-VAR assimilation window. This technology is 

under development at NOAA/NCEP/EMC and NOAA/OAR/ESRL. It was not ready for 

testing in the 2010 season but may be available for subsequent seasons. This hybrid 

approach is likely to define the operational global data assimilation system for NOAA in the 

5-year time frame.

The initial state for the regional models was generally produced by downscaling the global 

models’ analysis and forecasts. In addition, the Penn State Regional Ensemble model, the 

WRF/ARW/NCAR model used an EnKF initialization system. 

The operational HWRF utilizes an advanced vortex initialization and assimilation cycle 

consisting of four major steps: 1) interpolate the global analysis fields from the Global Forecast 

System (GFS) onto the operational HWRF model grid; 2) remove the GFS vortex from the 

global analysis; 3) add the HWRF vortex modified from the previous cycle’s 6-hour forecast 

(or use a synthetic bogus vortex for cold start); and 4) add satellite radiance and other 

observation data in the hurricane area (9 km inner domain) – no observations are used in the 

inner core of the storm. The major differences from the GFDL model initialization are steps 3) 

and 4).

4. The HFIP Baseline

HFIP 10-year goals for Atlantic guidance were set by the HFIP Executive Oversight Board 

(HEOB) as:

• Reduce average track errors by 50% for days 1 through 5

• Reduce average intensity errors by 50% for days 1 through 5

• Increase the probability of detection (POD) for rapid intensity change to 90% at Day 1 

decreasing linearly to 60% at day 5, and decrease the false alarm ratio (FAR) for rapid 
10

intensity change to 10% for day 1 increasing linearly to 30% at day 5. The focus on rapid 

intensity change is the highest forecast challenge indentified by the NHC.

• Extend the lead time for hurricane forecasts out to Day 7 (with accuracy of Day 5 

forecasts in 2003).

HFIP 5-year goals are to improve track and intensity guidance errors by 20% over the next 5

years. 

To measure progress toward meeting these goals, HFIP established a baseline against which 

results from experimental and operational HFIP model guidance will be measured. These HFIP 

Performance Goals Baselines were developed in a white paper authored by James Franklin dated 

5 May 2009 and summarized here. Tables 4 and 5 define the baselines for track and intensity, 

respectively. CONS in Table 4 is a consensus of operational models and is the primary baseline 

for measuring improvements. OCD5 is a simple statistical model baseline for measuring 

improvements in forecast skill, and OFCL is the average NHC official forecast errors from the 

baseline period. 

Table 4. HFIP Track Performance Baseline (nautical miles).

VT 

(h)

N OFCL OCD5 CONS

0 818 7.4 7.7 7.8

12 741 29.4 44.5 30.0

24 663 49.6 93.3 49.8

36 586 69.9 150.9 69.5

48 518 91.2 212.2 89.6

72 411 135.0 317.2 132.0

96 313 173.0 396.5 175.2

120 247 218.6 473.0 221.9

Table 5. HFIP Intensity Performance Baseline (knots).

VT 

(h)

N OFCL OCD5 CONS

0 820 1.9 2.2 2.2

12 745 7.2 8.3 7.7

24 667 10.4 11.5 10.1

36 590 12.6 14.2 11.7

48 522 14.6 16.1 13.7

72 415 17.0 17.8 16.0

96 316 17.5 19.3 16.6

120 250 19.0 19.3 17.0
11

5. FY2010 Demonstration System

During hurricane season of 2010 HFIP ran a number of models in real-time on its t-jet computer 

in Boulder. Much of the results reported below were from that demonstration system. During 

the period just after last year’s hurricane season and the start of the season in 2010, t-jet was used 

largely for development of the systems used in 2010 and for retrospective runs in part to 

establish the stream 1.5 components and for the development of statistics for the Multi Model 

Ensemble. An example of results for the retrospective runs is shown in figure 4 and figures 5 

and 6 for the Multi Model Ensemble.

As discussed before, Table 2 lists details of the global models in use by HFIP. Table 6 lists those 

that took part in the HFIP 2010 demonstration system.

Table 6. Global Models run in the 2010 Demonstration System.

 Global Models Run in 2010

FIM Deterministic models 

30 km (Initialized with EnKF)

Global Ensembles:

60 km GFS (initialized with EnKF) 20 members

55 km NOGAPS (4DVAR), 9 members

As shown above, Table 3 lists the various regional models used by HFIP during this last year. 

Included in this list are all the models used in the multi-model ensemble (MMEN). Table 7 

briefly lists those models that were part of the MMEN. Other regional models were part of 

stream 1.5 (WRF ARW NCAR, and GFD5). The GFDL model in addition was run as a 20 

member ensemble by varying the initial conditions provided to the model. Table 8 lists the 

various members of the GFDL ensemble.

Table 7. Multi Model Mesoscale Ensemble.

Multi Model Regional Ensemble

HWRF 9 km

GFDL 7.5 km

HWRF-x (H3HW) 3 km

WRF/ARW/NCAR (AHW1) 1.3 km

WRF/ARW/FSU (ARFS) 4 km

COAMPS-TC (COTC) 5 km

GFDL parallel (GFD5) 7.5 km
12

Table 8. Eleven member ensemble: 10 perturbed members and a control forecast.

GFDL Regional Ensemble

GP0: Control forecast (GFD5 run on Jet)

GPA: Unbogussed forecast

GPB: GFD5 with no asymmetries included

GPC: GFD5, but with the use of old environmental filter

GPD: Increase storm size (ROCI-based) by 25%

GPE: Decrease storm size (ROCI-based) by 25%

GPF: Increase wind radii 25%, increase storm size 25%

GPG: Decrease wind radii 25%, decrease storm size 25%

GPH: Old filter (GPC), plus both size increases from GPF

GPJ: Old filter (GPC), plus both size decreases from GPG

GPK: Set Rmax minimum to 45 km (GFD5 control uses 25 km)

6. Encouraging Results from FY10

Table 9 gives a high level overview of some encouraging results from the HFIP work during 

FY10. A more detailed description for each encouraging results is further expanded upon in the 

discussion below.
13

Table 9. Encouraging Results from FY10 Demonstration System.

The operational HWRF codes were fully synchronized with the community 

HWRF code repositories, allowing operations and research to work from a 

common code.

The operational GFS model initialized with EnKF gave the best track 

forecasts in the Atlantic out to 5 days even beating the ECMWF. This was 

achieved with a relatively low resolution version of the GFS (T256~60km). 

The improvement exceeded the 20% 5 year goal for HFIP on track 

demonstrating the potential to reach this goal.

High resolution global ensembles (30 km, 20 members) can be run in real 

time on available computing resources, which mean higher resolution is 

definitely possible. 

Using aircraft tail radar data collected in 57 cases over 3 years, the Penn 

State ARW regional model with an EnKF data assimilation scheme 

improved intensity forecasts beyond 24 hours by more than 20% relative to 

the HFIP baseline demonstrating the potential to reach the HFIP 5 year 

intensity goal.

The multi-model regional ensemble using a “Correlation Based Consensus” 

scheme substantially outperformed any of its components and showed a 40-

50% gain over the 5-day Statistical Hurricane Intensity Forecast model. 

(SHF5). This demonstrates the possibility that statistical models combined 

with use of additional data (especially aircraft data) can give even greater 

improvements over the HFIP 5 year goal.

When run as a seasonal prediction system, the Cubed Sphere model 

produced a remarkable prediction of the number of hurricanes and tropical 

storms for the period 2000-2010. This is powerful evidence of the ability of 

the global models to forecast genesis. 

For some 2010 storms the possibility of using ensemble information to 

narrow the “cone of uncertainty” from the operational cone for track was 

demonstrated.

The stream 1.5 testing and evaluation concept was successfully 

demonstrated. Four stream 1.5 candidate models were evaluated through a 

retrospective analysis study. Two of the models, AHW and GFDL, were 

accepted by NHC as part of stream 1.5 demonstration during the 2010 

hurricane season.
14

The operational HWRF codes were fully synchronized with the community HWRF code 

repositories, allowing operations and research to work from a common code.

During this past year both EMC and DTC worked to update the operational version of HWRF 

from version 2.0 to the current version of WRF (version 3.2.1). This makes the operational 

model completely compatible with the codes in the community code repositories and vice versa. 

This allows researchers access to the operational codes and makes improvements in HWRF 

developed by the research community easily transferable into operations. This was one of the 

initial goals of the WRF program and has now been fully implemented for the first time with the 

hurricane models. 

Figure 1 shows schematically how the process of moving the operational model into the 

community WRF code repository was achieved. The new version 3.21 codes were carefully 

compared to the original operations code until the two versions gave comparable answers. 

Subsequent changes to the operational code will now be entered directly into the community 

code 

repositories.

Figure 1. Schematic of the process for integrating the atmospheric forecast model 

component of the HWRF code onto the WRF community code repository.

The operational GFS model initialized with EnKF gave the best track forecasts in the Atlantic 

out to 5 days even beating the ECMWF. 
15

During the 2010 Demo project run on the HFIP t-jet system, a 20 member ensemble T256 

(~60km) version of the operational GSF global model was run. It is identical to the operational 

GFS including its physics package. The only exception was a different initialization system was 

used. For the operational GFS (deterministic T574 ~29km), the operational GSI (essentially 

3DVAR) data assimilation system was used for initialization. For the HFIP ensemble, an EnKF 

approach was used. The track verification for the Atlantic season is shown in Figure 2. Note:

Between 36 and 72 hours of forecast time the HFIP ensemble in green equals or exceeds 

the HFIP goal of a 20% improvement over the HFIP baseline track errors that it is trying 

to achieve within 5 years (we are in the second year of that 5 year period). This 

demonstrates that given the computer resources to run such a global ensemble 

operationally, HFIP may achieve its track goals at least out to 5 days. 

In the Atlantic the HFIP ensemble performed significantly better than the much higher 

resolution ECMWF T1299 ~15km model as well as the higher resolution operational 

GFS T547 ~20 km.

The global models performed considerably better on track at all lead times than the two 

operational regional models, HWRF and GFDL. This tendency for the global models to 

outperform the regional models on track has been frequently noted.

Figure 2. Average track errors for various models relative to the HFIP baseline in 2010 in 

the Atlantic. All storms in 2010 are included. HFIP 20% goal refers to the 5 year goal of 

reducing track errors by 20% relative to the HFIP baseline defined earlier.

Figure 3 shows the same as Figure 2, but for the West Pacific. We note the following result:

The HFIP ensemble performance is similar to the Atlantic.

HFIP 20% goal

Regional models Global Models

HFIP Ensemble

ECMWF

GFS Operational
16

The ECMWF model performs better than the HFIP ensemble at least out to 72 hours. In 

the Pacific in 2010, many of the typhoons were strongly affected by mid-latitude systems. 

It is known that the ECMWF is known to perform better in these situations. This is likely 

due to its much higher resolution and therefore the better resolution of mid-latitude 

frontal processes.

Again, the global models outperform the regional models on track.

Figure 3. Same as figure 2 but for the Western Pacific. In this case, the regional models (in 

shades of purple) are the Navy regional models.

High resolution global ensembles (30 km, 20 members) can be run in real time on available 

computing resources meaning higher resolution is definitely possible. 

A major accomplishment of the HFIP program over the last two years has been the development 

of a research computer system located at the NOAA facilities in Boulder, Colorado. This system 

is referred to as t-jet and is devoted to HFIP testing, evaluation and development. Currently, it 

consists of about 10,000 processors and is about equivalent to all of the operational computing at 

NCEP. During the demonstration program conducted during the summer hurricane season, it is 

focused on real-time experimental forecasts. In 2010, this included a 20 member 60 km global 

ensemble, results from which are displayed in the previous section. HFIP is planning to increase 

the number of processors on t-jet for the 2011 season to 16,000 processors. It will be possible 

given our experience with the 60 km global ensemble to increase the resolution to 30 km and run

the global model ensemble with an EnKF initialization in real-time. 

We note that the total cost of t-jet when it has been increases to 16,000 processors will have been 

$10M so it seems reasonable that even a relatively modest increase in size of a computer like tjet would allow global ensembles to reach at least 20 km resolution and perhaps considerably 
17

higher. The global models are useful for forecasts far beyond hurricane forecasting so we feel 

high resolution global ensembles will be the future centerpiece of forecasting in general.

Using aircraft tail radar data collected in 57 cases over 3 years, the Penn State ARW regional 

model with an EnKF data assimilation scheme improved intensity forecasts beyond 24 hours

by more than 20% relative to the HFIP baseline.

This was the first demonstration within HFIP to show that it will be possible to achieve its 5 year 

intensity goal. Figure 4 shows the results from the PSU ensemble when all aircraft data 

including the tail radar data are included in the initialization. We note the following from the 

results shown in Figure 4. 

Beyond 24 hours the errors meet or exceed the 20% goal set by HFIP as its 5 year 

intensity goal for storms for which there are aircraft radar data.

The intensity errors at 0 hours are roughly the same as the errors later on. The initial 

errors are a result of the ensemble spread in creating the initial states and it apparently 

takes about 24-36 hours for the initial error to settle down.

The right hand panel illustrates the possible use of statistical post processing to improve 

the forecasts. Here a very simple bias correction is used that removes the initial bias and 

uses this correction out to 36 hours with the correction being reduced linearly to zero at 

36 hours.

Note that very similar results to those shown in figure 4 (but for just the 2010 season) 

were obtained by HRD using their version of the HWRF model and EnKF DA.

After initial bias corrected

Operational HWRF

PSU Ensemble using aircraft data

HFIP Base

HFIP 20% goal

Simple Bias Correction first 36 hours

•All Aircraft data (radar, soundings, flight level) used in DA

•All storms for 2008-2010 with aircraft data

•EnKF DA

Figure 4. Intensity error from forecasts with the PSU ensemble run at 4 km and initialized 

with an EnKF system using all available aircraft radar data including radar data. All 

storms with aircraft tail radar data for three seasons, 2008-2010 are included in the 

statistics, purple line. Blue line is the forecast from the operational HWRF, black line is 

the HFIP baseline for track and the black dashed line represents a 20% improvement over 

the baseline. Numbers at the top of the figures indicate the number of cases. The left 

figure shows the raw forecasts in the purple line. The right figure is the same except that a 

simple bias correction is applied that reduces the bias to zero at zero hour and this 

correction is applied to other times decreasing linearly to zero at 36 hours. 
18

The multi-model regional ensemble using a “Correlation Based Consensus” scheme 

substantially outperformed any of its component models and showed a 40-50% gain over the 

5-day Statistical Hurricane Intensity Forecast model. (SHF5). 

Figure 5 shows results from the Multi model Ensemble defined in Table 7 where the red line 

indicates the Correlation Based Consensus (CBC) where weights are applied to each model 

result based correlation coefficients derived from a training phase where all storms but the 

storms being forecast are correlated with the observations. These coefficients are then used to 

compute a weighted ensemble mean. Note that in a real forecast situation the training would be 

done on storms from earlier years, however, for these calculations only the storms for 2010 were 

available. The calculation of the CBC is illustrated in Figure 7

In Figure 5 just the mesoscale models of the Multi Model Ensemble are shown are shown for 

both track and intensity. For most lead times the skill of the CBC relative to the statistical 

models (CLIPER for track and SHF5 for intensity) meets or exceeds the skill for even the most 

skillful of the regional models.

Figure 6 is just focused on intensity and now includes other models including the operational 

global models (GFS and NOGAPS) and two operational statistical models for intensity (SHF5 

and DSHP). Here the comparison is against the HFIP baseline defined in Table 5. The red bars 

are again the CBC result calculated from all the models shown in Table 7 and again it has the 

lowest error of any of the component models except at 120 hours. Furthermore the error of the 

CBC is less than the baseline and in fact 25% less at 72 hours. This exceeds the HFIP goal of 

20% improvement within 5 years.

As discussed earlier, we noted the introduction of aircraft data into the initialization of the 

regional models through EnKF provided a 20% improvement ads well. The results in Figure 6 

do not include any models that were initialized with radar data. We can speculate here that 

combining the two; use of a multi model ensemble with CBC and where each model is initialized 

with aircraft data should provide an even greater improvement. This demonstrates the possibility 

that statistical post processing combined with use of additional data (especially aircraft data) can 

give even greater improvements over the HFIP 5 year goal.
19

Figure 5. Track and intensity skill for the 2010 Multi-Model Ensemble (see Table 7). The 

various models are identified in the figure. CBC (red curve) is the correlation based 

consensus described in the text.

Figure 6. Another example of the CBC results but here including global models and two 

statistical models used in operations (SHF5 and DSHP). The error using CBC is shown in 

red, GFS is the operational global GFS, NGPS is the Navy global NOGAPS model and the 

black bar is the HFIP baseline.
20

Figure 7. Illustration of the Correlation Based Consensus (CBC) method.

When run as a seasonal prediction system, the Cubed Sphere model produced a remarkable 

prediction of the number of hurricanes and tropical storms for the period 2000-2010. 

Figure 8 shows results from the Cubed Sphere model (see Table 2) of GFDL was run coupled 

with the Princeton Ocean Model (POM) starting with ocean and atmospheric conditions in June 

then integrated through the hurricane season so it is a true prediction of the number of storms and 

hurricanes for the each season. Years 2000-2010 are included and the system was run several 

times to produce a small ensemble. Shown in the figure are the counts of hurricanes and tropical 

storms from each of the ensemble as well as the ensemble mean (red). The black line shows the 

observed numbers. 

The match is remarkable indicating that the model is not significantly over or under predicting 

genesis so we get nearly correct predictions of the number of tropical storms and hurricanes. 

Since the breakout between tropical storms and hurricanes is approximate, the model system is 

able to forecast intensity at least at two intensity levels—tropical storm and hurricane. Granted 

this is a seasonal forecast, not a forecast of individual storms. However it does indicate the 

ability of global models to provide reasonable forecasts of genesis and at least the large scale 

controls on intensity. CONS in Table 4 is a consensus of operational models and is the primary 

baseline for measuring improvements. OCD5 is a simple statistical model baseline for measuring

improvements in forecast skill, and OFCL is the average NHC official forecast errors from the 

baseline period. 
21

FORECAST HURRICANE COUNT

FORECASTTROPICAL STORM COUNT

Note: Not only are the forecast 

number of tropical storms and 

hurricanes close to observed, the 

breakout between tropical storms 

and hurricanes is good as well

OBS

Ensemble Mean

Figure 8. Number of hurricanes (upper figure) and tropical storms (lower figure) from a

ocean/atmosphere coupled run using the Cubed Sphere global model initialized in June.

For some 2010 storms the possibility of using ensemble information to narrow the “cone of 

uncertainty” from the operational cone for track was demonstrated.

One of the more popular products from the national hurricane center is the ―cone of uncertainty‖ 

that displays their estimate of the possible error in the forecast track. An example of this type of 

forecast is shown for Earl in Figure 9. The error bars for each forecast time are the average error 

for those lead times for all forecasts for all storms for the previous three years and so are constant 

at the various lead times throughout the hurricane season. This is a reasonable estimate of the 

uncertainty and is probably the best available at this time.

However, it is well known that the uncertainty of any forecast is widely variable from one 

forecast to another where there must be times when the error bars are less than the averages and 

there must be times when the uncertainty is much larger. One application of ensembles is to use 

the spread of the ensemble tracks at the various forecast times to estimate the uncertainty. An 

example of this is shown on the left panel of Figure 9 for the HFIP GFS ensemble with 20 

members and initialized with EnKF. Note that the error ellipse for the 2-day ensemble forecast 

off Norfolk, VA is roughly half the size the operational error range at that position. Thus the 

ensemble could, in principle have been used to narrow the cone of uncertainty in that region. On 

the other hand there will be times, such as late in the forecast period of Figure 9 that the 

uncertainty is much larger than shown by the three year average. So again, in principle, this 

information could be used to indicate that the confidence in the official forecast must be 

considered to be low. 
22

Figure 9. Left figure shows the ensemble of tracks from the GFS 20 member ensemble 

initialized with EnKF for Hurricane Earl in 2010. The elipses at the various forecast lead 

times in days encompase 80% of the tracks and repersent a track uncertainity given by the

ensemble. Right figure shows the operational forecast from NHC with the error cone 

defined as the average error over the last three years.

The stream 1.5 testing and evaluation concept was successfully demonstrated. Four stream 

1.5 candidate models were evaluated through a retrospective analysis study. Two of the 

models, AHW and GFDL, were accepted by NHC as part of stream 1.5 demonstration during 

the 2010 hurricane season.

As presented in Table 1, the concept of stream 1.5 is to identify models that look promising by 

the NHC forecasters as future operational models. These promising models are then configured 

to run in real time during the summer demonstration study, with products made available to 

NHC. The specific goals of the retrospective analysis is to (1) provide adequate statistics for 

assessing the skill of the stream 1.5 model candidates, (2) help identify modeling systems to 

could be included in future operational forecast guidance, and (3) provide information that may 

help to calibrate the multi-model ensemble forecasts. To determine if a model is a candidate for 

stream 1.5, the retrospective study is conducted using cases selected by NHC that were observed 

during previous seasons. 

The TCMT successfully conducted the 2010 retrospective analysis study, which was coordinated 

with NHC and the HFIP Project Office. The candidate models were evaluated using storms 

Note that error ellipse for 

the global ensemble is 

considerably smaller than 

the current error range
23

observed during the 2008 and 2009 hurricane seasons in the Eastern Pacific and Atlantic basins. 

A total of 27 storms were used for the evaluation. Four modeling groups participated in the 

retrospective analysis. The models included two configurations of the Weather Research and 

Forecasting (WRF) model, a new version of the NOAA Geophysical Fluid Dynamic 

Laboratory’s (GFDL) model, and the Navy’s tropical cyclone model. 

The models were evaluated using a variety of baselines for the comparisons (see Table 10). The 

study focused on evaluating (1) track error and (2) absolute intensity errors. For verification, we 

used the NHC Best Track analysis product. Verification was conducted using the NHC 

Verification System (NHCVx) and with evaluation tools developed by TCMT. The cases were 

verified and samples homogenized via case matching so the evaluation was conducted over the 

same sample. For the evaluation, pairwise differences were computed between the model 

forecast and NHC Best Track, error distributions were created and results were reported in 

graphics and statistical significant (SS) tables.

Table 10. Stream 1.5 baseline comparisons.

Operational 

Baseline

Stream 1.5 

Configuration Lead Times Evaluated

GFDL Stream 1.5 Every 6 h out to 120 h

Consensus 

(at least 2 

available) 

Consensus 

+ 

Stream 1.5

Every 6 h out to 120 h

GFS (Track)

DSHP 

(Intensity)

Stream 1.5 Official Forecast Times

Homogeneous 

average of 

previous 

year’s top 

flight models

Stream 1.5 Official Forecast Times

Consensus Track: GFS, UKMET, NOGAPS, GFDL, HWRF

Consensus Intensity: DSHP, LGEM, GFDL, HWRF

Homogeneous Track: GFS, UKMET, GFDL

Homogeneous Intensity: GFDL, HWRF, DSHP, LGEM

Official Forecast Times: 00, 12, 24, 36, 48, 72, 96, 120 h

An example result from this evaluation is shown in Figure 10 for the Atlantic Basin. Similar 

results were also created for the Eastern Pacific Basin (not shown). The plots show the number 

of lead times that were statistically significant for each of the stream 1.5 model candidates. The 

results are stratified by baselines (GFDL and Consensus) for track and intensity errors. The blue 

bars indicate the number of lead times that the baseline was statistically significant. The red bars 

show the number of lead times the stream 1.5 model has statistically significant results. For 

example, the AHW (MMM in the plot) had no statistically significant results for track compared 

to the GFDL baseline (upper left plot of Figure 10). However, the operational GFDL baseline 
24

was statistically significant for 13 lead times. When compared to the Consensus Baseline, the 

AHW model had statistically significant (improved) results for 14 (21 – 7) lead times. 

To summarize, two candidate models were accepted for stream 1.5: GFDL and AHW (MMM). 

GFDL was accepted prior to the TCMT evaluation based on prior assessment by NHC. The 

model showed some improvement when added to the operational consensus. The AHW model 

showed statistically significant improvements at numerous time periods when added to the 

operational consensus with no statistically significant degradations. The FSU (ARW) and NRL 

(COAMPS-TC) models were not accepted as stream 1.5 candidates. The FSU model had a 

neutral impact on the consensus. There was only limited sample size for evaluation. The NRL 

results were not sufficiently strong or consistent enough to warrant inclusion.

The stream 1.5 evaluation study was successful. The TCMT developed the procedures and 

successfully conducted the evaluation of the candidate models. Based on the evaluation, NHC 

selected two models (AHW and GFDL) that were included in the stream 1.5 for the 2010 Demo. 

Products from these models were delivered to NHC in real time or near real time during the 

hurricane season. TCMT is currently refining the retrospective analysis procedures for the 2011 

stream 1.5 analysis.

Figure 10. Plots showing the statistically significant differences between the baselines 

(GFDL: left; consensus: right) and the candidate models for the Atlantic Basin. The blue 

bar indicates that the operational model was the better model for the displayed number of 

lead times and the red bar indicates the stream 1.5 candidate model was the better model 

for the number of lead times. The gray bar indicates that neither model was statistically 

significant.
25

7. Lessons Learned from FY09 HFIP Activities 

Table 11 gives a high level overview of some lessons learned from the HFIP work during FY09. 

A more detailed description of the lessons learned is provided in the following section.

Table 11. Lessons Learned from FY09 HFIP Activities.

The major problem with initialization of regional models identified last year remains. It is a 

particularly severe problem for the first 24 hours of the forecast which results in a severe 

problem in forecasting rapid intensification.

Most of the regional models used operationally and in the Multi Model Ensemble use some sort 

of synthetic initial vortex adjusted to come close to the observed position and intensity of the 

storm at the initial time. This requires removing the vortex from the original global model fields 

given to the model and introducing the synthetic vortex. There are a number ways of developing 

the latter including using an axis-symmetric version of the model to spin up a vortex while 

forcing it to the correct intensity or using a sophisticated system to specify a vortex based on the 

previous run of the regional model (see last bullet in section 3.3). All this leads to some well 

known issues including a miss-match between the fields given the model and what the model 

would have developed itself or a mismatch between the introduced hurricane and the 

environment of the storm in the model. 

This often leads to a rapid initial adjustment in the vortex to something either stronger or weaker 

than what was initially introduced. Note in Figure 11 that several of the models show an initial 

rapid increase in the vortex strength in the first 6 hours. This is quite common in regional model 

predictions, often more pronounced than shown in Figure 11 and in fact often occurs in the first 

few minutes of the forecast probably because of the model adjusting the vortex to the 

The major problem with initialization of regional models identified last year 

remains. It is a particularly severe problem for the first 24 hours of the forecast 

which results in a severe problem in forecasting rapid intensification.

Global models consistently provide significantly better track forecasts than 

regional models.

Advanced data assimilation systems, such as y EnKF, is essential for improving 

both track and intensity forecasts.

Statistical post processing of ensemble output likely to provide additional skill 

improvement.

Significant improvement in the intensity forecast skill of dynamical models can 

be attained through advancements to the model physics and data assimilation, 

as illustrated by the 2010 COAMPS-TC performance in the W. Atlantic.

The delivery of complete, consistent Tier 1 (ATCF files) and Tier 2 (gridded 

model) data is necessary to provide consistent evaluations of the model forecasts 

for the same forecast challenge using a common (operational) tracker.
26

environment or to agree with the model physics. We often also see sudden decreases in the 

initial intensity.

OBS Experimental HWRF

Operational HWRF

Operational GFDL

Experimental GFDL

Ensemble Mean

RI

Figure 11. Wind speed forecasts from various members of the Multi Model Ensemble for 

hurricane Karl. The observed wind is shown as the black line.

In Figure 12, we illustrate one recent result from our examination of the problems related to 

initialization of regional models. The upper panel shows results from the operational HWRF and 

the lower panel from the operational GFDL. Shown are the wind speed and pressure bias after 6 

hours of forecast time binned by wind speed from weak to strong storms. Blue shows the wind 

speed bias and red the pressure error. Note that, for weak storms, at 6 hours both models have 

stronger winds and lower central pressure than observed. Thus the weaker storms have spun up 

(note that the scales in the two figures differ). For HWRF, at stronger initial wind speeds, the 

opposite is true. The winds have weakened considerably, spun down by as much as 30 knots for 

the strongest storms plus decrease in the central pressure. In the GFDL model, the results also 

shows an initial spin down of the vortex but an increase in the central pressure,
27

Figure 12. Wind Speed (blue bars) and minimum pressure (red bars) after six hours 

grouped by wind speed for HWRF (upper panel) and GFDL Model (lower panel).

One problem that can be attributed to the issues with initialization will be prediction of rapid 

intensification (RI) in the first 24 hours. One of the goals of HFIP is to reduce the false alarm 

rate and increase the probability of detection of RI especially during the first day of the forecast. 

Given the problems of initialization of the regional models, we have not yet been able to 

demonstrate an ability to meet that goal. Occasionally a model will forecast rapid intensification 

quite accurately. An example is shown in Figure 11 where the HWRF-x (e.g., experimental 

HWRF) run by HRD did a remarkable job forecasting the intensity changes of hurricane Karl 

including the period of rapid intensification. This of course wasn’t the case for the other models 

shown in the figure and in fact is not always true for the HWRF-x as well. Still Figure 11 does 

provide hope that we will eventually be able to meet the RI goal of HFIP. But this is likely only 

to happen once the general problem with initialization is resolved.
28

Some of the regional models used in HFIP (NCAR/ARW and PSU/ARW for example) use an 

EnKF initialization process and there is some hope that a system that uses the model itself for 

initialization and avoids the use of a synthetic initial vortex may perform better than those that 

do. This would allow the initial vortex to be consistent with the model and consistent with the 

hurricane environment in the model. However, if the PSU/ARW model results shown in Figure 4 

are reexamined, that model uses the EnKF approach yet the initial errors remain quite large. 

Finally it is generally accepted that much of the physics associated with RI occurs at small, 

perhaps the convective scales. To resolve these scales the models will require at least 3 km 

resolution and this will only be possible with the regional models for the next decade. Thus we 

will probably need high resolution to resolve the problem with initialization of the regional 

models. Note in Figure 11, of all the models shown, only HWRF-x used 3 km resolution.

Global models consistently provide significantly better track forecasts than regional models

Figures 2 and 3 are a clear demonstration that the regional models do not do as well as global 

models in predicting hurricane track at any lead time but particularly at the longer lead times. 

This is perhaps not surprising since the regional models use the global models for predicting 

their lateral boundary conditions so it might be expected that the regional models can be no 

better than the global models in predicting track. Track is largely determined by the large scale 

environment in which the hurricanes are imbedded which is better predicted by the global 

models than the regional models. Still the differences shown in Figures 2 and 3 are surprisingly

large. Figure 13 is another example of the same result where the two operational regional 

models (HWRF and GFDL) are compared to the operational GFS global model. Here the 

differences are shown relative to CLIPPER. The differences between the global and regional 

models are similar to those shown in Figures 2 and 3. 

Figure 13. Track forecast skill relative to CLIPPER (Climatology and persistence 

statistical model) for GFS operational global model (blue), GFDL Model (black) and 

HWRF (red). Here higher numbers are better.
29

In Figures 2 and 3, four different regional models are compared to two different global models. 

One can only conclude that the global models (even at relatively low resolution—the GFS EnKF 

was 60 km) out performs the regional models in track. Thus emphasis on improving track 

forecasting must focus on improving the global models. The since the global models will not 

reach the resolution necessary to account for inner core process for at least the next 5 years, 

regional models will be required for intensity forecasting, particularly RI as noted above with 

emphasis on forecast times out to 3-4 days. 

Advanced data assimilation systems, such as EnKF, are essential for improving both track and 

intensity forecasts.

The 2009 HFIP final report contained several examples of the relative value of the more 

advanced data assimilations systems (over 3DVAR). Though we didn’t show any examples of 

the results again this year, the same conclusions were evident for the 2010 results. Currently 

NCEP is in the process of replacing their GSI (3DVAR) system with a hybrid system involving 

the ensemble approach combined with first GSI and later possibly with 4DVAR. The results 

from HFIP suggest this is a good approach.

Statistical post processing of ensemble output likely to provide additional skill improvement

Much of the discussion above focused on using model improvement to achieve the HFIP goals. 

We have already noted in a previous section where we discussed results from the Multi Model 

Ensemble that statistical post processing adds considerably to the skill of the ensemble. In 

particular we noted the results of the CBC process (Figure 6) which gave improvements in 

intensity forecasts that are apparently similar to adding aircraft data to the initialization of a 

regional model (Figure 4). This is a very preliminary result and needs to be further explored 

using statistical data sets that are completely separate from the forecasts.

There is another class of statistical models that combine various sources of data beyond that 

derived from models and they have proved superior to the models historically. The one shown in 

Figure 14, SHIPS (Statistical Hurricane Intensity Prediction System), is one that is used 

operationally. These models can and do use data from model forecasts. Figure 14 shows the 

impact of using several of the HFIP models (the operational GFDL, HWRF and COAMPS-TC)) 

in SHIPS. Note that in the cases of HWRF and GFDL the statistical model provided better 

forecasts than the respective models that were included in SHIPS and in fact gave slightly better 

forecasts than the original SHIPS model beyond 24 hours except at the longest lead time.

Statistical post processing taken together with model improvements, use of ensembles, improved 

data assimilation and inclusion of more data such as aircraft and satellite data promises to be the 

keys to meeting the HFIP goals. 
30

Figure 14. The impact of including three of the operational regional models (HWRF, 

GFDL and TC-COAMPS) in the SHIPS statistical intensity model.

Significant improvement in the intensity forecast skill of dynamical models can be attained 

through advancements to the model physics and data assimilation, as illustrated by the 2010 

COAMPS-TC performance in the W. Atlantic.

Recent model upgrades following the FY09 demonstration forecasts to COAMPS-TC resulted in 

improvements to the overall intensity forecast skill. These upgrades included improved 

boundary layer and microphysics parameterizations, as well as inclusion of additional 

observations into the Navy 3D-VAR, NAVDAS. Figure 15 shows a homogeneous comparison 

of the intensity error for HWRF, COAMPS-TC, GFDL, GFDN, HWRF-X and the statistical 

models LGEM and DSHIPS. The COAMPS-TC performance is on par or superior to the 

statistical models in 36-60 h forecast periods, a significant improvement over of FY09 statistics 

resulting from model improvements to the physics and data assimilation. The statistical models 

and HFIP baseline are lagged by 6 h to reflect the delay in attaining the dynamical model output. 

The results highlight the sensitivity of intensity forecasts to the physical parameterizations and 

data assimilation. Another interesting lesson learned is that the COAMPS-TC intensity forecasts 

were superior to all other dynamical models in the 36-60 h forecast range, however, the track 

forecasts were not as skillful as the global or other regional models. The results suggest that 

reasonably skillful intensity forecasts can be attained using simple vortex initialization methods. 

Improvements to the track skill have been achieved through assimilation of additional 

observations including total precipitable water (TPW) and new averaging methods appropriate 

for the mesoscale for scatterometer observations, as illustrated in homogeneous track error 

statistics shown in Figure 16.
31

Figure 15. Homogeneous comparison of the intensity error for HWRF, COAMPS-TC, GFDL, 

GFDN, HWRF-X and the statistical models LGEM and DSHIPS. Note the COAMPS-TC 

performance is on par with the statistical models in 36-60 h forecast periods, a significant 

improvement over of FY09 statistics resulting from model improvements to the physics and data 

assimilation. The statistical models and HFIP baseline are lagged by 6 h to reflect the delay in 

attaining the dynamical model output.
32

Figure 16. Homogeneous comparisons of the track error for the E. Pacific are shown. 

Track error results are shown for a series of control forecasts and for a new version of the 

Navy 3D-Var, NAVDAS, that makes use of additional observations including total 

precipitable water (TPW) and new averaging methods appropriate for the mesoscale for 

scatterometer observations. The sample size is shown at the bottom as a function of 

forecast hour. 

The delivery of complete, consistent Tier 1 (ATCF files) and Tier 2 (gridded model) data is 

necessary to provide consistent evaluations of the model forecasts for the same forecast 

challenge using a common (operational) tracker.

The TCMT conducted several studies to show the importance of having a complete, consistent 

dataset for both Tier 1 (ATCF files) and Tier 2 (gridded model products) data. To show the 

impact on sample size, the NRL (COAMPS-TC), which had an almost complete dataset, was 

subset to match the number of cases that were delivered by other modeling groups. The stream 

1.5 analyses results were re-evaluated with the smaller sample size. The results (not shown) 

indicated an improvement in the number of lead times for both intensity and track that the NRL 

model was statistically significant compared to the consensus baseline. Clearly, the models that 

did not provide a complete, consistent dataset had different forecast challenges, which influenced 

the results. 

Figure 17 shows the impact of using different trackers. In this example, we compare the HWRF 

track errors from the tracker used at NCEP/EMC (Tier 1 products) with the track errors using the 

common GFDL tracker (Tier 2 products). The results shown in Figure 17 indicate there are 

significant differences as a result of the different trackers used. These results highlight the 

importance in having a complete Tier 2 dataset available to make consistent comparisons of the 

models with a common tracker. 
33

Figure 17. Results showing differences in absolute track errors when different trackers are 

used.

8. Challenges for HFIP beyond FY10

Table 12 lists what HFIP considers to be its main challenges for 2011 and beyond. All these

points have been covered in the earlier discussion so we simply list them here.

Table 12. HFIP Challenges.

9. Meeting the HFIP Performance Goals

In the discussion above, we showed evidence that it is possible to meet the 5 year HFIP intensity 

and track goals. It is less clear whether we can or cannot make the RI goals. The problem with 

the latter is that we still need to solve a major problem with regional model initialization. Most 

techniques provide the model with a vortex that is not consistent with the model or the hurricane 

environment so there is an initial adjustment that can be larger than a typical example of RI. A 

major HFIP focus this next year will be trying to solve the initialization problem.

Initialization systems for regional models need to be improved for forecasts in the first 

24 hours.

HFIP needs to develop systems to better make use of satellite observations in 

regional models.

Implementing the Hybrid DA system in operations as soon as possible.

Development of appropriate statistical post processing systems for ensemble output.

Operational computing not likely to be sufficient to meet HFIP goals within 3 years.
34

While it appears that use of aircraft data will likely help HFIP meet its intensity goals for storms 

for which such data is available, it will not be available for storms for a large majority of model 

initializations. For those, we will need to rely on better use of satellite data taken in the near 

vicinity of the hurricane. Another major focus this next year will be on improving satellite data 

assimilation in regional model initialization.

Except for the RI issue, we can now say with considerable confidence what a final end state 

operational configuration of the hurricane numerical prediction system should look like in 2014.

The longer range predictions, out to one week, of both track and intensity will be accomplished 

by global models run as an ensemble and initialized with a Hybrid data assimilation system and 

post processed with various statistical models. Resolution of these global models needs to be at 

least 20 km and the results will be improved if more than one global model is used in the 

ensemble. 

The earlier forecast periods—out to 48-72 hours will be accomplished with regional models run 

with at least 3 km resolution as a multi- model ensemble. All models will use all available 

aircraft and satellite data. These will also be post processed with statistical models. The focus 

with the regional models will be on intensity and with the high resolution there is confidence in 

the community that the RI goals can be met with the regional models. More specifically the end 

system might include:

• Global model ensemble with Hybrid Data Assimilation

– 20 members at 20 km

– Multi Model (at least two—e.g.: FIM, GSF, Cubed Sphere)

• Regional model ensemble

– 20 members at 3 km

– Multi model (at least two—e.g.: HWRF, AHW, COAMPS-TC)

– Using all available aircraft and satellite data in core and near environment of 

hurricane

• Statistical Post processing

– Bias correction, Correlation Based Correction, LGEM, SHIPS

The ability to run this system will however require about a 10 times increase in computer 

resources in operations with a major emphasis on the ability to run the high resolution 

ensembles.

10. List of HFIP Supported Publications and Presentations

This extensive list can be view and downloaded at the follow link: 

http://www.hfip.org/documents/HFIP_pubs_2010-012311.pdf
35

11. Appendix A: List of HFIP Teams

Bold face type denotes team leads.

1. Global Model/Physics Development Team 

Stan Benjamin (ESRL) Stan.Benjamin@noaa.gov

John Brown (ESRL) John.M.Brown@noaa.gov

Kevin Yeh (AOML) Kevin.Yeh@noaa.gov

Melinda Peng (NRL) melinda.peng@nrlmry.navy.mil

Shian-Jiann Lin (GFDL) Shian-Jiann.Lin@noaa.gov

Steve Lord (EMC) Stephen.Lord@noaa.gov

Jim Ridout (NRL) james.ridout@nrlmry.navy.mil

Jian-Wen Bao (ESRL) Jian-Wen.Bao@noaa.gov

John M Ward (EMC) John.M.Ward@noaa.gov

2. Regional Model/Physics Development Team

Morris Bender (GFDL) Morris.Bender@noaa.gov

Young Kwon (EMC) young.kwon@noaa.gov

Steve Lord (EMC) Stephen.Lord@noaa.gov

Gopal (AOML) Sundararaman.G.Gopalakrishnan@noaa.gov

Rich Hodur (NRL) hodur@nrlmry.navy.mil 

Shaowu Bao (ESRL) shaowu.bao@noaa.gov

Isaac Ginis (URI) iginis@gso.uri.edu

Jim Doyle (NRL) james.doyle@nrlmry.navy.mil 

Robert Rogers (AOML) Robert.Rogers@noaa.gov

Jian-Wen Bao (ESRL) Jian-Wen.Bao@noaa.gov

Bob Tuleya (Old Dominion Univ) Robert.Tuleya@noaa.gov

Ligia Bernardet (ESRL) ligia.bernardet@noaa.gov 

Chris Davis (NESL) cdavis@ucar.edu

3. Ensemble Systems Development Team

Zoltan Toth (ESRL) zoltan.toth@noaa.gov

Carolyn Reynolds (NRL) carolyn.reynolds@nrlmry.navy.mil

Sim Aberson (HRD) Sim.Aberson@noaa.gov

Tom Hamill (ESRL) tom.hamill@noaa.gov

Jeff Whitaker (ESRL) Jeffrey.S.Whitaker@noaa.gov

Fuqing Zhang (PSU) fzhang@psu.edu

Yuejian Zhu (EMC) Yuejian.Zhu@noaa.gov

Jun Du (EMC) Jun.Du@noaa.gov

Mike Brennan (NHC) Michael.J.Brennan@noaa.gov

Mrinal K Biswas (FSU) Mrinal.K.Biswas@noaa.gov

Krish (FSU) tkrishnamurti@fsu.edu

Teddy Holt (NRL) teddy.holt@nrlmry.navy.mil
36

4. Data Assimilation/Vortex Initialization Team)

Jeff Whitaker (ESRL) Jeffrey.S.Whitaker@noaa.gov

Bill Lapenta (EMC) Bill.Lapenta@noaa.gov

Mingjing Tong (EMC) mingjing.tong@noaa.gov

Jim Doyle (NRL) jim.doyle@nrlmry.navy.mil

Altug Aksoy (AOML) Altug.Aksoy@noaa.gov

Yuanfu Xie (ESRL) yuanfu.xie@noaa.gov

Milija Zupanski (CIRA) zupanskim@cira.colostate.edu

Liyan Liu (EMC) Liyan.liu@noaa.gov

Fuqing Zhang (PSU) fzhang@psu.edu

Tomislava Vukicevic (AOML) Tomislava.Vukicevic@noaa.gov

5. Verification Team

Tim Marchok (GFDL) timothy.marchok@noaa.gov

Barb Brown (RAL) bgb@ucar.edu

Jim Goerss (NRL) jim.goerss@nrlmry.navy.mil

Mark DeMaria (NESDIS/STAR) DeMaria@cira.colostate.edu

Robert Rogers (AOML) Robert.Rogers@noaa.gov

James Franklin (NHC) james.franklin@noaa.gov

Vijay Tallapragada (EMC) vijay.tallapragada@noaa.gov 

Michael Fiorino (ESRL) Michael.Fiorino@noaa.gov

Hao Jin (NRL) hao.jin@nrlmry.navy.mil

Ligia Bernardet (ESRL) ligia.bernardet@noaa.gov 

Louisa Nance (RAL) nance@ucar.edu

Tony Eckel ( NOAA/OST ) t.eckel5162@sbcglobal.net

6. Applications Development/Diagnostics Team 

Mark DeMaria (NESDIS/STAR) mark.demaria@noaa.gov

Ed Rappaport (NHC) edward.n.rappaport@noaa.gov

Vijay Tallapragada (EMC) vijay.tallapragada@noaa.gov

Yi Jin (NRL) yi.jin@nrlmry.navy.mil 

Buck Sampson (NRL) buck.sampson@nrlmry.navy.mil

Robert Rogers (HRD) Robert.Rogers@noaa.gov

Barb Brown (RAL) bgb@ucar.edu

Richard Pasch (NHC) richard.j.pasch@noaa.gov

Michael Fiorino (ESRL) Michael.Fiorino@noaa.gov

Louisa Nance (RAL) nance@ucar.edu

Bob Tuleya (Old Dominion Univ) Robert.Tuleya@noaa.gov

Jim Hansen (NRL) jim.hansen@nrlmry.navy.mil

John Knaff (NESDIS/STAR) john.knaff@noaa.gov

Gopal (AOML) Sundararaman.G.Gopalakrishnan@noaa.gov

Krish (FSU) tkrishnamurti@fsu.edu

Tony Eckel (NOAA/OST ) t.eckel5162@sbcglobal.net
37

7. Hurricane Observations Team 

Sim Aberson (AOML) Sim.Aberson@noaa.gov

John Knaff (NESDIS/STAR) john.knaff@noaa.gov

Robert Atlas (AOML) Robert.Atlas@noaa.gov

Jack Beven (NHC) john.l.beven@noaa.gov

John Derber (EMC) john.derber@noaa.gov

Michelle Mainelli (NCO) michelle.m.mainelli@noaa.gov

George Halliwell (AOML) ghalliwell@rsmas.miami.edu

Daniel Melendez (NWS/OST) Daniel.Melendez@noaa.gov

Jeff Hawkins (NRL) Jeffrey.Hawkins@nrlmry.navy.mi

Chris Fairall (ESRL) chris.fairall@noaa.gov

Isaac Ginis (URI) iginis@gso.uri.edu

Nick Shay (RSMAS) nshay@rsmas.miami.edu

Paul Chang (NESDIS)Paul.S.Chang@noaa.gov

Jim McFadden (AOC) jim.d.mcfadden@noaa.gov

Tara Jensen (RAL) jensen@ucar.edu

Thiago Quirino (AOML) Thiago.Quirino@noaa.gov

Yuanfu Xie (ESRL) yuanfu.xie@noaa.gov

8. Coupled Ocean/Wave model Team 

Hendrik Tolman (EMC) hendrik.tolman@noaa.gov

George Halliwell (AOML) george.halliwell@noaa.gov

Isaac Ginis (URI) iginis@gso.uri.edu

Shaowu Bao (ESRL) shaowu.bao@noaa.gov

Sue Chen (NRL) sue.chen@nrlmry.navy.mil

Jian-Wen Bao (ESRL) Jian-Wen.Bao@noaa.gov

Chris.Fairall (ESRL) Chris.Fairall@noaa.gov

Nick Shay (RSMAS) nshay@rsmas.miami.edu

Daniel Melendez (OAR) Daniel.Melendez@noaa.gov 
38

12. Appendix B: Organization Acronyms

AOC—Aircraft Operations Center, NOAA

AOML—Atlantic Oceanographic and Meteorological Laboratory, OAR/NOAA

EMC—Environmental Modeling Center, NCEP/NOAA

ESRL—Earth System Research Laboratory, OAR/NOAA

FSU—Florida State University

GFDL—Geophysical Fluid Dynamics Laboratory, OAR/NOAA

NCAR—National Center for Atmospheric Research

NCEP—National Centers for Environmental Modeling, NWS/NOAA

NESDIS—National Environmental Satellite Data Information Service, NOAA

NHC—National Hurricane Center, NWS/NOAA

NOAA—National Oceanic and Atmospheric Administration

NRL—Naval Research Laboratory, Monterey

NWS—National Weather Service, NOAA

OAR—Ocean and Atmospheric Research, NOAA

ODU—Old Dominion University

OST—Office of Science and Technology, NWS/NOAA

RAL—Research Applications Laboratory, NCAR

RSMAS—Rosenstiel School of Marine and Atmospheric Science, University of Miami

URI—University of Rhode Island