2014 HFIP R&D Activities Summary:

Recent Results and Operational Implementation

May 2015

HFIP Technical Report: HFIP2015-1

NOAA NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

United States Department of Commerce
Image on cover page

used with permission from the

Joint Typhoon Warning Center, Hawaii
2014 HFIP R & D Activities Summary:

Recent Results and Operational Implementation

R. Gall14, F. Toepfer14, F. Marks1, E. N. Rappaport12, A. Aksoy22, S. Aberson1, J. W. Bao5, 

M. Bender7, S. Benjamin5, L. Bernardet3,5, M. Biswas10, B. Brown10, J. Cangialosi12, C. Davis7, 

M. DeMaria12, J. Doyle13, R. Falvey9, M. Fiorino5, S. Forsythe-Newell18, J. Franklin12, T. 

Ghosh6, I. Ginis24, S. Goldenberg8, S. Gopalakrishnan1, T. Hamill5, R. Hodur17,13, H. S. Kim4, J. 

Knaff11, T. Krishnamurti6, P. Kucera10, Y. Kwon4, W. Lapenta4, N. Lett16, S. Lord4, T. 

Marchok7, D. Meléndez14, M. Morin7, J. Moskaitis13, K. Musgrave2, L. Nance10,20, A. 

Reinecke13, C. Reynolds13, Brian Strahl9, V. Tallapragada4, H. Tolman4, R. Torn19, G. 

Vandenberghe4, T. Vukicevic12, X. Wang23, Y. Weng15, J. Whittaker5, R. Yablonsky24, D. A. 

Zelinsky12, D-L Zhang21, F. Zhang15 , J. Zhang22, X. Zhang22.

May 2015

1

Atlantic Oceanographic and Meteorological Laboratory OAR/NOAA

2

Cooperative Institute for Research in the Atmosphere, Colorado State University

3

Cooperative Institute for Research in the Environmental Sciences, University of Colorado 4

Environmental Modeling Center NCEP/NOAA

5

Earth System Research Laboratory OAR/NOAA 

6

Florida State University

7

Geophysical Fluid Dynamics Laboratory OAR/NOAA 

8

Hurricane Research Division AOML/NOAA

9

Joint Typhoon Warning Center, Hawaii 10National Center for Atmospheric Research

11National Environmental Satellite Data Information Center NOAA

12National Hurricane Center NWS/NOAA

13Naval Research Laboratory, Monterey

14National Weather Service Science and Technology Integration, NWS/NOAA 15Pennsylvania State University

16Science and Technology Corporation

17Science Applications International Corporation

18Syneren Technologies Corporation

19University at Albany, State University of New York

20University Corporation for Atmospheric Research

21University of Maryland

22University of Miami

23University of Oklahoma

24University of Rhode Island
Table of Contents

Executive Summary...................................................................................................................... 1

1. Introduction ........................................................................................................................... 3

2. The Hurricane Forecast Improvement Project .................................................................. 3

3. The HFIP Model Systems ..................................................................................................... 6

a. The Global Models .................................................................................................................... 6

b. The Regional Models................................................................................................................. 8

c. Initialization and Data Assimilation Systems.......................................................................... 9

d. The HWRF Community Code Repository and User Support............................................. 10

4. Meeting the HFIP Goals...................................................................................................... 10

a. The HFIP Baseline................................................................................................................... 10

b. Meeting the Track Goals......................................................................................................... 12

c. Reaching the Intensity Goals.................................................................................................. 12

5. HFIP Stream 1.5 .................................................................................................................. 13

a. Stream 1.5 results .................................................................................................................... 14

6. Performance of Stream 2 models....................................................................................... 17

7. Operational Hurricane Guidance Improvements ............................................................ 19

a. Global Model (GFS)—Operational........................................................................................ 19

b. Global Model Ensembles (GFS based)—HFIP Experimental............................................. 20

c. Hurricane WRF (HWRF)....................................................................................................... 24

1) Atlantic......................................................................................................................................................24

2) West Pacific (WPAC) and Other Basins................................................................................................25

3) A Comment on Intensity Forecast Errors..............................................................................................26

4) The Multi-Model Ensemble.....................................................................................................................26

d. GFDL Ensemble ...................................................................................................................... 28

8. Impact of Inner Core Reconnaissance Data...................................................................... 31

9. Data Assimilation and Physics Development.................................................................... 32

a. Data assimilation ..................................................................................................................... 32

b. Physics Development............................................................................................................... 35

10. Post Processing of Model Output....................................................................................... 36

a. Statistical Post Processing of Model Output: The FSU Multi-Model Ensemble ............... 36

b. The HFIP Web Page................................................................................................................ 38

11. Societal Impact Work.......................................................................................................... 39

12. Future Configuration of a Numerical Model Hurricane Forecast Guidance System to 

meet the HFIP goals.................................................................................................................... 42

References .................................................................................................................................. 45

Appendix A: NOAA Opportunity Announcement ................................................................. 47

Appendix B: Model Acronyms .................................................................................................. 48
List of Tables

Table 1. Future Numerical Model Hurricane Forecast Guidance System.................................... 2

Table 2. Strategic Teams 2014....................................................................................................... 4

Table 3. Tiger Teams 2014 ............................................................................................................ 5

Table 4. Specifications of the HFIP Global Models...................................................................... 7

Table 5. Specifics of the regional models used by HFIP in 2014.................................................. 8

Table 6. Participating modeling groups in the 2014 HFIP Retrospective Evaluation................ 13

Table 7. 2014 HFIP Stream 1.5 Real-time runs and model guidance to NHC ........................... 14

Table 8. Stream 2 Models............................................................................................................ 17

Table 9. Automated Tropical Cyclone Forecasting System (ATCF) ID Descriptions.................. 29

Table 10. Numerical Model Hurricane Forecast Guidance System............................................ 44

List of Figures

Figure 1. HFIP Track and Intensity Error Baseline and Goals. ................................................... 11

Figure 2. Tracking and Intensity Forecast Skill in the Atlantic Basin (2014).............................. 15

Figure 3. Track and Intensity Forecast Skills for the East Pacific Basin..................................... 16

Figure 4. Track and Intensity Skills for the 2014 Atlantic Basin hurricane season..................... 18

Figure 5. Hurricane track forecast skill errors for GFS, ECMWF, and FIM for the 2011-2013 

Atlantic and East Pacific hurricane seasons................................................................... 20

Figure 6. Operational GEFS (T254) and HFIP GFS Ensemble System (T574) Track and 

Intensity (Max Wind) Errors.......................................................................................... 21

Figure 7. EnKF HFIP GFS Ensemble T.S. Force Wind Probabilities 84 Hours for Hurricane 

Arthur............................................................................................................................. 22

Figure 8. EnKF HFIP GFS Ensemble Systems Forecasts and Ellipses (July 1, 2014 Forecast). 22

Figure 9. Operational GEFS and HFIP Ensemble for Verification of Probability of Storm Force 

Winds for 2013 and 2014............................................................................................... 23

Figure 10. HWRF Intensity Forecast Improvement for the Atlantic Basin................................. 24

Figure 11. 2014 HWRF Track and Intensity Error Statistics in the Western Pacific .................. 25

Figure 12. OFCL Intensity Error Distribution ............................................................................. 26

Figure 13. The HFIP Multi-Model Ensemble for Tropical Storm Gonzalo (2014)..................... 27

Figure 14. Comparing Track & Intensity Errors between Multi-Model Ensemble Components. 28

Figure 15. Mean Track and Intensity Skills in the Atlantic Basin............................................... 30

Figure 16. 2014 Track, Intensity and Bias comparisons with Inner Core Reconnaissance Data 31

Figure 17. Operational HWRF Workflow ................................................................................... 32

Figure 18. Cycling HWRF GSI/EnKF Workflow ....................................................................... 33

Figure 19. HWRF GSI/EnKF Method for Model Initialization (Hurricane Arthur in 2014)...... 34

Figure 20. 2014 HWRF Formulations Testing and Evaluations for Cp and Ch ........................... 35

Figure 21. 2014 Atlantic Hurricane Season Results using BLUE 96-h+ Difference Weighting 

Scheme........................................................................................................................... 37

Figure 22. Example of the HFIP Webpage.................................................................................. 39

Figure 23. Assessment of Potential Storm Surge Product from the NHC................................... 40

Figure 24. Assessment of how various groups rate the cone of uncertainty for the forecast 

hurricane track. .............................................................................................................. 40

Figure 25. Onset of Storm Force Winds Product:........................................................................ 41

Figure 26. Example of the experimental global HWRF System in NMMB NEMS framework: 43

Figure 27. Track and Intensity Forecasts for HWRF Basin-Scale and the Operational HWRF. 44
1

Executive Summary

This report describes the activities and results of the Hurricane Forecast Improvement Project

(HFIP) in 2014. It should be generally noted that 2014 was not a representative season for the 

Atlantic due to very low storm activity, not many rapid intensification (RI) events, and a relatively 

small sample size of total storms. Like last year, we focused on the improvements in the 

operational Global Forecast System (GFS) global model and Hurricane Weather Research and 

Forecasting (HWRF) regional model. HFIP is organized around the three “streams”: 1) Stream 1;

operational model development, 2) Stream 1.5; a group of experimental models evaluated by the 

National Hurricane Center (NHC) pre-season and then made available to NHC forecasters during 

their forecast cycle in real-time, and 3) Stream 2; HFIP experimental models which test and 

evaluate new techniques and strategies for model forecast guidance prior to testing for possible 

operational implementation. Stream 2 also tests techniques that cannot be tested on current 

operational computers because of size and time requirements, but can be tested on HFIP computer 

facilities in Boulder, CO. Those studies are looking ahead to possible future operational 

computational capability. This report outlines HFIP, how it is organized, its goals, its models and 

the results from each of the three Streams.

Stream 1.0 Results and Accomplishments

• The 2014 HWRF continued to improve its intensity forecasts (Figure 10) but this year it 

was in the middle of the pack (Figure 2) for intensity performance with respect to other 

models, especially the statistical models.

• This year however, HWRF track forecasts were better than other models for the later lead 

times (Figure 2).

• However, model initialization is still an area needing additional improvements, 

particularly for initially strong tropical cyclones, where the focus is to obtain more 

accurate initial vortex structure and environment.

• The Geophysical Fluid Dynamics Laboratory (GFDL) model and the HWRF model are 

now being run operationally in all hurricane basins, i.e. North Atlantic (NATL), East 

Pacific (EPAC), the north and south West Pacific (WPAC), and Indian (north and south) 

oceans.

• The GFS continues to provide excellent track guidance superior to most other models and 

comparable to the European Centre for Medium-range Weather Forecasts model

(ECMWF) guidance and exceeds the 5-year HFIP goal out to 4-day lead time.

Stream 1.5 Results and Accomplishments

• The HWRF was run as a 20 member ensemble and exhibited a 15%-20% improvement of 

intensity over the control run (operational version of the model).

• The GFDL ensemble was run as a 10 member ensemble. Performance was acceptable

but the spread of the ensemble, particularly for track was poor.

• This year a multi-model regional ensemble HWRF, GFDL, and Coupled 

Ocean/Atmosphere Mesoscale Prediction System-Tropical Cyclone (COAMPS-TC) was 

inaugurated.

• The best performer of the three ensembles for intensity was the COAMPS-TC ensemble 

that even outperformed the multi-model ensemble mean.
2

• The other Stream 1.5 models are shown in Table 7. Performance shown in Figure 3 was 

mixed.

• A web page http://www.hfip.org/products/ to display HFIP Stream 1.5 and other HFIP 

sponsored/run models in real time have been developed and maintained for the past two 

years. New products were added and improvements to existing products were made (e.g., 

Figure 22).

Stream 2.0 Results and Accomplishments

• The HFIP version of GFS was again run in parallel to the GFS operational model. The 

HFIP system used a semi-Lagrangian differencing scheme and was run at twice the 

resolution of both the GFS deterministic model and the National Centers for 

Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). As with 

the previous year, the higher resolution system did not perform as well as the operational 

system. This disappointing result is likely because the physics and Data Assimilation 

(DA) packages were not optimized for the higher resolution and the semi-implicit 

scheme.

• Forecasts of the 35 knot wind probability for ATL, EPAC, and WPAC by the HFIP 

global GFS ensemble were excellent for days 3-4 (see Figure 9).

• The global version of the HWRF was tested for the first time this year, see Figure 26.

• Performance of the multi-storm basin scale HWRF (from which the global version was 

developed) showed improvement in intensity over each storm run individually in the 

current HWRF (not the upgraded operational version).

• DA using the HWRF ensemble to generate background errors is progressing (Figures 17

and 18).

• A new product on presentation of the onset of storm force winds is being developed at the 

NHC. A prototype is shown in Figure 25.

Future configuration of the Hurricane Forecast System

Based on five years of results from HFIP, the projected future operational hurricane forecast 

guidance system would be as described in Table 1 below.

Table 1. Future Numerical Model Hurricane Forecast Guidance System

Component Specifications

Global model ensemble with 

Hybrid Data Assimilation

20 members at 10-20 km

Multiple moving nests to 3 km 

within the global model 

Double nests (9 and 3km), one for each hurricane 

Using all available aircraft and satellite data in the inner core and 

near environment of hurricane

Additional models to make a 

multi model ensemble (possibly 

run as a global model with 

internal nests).

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

Statistical Post Processing

Logistics Growth Equation Model (LGEM), Statistical Hurricane 

Intensity Prediction System (SHIPS), Statistical Prediction of 

Intensity from a Consensus Ensemble (SPICE), and others.
3

1. Introduction

This report describes the Hurricane Forecast Improvement Project (HFIP), its goals, proposed 

methods for achieving those goals, and recent results from the program with an emphasis on 

recent advances in the skill of the operational hurricane forecast guidance.

The first part of this report is very similar to previous versions of the annual report since it 

basically sets the background of the program. This year’s version is shortened somewhat from 

previous years but some of the same material is repeated for reference. For more background 

information the reader is referred to earlier reports available at: 

http://www.hfip.org/documents/reports2.php.

2. The Hurricane Forecast Improvement Project

HFIP provides the unifying organizational infrastructure and funding for NOAA and other 

agencies to coordinate the hurricane research needed to significantly improve guidance for 

hurricane track, intensity, and storm surge forecasts. HFIP’s 5-year (for 2014) and 10-year goals 

(for 2019) are:

• Reduce average track errors by 20% in 5 years, and 50% in 10 years for days 1-5.

• Reduce average intensity errors by 20% in 5 years, and 50% in 10 years for days 1-5.

• Increase the probability of detection (POD)1 for RI2 to 90% at Day 1 decreasing linearly 

to 60% at day 5, and decrease the false alarm ratio (FAR) for RI change is the highestpriority forecast challenge identified by the National Hurricane Center (NHC).

• Extend the lead-time for hurricane forecasts out to Day 7 (with accuracy equivalent to 

that of the Day 5 forecasts when they were introduced in 2003).

It is hypothesized that HFIP goals could be met with high-resolution (~10-15 km) global 

atmospheric numerical forecast models run as an ensemble in combination with, and as a 

background for, regional models at even higher resolution (~1-5 km). In order to support the 

significant computational demands of such an approach, HFIP has developed a high-performance 

computational system in Boulder, Colorado. Demonstrating the value of advanced science, new 

observations, higher-resolution models, and post-processing applications is necessary to justify 

obtaining the commensurate resources required for robust real-time use in an operational 

environment.

In FY2014, HFIP funded approximately $11,000,000 of research and development (R&D) based 

on recommendations from 4 strategic teams focused on various components of the hurricane 

forecast problem. That research and development was performed by: 1) various NOAA

laboratories and centers: Atlantic Oceanographic and Meteorological Laboratory (AOML), Coast 

Survey Development Laboratory (CSDL), Environmental Modeling Center (EMC), Earth 

 1 POD, Probability of Detection, is equal to the total number of correct RI forecasts divided by the total number of 

forecasts that should have indicated RI: number of correctly forecasted ÷ (correctly forecasted RI+ did not, but 

should, have forecasted RI). FAR, False Alarm Ratio, is equal to the total number of incorrect forecasts of RI 

divided by the total number of RI forecasts: forecasted RI that did not occur ÷ (forecasted RI that did occur + 

forecasted RI that did not occur).

2 RI for hurricanes is defined as an increase in wind speed of at least 30 knots in 24 hours. This goal for HFIP also 

applies to rapid weakening (RW) of a decrease of 25 knots in 24 hours.
4

System Research Laboratory (ESRL), Geophysical Fluid Dynamics Laboratory (GFDL), 

National Environment Satellite Data and Information Service (NESDIS), and National Hurricane 

Center (NHC); 2) the National Center for Atmospheric Research (NCAR); 3) the Naval Research 

Laboratory in Monterey (NRL); and 4) several universities (awarded through a NOAA

Announcement of Opportunity, see Appendix A): University of California, Los Angeles 

(UCLA), Colorado State University (CSU), University of Colorado (UC), Florida International 

University (FIU), Florida State University (FSU), University of Maryland (UMD), University of 

Miami (UM), the State University of New York (SUNY), Albany, University of Oklahoma 

(OU), The Pennsylvania State University (PSU), University of Rhode Island (URI), University 

of Utah (UT), and the University of Wisconsin (UW).

The teams, made up of over 50 members drawn from the hurricane research, development and 

operational communities, and team co-leaders are listed in Table 2. Tiger teams, focused on 

specific, shorter-term (1 or 2 years) tasks are listed in Table 3.

Full team membership is available at: http://www.hfip.org/teams/.

HFIP dedicated $2,300,000 to operating and maintaining the high performance computer system 

in Boulder, Colorado, and an additional $1,600,000 to enhancing computer capacity.

Table 2. Strategic Teams 2014

FY 2014 Teams FY 2014 Team Leads 

HFIP Model/Physics Strategy Vijay Tallapragada (EMC), Jian-Wen Bao 

(ESRL) 

Data Assimilation / Initialization / Ensemble 

Development 

Jeff Whitaker (ESRL), Xuguang Wang 

(OU) 

Post Processing and Verification Development Mark DeMaria (NHC), David Zelinsky 

(NHC), Tim Marchok (GFDL) 

Societal Impacts Jennifer Sprague (NWS), Rick 

Knabb(NHC) 
5

Table 3. Tiger Teams 2014

FY 2014 Teams FY2014 Team Leads

Web Page Design Paula McCaslin (ESRL), Laurie Carson (ESRL)

Regional Hybrid DA System / Use of Satellite 

Data Jeff Whitaker (ESRL), Xiaolei Zou (FSU) 

Stream 1.5 and Demonstration System 

Implementation James Franklin (NHC), Barb Brown (NCAR) 

HFIP’s focus and long-term goal is to improve the numerical model guidance that is provided by 

the NCEP operations to NHC as part of the hurricane forecast process. To accomplish this goal, 

the project is structured along three somewhat parallel development paths, known as “streams”. 

Stream 1 is directed toward developments that can be accomplished using operational computing 

resources (either existing or planned). This stream covers development work planned, budgeted, 

and executed over the near term (mostly one to two years) by EMC in collaboration with others,

with HFIP augmenting support to enable participation by the broader modeling community. 

Since Stream 1 enhancements are implemented into operational forecast systems, these advances 

are automatically available to Hurricane Specialists at NHC in the preparation of official forecast 

and warning products.

While Stream 1 works within presumed operational computing resource limitations, Stream 2 

activities assume that resources will be found to greatly increase available computer power in 

operations above that planned for the next five years. The purpose of Stream 2 is to demonstrate 

that the application of advanced science, technology, and increased computing will lead to the 

desired increase in accuracy, and other improvements of forecast performance. Because the 

level of computing necessary to perform such a demonstration is larger than can be 

accommodated by the current operational computing resources, HFIP has developed its own 

computing system at NOAA/ESRL in Boulder, Colorado.

A major component of Stream 2 is an Experimental Forecast System (EFS) that HFIP runs each 

hurricane season. The purpose of the EFS (also known as the Demonstration Project) is to 

evaluate the strengths and weaknesses of promising new approaches that are testable only with 

enhanced computing capabilities. The progress of Stream 2 work is evaluated after each season 

to identify techniques that appear particularly promising to operational forecasters and/or 

modelers. These potential advances can be blended into operational implementation plans 

through subsequent Stream 1 activities, or further developed outside of operations within Stream 

2. Stream 2 models represent cutting-edge approaches that have little or no track record; and 

therefore are not used by NHC forecasters to prepare their operational forecasts or warnings.

HFIP was originally structured around this two-stream approach. However, it quickly became 

apparent that some Stream 2 research models were producing forecast guidance that was 

potentially useful to forecasters. Because these models could not be implemented at NCEP due 

to insufficient operational computing resources, a third activity, known as Stream 1.5, was 

initiated to expedite the testing and availability of promising new models to forecasters. Stream 
6

1.5 is an approach that accelerates the transfer of successful research from Stream 2 into realtime forecasting, by following a path that temporarily bypasses the budgetary and technical 

bottlenecks associated with traditional operational implementations.

The Stream 1.5 process for each hurricane season involves extensive evaluation, by the Tropical 

Cyclone Modeling Team (TCMT) at NCAR, of the previous season’s most promising Stream 2 

models or techniques. This testing involves rerunning the models or techniques over storms 

from the demonstration period (August 1 to October 31 – the peak three months of the hurricane 

season) for the three previous seasons involving several hundred cases. If operational computing 

resources are not available for immediate implementation, those models or techniques that meet 

certain pre-defined standards for improvement over existing techniques, can be run on HFIP 

computing resources and the output guidance provided to NHC forecasters in real-time during 

the upcoming hurricane season as part of the EFS. This process expedites the availability of 

real-time advances to forecasters by one or more years. It also serves as a proof of concept for 

both the developmental work (Stream 2) and augmented computational capabilities.

3. The HFIP Model Systems

HFIP believes that the best approach to improving hurricane track forecasts, particularly beyond 

four days, involves the use of high-resolution global models with at least some run as an 

ensemble. However, global model ensembles are likely to be limited by computing capability 

for at least the next five years to a resolution no finer than about 10-15 km, which is inadequate 

to resolve the inner core of a hurricane. It is generally assumed that the inner core must be 

resolved to see consistently accurate hurricane intensity forecasts (NOAA SAB, 2006). 

Maximizing improvements in hurricane intensity forecasts will therefore require high-resolution 

regional models or global models with moveable high-resolution nests, perhaps also run as an 

ensemble. Below we outline the modeling systems currently in use by HFIP.

a. The Global Models

Global models provide the foundation for all of HFIP’s modeling effort. They provide hurricane 

forecasts of their own, and are top-tier performers for hurricane track forecasts. They also 

provide the background data and/or boundary conditions for regional and statistical models, and 

can be used to construct single-model ensembles, or be members of multi-model ensembles. The 

global models used in 2014 by HFIP are listed in Table 4 along with their characteristics.
7

Table 4. Specifications of the HFIP Global Models

Models Horizontal 

resolution

Vertical 

levels

Cumulus 

Parameterization Microphysics

Planetary 

Boundary Layer 

(PBL)

Land Surface

Model (LSM) Radiation Initialization

FIM (ESRL)

5-member 

ensemble

27 km 64

From 2011 GFS -

Simplified Arakawa 

Schubert

Zhao-Carr GFS Non-Local 

PBL

Noah LSM

Rapid Radiative 

Transfer Model 

(RRTMG)

ESRL EnKF

FIM – 2014 

numerics w/ 1/8 

deg. output

(ESRL)

15km 64

From 2011 GFS –

Simplified Arakawa 

Schubert

Zhao-Carr GFS Non-local 

PBL

Noah LSM RRTMG

GFS GSI 

operational 

hybridensemble 

variational

GFS/Hybrid 

EnKF-GSI

(NCEP)

27 km 64 Simplified Arakawa 

Schubert

Zhao-CarrMoorthy

GFS Non-Local 

PBL Noah LSM RRTMG

Hybrid 

EnKF-3DVAR GSI

20-member 

Global Ensemble 

GEFS (NCEP)

60 km 42 Simplified Arakawa 

Schubert

Zhao-CarrMoorthy

GFS Non-Local 

PBL Noah LSM RRTMG Bred Vector ETR + STTP

20-member HFIP 

Global Ensemble 

GFS/EnKF 

(ESRL)

27 km 42 Simplified Arakawa 

Schubert

Zhao-CarrMoorthy

GFS Non-Local 

PBL Noah LSM RRTMG

ESRL EnKF 

+ New 

Stochastic 

Physics

NAVGEM

T359 

37 km

(T425 ~31 km in 

transition)

50

(60 in 

transition)

Simplified Arakawa 

Schubert

Modified 

Zhao-Carr NAVGEM NAVGEM LSM RRTMG

NRL 

Atmospheric 

Variational 

DA Systemaccelerated 

representer 

(NAVDASAR)
8

b. The Regional Models

Specifications of regional models used by HFIP in 2014 are shown in Table 5.

Table 5. Specifics of the regional models used by HFIP in 2014

Models

Domains / 

Horizontal 

Resolution (km)

Vertical 

Levels

core

Cumulus 

Parameterization Microphysics PBL Land Surface Radiation

Initial and 

Boundary 

Conditions

Initialization SST

HWRF (OPS)

3

27/9/3

(9/3 following the 

storm)

61

NMM

Simplified 

Arakawa Schubert

for 27/9 nests

Ferrier GFS NonLocal PBL

GFDL Slab 

Model

GFDL

Scheme GFS

One-way 

hybrid

GSI-EnKF 

with vortex 

initialization

3D POM

HWRF in nonNWS basins 

(WP/SH/SL/IO)

3

27/9/3

(9/3 following the 

storm)

43

NMM

Simplified 

Arakawa Schubert 

for 27/9 nests

Ferrier GFS NonLocal PBL

GFDL Slab 

Model

GFDL

Scheme GFS vortex 

initialization GFS (static)

HWRF (Ens)

20 members

3

27/9/3

(9/3 following the 

storm)

43

NMM

Simplified 

Arakawa Schubert 

with Stochastic 

Perturbations for 

27/9 nests

Ferrier

GFS NonLocal PBL 

with 

stochastic 

perturbation

GFDL Slab 

Model

GFDL

Scheme GEFS

vortex 

initialization 

with initial 

position 

perturbed

3D POM

GFDL (OPS)

3

55/18/6

(18/6 following 

the storm)

42

GFDL

Simplified 

Arakawa Schubert Ferrier GFS NonLocal PBL

GFDL Slab 

Model

Schwarzkopf

-Fels 

(longwave) / 

Lacis-Hansen 

(shortwave)

GFS

GFDL 

Synthetic 

Bogus Vortex

3D POM

GFDL (WP, 

HFIP version)

3

55/18/6

(18/6 following 

the storm)

42

GFDL

Simplified 

Arakawa Schubert Ferrier GFS NonLocal PBL

GFDL Slab 

Model

Schwarzkopf

-Fels 

(longwave) / 

Lacis-Hansen 

(shortwave)

GFS

GFDL 

Synthetic 

Bogus Vortex

3D POM

GFDL (Ens)

10 members

3

55/18/6

(18/6 following 

the storm)

42

GFDL

Simplified 

Arakawa Schubert Ferrier GFS NonLocal PBL

GFDL Slab 

Model

Schwarzkopf

-Fels 

(longwave) / 

Lacis-Hansen 

(shortwave)

GFS.

GFDL 

Synthetic 

Bogus Vortex

with inner 

core 

perturbations

3D POM

HYCOMCoupled HWRF

3

27/9/3

(9/3 following the 

storm)

61

NMM

Simplified 

Arakawa Schubert Ferrier GFS NonLocal PBL

GFDL Slab 

Model

GFDL

Scheme GFS

One-way 

hybrid

GSI-EnKF 

with vortex 

initialization

3D HYCOM

HWRFHRD/EMC

Basin Scale

3

27/9/3

(9/3 following 

each storm)

61

NMM

Simplified 

Arakawa Schubert Ferrier GFS NonLocal PBL

GFDL Slab 

Model

GFDL

Scheme GFS Vortex 

initialization GFS (static)

HWRF-HRD

(HEDAS)

2

9/3

(3km following 

the storm)

42

NMM

Simplified 

Arakawa Schubert Ferrier GFS NonLocal PBL

GFDL Slab 

Model

GFDL 

Scheme GFS

EnKF with 

aircraft and 

satellite

(AMVs, 

AIRS and 

GPS-RO 

retrievals) 

data

GFS (static)

AHW (NCAR)

15-member 

ensembles

3

36/12/4

36

ARW

Tiedtke (36/12 km 

only) WSM6 YSU NOAH LSM RRTMG 

(LW+SW)

GFS (BC 

only)

96-member 

DART EnKF 

method in a 

6-hour 

cycling mode

Pollard 1-D 

Column Ocean

COAMPS-TC©

(HFIP version)

3

45/15/5

(15/5 km 

following the 

storm)

40

COAMPS

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

Fu-Liou GFS

Balanced 

vortex 

initialization

(4D-VAR, 

EnKF 

options)

GFS (static)
9

c. Initialization and Data Assimilation Systems

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

the HFIP EFS:

1. Grid-point Statistical Interpolation (GSI): The GSI system developed by NCEP is a unified

3-dimensional variational (3D-VAR) data assimilation system for both global and regional 

applications, and has been widely used by many modeling systems across NOAA and other 

agencies (DTC 2012; Wu, et al, 2002; Parrish and Derber 1992; Cohn and Parrish, 1991). 

This system has been used by NCEP GFS since 2006 in operations.

2. Ensemble Kalman Filter (EnKF): This is an advanced assimilation approach, somewhat like 

4D-VAR, that uses an ensemble to create background error statistics for a Kalman Filter 

(Tippett et al, 2003; Keppenne, 2000; Houtekamer et al, 1998; Evensen, 1994;). Several 

HFIP models (e.g., AHW, HFIP GFS ensembles, PSU etc., see Tables 4 & 5 above) are using 

the EnKF approach for DA. The Hurricane Research Division (HRD)/AOML developed a 

variant of the EnKF based DA system using the HWRF model, known as Hurricane 

Ensemble Data Assimilation System (HEDAS) as noted by Aksoy et al, (2012).

3. Vortex initialization: The initial vortex for the regional models is produced by a vortex 

initialization procedure. In general, the vortex circulation is filtered from the first guess 

fields interpolated from global model; then a new vortex modified by the observed intensity 

is inserted back in the filtered environment. The new vortex is the model balanced vortex 

cycled from previous six-hour forecast, from a parent global model, or defined based on a 

synthetic vortex profile. On the first initialization for a particular storm, the size and 

intensity of the vortex are modified based on real-time observations. In the HWRF system, 

the tropical cyclone vortex is cycled from the previous six hour forecast and the vortex is 

relocated based on the observed position. The one-way hybrid GSI-EnKF DA system 

assimilates the modified vortex and ambient fields to generate initial conditions for the 

Models

Domains / 

Horizontal 

Resolution (km)

Vertical 

Levels

core

Cumulus 

Parameterization Microphysics PBL Land Surface Radiation

Initial and 

Boundary 

Conditions

Initializatio

n SST

COAMPS-TC©

(OPS)

3

45/15/5

(15/5 km 

following the 

storm)

40

COAMPS

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

Fu-Liou NAVGEM

Balanced 

vortex 

initialization

(4D-VAR, 

EnKF 

options)

NAVGGEM 

(static)

COAMPSTC© (Ensemble)

10-members

3

27/9/3

(9/3 km 

following the 

storm)

40

COAMPS

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

Fu-Liou

GFS with

initial and 

boundary 

condition 

synopticscale 

perturbatio

ns

Balanced 

vortex 

initialization 

with 

perturbations

GFS (static)

Wisconsin NMS

2

45/4,1

(4/1 following the 

storm)

42

UW-NMS Modified Emanuel

Tripoli-Flatau

Bulk 

microphysics (1 

liquid, 2 ice 

categories)

1.5 Order 

Closure NOAH LSM RRTMG SW 

and LW GFS

Bogus vortex 

with 12-hour 

dynamic 

initialization

GFS (static)

Penn State 

ARW

3

27/9/3 (9/3 

following the 

storm)

42

ARW

Grell-Devenyi 

ensemble scheme

(27 km only)

WSM 6-class 

graupel scheme YSU 5-layer thermal 

diffusion scheme

RRTM 

(longwave) / 

Dudhia 

(shortwave)

GFS

Cycling 

EnKF with 

all Recon 

data

GFS (static)
10

HWRF system. Vortex relocation is also utilized by the current operational GFS and Global 

Ensemble Forecast System (GEFS) in NCEP. An advanced vortex initialization and 

assimilation cycle for the operational HWRF consists of four major steps: 1) interpolation of 

the global analysis fields from the Global Data Assimilation System (GDAS) onto the 

operational HWRF model grid; 2) removal of the GFS vortex from the global analysis; 3) 

addition of the HWRF vortex modified from the previous cycle’s six-hour forecast based on 

observed location and strength (or use of a corrected GDAS or bogus vortex for a cold start); 

and 4) addition of observation data outside of the hurricane area using one-way hybrid GSI 

and EnKF. This is a version of the Hybrid Ensemble-Variational DA System (HEVDAS)

where instead of using an ensemble from the HWRF to create the ensemble part of the 

background error, it uses ensemble members created by the GFS EnKF. The DA system is 

capable of ingesting inner core data to optimize the vortex initialization.

4. Global Forecast System (GFS): The initial state created for the current operational global 

model, the GFS, is interpolated to the grids used by HFIP global models. The GFS in 2012 

used the HEVDAS. HEVDAS is a combination of the GSI 3D-VAR and an ensemble-based 

system to define the background error matrix.

5. Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System

(NAVDAS): This is the system used to provide the initial conditions to NAVGEM. 

Previously a 3D-VAR system, it was upgraded in September 2009 to NAVDAS-AR, a fourdimensional variational (4D-VAR) approach (Daley and Barker, 2001). The 3D-VAR 

version of NAVDAS is used to initialize COAMPS-TC.

d. The HWRF Community Code Repository and User Support

During 2009-2014, both EMC and the Developmental Testbed Center (DTC) worked to update 

the operational version of HWRF from version 2.0 to the current community version of HWRF, 

version 3.6a (Bernardet et al, 2014; Tallapragada et al, 2014). This makes the operational model 

completely compatible with the codes in the community repositories, 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 been supported by HFIP for developing a repository for a community based 

hurricane modeling system. A community based hurricane modeling system ensures the same 

code base can be used for research and in operations. Support provided by DTC in 2014 

included two HWRF tutorials, taught in College Park, MD, and in Taiwan.

4. Meeting the HFIP Goals

a. The HFIP Baseline

To measure progress toward meeting the HFIP goals outlined in the introduction, a baseline level 

of accuracy was established to represent the state of the science at the beginning of the program. 

Results from HFIP model guidance could then be compared with the baseline to assess progress. 

HFIP accepted a set of baseline track and intensity errors developed by NHC, in which the 

baseline was the consensus (average) from an ensemble of top-performing operational models, 

evaluated over the period 2006-2008. For track, the ensemble members were the operational 

aids GFSI, GFDI, UKMI, NGPI, HWFI, GFNI, and EMXI, while for intensity the members were 

GHMI, HWFI, DSHP, and LGEM (Cangialosi and Franklin, 2011). Figure 1 shows the mean 
11

errors of the consensus over the period 2006-2008 for the Atlantic basin and the 5- and 10-year 

error goals represented in black and labeled on the left side of the graph. A separate set of 

baseline errors (not shown) was computed for the eastern North Pacific basin.

The baseline errors in Figure 1 are also compared to the errors for the same cases for the 

climatology and persistence model (CLIPER5) for track and the Decay Statistical Hurricane 

Intensity Forecast (Decay-SHIFOR5) model for intensity (NHC, 2009). Errors from these two 

models are large when a storm behaves in an unusual or rapidly changing way, and therefore are 

useful in assessing the inherent difficulty in a set of forecasts. When a track or intensity model 

error is normalized by the CLIPER5 or Decay-SHIFOR5 error, the normalization yields a 

measure of the model’s skill.

Because a sample of cases from, say, the 2013 season might have a different inherent level of 

difficulty from the baseline sample of 2006-2008 (for example, because it had an unusually high 

or low number of rapidly intensifying storms), evaluating the progress of the HFIP models in 

terms of forecast skill provides a more representative longer-term perspective. Figure 1 shows 

the baseline errors and the 5- and 10-year goals as skill, represented in blue and labeled on the 

right side of the graph. Skill in the figure is the percentage improvement over the DecaySHIFOR5 and CLIPER5 forecasts for the same cases. Note the skill baseline and goals for 

intensity at all lead times is roughly constant with the baseline representing a 10% improvement 

over Decay-SHIFOR5 and the 5- and 10-year goals, representing 30% and 55% improvements, 

respectively. It’s important to remember, however, that normalization by CLIPER or 

(especially) Decay-SHIFOR5 can fail to adequately account for forecast difficulty in some 

circumstances. A hurricane season that features extremely hostile environmental conditions will 

lead to very high Decay-SHIFOR intensity forecast errors (because climatology will be a poor 

forecast in such years), but relatively low dynamical model and NHC official forecast errors 

(because few storms will intensify rapidly, making life easy on both models and forecasters). 

This combination of baseline and model errors yields an unrealistic skill estimate.

Figure 1. HFIP Track and Intensity Error Baseline and Goals.

The baseline errors (black lines) were determined from an average of the top-flight operational models 

during the period 2006-2008. The HFIP expressed goals (dashed lines) are to reduce this error by 20% in 5 

years and 50% within 10 years. Comparisons of forecasts over non-homogenous samples, however, are 

best done in terms of skill. To obtain the 5-year and 10-year HFIP goal in terms of skill (blue lines—

baseline skill in solid, HFIP goals dashed), the goals are expressed as the percentage improvement over the 

CLIPER5 errors (track) and Decay-SHIFOR5 (intensity) of the baseline sample (see text).
12

It is important to note that the HFIP performance baselines were determined from a class of 

operational aids known as “early” models. Early models are those that are available to 

forecasters early enough to meet forecast deadlines for the synoptic cycle. Nearly all the 

dynamical models currently in use at tropical cyclone forecast centers, however, (such as the 

GFS or the GFDL model, referred to as GFDL, for short) are considered “late” models because 

their results arrive too late to be used in the forecast for the current synoptic cycle. For example, 

the 1200 Coordinated Universal Time (UTC) GFDL run does not become available to forecasters 

until around 1600 UTC, whereas the NHC official forecast based on the 1200 UTC initialization 

must be issued by 1500 UTC, one hour before the GFDL forecast can be viewed. It’s actually 

the older, 0600 UTC run of the GFDL model that would be used as input for the 1500 UTC 

official NHC forecast, through a procedure developed to adjust the 0600 UTC model run to 

match the actual storm location and intensity at 1200 UTC. The procedure also adjusts the 

forecast position and intensity at some of the forecast times as well and then applies a smoother 

to the adjusted forecast. This adjustment, called “interpolation”, procedure creates the 1200 

UTC “early” aid GFDI that can be used for the 1500 UTC NHC forecast. Model results so 

adjusted are denoted with an “I” (e.g., GFDI). The distinction between early and late models is 

important to assessing model performance, since late models have an advantage of more recent 

observations/analysis than their early counterparts. It is interesting to note however that although 

the early version loses about 3-5% of the skill for track forecasts compared to the late version, 

the skill for intensity forecasts are virtually the same for late and early versions (for details see 

Goldenberg et al, 2015).

b. Meeting the Track Goals

Accurate forecasts beyond a few days require a global domain because influences on a forecast 

for a particular location come from weather systems at increasing distance from the local region 

over time. One of the first efforts in HFIP was to improve the existing operational global 

models. Early in the program, it was shown that forecasts were improved, particularly in the 

tropics, by using a more advanced DA scheme than the one employed operationally at that. A 

version of this advanced DA went operational in the GFS model in May 2012. Results from that 

model are presented in this report.

c. Reaching the Intensity Goals

HFIP expects that its intensity goals will be achieved through the use of regional models or 

global models that have moveable nests with a horizontal resolution near the hurricane’s inner 

core finer than about 3 km. In addition, early results suggest that output from individual HFIP 

models can be used in statistical models such as the Statistical Hurricane Intensity Prediction 

System (SHIPS), (DeMaria and Kaplan, 1994; NHC 2009) or Logistics Growth Equation Model 

(LGEM) (DeMaria, 2009; NHC 2009) to further increase the skill of the intensity forecasts. The 

operational HWRF model operating at 3km resolution has started showing its potential for 

improved intensity forecasts, producing comparable and sometimes superior results versus the 

statistical models and NHC official forecasts as demonstrated through a large set of retrospective 

forecasts. Results from HWRF model for intensity forecasts are presented in this report.

Eventually the regional model will be able to interact within the global model. More 

specifically, there would be one set of nests for each hurricane in the global model, thereby 

accomplishing the track and intensity forecast goals through a unified global-to-regional scale 

modeling system.
13

5. HFIP Stream 1.5 

HFIP and NHC agreed in 2009 to establish a pathway to operations known as “Stream 1.5.” As 

explained previously, Stream 1.5 covers improved models and/or techniques that NHC, based on 

prior assessments, wants to access in real-time during a particular hurricane season, but which 

cannot be made available to NHC by the operational modeling centers in conventional 

production mode. HFIP’s Stream 1.5 supports activities that intend to bypass operational 

limitations by using non-operational resources to expedite by one or more hurricane seasons the 

delivery of real-time guidance for a particular model to NHC. Stream 1.5 projects are run as part 

of HFIP’s annual summertime “Demo Project”.

Nine modeling groups provided retrospective tests of their models for consideration by NHC for 

inclusion as a Stream 1.5 model for the 2014 season. Those groups and their models are listed in 

Table 6. Table 7 shows the groups selected for Stream 1.5 and the form of the output from their 

models provided to NHC.

Note that 4 of the 9 candidate models were admitted into Stream 1.5 based on the models’ 

performance for track and/or intensity. In addition, two HFIP Stream 1.5 consensus aids were 

constructed: (1) the TV15 Track Consensus Model used operational model members: GFSI, 

EGRI, GHMI, HWFI, GFNI, EMXI and the GFDL Stream 1.5 model, while (2) the IV15 

Intensity Consensus Model used of operational model members: Decay-SHIP (DSHP), LGEM, 

GHMI, HWFI and the Stream 1.5 NRL, UW-Madison, and GFDL.

Table 6. Participating modeling groups in the 2014 HFIP Retrospective Evaluation.

40

Organization Model Type

EMC HWRF coupled with HYCOM Regional-dynamic-deterministic

EMC 20-member HWRF Ensemble Regional-dynamic-ensemble

NRL COAMPS-TC with GFS IC/BC Regional-dynamic-deterministic

UW-Madison UW-NMS Regional-dynamic-deterministic

AOML/HRD Basinscale HWRF Regional-dynamic-deterministic

GFDL 10-member GFDL hurricane model

Ensemble Regional-dynamic-ensemble

ESRL FIM Global-dynamic-deterministic

FSU Multi-model superensemble Weighted-consensus

NESDIS/STAR and CIRA SPICE Statistical-dynamical-consensus
14

Table 7. 2014 HFIP Stream 1.5 Real-time runs and model guidance to NHC

All evaluations were based on homogeneous samples and applied the appropriate method for 

assessing statistical significance. A detailed description of the metholodgy used for the 

evaluation, reports for each participating modeling group (Tables 6 and 7). All the verification 

plots generated during the evaluation, and information on the participating models and cases 

included in the evaluation are available on the TCMT 2014 Retrospective HFIP Testing website 

(http://www.ral.ucar.edu/projects/hfip/d2014).

a. Stream 1.5 results

The results presented here reflect the Stream 1.5 runs that were successfully transmitted to NHC 

in real time during 2014. The Stream 1.5 models arriving at NHC underwent standard 

interpolation processing to convert “late” dynamical guidance into “early” interpolated guidance 

that could be used by the forecasters, (see Section 4a). Figure 2 (upper left panel) portrays the 

skill plots for a homogeneous verification of the Stream 1.5 track models that met availability 

standards (regardless of whether they were intended for use explicitly or in a consensus), along 

with selected operational models.

Early in the forecast period the track consensus (TVCA) was (marginally) the best model. The 

GFSI (operational GFS model) was close to the official, and similar to the European model 

(EMXI) for all times except 120 h when GFSI had much higher skill. So overall, TVCA and 

HWFI showed the highest skill for track. The GFDL models [green lines: GFDM - GFDL 

ensemble mean and GHMI (operational GFDL)] had the lowest overall skill for track.

39

Organization Model

Track Track

consensus

Intensity Intensity

Consensus

NRL COAMPS-TC  

UW-Madison UW-NMS 

GFDL 10-member GFDL hurricane 

model ensembles    

NESDIS/STAR and CIRA SPICE 
15

Figure 2. Tracking and Intensity Forecast Skill in the Atlantic Basin (2014)

It should be noted that the results discussed here are for only one (low activity) year. This 

relatively small sample size (only 110 and 25 cases at 12 and 120 h, respectively) is not 

necessarily representative of results that would be seen from a multi-year sample.

Figure 3 shows that in 2014 the HWFI was competitive with the top-tier operational models and 

in fact exceeded the official intensity skill after the first 48 hours and was more skillful in track 

forecasts than even GFSI from 24 to 36h and after 72 h.
16

 

 

Figure 3. Track and Intensity Forecast Skills for the East Pacific Basin

As in Fig. 2 track and intensity skills are shown in upper and lower panels, respectively. TV15 and TVCE 

depicted in the upper right panel are Stream 1.5 and operational track consensus respectively, while TV15 

and IV15 in the lower right panel are the corresponding consensus with the Stream 1.5 models included.

The intensity results in Figure 3 show that the best performers in the early part of the forecast 

period were the intensity consensus (ICON) and two statistical models (SPC3 and DSHPS). In 

general, the LGEM statistical model did not perform as well as the other models (except at 120 

hours where there are fewer cases). Although the 2014 HWFI had shown relatively high skill for 

intensity forecasts in retrospective runs, it did not perform as well for the 2014 sample. HWRF 

and the Navy model (CXTI) were among the lowest performers. In the upper right panel the 

consensus with and without the Stream 1.5 models (TV15 and TVCE, respectively) are 

compared for the East Pacific Basin. The addition of the Stream 1.5 models to the consensus 

produced almost no change for track forecast skill. However, when comparing the consensus for 

intensity with and without the Stream 1.5 models (IV15 and IVCN, respectively), the addition of 

the Stream 1.5 models resulted in increased skill at all times past 24 h (lower right panel).
17

6. Performance of Stream 2 models

Table 8. Stream 2 Models

Organization Model Identifier

GSD FIM9I

University of Utah A3UI

PSU APSI

GFDL Ensemble Mean GTMI

HRD H3WI, HECI, HEDI

EMC Ensemble Mean HWMI

FSU MMSI

In addition to the Stream 1.5 models run as part of the real-time experimental forecast system, 

HFIP also ran several models as Stream 2 candidates. Table 8 shows the various groups that ran 

Stream 2 candidates with the identifier for the various models run by each group shown in the 

right hand column.

The results from track and intensity skill for Stream 2 models for the 2014 Atlantic hurricane 

season are shown in Figure 4. Note that the sample size is even smaller than for Figure 2 since 

the Stream 2 models were run for fewer cases. The results are similar in perfomance patterns to 

those shown for the Stream 1.5 models as well as the operational models. For track, from 72-96 

h, HWFI had the highest skill.
18

Figure 4. Track and Intensity Skills for the 2014 Atlantic Basin hurricane season

Forecast skill of Stream 2 models compared to operational models. As in Fig. 3 track and intensity skills are 

shown in the upper and lower panels, respectively. The number of cases is shown across the top of each 

panel.

For intensity DSHIPS (statistical) is still the best model overall, but at mid-forecast lead times 

dynamical models from HRD (H3WI) and Pennsylvania State University’s APSI model were 

comparable. However, for intensity forecasts the Official forecast still shows the highest skill. 
19

Several of the dynamical models show problems for intensity for the earliest lead times due to 

vortex initialization (spin up) issues.

7. Operational Hurricane Guidance Improvements

The HFIP goals described in section 4 are considered met when the model guidance provided to 

NHC by NCEP reaches those goals. Since 2014 represents the fifth year of the project, it is 

expected to see progress toward meeting the five year goals in both operational models and 

experimental models (e.g., Stream 1.5 models described in the previous section that provide 

model guidance to NHC in real time). In this section the emphasis will be on improvements in 

the hurricane forecasts from the models that were fully operational in 2014. This includes the 

GFS, and the HWRF operational regional models.

a. Global Model (GFS)—Operational

In May of 2012 the GSI data assimilation system in the GFS was replaced by the hybrid data 

assimilation system. The hybrid system uses an ensemble to generate a flow dependent 

background error covariance matrix that is then used in the GSI for the analysis. In previous 

annual reports starting with the first one in 2010 the impact of changing the DA system in the 

global models was described, particularly the GFS from the 3D-VAR GSI to an ensemble based 

system, called (EnKF). The hybrid system is basically a combination of the EnKF and the GSI 

which has shown to provide somewhat better results than EnKF alone. The global hybrid system 

merge with regional models is considered an important mechanism for the transference of HFIP 

results into operations. In addition, the GFS has undergone other improvements including 

improved physics and increased resolution. In 2014 the deterministic operational GFS was run 

at T574 (~27 km) and the GFS ensemble (GEFS) at T254 (~60 km). Recently (January 2015) 

the resolution of the GFS has been increased to T1534 (~13 km) but it is too early to show 

performance results for that configuration.

Using retrospective testing, Figure 5 shows retrospective testing results of the skill for the 2011-

2013 Atlantic and East Pacific hurricane seasons of the 2012 GFS operational version as 

compared to the ECMWF and the FIM models which was run as a Stream 2 model in 2014. The 

upper panel is similar to a figure shown in the previous annual report but contains one additonal

year. The 3-year results are similar to the 2-year results in that the GFS compares favorably to 

the higher resolution ECMWF model in the Atlantic. The FIM model using the same DA system 

as the GFS and the same physics performs comparably in the Atlantic to the GFS or ECMWF

from 36 to 96 h.

In the East Pacific, FIM has higher skill than GFS for most forecast times. However, starting at 

36 h, ECMWF has substantially higher skill than both the GFS and FIM. It will be interesting to 

see how the higher resolution GFS (T1534) will perform in the upcoming hurricane season for

track forecasts.
20

Figure 5. Hurricane track forecast skill errors for GFS, ECMWF, and FIM for the 2011-2013 Atlantic and 

East Pacific hurricane seasons

The FIM is an HFIP Stream 2 model. The resolutions used in each model are shown at the top. The 

number of cases for each time period and each model are shown in parentheses. The percentage gain is 

relative to the baseline.

b. Global Model Ensembles (GFS based)—HFIP Experimental

The operational NCEP Global Ensemble Forecast System (GEFS) is run at T254, and HFIP (and 

ESRL) high-resolution version (T574) GFS ensemble system, was run as part of the Stream 2 

during the 2014 hurricane season. In this section, which is almost identical to the same section 

in the 2013 Annual Report except for updated figures, emphasis is placed upon ensemble 

systems comparison. Both GEFS and fine-grid HFIP ensemble systems use the same physics 

package and hybrid DA system that the operational GFS employs. But, while GEFS used the 

bred-vector ensemble transform with rescaling (BV-ETR) and stochastic total tendency 

perturbation (STTP) method for generating the perturbations, the HFIP system (semiLagrangian) used the EnKF based perturbations and a new stochastic physics package in its 

configuration.
21

Figure 6. Operational GEFS (T254) and HFIP GFS Ensemble System (T574) Track and Intensity (Max 

Wind) Errors

For the 2013 and 2014 Atlantic hurricane seasons: Track (left panels) and intensity (right panels) error for 

the HFIP GFS Ensemble System (T574; HFIP demo; blue lines and the operational GEFS (T254; OPNL; red 

lines for 2014 (top panels) and 2013 (bottom panels). The HFIP ensemble systems use semi-Lagrangian 

differencing. The dashed lines show the ensemble spread and the number of cases are shown in parentheses 

above each panel. Error bars are shown for XX% confidence interval. The samples are homogeneous

particular to a given year.

Figure 6 compares the track and intensity errors for the 2013 and 2014 Atlantic hurricane 

seasons. The results from the two years are similar. Both GEFS and the HFIP system had

almost identical average track errors this year (2014) whereas GEFS performed better in 2013 for 

most lead times than the HFIP system. The spread for the HFIP system is still a little better than 

for GEFS. The GFS model is not prized for its intensity forecasts, but it is noted here that GEFS 

has slightly lower average errors than the HFIP ensemble and the ensemble spread of the two 

systems is rather low but similar.

The poorer performance of the HFIP versus GEFS for intensity is, as last year, related to 

incomplete tuning of the physics package to the semi-Lagrangian time differencing used in the 

HFIP system. At the current time, the HFIP developers are not emphasizing the development of 

global model techniques and 2014 is probably the last time we will run this global system on the 

HFIP computers.

Figures 7 and 8 show examples of products produced by the HFIP GFS ensemble system. 

Figure 7 shows the probability of winds greater than 34 knots (at least tropical storm force). 
22

Each of the colored ellipses in Figure 8 contain 80% of the member’s positions for each lead 

time, giving an idea of the probability of the storm position falling within the ellipse. Ellipses 

with their major axis aligned along the ensemble mean track indicate a speed uncertainty and

those aligned perpendicular to the mean track indicate a directional uncertainty. Those aligned 

at some other angle with respect to the track indicate a speed-bias dependent on tracks to the 

left of the mean versus those aligned to the right. Both of these products were widely used by 

the HFIP team during the hurricane season.

Figure 9 shows the verification of Figure 7, probability of winds greater than 34 knots calculated 

from the HFIP ensemble system. A similar figure was shown in the last year’s Annual report for 

the 2013 season. Here the results are shown from both years. For the 3-4 day forecasts the HFIP

ensemble performed with significant improvement in 2014 compared to 2013 but GEFS was 

worse. The bottom panels show the 5-7 day forecasts. The HFIP ensemble did not perform as 

well in 2013 while GEFS was unchanged. Both models over forecast the probability of 34 knot 

winds at 5-7 days.

Figure 7. EnKF HFIP GFS Ensemble T.S. 

Force Wind Probabilities 84 Hours for 

Hurricane Arthur

This provides an example of a probability of 

winds greater than 34 knots at the various 

points during a 3.5 day period starting at 18 Z 

July 1, 2014 for hurricane Arthur. A 100% 

probability implies that all 20 members of the 

ensemble forecast a 34 knot wind at some time 

during the 84 hours of the forecast. Forecast is 

from July 1 at 18Z 2014.

Figure 8. EnKF HFIP GFS Ensemble 

Systems Forecasts and Ellipses (July 1, 2014 

Forecast)

This product created from the HFIP GFS 

ensemble shows all the tracks from the 

forecast of July 1, 2014, 18 Z out to 3.5 days 

for hurricane Arthur. There are twenty 

ensemble members shown in light gray and 

the ensemble mean is shown in black. The 

various ellipses enclose 80% of the members 

at various lead times in days from the 

beginning of the forecast
23

Figure 9. Operational GEFS and HFIP Ensemble for Verification of Probability of Storm Force Winds

for 2013 and 2014.

Verification statistics for probability of storm force winds (greater than 34 knots) at points shown in 

the black area in the lower panel. HFIP GFS (T574) ensemble system and the operational GEFS 

(T256) are shown in black and red, respectively. 100% (i.e., 1) in the figures would imply that all 

ensemble members forecast a 34 knot wind at that point during days 3-4 (upper panel) and days 5-7 

(lower panel). Probabilities are computed on a 1x1-degree box. Reliability Scores are aggregated over 

domain shown below. Observed probabilities are shown on the y axis and forecast probabilities on the 

x axis. A perfect score would be along the dashed line. Forecast probabilities are over forecast below 

the line and under forecast above.
24

Figure 10. HWRF Intensity Forecast Improvement for the 

Atlantic Basin

Improvements in the HWRF system from the 2011 to the 

2014 are shown as error. Seasons for which models were 

run are shown on each line. Note that some the samples 

(years) are not homogeneous between the models.

c. Hurricane WRF (HWRF)

1) Atlantic

Figure 10 reflects the story of where HFIP is with the HWRF hurricane model system. In earlier 

figures (e.g., Figure 2) it was shown that the most recent version of the HWRF (2014) model was 

performing very well for track. In fact, in 2014 HWRF provided among the best (if not the best) 

track guidance beyond 72 hours.

However, intensity is a different story. 

Again earlier figures (e.g., Figure 2) 

showed HWRF to be near the middle 

(or bottom) of the group of models 

shown in that figure. However, that 

was for a relatively small (2014 only) 

sample. The best performers (as is the 

case most years) were the statistical 

models, specifically DSHP. In Figure

10 the progress of HWRF in 

forecasting intensity is shown as error. 

There has been a steady decrease of 

intensity error from 2011 to present by

15% to 20% per year; although some 

of the samples (years over which 

models were run) shown in the figure 

are not homogeneous. In fact, in terms of 

error related to the five-year goal the 

2014 version of HWRF has met or 

exceeded that goal beyond 72 hours. In 

contrast, there has much work to be done

in improving the HWRF intensity 

forecasts for the first 24 hours; although the 2013 and 2014 operational versions are at least 

better than the HFIP baseline error in that forecast range.

When the intensity errors for HWRF are shown relative to the Statistical model DSHP the steady 

improvement is still evident but even in 2014 (the red line in the skill figure) the skill is less than 

the HFIP 5-year goal. Most striking feature in this figure is the consistently very low skill early 

in the forecast (purple ellipse). This is related to the ongoing problems with initialization of the 

models. This problem is not unique to HWRF, see Figure 4. All the dynamical models seem to 

suffer from a common initialization problem. The initialization of the dynamical models 

continues to be a high priority for HFIP. Keep in mind however, that statistical models as well 

as the “early” (aka interpolated) versions of dynamical models’, show better results in the first 24 

hours since the initial times are adjusted to the operational values. The results shown here are for 

the “late” (aka non-interpolated) versions and therefore the forecast errors at early lead times are 

generally larger than with the “early” versions.
25

2) West Pacific (WPAC) and Other Basins

In 2012 HFIP began running real-time forecasts for the WPAC and in 2013 for the Indian Ocean. 

In 2014 HWRF runs were also extended into the southern Pacific and Indian Oceans. These runs 

were done in real time on the HFIP computers in Boulder, CO rather than the operational 

computers. The forecasts were transmitted to the Joint Typhoon Warning Center (JTWC) where 

they were used extensively in their forecasts. Figure 11 shows the performance for 2013-2014 

for WPAC storms of HWRF compared to various other models. In this basin HWRF track 

forecasts (red) were as good as the GFS forecasts (black) although the ECMWF (yellow) track 

forecasts were even better. Traditionally, the ECMWF has provided the best forecasts of track in 

the WPAC.

Figure 11. 2014 HWRF Track and Intensity Error Statistics in the Western Pacific

Track and intensity errors in the WPAC for 2013-2014 compared to several other models run in the WPAC.

For the first several years the intensity performance of HWRF in the WPAC has been excellent. 

Unlike the results for the Atlantic, HWRF provided the best (lowest) intensity forecasts at all

forecast times except for 120 h. It is possible that the explanation for the different performance 

between HWRF in the WPAC and in the Atlantic is frequent occurrence of strong typhoons in 

the WPAC. Earlier studies show that HWRF tends to do better with initially stronger storms, 

which perhaps allow the HWRF model to demonstrate the importance and usefulness of highresolution deterministic models for intensity forecasts in this basin.
26

3) A Comment on Intensity Forecast Errors

Within HFIP it has been discussed if there is a limit to how much intensity errors can be reduced. 

Some of the error reduction proposed by the program, especially the 50% 10-year goal may be 

unrealistic because the errors in the observations (~10 percent or 5-10 knots, see Figure 12) may 

be larger than the proposed reduction (to errors less than 10 knots) especially in the early forecast 

times (see Figure 1). Figure 12 shows the cumulative distribution of errors in the NHC official 

forecasts. The average error over the whole of the distribution is about 14 knots for 48 hours, 

mainly because of the relatively high number of forecasts with very large errors (up to 70 knots), 

and 65% of all errors at 48 hours being greater than 10 knots. It is hypothesized that it is 

possible to make substantial reduction in average error by reducing the magnitude of the extreme 

errors. Thus a focus on reducing the large errors (termed as outliers) may reduce the average 

errors to close to the observation error.

 Figure 12. OFCL Intensity Error Distribution

Cumulative percentage of NHC Official Forecast errors are depicted as a function of the 

magnitude of the error. Forecast lead times of 24, 48 and 72 hours are shown.

4) The Multi-Model Ensemble 

This year HFIP began testing a multi-model regional ensemble. Three ensembles were used, the 

HWRF, COAMPC-TC and the GFDL Ensembles (which will be described in more detail below.

The HWRF ensemble was the same system used for the HWRF ensemble last year with a few 

additional physics perturbations:

• 27-, 9-, 3-km horizontal grid spacing

• 20 members plus 1 control, the operational HWRF

• IC/BC Perturbations were from the GEFS- and Ensemble Transform with Rescaling 

(ETR) system was used to create the ensemble members:
27

o Stochastic boundary layer height perturbations in PBL scheme, -20% to +20%

o Stochastic initial wind speed perturbations with zero mean and -3kts to +3kts

• Model Physics Perturbations (vortex scale):

o Stochastic Convective Trigger in SAS, -50hPa to + 50hPa white noise

Thus the large-scale perturbations of the ensemble came from the GEFS to initially define each 

member and then a stochastic convective trigger, initial wind and PBL height perturbations were

added within each member.

Figure 13. The HFIP Multi-Model Ensemble for Tropical Storm Gonzalo (2014)

This figure depicts an example of a forecast from the HFIP Multi-Model ensemble consisting of HWRF, 

COAMPS_TC and GFDL. Ellipses enclose 80% of member positions at indicated time. Black lines are the 

ensemble means for the individual models ensemble (bottom panels) and combined ensemble (top panel). 

Forecasts shown are for Tropical Storm Gonzalo from initial time on October 13, 2014 at 06 Z.

For COAMPS-TC ensemble:

• 27-, 9-, 3-km horizontal grid spacing

• 1 control + 10 members with initial and boundary condition perturbations

• No physics perturbations

• No data assimilation

• Control forecast:

 Initialized from the GFS analysis

 Vortex initialized with a Rankine vortex based on TC vitals

• Ensemble members IC’s perturbed about the control:

 Synoptic perturbations drawn from static covariance (e.g. WRFVAR 

cv3)

 Vortex IC’s based on perturbed TC vitals

The GFDL ensemble is described in subsection (d).
28

Figure 13 shows an example of one forecast from the multi-model ensemble for Gonzalo for 

each of the three component models and the combined model (EPS) with the combined ensemble 

including the combined mean (black line). It is interesting to note that the dispersion of the 

tracks for the GFDL model is very narrow while the dispersion is greatest for the COAMPS-TC.

Figure 14 shows the performance of control, each of the individual three ensemble means and the 

Figure 14. Comparing Track & Intensity Errors between Multi-Model Ensemble Components

Track (left panels) and Intensity (right panels) show errors (top panels) and skill (lower panels) relative to the 

HWRF deterministic/control model (bottom panels) for 2014 in the Atlantic. The figure compares the 

various components of the multi-model ensemble: Blue (GFDL ensemble), green (COAMPS-TC), light blue 

(HWRF ensemble), purple (operational deterministic HWRF) and red (multi-model ensemble mean).

mean of the three-component ensemble. With track the overall ensemble mean was the best 

performer giving about a 15-20% improvement over the HWRF operational model (control). For 

intensity the best performer by far was the COAMPS-TC ensemble mean though the three 

component ensemble mean was still a 15-20% improvement over the operational HWRF. The 

GFDL ensemble performed relatively poorly for track but comparable to the HWRF ensemble 

after 24 h.

d. GFDL Ensemble

For the last five hurricane seasons HFIP has promoted running an ensemble of the operational 

GFDL model. It uses the same model as the operational GFDL (which forms the control forecast 

for the ensemble, see Table 9. Working with the forecasters at NHC, scientists at GFDL 

constructed an ensemble by modifying various parameters in the initial conditions, sea surface 
29

temperatures and surface fluxes used by the model. The unbogussed forecasts start from the 

GFS without modification to the vortex from what was in the GFS initially. The other members 

use the GFDL initialization scheme but modified as described in Table 9.

Table 9. Automated Tropical Cyclone Forecasting System (ATCF) ID Descriptions.

Figure 15 shows the results for the GFDL model in the Atlantic. The upper panel shows the 

track skill of the GFDL ensemble mean compared to the control run (the operational GFDL, 

GFSI) and the bottom panel shows the intensity skill compared to DSHP. Also shown are the 

five year 20% HFIP goals for track and intensity. Note that for track the ensemble did better 

than the HFIP goal for all lead times but not as well as the operational GFDL. For intensity the 

GFDL ensemble mean performed better than the goal starting from 72 hours though it performed 

very poorly early in the forecast. However, DSHP was the superior intensity model for all lead 

times. Note in Figure 13 that the GFDL ensemble had very narrow spread in track. This low 

dispersion is likely why the ensemble did not compare well with the deterministic operational 

model.
30

Figure 15. Mean Track and Intensity Skills in the Atlantic Basin.

Results Skill for the GFDL ensemble mean (interpolated version) for 2014 in the Atlantic for track (upper 

panel) and intensity (lower panel) compared to GFSI (track) and DSHP (intensity). Skill calculated relative 

to CLIPER5 for track and Decay-SHIFOR5 for intensity.
31

8. Impact of Inner Core Reconnaissance Data

Figure 16 shows results from that effort using the 2013 operational version of HWRF applied to 

storms in 2009-2013. Note that these are rather small samples, especially for the two hurricane 

samples. For tropical storms the aircraft data substantially improved the intensity forecasts at 

almost all lead times. For category 1-2 hurricanes the results for intensity were mixed and there 

did not seem to be an overall positive impact. For major hurricanes there is a very large initial 

error when the aircraft data were included due to an initial rapid spin down from the analysis 

although for later forecast periods the impact was slight. Including the Tail Doppler Radar 

(TDR) data had almost no overall impact. For track the impact of including the aircraft, with 

and without TDR, was minimal. So from this sample, the only overall improvement appears to 

be for intensity by including aircraft data for the tropical storm strength, perhaps aided by 

initializing a more realistic vortex. But even for these storms the addition of TDR data did not 

appear to impact the results.

Figure 16. 2014 Track, Intensity and Bias comparisons with Inner Core Reconnaissance Data

Comparison between the 2014 operational HWRF model (black lines), the same model but where the 

reconnaissance data excluding the TDR data are used (dark blue line) and using all the RADAR data 

including the TDR data (light blue line). Track errors are shown in the top row, intensity errors in the 

second row, and intensity bias in the third row. The left column includes only tropical storm strength, the 

middle column-category 1 and 2 hurricanes and the right column category major hurricanes. The number of 

cases in each panel is shown in blue figures below the panel.
32

Last year a detailed study of the impact of tail TDR on hurricane forecasts was described. That 

study was rather inconclusive in that with the three separate models considered (Pennsylvania

State University, HWRF, and the AOML model) the impacts were not consistent between the 

models and overall the impact was small. It was felt that further work with the models,

especially improvement in the DA systems, was necessary before we would see a consistent 

impact. This study was not repeated this year but the HWRF team continued to experiment 

with including the data in the HWRF initialization.

9. Data Assimilation and Physics Development

a. Data assimilation

Across the HFIP program there are many attempts to develop and test new techniques that will 

result in future improvements to the operational models. Here we discuss two such attempts.

In Figure 17 the work flow for initialization of the current operational HWRF system is shown. 

Basically it uses the GFS ensemble to define the background error and adjustments to the 

position and intensity of the resulting vortex to match observations at the time of initialization. 

Thus the DA cycle uses perturbations from the large scale coarse resolution ensemble to define 

the background error.

 Figure 17. Operational HWRF Workflow

A schematic of the workflow within the current HWRF operational model initialization.
33

Figure 18. Cycling HWRF GSI/EnKF Workflow

A schematic of the workflow within an experimental version of HWRF Operational model 

initialization implemented at the University of Oklahoma. The main difference from Figure 18 is the 

incusion of cycling data using the members of an HWRF ensemble in the initialization.

Figure 18 shows an experimental version of the HWRF initializaton system developed by the 

University of Oklahoma. The main difference from Figure 17 is the inclusion of a cycling DA

system that uses a background error defined by the HWRF ensemble which employs the EnKF 

DA technique developed within the GSI framework. Figure 1 shows some preliminary results 

for the experimental system.
34

Figure 19. HWRF GSI/EnKF Method for Model Initialization (Hurricane Arthur in 2014)

An example from Hurricane Arthur in 2014 where the method diagrammed in Figure 18 was used to 

initialize the model.
35

b. Physics Development

There is an ongoing effort to better parameterize the flux coefficients for heat (Ch) and 

momentum (Cd). We have discussed earlier attempts to improve these parameterizations in 

previous annual reports. This year GFDL experimented with a radically different set of values 

(as a function of wind speed). The old values are shown in the right upper panel of Figure 20

with dashed lines. The new values are shown by solid lines.

Figure 20. 2014 HWRF Formulations Testing and Evaluations for Cp and Ch

Upper panels show changes to CD and CH that were included in the 2014 GFDL model. Left upper panel 

shows data on Cd and a theoretical parameterization from Soloviev et al (2014). The right panels compare 

the new Cd and Ch parameterizations where Cd formulation is based on the new COARE 3.5 (Edson et al. 

2013; Soloviev et al, 2014) –left upper panel and CH is based on Andreas (2011) that includes the effects of 

both interfacial transfer by molecular processes at the air–sea interface and transfer spray. The lower panels 

compare intensity errors (left lower panel and bias (right lower panel for the 2014 HWRF using the old 

parameterization for Cd and CH (red lines) and with the new values (blue line used in the GFDL model).
36

The impact of these changes in Ch and Cd on intensity are illustrated in the lower panels through 

forecasts made by the 2014 HWRF model with the old (red lines) and new (blue lines) set of 

values for Dh and Cd. Note that both the intensity error and the intensity bias are reduced with 

these new values of Ch and Cd. These results are expected to be somewhat representative since 

the results for intensity forecasts are from a reasonably large sample size.

10. Post Processing of Model Output

a. Statistical Post Processing of Model Output: The FSU MultiModel Ensemble

Much of the discussion above focused on using numerical model improvements to achieve the 

HFIP goals. Typically statistical models (for example DSHP) are among the best predictors of 

hurricane intensity. A statistical model is one where a limited number of predictors (measured in 

single to double digits) are combined with weights that are determined by correlation with past 

data. These predictors are generally selected from parameters describing the current state of the 

hurricane or various environmental data. Those using environmental data can specify their 

values from current observations or from model forecasts. There is another class of statistical 

model that takes a particular prediction from a dynamical model (e.g., track or intensity) and 

combines it with a weighted average from other models in a multi-model ensemble. The weights 

are determined by comparing the performance of the various models over a period of years. 

Perhaps the simplest statistical model for intensity is SHIFOR5 (also called OCD5) where the 

variables are current position, and intensity including intensity 12 hours prior to the current time 

(CLIPPER5 is a similar model for track), see Figure 2.

More complex statistical models used operationally for intensity are: SHIPS (aka DSHIP), 

LGEM and SPICE (SPC3). SPC3, in recent years showed improvement compared to the 

operational statistical and dynamical models, by using multiple operational numerical models 

(GFS, HWRF, and GFDL) as input for the environmental predictors of DSHIP and LGEM. This 

gives six variations which are then averaged as an ensemble.

Figure 21 on the following page illustrates results for the Atlantic Basin during the 2014 

hurricane season. 
37

Figure 21. 2014 Atlantic Hurricane Season Results using BLUE 96-h+ Difference Weighting Scheme.

The upper panel of depicts intensity errors for all components of the FSU Multi-Model Ensemble for various 

forecast lead times. The acronyms for the various models are shown on the right, see Appendix B for 

definitions. The ensemble mean (EM) of the models shown is in orange and the weighted ensemble mean, 

MMSE, is shown by the black bar. The official forecast is dark red. The bottom panel is the same as the 

upper panel (but note that the some of the colors have changed so refer to the label on the right of the panel. 

The difference from the above panel is that a difference weighting scheme is used for MMSE called BLUE. 

See text for details on BLUE.

0

2

4

6

8

10

12

14

16

18

12(89) 24(83) 36(77) 48(71) 60(64) 72(56) 84(48) 96(40) 108(32) 120(25)

Mean absolute errors in kt

Forecast lead in hours (Cases)

Season 2014

AVNI

DSHP

GHMI

HWFI

IVCN

OFCI

SHIP

EM

MMSE

0

2

4

6

8

10

12

14

16

18

12(89) 24(83) 36(77) 48(71) 60(64) 72(56) 84(48) 96(40) 108(32) 120(25)

Mean Absolute error in kt

Forecast lead in hours (#Cases)

Season 2014 using BLUE for 96+ hours

AVNI

DSHP

GHMI

HWFI

IVCN

OFCI

SHIP

EM

MMSE
38

In most years the statistical models provide forecasts of intensity almost as good as any of the 

current dynamical models. As illustrated in Figure 2, last year the dynamical models were 

comparable. 

As in past years the FSU Multi-Model Ensemble was among or the best performer of the 

statistical models. The FSU system employs a process that finds an optimum linear combination 

of model outputs to provide a better forecast. This process forms a multiple linear regression 

equation using least squares. Previous years of data are used to fit the regression equation 

(known as the training phase). The system was then used to forecast in real-time (forecast 

phase).

Figure 21, upper panel shows the intensity errors for the various models that went into the FSU 

Multi-Model Ensemble. The black and purple bars on the right side of the groups for each 

forecast lead time are the equally-weighted ensemble mean (EM; purple line) and for the 

variably-weighted ensemble mean (MMSE; black line). At most lead times MMSE was better 

than EM and from 24-84 h, MMSE was better than all other models. After that the performance 

of MMSE dropped off and was almost the worst performer.

It was found that the problem with the later times is related to a correlation between errors of 

different models used in the regression calculations. If two or more regression variables are 

highly correlated, test of significance of the affected coefficients will give misleading results. In 

attempt to correct this defect in the FSU system, a new system known as the Best Linear 

Unbiased Estimator (BLUE): a linear combination of estimators with properties of no bias and 

minimum variance). The results using BLUE starting at 96 h are shown in the lower panel of 

Figure 21. Forecasts from 12-84 h are calculated as in the top panel. The use of BLUE for 

longer lead times greatly reduced the average errors, up to 69% at 120 hours. MMSE (with 

BLUE) is superior to EM at all lead times past 12 h, and is better than all of the other models at 

most lead times.

b. The HFIP Web Page

Each hurricane season HFIP runs a real-time system on the HFIP machines in Boulder, CO. This 

includes global models as well as regional hurricane models. The Stream 1.5 models, described 

in section 5, are part of this real-time system. For the purpose of presenting results of the runs on 

the HFIP computer and as presenting data run elsewhere (e.g., NRL and operational models), 

HFIP has developed a web page (http://www.hfip.org/products/) where the various results are 

presented. Figure 22 shows an example of a product from the HFIP web site. This product has 

been available for a couple of years and some enhancements were added in 2014, e.g., intensity 

color coding for the ensemble tracks and allowing display of various models for comparison as 

an inset (see Figure 22).
39

Figure 22. Example of the HFIP Webpage

11. Societal Impact Work

In 2014 HFIP provided modest support for the Societal Impacts Team (SIT) with most of this 

support going to NHC. Figure 23 shows an example of a storm surge impact product that has 

been developed at NHC and how it was rated. This type of product was displayed last year in the 

HFIP annual report with Tampa Bay as the example and was evaluated by SIT by surveying 

Emergency Managers, the various media outlets, the general public, and Warning Coordinator 

Meteorologists (WCMs) for effectiveness of the presentation. A very large percentage rated it 

very high for ease of understanding and for providing useful information.

Figure 24 is a similar survey for how these groups rate the uncertainty cone for forecast 

hurricane track that has been used operationally for many years. The results were mixed with the 

public giving it high marks for ease of understanding and usefulness, while only about half of the 

WCMs rated it favorably.
40

Figure 23. Assessment of Potential Storm Surge Product from the NHC.

The storm is hypothetical but was motivated by Sandy.

Figure 24. Assessment of how various groups rate the cone of uncertainty for the 

forecast hurricane track.
41

NWS Service Assessments, such as those for Hurricanes’ Irene and Sandy, identified a number 

of gaps in the way NOAA articulates the risk from tropical cyclones in its various products, 

information, and services—at both the national and regional levels. Additionally, NWS partners 

have expressed a strong desire to have NHC create a separate product depicting the potential 

time of arrival of sustained winds of tropical storm force. Such a product will enable emergency 

managers and other officials to make more informed decisions on when to conduct and complete 

preparations as a tropical cyclone approaches. Broadcast meteorologists also have a need for this 

information for use in their on-air programming, websites, and social media.

The HFIP Socio-Economic Team has begun development of an “Onset of Tropical Storm Force 

Winds” product (scheduled completion date: 11/30/2015). There are no finalized graphics at this 

time. However, an early graphic that was tested during the Tropical Storm Surge research is 

presented here (Figure 25).

Figure 25. Onset of Storm Force Winds Product:

This figure illustrates a prototype of the arrival of a storm force winds product.
42

12. Future Configuration of a Numerical Model Hurricane 

Forecast Guidance System to meet the HFIP goals

It has already noted that the HWRF undergoes considerable testing when new techniques and 

technology are added to the system. This has led to some significant improvements (Figure 10). 

HWRF development for the remainder of the HFIP program, originally scheduled to be a 10 year 

program ending in 2019, is charted out here. The general strategy, for the next 5 years is to 

gradually evolve the HWRF into the NOAA Environmental Modeling System (NEMS)

framework, the infrastructure that has been adopted for other models at EMC. In addition,

HWRF will move from EMC Non-hydrostatic Mesoscale Model (NMM) on the E-grid to NMM

on the B-grid (NMMB) which is currently being used in other mesoscale models at EMC. At the 

same time, in collaboration with HRD, the HWRF system is evolving into a basin-scale (large 

domain) system where there are multiple moving double nests (one set per hurricane) where each 

set of nests interact with the large basin-scale domain. This allows for interactions between 

relatively close hurricanes via the larger scale domain. These interactions can occasionally have 

an important impact on hurricane track forecasts and perhaps a lesser impact on intensity. It also 

saves computer time since the basin-scale domain only has to be computed once for all of the 

hurricanes rather than separately for each hurricane. In addition, since NMMB can easily be 

made global, the basin-scale domain can be made larger depending on computer resources and 

even eventually evolve into a global model. Such a global model is currently undergoing 

development and testing and an example of a forecast with five simultaneous tropical cyclones in 

the Atlantic and West Pacific is shown in Figure 26. Due to limited computer resources, it has 

not yet been possible to test the global system over a large number of cases.

Figure 27 shows the comparisons for track and intensity forecasts between the 2013 versions of 

the basin-scale HWRF (HWHI: red lines) and the operational HWRF (HWFI: black lines) for a 

large sample size (large number of storms) from 2011-2013. For storms that were simultaneous 

in time, they were run together in the basin-scale system and individually in the operational 

HWRF. HWHI out-performs HWFI at almost all lead times for both track and intensity

suggesting the potential of the basin-scale version to produced improved forecasts.
43

Figure 26. Example of the experimental global HWRF System in NMMB NEMS framework:

This depicts an example of the experimental global HWRF system with multiple moving inner nests about 

each hurricane. Each hurricane has a pair of moving nests that interact with the global modeling system. 

This image is from an actual forecast of the five hurricanes shown. Four inner nests are shown in the upper

panels.
44

Figure 27. Track and Intensity Forecasts for HWRF Basin-Scale and the Operational HWRF.

Comparison of the track (left panel) and intensity (right panel) forecast errors for Atlantic storms from 2011-

2013 from the 2013 versions of the basin scale HWRF (HWHI: red lines and operational HWRF (HWFI:

black). The blue line is the difference in error HWFI-HWHI). The number of cases is shown across the top 

of each panel. Error bars for 95% confidence interval are shown for each model and for the difference.

In past years we have speculated on what the final configuration of the hurricane model might 

look like at the end of the project. Table 10 shows the anticipated configuration and assumes 

that the hurricane model will be a single global model (likely run as an ensemble) with doubly 

nested inner nests over each hurricane.

Table 10. Numerical Model Hurricane Forecast Guidance System

Component Specifications

Global model ensemble with Hybrid Data 

Assimilation

20 members at 10-20 km

Multiple moving nests with the inner nest at 3 

km or higher resolution within the global 

model 

Double nests, one set for each hurricane 

using all available aircraft and satellite 

data in inner core and near environment of 

the hurricane

Additional models to make a multi-model 

ensemble (possibly run as a global model with 

internal nests).

Multi-model (at least two – e.g. HWRF, 

AHW, TC-COAMPS)

Statistical Post Processing LGEM, SHIPS, SPICE, and others
45

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Appendix A: NOAA Opportunity Announcement

Through an Announcement of Opportunity (AO) in March 2011, 2012, the HFIP Program Office 

invited proposals from academic institutions and private industry offering two-year support of 

their expertise and experience to contribute towards the advancement of hurricane science and 

modeling to improve operational hurricane forecasts. The priorities set for this AO include 

advancements in DA techniques focusing on use of satellite datasets and in-situ measurements 

obtained through: (a) aircraft reconnaissance; (b) advancements in numerical weather prediction 

techniques with emphasis on high-resolution physics for more accurate representation of moist 

processes; (c) air-sea interactions in the hurricane inner-core region; (d) advanced model 

diagnostics to test model improvements; (e) advanced ensemble techniques for improved track 

and intensity forecast guidance; (f) enhanced observing strategies; and (g) use of observations for 

hurricane environment-scale and hurricane inner core-scale circulations.

Of the 32 proposals received in response to this AO, 12 were selected for funding based on their 

relevance to the program priorities, feasibility of transition to operations, active participation in 

HFIP supported activities, and recommendations from the review committee. Among the 12 

selected proposals: two address DA priorities; four address operational hurricane model 

improvements; and five address high-resolution physics and ensembles. Progress of each of 

these proposals is monitored through quarterly reports from the principle investigators (PIs) and 

through scientific presentations from the PIs in various forums including HFIP bi-weekly 

teleconferences, monthly EMC-HRD modeling meetings and EMC HWRF weekly meetings. 

All of the funded efforts showed significant progress during the first year and HFIP extended 

funding to each of them for the second year. Most of these awards are now closing, and final 

reports being prepared though some investigations have requested and received no-cost 

extensions. These reports serve to identify results that could be transitioned to operations with 

varying levels of further development.

In 2014, HFIP launched a second round of awards focused on advanced physics and ensembles 

for: hurricane intensity predictability, global-to-local scale modeling techniques, air-sea-wave 

coupling, and continuous improvements to operational hurricane models and DA techniques. 

The review process for these proposals has been completed and announcements of awards were 

made in Spring 2014. The list of the winning proposals is made available on http://HFIP.org

(http://www.hfip.org/documents/HFIP_Projects_Funded_Round2.pdf). The majority of these 

proposals are geared towards improving the operational HWRF model by more direct and 

efficient transition of research to operations. DTC has setup an advanced support system for 

HWRF model developers (http://www.dtcenter.org/HurrWRF/developers/) to provide training 

and to facilitate model development in a community repository commonly used by researchers 

and operational model developers. There are plans for a review of the first year progress of the 

14 awards made.
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Appendix B: Model Acronyms

The following is a list of acronyms used to identify models in this document. Many of the 

acronyms follow the four-character naming convention in the Automated Tropical Cyclone 

Forecasting (ATCF) system. For example, 6-hour “early” (aka “interpolated”) forecasts from 

“late” models (see Section 4a) are adjusted so that the previous 6-hour forecast matches the 

conditions at the beginning of the current forecast. Forecasts of those future conditions are 

denoted with an “I” at the end (12-hour interpolations are denoted with a “2”).

Other conventions occasionally used in the model naming include the acronym “A” to denote 

advanced version, “D” to denote the addition of inland decay, “E” to denote ensemble, “H” to 

denote hurricane, “R” to denote research, “S” to denote statistical, “T” to denote track, “V” to 

denote Variable (ensemble of at least 2, for example), and beginning with an “I” to denote 

intensity.

AEMI: GEFS with 6-hour interpolation.

AVNI: GFS with 6-hour interpolation.

AHW: National Center for Atmospheric Research Advanced Hurricane WRF.

AHWI: AHW with 6-hour interpolation.

APSI: Advanced Weather Research and Forecasting model with 6-hour 

interpolation.

ARW: Pennsylvania State University Advanced Research WRF

BLUE: Best Linear Unbiased Estimator that is a linear combination of estimators 

with properties of no bias and minimum variance designed to resolve 

misleading results from a test of significance of affected coefficients 

associated with two or more highly correlated regression variables.

CMC: Canadian Meteorological Centre model.

CMCI: CMC with 6-hour interpolation.

CLIPER: Climate and Persistence model.

COAMPS-TC: Coupled Ocean/Atmosphere Mesoscale Prediction System-Tropical 

Cyclone model at the Fleet Numerical Meteorology and Oceanography 

Center.

COTI: COAMPS-TC with 6-hour interpolation.

CXTL: Experimental COAMPS-TC Forecast Interpolated ahead 6 hours.

Decay-SHIFOR5: Decay Statistical Hurricane Intensity Forecast model.
49

DSHP: Decay SHIPS.

ECMWF: European Centre for Medium-range Weather Forecasts model.

EGRI: United Kingdom Meteorological Office model, subjective tracker, with 6-

hour interpolation.

EM: Equally-weighted Ensemble Mean for models used in MMSE.

EMXI: ECMWF with 6-hour interpolation.

FIM: Flow-following finite-volume Icosahedral Model.

FM9I: FIM with 6-hour interpolation.

FSSE: Florida State University Super Ensemble.

G01I: GFDL ensemble member 01 with 6-hour interpolation (in general, G##I 

denotes GFDL ensemble member ## with 6-hour interpolation).

GEFS: National Centers for Environmental Prediction Global Ensemble Forecast 

System.

GFDI: GFDL with 6-hour interpolation.

GFDL: Geophysical Fluid Dynamics Laboratory model.

GFNI: Navy version of GFDL with 6-hour interpolation.

GFS: Global Forecast System.

GFSI: GFS with 6-hour interpolation.

GHMI: GFDL adjusted using a variable intensity offset correction that is a 

function of forecast time, with 6-hour interpolation.

GPMN: GFDL ensemble mean.

GPMI: GFDL ensemble mean (note all members of the ensemble include 6-hour 

interpolation).

GTMI: Geophysical Fluid Dynamics Laboratory model’s regional and dynamic 

ensemble.

H3WI: Hurricane Research Division’s regional, dynamic and deterministic 

ensemble

HWHI: Basin-scale HWRF with 6-hour interpolation.

HWMI: HWRF Ensemble Mean Forecast Interpolated Ahead 6 hr.
50

HWFI: HWRF with 6-hour interpolation.

HWRF: Hurricane WRF.

ICON: National Hurricane Center Intensity Consensus.

IV15: Intensity forecast ensemble including 2012 stream 1.5 forecasts.

IVCN: Intensity forecast ensemble (operational) without stream 1.5 forecasts.

LGEM: Logistics Growth Equation Model.

MMSE: FSU Multi-Model Ensemble (variably weighted mean).

NAVGEM: Fleet Numerical Meteorology and Oceanography Center Navy Global 

Environmental Model (replaced NOGAPS February, 2013).

NEMS: NOAA Environmental Modeling System.

NGPI: NOGAPS with 6-hour interpolation.

NGXI: Experimental NOGAPS with 6-hour interpolation.

NMM: Environmental Modeling Center Non-hydrostatic Mesoscale Model.

NMMB: NMM on the B-grid.

NOGAPS: Fleet Numerical Meteorology and Oceanography Center Navy Operational 

Global Atmospheric Prediction System (replaced by NAVGEM February, 

2013).

OFCL: Official National Hurricane Center Forecast.

RI: Rapid Intensification (an increase of 30 knots in 24 hours).

RW: Rapid weakening (a decrease of 25 knots in 24 hours).

SHIPS: Statistical Hurricane Intensity Prediction System.

SPC3: Six member weighted SPICE ensemble using output from GFS, HWRF, 

and GFDL as input for DSHP and LGEM. The ensemble weights vary 

with forecast lead time.

SPICE: Statistical Prediction of Intensity from a Consensus Ensemble.

TV15: Track forecast ensemble including 2012 Stream 1.5 forecasts.

TVCA: Track Variable Consensus of at least two of AVNI, EGRI, EMXI, NGPI, 

GHMI, HWFI forecasts.
51

TVCN: National Hurricane Center Track Variable Consensus.

UKMI: United Kingdom Meteorological Office model, automated tracker, with 6-

hour interpolation.

UW4I: University of Wisconsin’s Non-hydrostatic Modeling System (4 km).

UWNI: UW-NMS with 6-hour interpolation.

UW-NMS: University of Wisconsin Non-hydrostatic Modeling System.

WRF: Weather Research and Forecasting model. It is a regional system with 

options for the dynamic core, physics, initialization, post processing and 

verification. Variations include the Hurricane WRF (HWRF), PSU 

Advanced Research WRF (ARW), and NCAR Advanced Hurricane WRF 

(AHW).