2013 HFIP R&D Activities Summary:

Recent Results and Operational Implementation

April 15, 2014

HFIP Technical Report: HFIP2014-2

NOAA NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

United States Department of Commerce

20130914

2013 HFIP R & D Activities Summary:

Recent Results and Operational Implementation

R. Gall11, F. Toepfer11, F. Marks1, E.N. Rappaport9, A. Aksoy20, S. Aberson1, J.W. Bao4, 

M. Bender6, S. Benjamin4, L. Bernardet2,4, M. Biswas7, B. Brown7, J. Cangialosi9, C. Davis6, 

M. DeMaria9, J. Doyle10, M. Fiorino4, J. Franklin9, I. Ginis18, S. Gopalakrishnan 1, T. Hamill4, 

R. Hodur14, H.S. Kim3, J. Knaff8, T. Krishnamurti 5, P. Kucera7, Y. Kwon3, W. Lapenta3, 

N. Lett13, S. Lord3

, T. Marchok6, E. Mifflin16, M. Morin6, K. Musgrave2, L. Nance7, 

C. Reynolds10, V.Tallapragada3, H. Tolman3, R. Torn15,G. Vandenberghe3, T. Vukicevic9, 

X. Wang21, Y. Weng12, J. Whittaker4, R. Yablonsky18, D-L Zhang19, F. Zhang12 , J. Zhang20, 

X. Zhang20 , D. A. Zelinsky9

March 2014

1

Atlantic Oceanographic and Meteorological Laboratory OAR/NOAA

2

Cooperative Institute for Research in the Atmosphere, Colorado State University 3

Environmental Modeling Center NCEP/NOAA

4

Earth System Research Laboratory OAR/NOAA 

5

Florida State University

6

Geophysical Fluid Dynamics Laboratory OAR/NOAA 

7

National Center for Atmospheric Research

8

National Environmental Satellite Data Information Center NOAA 9

National Hurricane Center NWS/NOAA

10Naval Research Laboratory, Monterey

11Office of Science and Technology, NWS/NOAA

12Pennsylvania State University 

13Science and Technology Corporation 14Science Applications International Corporation 15University at Albany, State University of New York 16Syneren Technologies Corporation

17University Corporation for Atmospheric Research

18University of Rhode Island

19University of Maryland

20University of Miami

21

University of Oklahoma

Table of Contents

Executive Summary....................................................................................................................... i

1. Introduction.......................................................................................................................... 1

2. The Hurricane Forecast Improvement Project.............................................................. 1

3. The HFIP Model Systems .................................................................................................... 4

a. The Global Models ................................................................................................................... 4

b. The Regional Models............................................................................................................... 5

c. Initialization and Data Assimilation Systems .................................................................... 5

d. The HWRF Community Code Repository ............................................................................ 8

4. Meeting the HFIP Goals....................................................................................................... 8

a. The HFIP Baseline.................................................................................................................... 8

b. Meeting the Track Goals......................................................................................................... 9

c. Reaching the Intensity Goals ............................................................................................... 10

5. HFIP Stream 1.5.................................................................................................................. 10

a. Stream 1.5 results.................................................................................................................. 12

6. Operational Hurricane Guidance Improvements ....................................................... 14

a. Global Model (GFS)—Operational...................................................................................... 14

b. Global Model (GFS)—HFIP Experimental ......................................................................... 16

c. Hurricane WRF (HWRF) ....................................................................................................... 21

1) Atlantic .....................................................................................................................................................21

2) West Pacific (WPAC) ..............................................................................................................................22

3) Rapid Intensification .............................................................................................................................24

4) HWRF ensemble .....................................................................................................................................25

5) Evolution of the HWRF System ............................................................................................................27

7. GFDL Ensemble................................................................................................................... 28

8. Impact of Inner Core Reconnaissance Data.................................................................. 32

9. Post Processing of Model Output.................................................................................... 36

a. Statistical Post Processing of Model Output..................................................................... 36

b. Other Post Processing........................................................................................................... 38

c. The HFIP Web Page ............................................................................................................... 40

10. Storm Surge......................................................................................................................... 41

11. Societal Impact Work........................................................................................................ 42

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

meet the HFIP goals .................................................................................................................. 43

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

Appendix A: HFIP Funding Opportunity for Academia and Private Industry............... 47

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

Table 1. Strategic Teams 2013....................................................................................................... 2

Table 2. Tiger Teams 2013 ............................................................................................................ 3

Table 3. Specifications of the HFIP Global Models...................................................................... 5

Table 4. Specifications for the HFIP Regional Models................................................................. 6

Table 5. 2013 HFIP Retrospective Evaluation Modeling Groups................................................ 11

Table 6. 2013 HFIP Stream 1.5 Real-time Runs Model Guidance Provided to NHC................. 11

Table 7. HWRF development 2013-2018..................................................................................... 28

Table 8. GFDL hurricane model ensemble members.................................................................. 29

Table 9. Numerical Model Hurricane Forecast Guidance System.............................................. 44

List of Figures

Figure 1. HFIP Track (left panel) and Intensity (right panel) Error Baseline and Goals. ............. 8

Figure 2. Stream 1.5 Track Skills, 2013 Atlantic ........................................................................ 12

Figure 3. Stream 1.5 Intensity Errors 2013 Atlantic.................................................................... 13

Figure 4. GFS and ECMWF Track Errors Relative to HFIP Baseline, Atlantic. ........................ 15

Figure 5. GFS and EMCWF Track Errors Relative to HFIP Baseline (2012-2013), WPAC ..... 16

Figure 6. Performance Comparison Operational and Experimental GFS Systems..................... 17

Figure 7. Tropical Storm Force Wind Probabilities within 168 Hours........................................ 18

Figure 8. Verification of Tropical Storm Force Wind Probabilities............................................ 19

Figure 9. Ensemble Member Tracks............................................................................................ 20

Figure 10. HWRF System Improvements 2011-2013 ................................................................. 21

Figure 11. HWRF Track and Intensity Errors 2013 WPAC........................................................ 22

Figure 12. JTWC Official Intensity Forecast Errors, WPAC, 2000-2013.................................. 23

Figure 13. NHC Official Intensity Forecast Errors, Atlantic basin, 1990-2013.......................... 23

Figure 14. Forecasted 24-hour Intensity Changes in WPAC, 2013............................................. 24

Figure 15. HWRF 2013 Experimental Ensemble Track Performance, Atlantic Basin ............... 25

Figure 16. HWRF 2013 Experimental Ensemble Intensity Performance, Atlantic Basin........... 26

Figure 17. Basin Scale System with Three Moving Nests: Kirk, Isaac, and Ileana, 2012 .......... 27

Figure 18. 2013 GFDL Ensemble Intensity Errors, EPAC.......................................................... 30

Figure 19. Intensity Errors for GFDL Ensemble Members: Hurricane Raymond. ..................... 31

Figure 20. HWRF Initial Condition Calculation Flow Chart. ..................................................... 33

Figure 21. Example RDITT Test Results: EMC HWRF, All Data. ............................................ 34

Figure 22. Outlier Example: RDITT Test PSU Model, All Data and Control ............................ 35

Figure 23. FSU Multi-model Ensemble: Members and Mean Performance (2012 Atlantic)...... 37

Figure 24. Post Processing Diagnostics: Hurricane Sandy 250 hPa Geopotential Heights........ 38

Figure 25. Sensitivity of Intensity to Precipitable Water: Hurricane Bill.................................... 39

Figure 26. HFIP.org Products Page. ............................................................................................ 40

Figure 27. Storm Surge Advisory Product, Tropical Storm Karen.............................................. 41

Figure 28. Preliminary Storm Surge Advisory Product............................................................... 42
i

Executive Summary

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

(HFIP) in 2013. Since this is the fourth year of the first five years of the project, we, like last year,

focus on the improvements in operational Global Forecast System (GFS) global model and the 

Hurricane Weather Research and Forecasting (HWRF) regional model. HFIP is organized around 

the three “streams”: Stream 1 or the operational model development, Stream 1.5 which comprises a 

group of experimental models that have been evaluated by the National Hurricane Center (NHC)

pre-season and then made available to NHC forecasters during their forecast cycle, and Stream 2 

representing HFIP experimental models which test and evaluate new techniques and strategies for 

model forecast guidance before testing is begun 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. The report outlines the 

HFIP program, how it is organized, its goals, its models and then results from each of the three 

Streams.

Stream 1.0 Accomplishments

• The 2013 HWRF provided intensity forecasts that were the best ever for the model and 

which beat out the statistical models in the 24- to72-hour forecast lead time, even when 

run over three years of retrospective data. 

• There were few long-lived storms in the Atlantic in 2013 so the error means reported

beyond 72-hour forecast lead time are not statistically significant. At the earlier lead 

times HWRF continued to show the accuracy in intensity forecasts as shown by the 

retrospective runs.

• The HWRF was run in the western Pacific basin (WPAC) as a real-time system in 2013 

and results made available to Joint Typhoon Warning Center (JTWC). The intensity was

among the best guidance products for JTWC. Track guidance was also excellent. 

• The global GFS continues to provide track guidance comparable to the European Center 

for Medium-range Weather Forecasts (ECMWF) guidance and exceeds the 5-year HFIP 

goal out to 4-day lead time.
ii

• At longer lead times the improvement in track accuracy falls off dramatically for 

unknown reasons so that there has been little, if any, improvement at 7 days. The 

ECMWF model also did not seem to improve at 7 days indicating that the lack of 

improvement is likely a problem with global models in general.

Stream 1.5 Results

• The HWRF was run as a 20 member ensemble (using the same model as used 

operationally—called the control run), with as much as a 50% improvement over the 

control run.

• It was hard to measure “skill” of intensity forecasts since the model to which all models 

are usually compared to determine skill, SHIFOR5 (OCD5) performed very poor this 

year—see Figure 2.

• The six-member weighted Statistical Prediction of Intensity from a Consensus Ensemble 

(SPC3) statistical model was the best performing Stream 1.5 intensity model in 2012.

This year it was among the worst. The best model out to 48 hours was the operational 

HWRF (HWFI) and the Florida State University super ensemble (FSSE). IV15/IVCI 

composites performed well as did the Wisconsin model (UW4I) in the 24- to 48-hour 

forecast lead times.

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

sponsored/run models has been running for the past two years. 

Stream 2.0 Accomplishments

• The Reconnaissance Data Impact Tiger Team (RDITT) was formed to evaluate the 

impact of inner core aircraft derived data (flight level, dropsondes, SMFR and TDR data) 

on intensity and track forecasts. The TDR data did not show a clear or consistent benefit 

beyond what the standard reconnaissance data provided. The RDITT results suggest that 

inner core observations offer promise for improving operational tropical cyclone 

guidance, but that much work lies ahead to make optimal use of these data.

• Experimental GFS global deterministic and ensemble systems were again run 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 

GFS ensemble (GEF). Results were disappointing in that the higher resolution system 

did not perform as well as the operational system, likely because the physics and data 

assimilation packages had not been optimized for the higher resolution and the semiimplicit scheme because of time constraints in preparing the system for real-time runs in 

2013. 

• One major improvement noted from the global ensemble runs was that use of a stochastic 

physics process in the ensemble members gave much better ensemble spread than the 

current operational system.

• Forecasts of the 35-knot wind probability for Atlantic basin (ATL), eastern Pacific basin 

(EPAC), and western Pacific basin (WPAC) by the HFIP global GFS ensemble were 

excellent—see Figure 5.
iii

• The performance of HWRF in forecasting rapid intensification (RI) in the WPAC showed 

a significant improvement in the probability of detection (POD) for RI in that region over 

the operational forecasts. The POD was 23%, the false alarm ration near was 0%.

• HFIP began closely working with the hurricane surge models in 2013, running ADCIRC 

on the Boulder system. There were few land falling systems this year so this year we 

primarily focused on developing the surge system. A forecast from Karen for southern 

Louisiana looked very good.

• A new product on presentation of storm surge data has been developed at NHC with 

partial support from HFIP. This product is likely to be operational this coming hurricane 

season. 

Future configuration of the Hurricane Forecast System

Based on four years of results from HFIP, we project that the future operational hurricane 

forecast guidance system would be as described in the table below.

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 

1) Double nests (9 and 3km), one for 

each hurricane 

2) HWRF

3) Using all available aircraft and 

satellite data in 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, TC-COAMPS)

Statistical Post Processing LGEM, SHIPS, SPICE, others

1

1. Introduction

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

methods for achieving those goals, and recent results from the project 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, 50% in 10 years for days 1 through 5.

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

• Increase the probability of detection (POD)1 for rapid intensification (RI)2 to 90% at Day 

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

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

intensity change is the highest-priority 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 the 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 

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 Rapid Intensification (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 of a decrease of 25 knots in 24 hours.

 
2

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

environment.

In FY2013, HFIP funded approximately $11,000,000 of research and development based on 

recommendations from six 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 

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); 4) the Joint Center for Satellite Data Assimilation 

(JCSDA); and 5) 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 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), and University of Wisconsin (UW). 

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

operational communities, and the team co-leaders are listed in Table 1. Tiger teams, focused on 

specific, shorter term (1 or 2 years) tasks are listed in Table 2. Full team membership is 

available at: http://www.hfip.org/teams/. 

HFIP dedicated an additional $2,000,000 to operating and maintaining the high performance

computer system in Boulder, Colorado. 

. 

Table 1. Strategic Teams 2013

FY 2013 Teams FY 2013 Team Leads

1. HFIP Model Strategy Vijay Tallapragada, Stan Benjamin

2. Model Physics Brad Ferrier, Jian-Wen Bao

3. Data Assimilation/Initialization John Derber, XuguangWang

4. Ensemble Development Jeff Whitaker, Jiayi Peng

5. Post Processing and Verification 

Development Team

Mark DeMaria, David Zelinski, Tim 

Marchok

6. Societal Impacts Jennifer Sprague, Rick Knabb
3

Table 2. Tiger Teams 2013

FY 2013 Teams Strategic 

Team

FY2013 Team Leads

1. Web Page Design 5 Paula McCaslin, Laurie Carson

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

3. Regional Hybrid DA 

System/Use of Satellite Data

3 Jeff Whitaker, Xiaolei Zou

4. Stream 1.5 and Demo 

System Implementation

1 James Franklin, Barb Brown 

5. Reconnaissance Data 

Impact

1 James Franklin, Vijay Tallapragada

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

the National Centers for Environmental Prediction (NCEP) operations to NHC as part of the 

hurricane forecast process. To accomplish this goal, the program 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, particularly AOML, 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 the 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 aspects of forecast performance. Because the level of 

computing necessary to perform such a demonstration is large, HFIP is developing 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 each off-season to 

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

These potential advances can be blended into the operational implementation plans through 

subsequent Stream 1 activities, or developed further outside of operations within Stream 2. 
4

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

consequently NHC forecasters do not use these models 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 

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 the 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) 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 moves forward the real-time availability of advances to 

forecasters 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 15-20 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 (e.g., NOAA SAB 2006). Maximizing 

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

regional models, 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. They also provide 

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 2013 by HFIP are listed in table 3 along with their characteristics. 
5

Table 3. 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

5-member 

ensemble 27 km 64

From 2011 GFS -

Simplified 

Arakawa Schubert Zhao-Carr GFS NonLocal PBL

Noah 

LSM

Rapid 

Radiative 

TransferModel 

(RRTM)

ESRL EnKF

FIM –

2014 

numerics

w/ 1/8 deg 

output

15km 64

From 2011 GFS –

Simplified 

Arakawa Schubert Zhao-Carr GFS Nonlocal PBL

Noah 

LSM RRTM

GFS GSI 

operational 

hybridensemble 

variational

GFS/EnKF 27 km 64 Simplified 

Arakawa Schubert Ferrier GFS NonLocal PBL

Noah 

LSM

GFDL

scheme ESRL EnKF

GFS/GSI 27 km 64 Simplified 

Arakawa Schubert Ferrier GFS NonLocal PBL

Noah 

LSM GFDL GSI

NOGAPS 41 km 42 Emanuel N/A NOGAPS NOGAPS Harshvardhan/ 

Fu-Liou

NRL 

Atmospheric 

Variational 

Data 

Assimilation 

Systemaccelerated 

representer 

(NAVDASAR)

b. The Regional Models

Specifics of the regional models used by HFIP in 2013 are shown in Table 4. 

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

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

used the new Hybrid Ensemble-Variational Data Assimilation System (HEVDAS—see 

below) that is a combination of the Grid-point Statistical Interpolation (GSI) system 

formerly used and an ensemble based system to define the background error matrix. The 

GSI initialization system that has been run operationally since 2006 is a threedimensional variational approach, 3D-VAR (DTC 2012; Wu et al. 2002; Parrish and 

Derber 1992; Cohn and Parrish 1991).
6

Table 4. Specifications for the HFIP Regional Models

2. HWRF: The operational HWRF uses an advanced vortex initialization and assimilation 

cycle consisting 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 Ensemble 

Kalman Filter. The Data Assimilation (DA) system is capable of ingesting inner core 

data to optimize the vortex initialization.

Models

Domains / 

Horizontal 

Resolution 

(km)

Vertical 

Levels

core

Cumulus 

Parameterizatio

n

Microphysics PBL Land 

Surface Radiation

Initial and 

Boundary 

Conditions

Initialization

HWRF 

(OPS)

3

27/9/3

42

NMM

Simplified 

Arakawa Schubert

(27/9 km only)

Ferrier

GFS 

NonLocal 

PBL

GFDL 

Slab 

Model

GFDL

Scheme

GDAS and 

GFS

One-way 

hybrid

GSI-EnKF 

with vortex 

initialization

GFDL 

(OPS)

3

55/18/9

42

GFDL

Simplified 

Arakawa Schubert Ferrier

GFS 

NonLocal 

PBL

GFDL 

Slab 

Model

Schwarzko

pf-Fels 

(longwave) 

/ LacisHansen 

(shortwave)

GFS

GFDL 

Synthetic 

Bogus Vortex

GFDL (Ens)

3

55/18/9

42

GFDL

Simplified 

Arakawa Schubert Ferrier

GFS 

NonLocal 

PBL

GFDL 

Slab 

Model

Schwarzko

pf-Fels 

(longwave) 

/ LacisHansen 

(shortwave)

GFS.

GFDL 

Synthetic 

Bogus Vortex

HWRFHRD/EMC

Basin Scale

3

27/9/3

61

NMM

Simplified 

Arakawa Schubert Ferrier

GFS 

NonLocal 

PBL

GFDL 

Slab 

Model

GFDL

Scheme GFS Vortex initialization

HWRFHRD

(HEDAS)

2

9/3

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

AHW 

(NCAR)

3

36/12/4

36

ARW

Tiedtke (36/12 

km only) WSM6 YSU NOAH LSM

RRTMG 

(LW+SW)

GFS (BC 

only)

EnKF method 

in a 6-hour 

cycling mode

COAMPSTC©

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 in 

WATL and 

EPAC

3D-Var data 

assimilation 

with synthetic 

observations

Wisconsin 

NMS

2

45/4,1

42

UWNMS

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

Penn State 

ARW

3

27/9/3

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
7

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

used to provide the initial conditions to NOGAPS. Previously a 3D-VAR system, it was

upgraded in September 2009 to NAVDAS-AR, a four-dimensional variational (4D-VAR) 

approach (NRL 2001, Daley and Barker 2001). The 3D-VAR version of NAVDAS is 

used to initialize COAMPS-TC.

4. 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, Evensen 1994, Houtekamer et al 1998). This 

approach has shown considerable promise (Hamill et al 2011).

5. Hurricane Ensemble Data Assimilation System (HEDAS): HEDAS is an EnKF system 

applied to the HWRF and was developed at the AOML (Aksoy et al 2012).

6. Hybrid Ensemble-Variational Data Assimilation System (HEVDAS): This system 

combines aspects of the EnKF and 3D- or 4D-Var, such as using the ensemble of 

forecasts to estimate the covariances at the start of the variational component of the DA 

system. This technology was developed at EMC, ESRL and AOML/Hurricane Research 

Division (HRD) and was used in operations for the 2012 season. 

7. One Way Hybrid: This is a version of the HEVDAS system that is currently being used 

by the Operational HWRF regional model. Instead of using an ensemble from the 

HWRF to create the ensemble part of the background error, it uses ensemble members 

created by the global GEFS. 

8. Vortex Initialization: The initial vortex for some of the regional models is produced by a 

vortex initialization procedure. First, 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 HWRF system. Vortex relocation is also utilized by the current 

operational GFS and Global Ensemble Forecast System (GEFS) in NCEP. 
8

d. The HWRF Community Code Repository

During 2009-2013, 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 3.5a (Bernardet et al, 2013; Tallapragada et al, 2013). 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. 

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 

errors of the consensus over the period 2006-2008 for the Atlantic basin. A separate set of 

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

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).

Figure 1. HFIP Track (left panel) and Intensity (right panel) Error Baseline and Goals. 
9

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 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 Decay-SHIFOR5 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 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 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. This adjustment, or 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. 

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 using a more advanced data assimilation scheme than the 

one employed operationally at that time improved forecasts, particularly in the tropics. A 

version of this advanced data assimilation went operational in the GFS model in May, 2012.

Results from that model are presented in this report.
10

c. Reaching the Intensity Goals

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

horizontal resolution near the 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.

5. HFIP Stream 1.5 

The HFIP and the NHC agreed in 2009 to establish a pathway to operations known as “Stream 

1.5.” Stream 1.5 covers improved models and/or techniques that the 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 nonoperational resources to move forward the delivery of guidance to NHC by one or more 

hurricane seasons. 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 2013 season. Those groups and their models are listed in 

table 5. 

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 and all the verification plots generated 

during the evaluation are available on the TCMT 2013 Retrospective HFIP Testing website 

(http://www.ral.ucar.edu/projects/hfip/d2013), as well as information on the participating models 

and cases included in the evaluation. 

 

The groups selected for Stream 1.5 in 2013 and the form of the output from their models 

provided to NHC are listed in Table 6.

Note that most models were admitted into Stream 1.5 based on the models’ performance 

forecasting either track or intensity, but generally not both. For example, forecasters were 

instructed to consult the ESRL FIM model, interpolated ahead 6 hours, (FM9I—FIM in the tables 

below) track forecasts but not the FM9I intensity forecasts. Two HFIP Stream 1.5 consensus 

aids were constructed: the track consensus TV15 comprised the operational models GFSI, EGRI, 

GHMI, HWFI, GFNI, EMXI and the Stream 1.5 models APSI, GHMI, and FM9I, while the 

intensity consensus IV15 comprised the operational models Decay-SHIP (DSHP), LGEM, 

GHMI, HWFI and the Stream 1.5 models COTI, APSI, and UWNI. 
11

Table 5. 2013 HFIP Retrospective Evaluation Modeling Groups

Organization Model Type Configurations

NCAR/MMM and SUNYAlbany AHW Regional-dynamic-deterministic 1

NRL COAMPS-TC Regional-dynamic-deterministic 1

PSU ARW Regional-dynamic-deterministic 3

UW-Madison UW-NMS 4-km Regional-dynamicdeterministic/ensemble 3

EMC HWRF Regional-dynamic-ensemble 1

GFDL GFDL hurricane 

ensemble Regional-dynamic-ensemble 1

ESRL FIM Global-dynamic-deterministic 1

FSU Multi-model super 

ensemble Weighted-consensus 1

NESDIS/STAR and CIRA SPICE Statistical-dynamical-consensus 1

Table 6. 2013 HFIP Stream 1.5 Real-time Runs Model Guidance Provided to NHC

Organization Model Track Track 

Consensus Intensity Intensity Consensus

NRL COAMPS-TC ●

PSU ARW ● ●

UW-Madison UW-NMS ●

EMC HWRF ● ●

GFDL GFDL hurricane 

model ● ●

ESRL FIM ●

NESDIS/STAR and CIRA SPICE ●
12

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 2013. The Stream 1.5 models arriving at NHC underwent standard 

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

that could be used by the forecasters. Figure 2 presents a homogeneous verification of the 

Stream 1.5 track models that met availability standards (and regardless of whether they were 

intended for use explicitly or in a consensus), along with selected operational models. The figure 

shows that in 2013 the FM9I was competitive with the top-tier operational models, although the 

HWFI was not. The TV15, the composite that included the ARW of the stream 1.5 models was 

the best performer throughout the forecast lead times (though the TVCA -- the consensus without 

the stream 1.5 models – was almost the same) and outperformed the European Center Model 

(EMXI) in this sample. The operational GFS (GFSI) was similar to EMXI except for the 36- and 

48- hour lead times.

Track skill (relative to CLIPER5) of Stream 1.5 and other models for the Atlantic basin 2013 (homogeneous 

comparison)

Figure 2. Stream 1.5 Track Skills, 2013 Atlantic
13

Stream 1.5 intensity results are shown as absolute error in Figure 3, for a sample that, because of

limited availability, excludes the PSU Doppler runs. In addition we show this chart as absolute 

error rather than skill (relative to SHIFOR5, labeled OCD5) because OCD5 was an especially 

poor performer in 2013 (dotted line in the figure). Note also that there are very few cases at 120 

hours—very few of the storms in the Atlantic in 2013 lasted more than three days. 

In 2012 the SPC3 statistical model was the best performing Stream 1.5 intensity model. In 2013 

it was among the worst. None of the statistical models performed well in 2013. The best model 

out to 48 hours was the operational HWRF (HWFI) and the FSU super ensemble (FSSE). 

IV15/IVCI composites performed well as did the Wisconsin model (UW4I) in the 24-48 hour 

forecast lead times.

Intensity errors of Stream 1.5 and other models for Atlantic basin 2013 (homogeneous comparison)

The shining lights in the stream 1.5 models this year were the GFS and FIM for track and the 

HWRF for intensity. The HWRF and GFS operational systems are discussed in the next section. 

The GFDL ensemble is discussed in section 7.

Figure 3. Stream 1.5 Intensity Errors 2013 Atlantic 
14

6. Operational Hurricane Guidance Improvements

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

by NCEP reaches those goals. Since 2013 represents the fourth year of the program we would 

expect to see considerable progress toward meeting the five year goals in the operational models

and not just in the experimental models such as the stream 1.5 models described in the previous 

section. In this section the emphasis will be on improvements in the hurricane forecasts from the 

models operational in 2013. This includes the global GFS model, 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 which is then used in the GSI for the analysis. The reader 

may note that previous annual reports, starting with the first one in 2010, described the impact of 

changing the data assimilation system in the global models, particularly the GFS from the 3DVAR GSI to the ensemble-based EnKF system. The hybrid system is basically a combination of 

the EnKF and the GSI and has been shown to provide somewhat better results than EnKF alone. 

The implementation of the hybrid system is an example of transferring HFIP results into 

operations.

The GFS has undergone other improvements including improved physics and increased 

resolution. In 2013 the deterministic operational GFS was run at T574 and the GFS ensemble 

(GEFS) at T265. Figure 4 illustrates the progress that has been made with the GFS since 2006

in forecasting track. The upper panel shows the performance of the GFS and the ECMWF 

operational models over three years starting in 2006. Data shown on both charts in Figure 4 are 

plotted relative the HFIP baseline for track. Note that, compared to the HFIP baseline, both the 

ECMWF and the GFS performed poorly early in the forecast during that three-year period. The 

ECMWF was better than the HFIP baseline at later forecast lead times. On the other hand, by 

120 hours, the GFS forecasts were worse than the forecasts at the earlier lead times.

The bottom panel shows the global model performance in 2012-2013. Again, results from both 

ECMWF and GFS are shown for the Atlantic. Note that both models have improved 

dramatically in the first 4 days of forecast lead time. ECMWF beats the GFS in the first 36 hours 

and the GFS beats ECMWF from 48 to 96 hours. Up to 3 days both models exceed the 5-year,

20% HFIP goal and the GFS does so out to 4 days. However, the charts indicate essentially no 

improvement in track forecasts beyond 4 days since 2006. 

This sudden decrease in track skill improvement is dramatic and apparently is shared by both 

models shown. It flies in the face of the assumption, often made, that just because the forecasts 

get better say at day 4 they should also get better at later forecast lead times. This apparently 

isn’t the case.
15

Track Skill (error relative to HFIP baseline) for the NCEP (GFS) and ECMWF Operational models. 

Upper panel shows error for all storms in the Atlantic for 2006-2008 and number of cases is shown in 

parentheses at the bottom of the figure. The lower panel is for 2012-2013 and otherwise is the same as the 

upper panel.

Figure 4. GFS and ECMWF Track Errors Relative to HFIP Baseline, Atlantic. 
16

The reasons for the sudden decline in track skill beyond 4 days are an interesting and important 

problem. There was some speculation that the reason for the sudden drop-off in the Atlantic at 

day 4 was related to higher observation error propagating into the Atlantic from say the Pacific. 

But if that were true the performance beyond 4 days should be different in different basins where 

upstream error properties should be different. Figure 5 shows the same plot as as the bottom 

panel in Figure 4 but for the West Pacific (WPAC). Note that the drop-off at day 4 is just as 

apparent in that basin. HFIP will put some resources towards answering this question in 2014.

Note also that while Figure 5 is similar to the lower panel on Figure 4, in the WPAC, the 

performance of the GFS is not generally superior to the ECMWF.

b. Global Model (GFS)—HFIP Experimental

The operational GFS deterministic model was run at a resolution of T547; and the ensemble 

(GEFS) was run at a resolution of T256. Experimental versions were run on the HFIP high 

performance computer using a semi-Lagrangian differencing scheme and at higher resolutions: 

T1149 for the deterministic model; and T547 for the ensemble. Comparisons of the operational 

and experimental systems performances are shown on Figure 6. 

Figure 5. GFS and EMCWF Track Errors Relative to HFIP Baseline (2012-2013), WPAC
17

Track and intensity error for the HFIP experimental GFS T1148 (blue) and operational GFS T574 (red) deterministic 

model (left panels) and the HFIP experimental GFS T574 (blue) and operational GEFS T256 (red) ensembles (right 

panels) for the Atlantic in 2013. Track errors above and maximum wind (intensity) errors below. Both the HFIP

experimental deterministic and ensemble systems use a semi-Lagrangian differencing. The dashed lines on the right 

hand panels show the ensemble spread.

Note that the operational versions of the GFS systems outperformed their experimental 

counterparts in both track and intensity forecasting. The primary reasons for this disappointing 

performance can most likely be attributed to not adjusting the physics and data assimilation 

packages to accommodate the revised resolutions and differencing scheme:

1. Increasing resolution does not always improve forecast skill. Rather, careful matching 

and testing of the physics and DA system to the higher resolution must be performed 

before the benefits of the higher resolution can be seen. HFIP has noted this in the past 

for the regional models where a simple test of several regional models at different 

resolutions did not reveal an advantage of taking the regional model grid to 3 km. 

However, the recent introduction of a high resolution inner core (3km) in the HWRF 

along with additional improvements in the overall physics package led to significant 

improvements over the 9 km version of HWRF.

2. This was the first real-time test of the semi-Lagrangian difference scheme in the GFS 

and, like the resolution, the physics package may also need to be tuned to the differencing 

scheme.

Figure 6. Performance Comparison Operational and Experimental GFS Systems
18

The right-hand panels in Figure 6 show the spread of the ensemble errors as well as the mean of 

the errors. The spread at any forecast lead time is the mean of the distances each of the ensemble 

members are from the mean location of the forecast. It is encouraging that the spread associated 

with the ensemble track errors is close to the mean errors. As a rule of thumb, the spread of the 

ensemble members should approximately equal the error of the ensemble mean otherwise it is 

said that the ensemble is under dispersed. The track error spread (blue dashed line) is much 

closer to the mean (blue solid line) for the experimental ensemble than that for the operational 

ensemble (red lines). That improvement may be the result of using a stochastic physics 

perturbation process in generating the experimental ensemble. There is, however, no 

corresponding improvement in the spread of the intensity forecast errors.

One product derived from the experimental HFIP GFS ensemble is the prediction of winds 

greater than 34 knots. The predictions are in terms of probabilities that tropical storm force 

winds will be exceeded at a location within a particular time frame. Figure 7 shows the 

probability forecast for winds 34 knots or greater over a 168-hour period starting 12Z September 

11, 2013. The storm in the eastern Atlantic is Humberto which was a hurricane at that time. 

An example of a probability of winds greater than 34 knots at the various points during a 7 

day period starting at 12 Z September 11 2013. A 100% probability implies that all 20 

members of the ensemble forecast a 34 knot wind at the point at some time during the 7 

days of the forecast. Forecast is from Sept 11 at 12Z 2013. Humberto is the storm in the 

east Atlantic that formed on Sept 8, 2013, Ingrid in the Gulf of Mexico that formed on 

September 12, 2013 and Manual in the East Pacific that formed on September 13, 2013.

Figure 7. Tropical Storm Force Wind Probabilities within 168 Hours
19

Humberto later weakened rapidly as indicated in the figure and then regained tropical storm 

force winds as it turned northeast. The storm in the Gulf formed on the 12th and became 

hurricane Ingrid on the 14th. The storm in the EPAC is Manuel which formed on the 13th.

Figure 8 shows the verification of probability calculated from the experimental HFIP GFS 

ensemble of winds greater than 34 knots at a point during the time period indicated on each 

panel. 

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

black area in the lower panel. This is from the HFIP GFS T574 ensemble and 100% or 1 in the figures would 

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

(right panel), black and the operational GEFS (T256), red. Reliability Scores are aggregated over the 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.

Figure 8. Verification of Tropical Storm Force Wind Probabilities
20

Another product derived from the experimental HFIP GFS ensemble is a map showing the 

different tracks forecast by ensemble members. Figure 9 shows the 7-day tracks forecast for 

Humberto by the 20 ensemble members and the track of the ensemble mean. Ellipses, also 

shown, are drawn, centered on the mean and enclosing 80-percent of the ensemble members’ 

forecasts associated with each lead time. 

Both of these products can be generated from the operational GFS system.

A product created from the HFIP GFS ensemble that shows all the tracks from the forecast of September 11, 

2013 out to 7 days. 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. The storm shown is Humberto.

Figure 9. Ensemble Member Tracks.
21

c. Hurricane WRF (HWRF)

1) Atlantic

In 2012 a third nest was introduced into the HWRF allowing the resolution of the inner core to 

be increased to 3 km from the 9 km employed in 2011. That change, changes in the DA part of 

the initialization system in 2013 and various physics changes added in 2012-2013 have led to a 

15%-20% per year improvement in intensity forecasts from the 2011 HWRF system. This 

improvement is illustrated in Figure 10. Although the intensity errors have not yet gotten to the 

HFIP 5-year goal (green dashed line in top left panel, Figure 10), the rate of improvement 

suggests that this goal will be met in 2014. Improvements in 2014 will include a higher model 

top (allowing more satellite data to be included in the initialization) and more frequent calls to 

the physics routines which preliminary indications suggest may contribute another 10-20% 

improvement in intensity forecasts.

Improvements in the HWRF system from the 2011 HWRF system to the 2013 HWRF system (upper left 

panel) and comparison with other models in lower left panel. The right hand lower panel shows results 

from 2013. The figure is mostly self explanatory.

Figure 10. HWRF System Improvements 2011-2013
22

2) West Pacific (WPAC)

HFIP began running real-time HWRF forecasts for the WPAC in 2012 and for the Indian Ocean 

in 2013. These runs were done on the HFIP computers in Boulder, CO. Even though they were

not run on the operational system, the reliability of those runs was quite high. The forecasts 

were transmitted to JTWC where they were used extensively in their forecasts. Figure 11 shows 

the track and intensity forecast performance of HWRF and other modeling systems in WAPC.

The HWRF in red is compared to COAMPS-TC in dark blue, the GFDLN (GFDL model run by the Navy) in light 

blue, the GFS in black and the official JTWC forecast in purple.

Figure 12 shows the official forecast errors in intensity for the JTWC forecasts in the WPAC 

starting in 2000 and going through 2013. The dramatic drop in the error for lead times of 72, 

96, and 120 hours in 2013 is noteworthy. We point this out somewhat reluctantly since the drop 

is only for a couple of years and there have been other years with a similar decrease (2009-2010). 

However this was an exceptional year in the WPAC with many storms, a relatively large number 

of which developed rapidly and storms reaching major levels (category 3 or higher). In addition,

we have been noticing a similar drop in the NHC intensity forecast error in the Atlantic where 

the errors curves flatten out at lead times greater than 48 hours (this is the first year we have seen 

that in the WPAC). 

Figure 13 shows the official forecast errors in intensity for the NHC forecasts in the Atlantic 

basin from 1990 through 2013. Again there has been a tendency for the intensity forecast errors 

to be greatly reduced at the longer lead times. Whether this trend is real or not remains to be 

determined. The 2013 season in the Atlantic had few storms, fewer that rapidly intensified, and 

only two hurricanes. So, 2013 could be regarded as an “easy season” for intensity forecasts. As 

noted above, that was not true for the WPAC yet we see a similar trend. That there may be a 

similar trend in the WPAC gives us hope that it is not just a statistical quirk. In addition, at this 

point it is not possible to tell whether the improvements, if they are real, are related to HFIP 

activities such as an improved HWRF or something else.

Figure 11. HWRF Track and Intensity Errors 2013 WPAC.
23

Figure 12. JTWC Official Intensity Forecast Errors, WPAC, 2000-2013

Figure 13. NHC Official Intensity Forecast Errors, Atlantic basin, 1990-2013
24

3) Rapid Intensification

One of the goals for HFIP is to increase the probability of detection (POD) for rapid 

intensification (RI) to 90% at Day 1 decreasing linearly to 60% at day 5, and decreasing the false 

alarm ratio (FAR) for RI to 10% for day 1 increasing linearly to 30% at day 5. 

We were not able to test the ability of the 2013 HWRF to forecast rapid intensification in the 

Atlantic this year because of too few cases. There were enough cases for such a test in the 

WPAC. Figure 14 shows a comparison of the observed 24-hour intensity change in the West 

Pacific plotted against the HWRF model forecast, left panel, and observed compared to the 

JTWC official forecast in the right panel. The numbers in the lower right quadrant of each panel 

show the POD and FAR for each set of forecasts. The red squares show the points that were 

correctly forecast as rapid intensifiers. While the POD was still considerably below the 10 year 

goal for HFIP, the result shown in figure 14 is promising: note that the HWRF RI forecasts are 

better than the official forecasts.

Forecast intensity change along the x-axis and best track intensity change along the y-axes. The red square denotes the 

region of increased intensity greater than 30 knots in 24 hours—the region regarded as rapid intensification. The 

Probability Of Detection (POD) and the False Alarm Rates (FAR) are shown. The left panel is for HWRF and the right 

panel is for the official JTWC forecast. Note that multiple data points with the same forecast-best track value appear as a 

single blue dot in the plots

PPPPPP = 5555

5555 + 111111 = 2222. %

FFFFFF = 5555 + = . %

PPPPPP = 9999 + = . %

FFFFFF = + = %

Figure 14. Forecasted 24-hour Intensity Changes in WPAC, 2013 
25

4) HWRF ensemble

For the first time HFIP and EMC conducted a real-time experiment on the HFIP machines in 

Boulder where the HWRF system was run as an ensemble. The basic model used in the 

ensemble was identical to the operational HWRF for 2013. A 20 member ensemble was run 

where the initial- and boundary-condition perturbations were from the GEFS. The members 

were created using the ensemble transform with a rescaling scheme. Additionally, the model 

physics were perturbed by adding a stochastic component to the convective trigger function in 

the operational HWRF. In particular, sub-grid scale convection is triggered only when the 

difference between partial pressure at the convection starting level and that at the level of free 

convection is less than the (large-scale-vertical-velocity-dependent) trigger function (120 to 180 

hPa) plus a random component between -50 and 50 hPa. Random components are generated 

separately for each member at each cycle thereby avoiding spatial or temporal correlations. Thus 

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

member and then a stochastic convective trigger was added within each member.

Figures 15 and 16 compare the intensity and track forecasts of the operational model (red line) 

and the ensemble mean (blue line) from the ensemble experiment in the Atlantic in 2013. 

Because there were few long-lived storms in the Atlantic this season, there are not enough cases 

beyond 72 hours to draw reliable conclusions. Figure 15 shows significant improvements in the 

track forecast out to 72 hours. 

Red shows the operational HWRF and blue the ensemble mean. The ensemble model was identical to the 

operational HWRF, it used an inner grid resolution of 3 km.

Figure 15. HWRF 2013 Experimental Ensemble Track Performance, Atlantic Basin
26

Figure 16 shows even more improvement in the intensity forecast, approaching 50% at some 

lead times. This effect alone (from this type of ensemble) might give the additional 

improvement overall in intensity guidance from the HWRF system to meet the HFIP goal at 5 

years (Figure 10). But there are additional improvements in the model as noted earlier that could 

contribute to exceeding the 5-year intensity goal.

Again, red shows the operational HWRF and blue the ensemble mean. The ensemble model was identical 

to the operational HWRF, it used an inner grid resolution of 3 km.

The HWRF ensemble was a stream 1.5 model for the 2013 season. Hence, it was factored into 

the official forecast.

Figure 16. HWRF 2013 Experimental Ensemble Intensity Performance, Atlantic Basin.
27

5) Evolution of the HWRF System

The HWRF undergoes considerable testing each year for adding new techniques and technology.

This has led to some extraordinary improvements (Figure 10). The general strategy, for the next 

5 years, is to gradually evolve the HWRF into the NCEP Earth Modeling System (NEMS), the 

infrastructure that has been adopted for other models at EMC. In addition, HWRF will move 

from the E-grid NMM to the B-grid NMM which is 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 system where there are multiple moving doubly-nested grids, one for each hurricane,

interacting with the larger basin scale domain. Figure 17 shows the extent of the basin scale 

system in 2012 when Kirk, Isaac, and Ileana were concurrently in the Atlantic, Gulf of Mexico, 

and Eastern Pacific, respectively. 

The basin scale system allows modeling not only the interactions with the larger scale domain 

and the systems within that domain (which play a large role in hurricane track and perhaps a 

lesser role on intensity) but, also, interactions between simultaneous, relatively close hurricanes -

- an interaction which, occasionally, can be quite important. It saves computation time because 

the requirement to run a large scale domain for each of the concurrent hurricanes is satisfied by 

Figure 17. Basin Scale System with Three Moving Nests: Kirk, Isaac, and Ileana, 2012
28

one basin scale run. In addition, since the B-grid NMM can easily be made “global.” The basin 

scale domain can be made arbitrarily bigger depending on computer resources and, eventually,

can cover the globe. This will eliminate the need for a separate global model. In summary, the 

strategy is to smoothly transition hurricane modeling systems from the hurricane scale to the 

global scale. Table 7 outlines the steps that the HWRF group will undertake to reach a system 

composed of a global model with multiple moving nests - one for each hurricane. Note also this 

moving nest approach likely can be made to work well with mid-latitude systems such as 

mesoscale convective systems.

Table 7. HWRF development 2013-2018

System Current 

(Q4FY13)

Q3FY14 Q3FY15 Q3FY18

Atmosphere 27:9:3 km, 42 

levels

27:9:3, 61 levels 18:6:2 km, 64 

levels

18:6:2 km, 128 levels with 

10 member ensembles (in 

NMMB/NEMS framework)

Ocean POM (3D ATL and 

1D EPAC) 1/6o

resolution 23 

levels

POM (TransAtlantic domain at 

1/12o resolution, 23 

levels and 3D ocean 

for East Pacific

HYCOM (1/12o

resolution 32 

levels TransAtlantic and EPAC 

basin)

Global HYCOM (1/12o 

resolution, 100 levels)

Waves None None WAVEWATCH III WAVEWATCH III

Data 

Assimilation

One-way Hybrid 

EnKF-3DVAR with 

vortex 

initialization, 

inner core NOAAP3 TDR DA

One-way hybrid 

with inner core 

aircraft recon data 

(TDR/FL) and clear 

sky satellite 

radiance DA

One-way hybrid 

with inner core 

recon data 

(TDR/FL); clear 

and inner core 

cloudy radiance 

DA

Two-way hybrid 3D/4D EnVar with inner core aircraft 

and all sky satellite 

radiance DA

Hurricane 

Physics

Ferrier 

Microphysics 

with explicit 

convection in 

3km domain

Advanced 

Microphysics with 

high-resolution PBL 

& convection

Advanced 

Microphysics, 

NOAH LSM and

land-air-sea-wave 

interactions

Advanced Microphysics, 

NOAH LSM and land-airsea-wave interactions, 

coupled to wave, 

hydrology, surge and 

inundation models

Basins NATL, EPAC NATL, EPAC NATL, EPAC All Tropical Ocean Basins

7. GFDL Ensemble

For the last four 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 8). Working with the forecasters at NHC, scientists at GFDL 

constructed an ensemble by varying parameters in the initial conditions, sea surface temperatures

and surface fluxes used by the model. The “unbogussed” forecasts start from the GFS without 
29

initial modification to the vortex from what was in the GFS. The other members use the GFDL 

initialization scheme but modified as described in Table 8.

Table 8. GFDL hurricane model ensemble members

Figure 18 shows the results for the GFDL model in the Eastern Pacific. We show EPAC instead 

of the Atlantic since there were many more cases there than in the Atlantic. Figure 18 compares 

the GFDL ensemble mean to the control run (the operational GFDL) for intensity. The GFDL 

ensemble mean is compared to other models in the Atlantic basin in Figures 2 and 3. In those

figures the GFDL ensemble mean is denoted GPMI since it was a late model and so was 

interpolated as described in section 5a. 

ATCF ID Description

GP00 Control forecast (same model as 2013 operational GFDL at NCEP)

GP01 Unbogussed 

GP02 Increase NHC-observed Vmax 10%, 34-kt radii 25%, 50-kt radii 40%, ROCI 25%

GP03 Decrease NHC-observed Vmax 10%, 34-kt radii 25%, 50-kt radii 40%, ROCI 25%

GP04 Modification to increase inner-core moisture by a max of 10%

GP05 Modification to decrease inner-core moisture by a max of 10%

GP06 Increase SSTs by a max of 1°C within the initial extent of the TC

GP07 Decrease SSTs by a max of 2°C within the initial extent of the TC

GP08 Surface physics modification: GFDL 2011 operational formulation of CD & CH

(surface drag and enthalpy exchange coefficients)

GP09 Surface physics modification: HWRF 2012 operational formulation of CD & CH

(surface drag and enthalpy exchange coefficients)

GPMN Ensemble mean computed at each lead time where the member availability is at 

least 4 members (40% member availability threshold)
30

The black line (GP00) is the control run (the operational GFDL) and the red line is the ensemble mean. 

The blue dashed line is the ensemble spread for intensity. The green numbers along the bottom of the 

figure show % improvement over the control GP00.

Figure 18 shows that the ensemble mean provided a 6%-10% improvement over the operational 

deterministic GFDL for intensity. Figure 2 shows that for track in the Atlantic the GFDL 

ensemble mean was in the middle of the pack for the comparisons shown (remember, ignore the 

longer lead times as the number of cases is very low). 

Of the models shown in figure 3, out to 24 hours, the GFDL ensemble mean (green line) is 

among the best intensity predictors though the HWRF (pink line) does beat it. The ensemble 

spread shown by the dotted blue line on Figure 18 is only about half of what is considered to be 

ideal: where the spread and the mean error are about equal. However also note that the ensemble 

spread for intensity of the HWRF model (Figure 6 right lower panel) has roughly the same 

relation to the ensemble mean. We are not sure what that means.

Figure 19 shows the GFDL ensemble performance for hurricane Raymond in the EPAC. 

Raymond was a notoriously poorly forecast storm. Most models generally forecast no 

Figure 18. 2013 GFDL Ensemble Intensity Errors, EPAC.
31

intensification of Raymond though it went through rapid deepening in the middle of its life cycle 

to a strong hurricane. What is shown is the average intensity error for all cases from that storm 

of each individual member of the ensemble and the ensemble mean. The best forecasts were by 

the member using the HWRF surface flux coefficients and the member that decreases the sea

surface temperatures within the region of the cyclone. The worst were by the member that used 

the GFDL exchange coefficients and the member which increased the inner core moisture. 

Light blue dashed line is the ensemble spread; red line (GPMN) is the ensemble mean.

Figure 19. Intensity Errors for GFDL Ensemble Members: Hurricane Raymond.
32

8. Impact of Inner Core Reconnaissance Data

Previous annual reports describe several past experiments that indicated including aircraft data in 

forecast initialization appeared to substantially improve intensity forecasts -- even out to 5 days. 

This year, the Reconnaissance Data Impact Tiger Team (RDITT, see table 2) was convened to 

conduct a systematic investigation of the impact of using aircraft reconnaissance data from the 

inner core of tropical cyclones on the numerical guidance provided by regional tropical cyclone 

models. For this investigation: 

• 3 regional TC models were selected for comparison of results

– PSU EnKF ARW hurricane analysis & prediction system

– EMC operational 3-km HWRF (2013 version)

– HRD HEDAS-HWRF (v3.2)

• Cases selected were Atlantic storms from 2008-2012 for which conventional aircraft 

reconnaissance data and/or Tail Doppler Radar (TDR) data are available in the TC inner 

core

• For all configurations from a particular modeling group, the same end-to-end system was 

used, each configuration differing only in the data included in the forecast initialization. 

The configurations were:

– Control: no inner-core aircraft reconnaissance data assimilated - baseline for 

evaluating reconnaissance impact 

– Standard reconnaissance: conventional reconnaissance data (flight-level, 

dropsondes and Stepped Frequency Microwave Radiometer – SFMR, which 

provides estimates of surface winds) assimilated (PSU did not assimilate data 

obtained with the SFMR)

– All reconnaissance: conventional reconnaissance data and tail Doppler radar 

(TDR) data assimilated

– TDR only (delivered by EMC and HRD): only TDR data assimilated (no 

conventional reconnaissance data)

Figure 20 is the flow chart showing how the HWRF was initialized and how the inner core data 

were incorporated in the retrospective runs for the RDITT test. 
33

Flow chart for the initial condition calculation for the HWRF one way hybrid DA system when aircraft reconnaissance 

data is and is not available

For each modeling system, each configuration tested was compared with the control. The 

comparisons were also partitioned by the initial intensity of storm and whether the storm was 

over water only or water and land. 

Figure 21 shows the comparison of track and intensity errors for the all reconnaissance (flightlevel, dropsonde, SMFR and TDR) data in the EMC HWRF test. The formatting convention is 

shown in the lower left corner of the figure. Green shaded areas are where the addition of the 

inner core data improved the forecast in a statistically significant way; red shaded areas are 

where the addition of the data degraded the forecast in a statistically significant way. Here, a pvalue exceeding 0.95 is regarded significantly significant 

Note that, for intensity, there was a spin-down problem from the initial conditions when the data 

were added that degraded the forecast in the early lead times. In general, the track forecasts were 

only slightly improved with the addition of the inner core data -- a result that might be expected 

because track tends to be determined by the hurricane environment rather than details in the 

inner core.

Figure 20. HWRF Initial Condition Calculation Flow Chart.
34

Subtropical Depressions, Tropical Depressions, 

Subtropical Storms, Tropical Storms Sample Size: 72 -37

Category 1 and 2 Hurricanes Sample Size: 82 – 6

Major Hurricane Sample Size: 35 – 3

Pairwise difference tables for the EMC model for track and intensity using all aircraft data 

including the TDR. Here the data have been stratified by intensity with the weakest storms 

shown on top and the strongest on the bottom. 

Figure 21. Example RDITT Test Results: EMC HWRF, All Data.
35

The addition of the data did provide improvement in the intensity forecasts for the weaker storms 

(depression though tropical storms) for lead times between 24 and 48 hours, perhaps some 

improvement for weak hurricanes and little improvement for major hurricanes. For major 

hurricanes the spin-down problem was the worst of the three categories shown in Figure 21.

The problem with the HWRF behavior in the early part of the forecast for intensity is one that 

has been a problem for some time and it is hoped that improvements in the initialization system 

will change that. Once that is fixed, it is possible that inner core data will further improve 

intensity forecasts.

Figure 22 illustrates one possible advantage of adding the inner core data. The figure shows data 

provided by PSU as their part of the RDITT test. Shown is a plot with track error data from the 

control run (APCT--black) of the PSU model and track error data when the model is run with all 

of the inner core data (flight level, dropsonde and TDR-APAR--red). The difference in the 

means at each forecast lead time is small though perhaps the track performance was slightly 

improved when the inner core data were added. 

Whisker plots from the PSU model comparison using all reconnaissance data (dropsondes, flight level, and 

TDR data -- red) and control (no inner core data) -- black. Outliers are points greater than 1.5 times the 

Inter-Quartile Range (difference between the 75% and 25% quartiles) from the median. The outliers within 

the ellipses are discussed in the text.

Outlier

75% quartile

25% quartile

Median 

Furthest point 

from median 

not an outlier 

Figure 22. Outlier Example: RDITT Test PSU Model, All Data and Control
36

Of more interest however is the reduction in the number of large outliers around the 72-hour 

forecast lead time. Note the outliers within the black dotted ellipse. The red ellipse indicates the 

reduction in magnitude of those outliers when the inner core data was included. This effect was 

noted for the other models in the test though not as dramatically. Also, this effect was manifest 

in just a few cases and may be related to a single storm.

The detailed RDITT committee report is available on the HFIP website (http://HFIP.org) when 

final. This report provides a summary of the results and a few examples. The main conclusions 

are:

• Results suggest inner core observations offer promise for improving operational TC 

guidance, but more work lies ahead to make optimal use of these data. While including 

standard reconnaissance did result in some statistically significant improvements, 

particularly for track, in the lead times that were improved, results lacked consistency 

across the regional models tested. 

• The investigation of the impact of TDR data was not definitive, partly due to the small 

sample size.

• Results varied considerably across the three models.

Note that many more inner core radar observations are available from Air Force C130 aircraft 

reconnaissance. However, because those radars are not comparable to the TDRs on the two 

NOAA P3s, the C130 observations were not included in the RDITT investigations.

9. Post Processing of Model Output

a. Statistical Post Processing of Model Output

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

HFIP goals. Typically, statistical models (for example DSHP and LGEM) are among the best 

predictors of hurricane intensity. A statistical model is one where a limited number of variables 

(measured in single to double digits) are weighted, through correlation with past data, and 

combined. The variables are generally selected from parameters describing the current state of 

the hurricane or various environmental data. Values of environmental data can be specified 

using current observations or model forecasts. Perhaps the simplest statistical model for 

intensity is SHIFOR5 (labeled OCD5 in Figure 3) where the variables are current position and 

intensity, position and intensity 12 hours earlier, and date. CLIPER5 is a similar model for 

track.

Another class of statistical model takes particular predictions (say track or intensity) from several 

dynamical models in a multi-model ensemble and combines those predictions as a weighted 

average. The weights are determined by comparing the performance of the various models over 

a period of years. The FSU Multi-Model Ensemble is of that class. As in past years, the FSU 

Multi-Model Ensemble was among or the best performers of the statistical models (see Figures 2 

and 3) for 2013. Figure 23 shows the intensity and track errors in 2012 for the various models 

that went into the FSU Multi-Model Ensemble. The orange and red bars on the right side of the 

groups for each forecast lead time are the equally weighted ensemble mean (EM) and for the 
37

variably weighted ensemble mean MMEN. At all lead times the weighted mean was better than 

the equally weighted mean and at all lead times it was better than all the other models. At some 

lead times the official forecast was better.

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 A for details). 

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

shown by the red bar. The official forecast is dark purple. Top-to-bottom in legend is left-to-right in 

charts. 

Figure 23. FSU Multi-model Ensemble: Members and Mean Performance (2012 Atlantic).
38

More complex statistical models used operationally for intensity are SHIPS, LGEM and SPICE

(SPC3). SPC3, in recent years, showed improvement compared to the operational statistical and 

dynamical models, by using multiple operational numerical models as input for the 

environmental predictors of DSHIP and LGEM. SPC3 derives input for both DSHIP and LGEM 

from the operational GFS, HWRF and GFDL models. This gives six variations which are then 

averaged as an ensemble. 

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

current dynamical models. This year however the traditional statistical models (LGEM, DSHP) 

did very poorly. See for example Figure 3 where the SHIFOR5 results (again, labeled OCD5) 

are displayed. This year, the statistical models scored worse than most of the dynamical models 

(Figure 3). The poorer performing statistical models include LGEM, DSHP and SPC3. SPC3 

did perform better at the longer lead times but, again, recall there are few cases at the longer lead 

times.

Both SPC3 and the FSU Multi-Model ensemble system are examples of statistical post 

processing being pursued by HFIP.

b. Other Post Processing

HFIP promotes the development of new diagnostic methods that can be applied to understand the 

source and growth of errors associated with modeling tropical cyclones. Figure 24 shows the 

difference in 250 hPa geopotential height between high resolution GFS ensemble member 

forecasts initialized on 0000 UTC 25 October. Here, the color shading shows the difference in 

geopotential heights for the ensemble members that predicted Hurricane Sandy would move out 

to sea and the ensemble members that correctly predicted Sandy’s westward turn toward the New 

Jersey coast. 

Normalized difference in 250 hPa geopotential height between ensemble members predicting eastward-turning tracks and 

those predicting westward-turning tracks (shading; units: standardized anomaly) at (a) 0 hours, and (b) 24 hours. 

Dotted regions indicate where the difference is statistically significant at the 95% confidence level. The contours denote 

the ensemble-mean 250 hPa height.

Figure 24. Post Processing Diagnostics: Hurricane Sandy 250 hPa Geopotential Heights
39

At the analysis time, the largest differences were associated with the trough to the west of Sandy 

over the Gulf of Mexico. By 24 hours, the largest differences were associated with the 

magnitude of the ridge building to the north of Sandy off the coast of the Carolinas; the ensemble 

members that predicted Sandy would move out to sea predicted a less amplified ridge. In those 

forecasts, the less amplified ridge would impart a westerly steering wind on Sandy, forcing the 

storm out to sea. Additional analysis suggests that the amplification of this ridge is related to the 

amount of latent heat release predicted along a warm front associated with Sandy, a prediction 

which in turn is a function of how the cumulus scheme handles upper tropospheric dry-air.

Ensembles can also be used to study how initial condition errors impact subsequent intensity 

forecasts. Figure 25 shows the sensitivity of the AHW 72-hour maximum wind speed forecast 

for Hurricane Bill (2009) to the analysis (i.e., initial conditions) of precipitable water. This 

figure indicates that increasing the precipitable water near the TC core or in the northeast 

quadrant of the TC leads to a more intense storm 72 hours later in the forecast. During that time, 

Bill was experiencing northeasterly vertical wind shear, allowing air from the northeast to enter 

the TC core. Figure 25 suggests that moistening the environmental air before it reaches the core 

could help mitigate the dry air entrainment which slows down the development of the TC. In 

addition, this result suggests that taking observations in the northeast quadrant of the storm could 

have improved the subsequent forecast.

Sensitivity of the 72-hour forecast of Hurricane Bill’s axisymmetric maximum wind 

speed to the analysis of precipitable water (shading units: knots per analysis 

standard deviation of precipitable water) initialized at 0000 UTC 16 August, 2009. 

The contours are the ensemble-mean analysis fields (mm). The center of Bill is at the 

center of the plot.

Figure 25. Sensitivity of Intensity to Precipitable Water: Hurricane Bill.
40

c. The HFIP Web Page

Each hurricane season HFIP runs a real-time system on the HFIP machines in Boulder. 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. HFIP maintains a website where the various 

results of the runs on the Boulder computer and elsewhere (NRL for example) including the 

operational models, are presented. This website is located at http://www.hfip.org; the products 

page is http://www.hfip.org/products. Figure 26 is a “screen-shot” of the products page showing 

the various options available. The site is open to the public and is active during the hurricane 

season. Some products remain available through the site during the off season. The site 

undergoes development each year to add capabilities and include changes to the real-time system 

output.

Figure 26. HFIP.org Products Page.
41

10. Storm Surge

One of the goals of HFIP is to increase the skill of the forecast of the storm surge as a result of 

land falling hurricanes. HFIP provided the Advanced Circulation model (ADCIRC) storm surge 

development group within NOAA real-time access to the HFIP computers for forecasts during 

land-falling hurricanes in the 2013 season. The number of cases available in 2013 was quite low 

since there were few land-falling storms. Figure 27 shows the HFIP storm surge forecast for 

Tropical Storm Karen as it passed just south of Louisiana. The forecast surge heights are shown 

along the right side of the figure. 

Figure 27. Storm Surge Advisory Product, Tropical Storm Karen
42

11. Societal Impact Work

In 2013 HFIP provided modest support for the Societal Impacts Team. Most of this support 

went to NHC. Figure 28 shows an example of a product developed at NHC to depict the impact 

of storm surge. This type of product was displayed last year in the HFIP annual report with 

Tampa Bay as the example and it was evaluated by the Impacts Team 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 of

Emergency managers, the media, public, and WCMs rated it very high in ease of understanding 

and providing useful information. Figure 28 is a refinement of what was presented last year and 

this product will likely be operational in 2014. 

A potential storm surge product from NHC. The storm is hypothetical but was motivated by Sandy. 

Figure 28. Preliminary Storm Surge Advisory Product.
43

12. The Configuration of a Numerical Model Hurricane 

Forecast Guidance System to meet the HFIP goals

With the new Hybrid Data Assimilation system and increasing resolution in both the global 

operational deterministic and ensemble models as well as improvements in physics and changes 

to the data assimilated, we feel that the operational global model will achieve at least the 5-year 

goals (in fact it appears to already achieved the 5-year track goals at least out to 4 days) by the 

5th year of the project. Beyond 4 days there is strong evidence that the global models in general 

have not improved in the 5-7 day range. The reasons for this are not clear and it will be one of 

the scientific issues that HFIP will address in the next 5 years. However, given the success in 

forecasting track out to 4-5 days, we feel that not only the 5-year goals but likely the 10-year 

goals are within reach in those forecast lead times. 

The regional models (particularly the HWRF) are now beginning to reach the track skill of the 

global models but it is likely that we will have to continue to focus on the global models for track 

forecasts. The regional models (particularly the HWRF) are just now reaching the 5- year goal 

for intensity, see Figure 8, and it is likely that planned changes in the HWRF in 2014 including 

the use of the HWRF as an ensemble, will reach that goal on time.

While HFIP has been emphasizing improving the operational HWRF, HRD and EMC with HFIP 

support have been experimenting with a class of models known as basin scale models. These are 

simply the regional scale models with a large outer domain (so they cover a couple of basins like 

Atlantic/ East Pacific) and are capable of running high resolution nests over more than one storm 

at the same time (see Section 6.c.5). These systems ran successfully during the 2012-2013

hurricane season and are showing promise. In both the single storm models and the basin scale 

models with multiple storms, the various nests interact with the larger outer domain so with 

multiple moving nests within the outer domain, concurrent individual storms can interact with 

each other.

This can then be easily taken one step further: the basin size can be expanded to include the 

entire globe. Then the regional models in a sense would be eliminated though they basically 

remain as the individual nests interacting with the global scale model directly.

We also note that this nested concept with a global model could also be extended to mid-latitude 

systems like squall lines. In other words, the basin scale concept being tested in HFIP could also 

likely improve forecasts of other types of weather systems, not just hurricanes.

In past years we have speculated on what the final configuration of the hurricane model might 

look like at the end of the program. Table 9 shows this 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.
44

Table 9. 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 

1) Double nests (9 and 3km), one for 

each hurricane 

2) HWRF

3) Using all available aircraft and 

satellite data in 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, TC-COAMPS)

Statistical Post Processing LGEM, SHIPS, SPICE, others
45

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47

Appendix A: HFIP Funding Opportunity for Academia and 

Private Industry

Through an Announcement of Opportunity (AO) in March 2011, 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 data assimilation techniques focusing on use of in-situ measurements obtained 

through aircraft reconnaissance and satellite datasets; advancements in numerical weather 

prediction techniques with emphasis on high-resolution physics for more accurate representation 

of moist processes and air-sea interactions in the hurricane core region; advanced model 

diagnostics to support model improvements; advanced ensemble techniques for improved track 

and intensity forecast guidance; and enhanced observing strategies and use of observations for 

hurricane environment- and hurricane core-scale circulations.

Of the 34 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. A complete list of 

selected proposals is available on the HFIP website at 

http://www.hfip.org/HFIP%20Grant%20Selected%20Proposals.pdf. Among the 12 selected 

proposals, four address the data assimilation priorities; three address operational hurricane model 

improvements; two each address high-resolution physics and ensembles, and one addresses 

tropical cyclogenesis. Progress of each of these proposals is monitored through quarterly reports 

from the principle investigators and through scientific presentations from the Principal 

Investigators 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.

HFIP released a second round AO in 2013 with an increased focus on advanced physics for landair-sea interactions, hurricane intensity predictability, global-to-local scale modeling techniques 

and continuous improvements to operational hurricane models and data assimilation techniques. 

The review process for these proposals has been completed and announcements of awards will 

be made in the spring 2014. When they are announced, the list of the winning proposals will be 

made available on http://HFIP.org. 
48

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 “earlier” forecasts from “late” models (see 

Section 5.a) are adjusted so that the previous 6-hour forecast matches the conditions at the 

beginning of the current forecast. This is simply known as an interpolated forecast. Forecasts of 

those future conditions are denoted with an “I” at the end, for “interpolated” (12-hour 

interpolations are denoted with a “2”).

Other conventions (although not exclusively) in the model naming include using 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.

ADCIRC: Advanced Circulation Model for oceanic, coastal and estuarine waters

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: ARW with 6-hour interpolation

ARW: Pennsylvania State University Advanced Research WRF

CMC: Canadian Meteorological Centre model.

CMCI: CMC with 6-hour interpolation.

CLIPER5: Climate and Persistence model.

COAMPS-TC: Fleet Numerical Meteorology and Oceanography Center Coupled 

Ocean/Atmosphere Mesoscale Prediction System-Tropical Cyclone.

COTI: COAMPS-TC with 6-hour interpolation.

Decay-SHIFOR5: Decay Statistical Hurricane Intensity Forecast model.

DSHP: Decay SHIPS.
49

ECMWF: European Centre for Medium-range Weather Forecasts model.

EGRI: United Kingdom Meteorological Office model, subjective tracker, with 6-

hour interpolation.

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). 

GDAS: Global Data Assimilation System

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).

HWFI: HWRF with 6-hour interpolation.

HWRF: Hurricane WRF.

ICON: National Hurricane Center Intensity Consensus.
50

IV15: Intensity forecast ensemble including 2012 stream 1.5 forecasts.

LGEM: Logistics Growth Equation Model.

NAVGEM: Fleet Numerical Meteorology and Oceanography Center Navy Global 

Environmental Model (replaced NOGAPS February, 2013).

NGPI: NOGAPS with 6-hour interpolation.

NGXI: Experimental NOGAPS with 6-hour interpolation.

NOGAPS: Fleet Numerical Meteorology and Oceanography Center Navy Operational 

Global Atmospheric Prediction System (replaced by NAVGEM February, 

2013).

NMM: Environmental Modeling Center Nonhydrostatic Mesoscale Model.

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

TVCN: National Hurricane Center Track Variable Consensus 

UKMI: United Kingdom Meteorological Office model, automated tracker, with 6-

hour interpolation.

UWNI: UW-NMS with 6-hour interpolation.

UW-NMS: University of Wisconsin Nonhydrostatic 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)