1

2011 HFIP R & D Activities Summary:

Accomplishments, Lessons Learned, and Challenges

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

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

Goerss10, S. Gopalakrishnan 1, T. Hamill4, H.S. Kim3, T. Krishnamurti 5, P. Kucera7, Y. Kwon 3, 

W. Lapenta3, S. Lord3, T. Marchok6, F. Marks1, E. Rappaport9, C. Reynolds10, N. Surgi3, 

V.Tallapragada3, H. Tolman3, G. Vandenberghe

3

, Y. Weng12 J. Whittaker4, F. Zhang12 ,Xuejin 

Zhang1, M. Zupanski2

26 March 2012

1Atlantic Oceanographic and Meteorological Laboratory OAR/NOAA

2Colorado State University

3Environmental Modeling Center NCEP/NOAA

4Earth System Research Laboratory OAR/NOAA 

5

Florida State University

6Geophysical Fluid Dynamics Laboratory OAR/NOAA 

7National Center for Atmospheric Research

8National Environmental Satellite Data Information Center NOAA

9National Hurricane Center NWS/NOAA

10Naval Research Laboratory, Monterey

11Office of Science and Technology, NWS/NOAA

12Pennsylvania State University 

13University of Rhode Island
2

Table of Contents

EXECUTIVE SUMMARY.............................................................................................................................................3

1. INTRODUCTION ..................................................................................................................................................4

2. THE HURRICANE FORECAST IMPROVEMENT PROJECT.........................................................................4

3. THE HFIP MODEL SYSTEMS............................................................................................................................8

3.1. HIGH RESOLUTION ENSEMBLE APPROACH .....................................................................................................8

3.2. THE GLOBAL MODELS....................................................................................................................................9

3.3. THE REGIONAL MODELS..............................................................................................................................10

3.4. EVALUATION OF THE STREAM 1.5 CANDIDATES ..........................................................................................11

3.5. INITIALIZATION SYSTEMS .............................................................................................................................13

3.6. THE HWRF COMMUNITY CODE REPOSITORY...............................................................................................14

4. MEETING THE HFIP GOALS ..........................................................................................................................15

4.1. THE HFIP BASELINE...................................................................................................................................15

4.2. MEETING THE TRACK GOALS........................................................................................................................15

4.3. REACHING THE INTENSITY GOALS ...............................................................................................................19

4.4. STATISTICAL POST PROCESSING OF MODEL OUTPUT ..................................................................................22

4.5. REACHING RAPID INTENSIFICATION GOALS................................................................................................23

5. RECENT ACCOMPLISHMENTS ......................................................................................................................24

5.1. FORECASTING TROPICAL STORM GENESIS OUT TO 5 DAYS OR MORE. ........................................................25

5.2. TRACK FORECASTING SKILL .........................................................................................................................29

5.3. THE IMPACT OF HIGH RESOLUTION DATA...................................................................................................30

5.4. A 3 KM VERSION OF HWRF SHOWING SIGNIFICANT SKILL IMPROVEMENT................................................30

5.5. INITIALIZATION OF THE OPERATIONAL HWRF HAS BEEN SUBSTANTIALLY IMPROVED ...............................31

5.6. PHYSICAL PARAMETERIZATIONS IN HWRF .................................................................................................32

5.7. STATISTICAL MODELS ..................................................................................................................................33

5.8. NEW MODEL OUTPUT PRODUCTS ................................................................................................................36

5.9. COAMPS-TC ENSEMBLE FORECASTS ........................................................................................................38

5.10. NEW DIAGNOSTIC VERIFICATION TOOLS.....................................................................................................40

6. LESSONS LEARNED..........................................................................................................................................42

6.1. INITIALIZATION OF THE REGIONAL MODELS REMAINS A MAJOR PROBLEM................................................42

6.2. PHYSICS PACKAGES 3 KM MODELS NEED TO BE IMPROVED .........................................................................42

7. CHALLENGES ....................................................................................................................................................43

7.1. COMPUTER RESOURCES TO RUN THE HFIP OPERATIONAL SYSTEM ...........................................................43

7.2. PHYSICS PACKAGES FOR HIGH RESOLUTION (3KM) REGIONAL MODELS ....................................................43

7.3. BETTER USE SATELLITE DATA ......................................................................................................................44

8. FINAL HFIP NUMERICAL MODEL HURRICANE FORECAST SYSTEM .................................................44

9. CONCLUDING REMARKS ................................................................................................................................45

10. REFERENCES.....................................................................................................................................................50
3

Executive Summary

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

2011. It is organized around three themes, Accomplishments from 2011 HFIP testing, evaluation and 

development activities, lessons learned from negative results and the challenges faced by the 

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

Recent Accomplishments

 The GFS model initialized with an EnKF Data assimilation system is showing significant 

ability to forecast tropical storm genesis out to 5 days or more

 Track forecasting skill with the global GFS/EnKF system at or better than the skill of the 

best global models in the world with 20% improvement for most forecast lead times

 The impact of high resolution data near the hurricane center using aircraft data (including 

the tail Doppler radar data) noted last year using the Penn State ARW model has been 

confirmed using HWRF – 20 to 40% improvement in intensity forecast

 The initialization system for the operational HWRF has been greatly improved

 There have been dramatic improvements in physical parameterizations in HWRF

 A 3 km version of HWRF, to be operational in 2012, is showing significant skill 

improvement over the current operational 9 km version at 84 hours and beyond

 New advancements in the COAMPS-TC data assimilation, initialization and physical 

parameterizations resulted in a greatly improved intensity forecast skill in 2011. 

COAMPS-TC was the only dynamical model to outperform the statistical guidance and 

exceeded the HFIP intensity goal for forecast times at 48 hours and beyond

 Statistical post processing of model output from two different statistical model approaches 

give the best intensity forecasts as compared to current dynamical and statistical models

 New model output products are providing more useful information from the models, 

especially ensembles

 New diagnostic verification tools are providing more useful information for model 

evaluation.

Lessons Learned

 Initialization of the regional models has improved but it still remains a major problem. It 

will need to be solved before we can make much progress on forecasting rapid 

intensification

 Physics packages suitable for models with resolutions of 3 km need to be improved

Challenges

 The biggest challenge to the results from HFIP will be for NOAA to identify the computer 

resources necessary to run the operational system that will be required to meet its goals

 The program needs a focus on developing appropriate physics packages for high resolution 

(3km) regional models

 Find ways to better use satellite data at resolutions appropriate for the hurricane

In the body of the text we discuss each of the above and provide an overview of the program 

and its goals.
4

1. Introduction

The year, 2005, saw record tropical cyclone and hurricane activity in the Atlantic, including two 

major storms that crossed the US coastline, Katrina and Rita. There were a total of 27 named 

storms that year of which 13 were hurricanes and 7 were major hurricanes. Throughout the decade 

2000-2010, 13 hurricanes crossed the US coastline including the notable hurricanes of Charlie 

(2004), Wilma (2005) and Ike (2008), http://www.nhc.noaa.gov/archive/2011/index.shtml.

The heightened awareness of the danger to the US from hurricanes as a result of the record year in 

2005 led to a number of studies on NOAA’s ability to forecast hurricanes. The NOAA Science 

Advisory Board’s (SAB) Hurricane Intensity Research Working Group (HIRWG) filed a report in 

October 2006 on recommendations to improve forecasts of hurricane intensity ; 

http://www.sab.noaa.gov/Reports/HIRWG_final73.pdf. Forecasting skill for hurricane intensity 

has not improved over the last two decades; http://www.nhc.noaa.gov/verification/verify5.shtml

and the report makes recommendations on strategies to improve hurricane intensity forecasts. 

On January 11, 2007 NSF National Science Board issued a report on the need for a National 

Hurricane Research Initiative http://www.nsf.gov/nsb/committees/archive/hurricane/initiative.pdf. 

Then in November 2007 NOAA released its response to the HIRWG report that included a 

proposed Hurricane Forecast Improvement Program:

“In response to the HIRWG report, NOAA convened a corporate hurricane summit developing 

unified strategy to address hurricane forecast improvements. On May 10, the NOAA Executive 

Council (NEC) established the NOAA Hurricane Forecast Improvement Project (HFIP), a 10-year 

effort to accelerate improvements in one to five day forecasts for hurricane track, intensity, storm 

surge and to reduce forecast uncertainty, with an emphasis on rapid intensity change.” 

http://www.sab.noaa.gov/Reports/hirwg/2010/SAB_Nov07_HFIP_Response_to_HIRWG_FINAL.

pdf. 

The Hurricane Forecast Improvement Project (HFIP) was established within NOAA in June of 

2007. In July 2008-July 2009 the President’s Budget was amended to include +$13M for HFIP

and this increment became part of NOAA’s base budget.

The HFIP program, its goals and proposed methods for achieving those goals and recent results 

from the Program are described below and suggest that it is clearly on a path to the meet its goals 

on time. 

2. The Hurricane Forecast Improvement Project

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

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

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

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

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

increased certainty; and to increase confidence in hurricane forecasts. These efforts will require 

major investments in enhanced observational strategies, improved data assimilation, numerical 

model systems, and expanded forecast applications based on the high resolution and ensemblebased numerical prediction systems. 
5

The specific goals of the HFIP are:

 Reduce the average errors of hurricane track and intensity forecasts by 20% within five years 

and 50% in ten years with a forecast period out to 7 days 

 Increase the probability of detecting rapid intensification at day 1 to 90% and 60% at 

day 5

The benefits from HFIP will significantly improve NOAA’s forecast services through improved 

hurricane forecast science and technology. Forecasts of higher accuracy and greater reliability (i.e., 

user confidence) are expected to lead to improved public response, including savings of life and 

property. 

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

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

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

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

run as an ensemble in combination with regional models at even higher resolution (~1-5 km). 

Demonstrating this is very expensive, computationally, and hence HFIP has been building up a 

computational system in Boulder, Colorado where HFIP can demonstrate the techniques necessary 

to meet its goals. Only by demonstrating the value of high resolution and other techniques for 

improving the numerical guidance to hurricane forecasters is there any opportunity to obtain such a 

computational resource for operational hurricane forecasts. 

For FY11, the HFIP program consisted of about $23M with $3M dedicated to enhancing computer 

capacity available to the Program. The funding for computing was used to enhance the HFIP 

system established in Boulder, Colorado in FY2009 and resulted in a machine called t-jet with 

16,000 processors. About $7M of the $23M is part of the base funding for the National Hurricane 

Center (NHC) and Atlantic Ocean and Meteorology Laboratory (AOML) in Miami, the 

Environmental Modeling Center (EMC) at NCEP for hurricane model development and the Earth 

System Research Laboratory (ESRL). The remaining $13M was distributed to the following 1) 

various NOAA laboratories and centers: [Earth System Research Lab (ESRL), Geophysical Fluid 

Dynamics Laboratory (GFDL), National Environment Satellite Data and Information Service 

(NESDIS), and National Hurricane Center (NHC)] 2) the National Center for Atmospheric 

Research (NCAR) 3) the Naval Research Laboratory in Monterey (NRL), and 4) several 

universities: University of Wisconsin, The Pennsylvania State University, Colorado State 

University, Florida State University, University of Wisconsin and University of Rhode Island

(awarded through a NOAA Announcement of Opportunity) and the National Oceanographic 

Partnership Program (NOPP). Specifically, $1M was contributed each year for three years to the

NOPP, Announcement of Opportunity, for competed proposals related to improving understanding 

and prediction of hurricanes. The funding to NOPP from HFIP was matched by funding from the 

Office of Naval Research (ONR). 

Distribution of the $10M was accomplished through recommendations from 9 teams focused on 

various components of the hurricane forecast problem. Current teams are listed in Table 1 

including the co-leaders of each team and the various organizations represented on the teams.

These teams are made up of over 50 members drawn from the hurricane research, development 

and operational community. 
6

Table1. HFIP Development Teams

FY2010 Teams Team Leads and Member’s Organization

1. Global Model/Physics Stan Benjamin (ESRL), John Brown (ESRL), 

AOML, NRL, GFDL, EMC, NRL

2. Regional Model/Physics Morris Bender (GFDL), Young Kwon (EMC), 

AOML, NRL, ESRL URI, Old Dominion Univ, 

NCAR

3. Ensembles Zoltan Toth (ESRL), Carolyn Reynolds (NRL), 

AOML, PSU, EMC, NHC, FSU

4. Data Assimilation/Vortex Initialization Team Jeff Whitaker (ESRL), Bill Lapenta (EMC), AOML, 

NRL, CIRA, PSU

5. Verification Team Tim Marchok (GFDL), Barb Brown (NCAR), NRL, 

NESDIS/STAR, AOML, NHC, EMC, ESRL, 

NWS/OST

6. Applications Development and Diagnostics Ed Rappaport (NHC), Mark DeMaria

(NESDIS/STAR), EMC, NRL, AOML, NCAR, 

ESRL, OU, AOML, FSU, NHC, AOML, 

NWS/OST

7. Hurricane Observations Sim Aberson (AOML), John Knaff 

(NESDIS/STAR), NHC, EMC, NESDIS/STAR, 

ESRL, URI, NRL, AOC, NCAR, RSMAS, NCO, 

NCAR, NWS/OST

8. Ocean/Wave Models Hendrik Tolman (EMC), Halliwell ( AOML), URI, 

ESRL, NRL, RSMAS 

9. Societal Impacts Bill Read (NHC), Jennifer Sprague(NWS/OASST), 

NWS/SR, NWS/ER, FEMA,CT-EM, TX-EM, NCEM, FL-EM, Weather Channel

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

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

paths of development called Streams (Table 2). Stream1 assumes that the computing power 

available for operational hurricane forecast guidance will not exceed what is already planned by 

NOAA. The development for this stream has been in planning for several years by EMC and is 

augmented and expanded to involve the broad community by the HFIP including activities at the 

NOAA labs and centers. 

HFIP Stream 2 does not put any restrictions on the availability of computer power available to 

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

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

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

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

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

perform such a demonstration is large, the Program is developing its own computing system at 

NOAA/ESRL in Boulder Colorado. 

A major part of Stream 2 is an experimental forecast system that is run each hurricane season. The 

purpose of this system is to evaluate strengths and weaknesses of promising new technology. As a 

result of the experimental testing, some components may be found to be of particular interest to the 

operational forecasters, and, if resources do not permit its implementation in the operational 

infrastructure, the Experimental Forecast System for the following season will emphasize those 

components and will provide specific output that is made available to NHC forecasters for 
7

evaluation. We refer to this component of the Experimental Forecast System as Stream 1.5. The 

stream 1.5 candidates undergo a level of testing similar to the testing at EMC for stream 1

operational implementations where the candidate models are run over many storms and many 

seasons. This process is overseen by the Tropical Cyclone Modeling Testbed (TCMT) at NCAR 

and uses a list of storms that have been selected by NHC: 

http://www.ral.ucar.edu/projects/hfip/includes/2011_stream_1.5_test_cases.pdf. The results are 

evaluated by TCMT and final selection for the upcoming hurricane season is made by NHC. 

Table 2 outlines these various streams. Roughly half of the HFIP funding is going toward Stream 

1 development activities. The remaining portion goes toward Stream 2 development and Stream 

1.5 activities.

Table2. The Two Stream Strategy

Stream 1 Development to directly improve the current operational global 

and regional hurricane models. Assumes that the computing that 

will be available for operations is that currently being planned.

Stream 2 Assumes that operational computing can be substantially increased 

above current plans. Uses an HFIP developed computer system to 

test and evaluate new technology. Emphasis is on high resolution 

global and regional models run as ensembles. It will include a 

demonstration system run in real time each summer.

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. Stream 1.5 will be part of the summer 

demonstration system and will be forecaster defined. 

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

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

Testbed Center (DTC) to make available and support HWRF (Hurricane WRF—the operational 

model at EMC) to the community. Code downloads, extensive documentation, and datasets can be 

found at http://www.dtcenter.org/HurrWRF/users. This support started in February 2010 with the 

DTC/EMC/MMM Joint Hurricane Workshop and the WRF for Hurricanes Tutorial. During FY10 

and FY11, EMC and DTC established a common version of the operational codes for HWRF in 

the repository at DTC. As DTC ported the code to various platforms, conducted testing and 

evaluation, and established Reference Configurations (benchmarks of the code that can be used to 

assess future development or to point directions for model improvement), several model issues 

were uncovered and addressed, increasing the quality of the model. This unified version has been 

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

repository in Boulder at the DTC, and rigorous code management protocols implemented by DTC 

and EMC, both the operations and research community are now drawing from the same central 

code repository facilitating the transitions of research results to operations. 
8

3. The HFIP Model Systems

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

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

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

in the 1 to 5 day time range, the best approach is likely high resolution regional models, also run as

an ensemble. The global model ensembles are likely to be limited in resolution to about 15-25 km 

for at least the next 5 years, because of computer limitations. Thus the only way to achieve the 

very high resolution of about 1-3 km necessary for resolving the inner core of the hurricane is with 

regional models. It is generally assumed that the inner core must be resolved before we can expect 

to see consistently accurate hurricane intensity forecasts (HIRWG Report).

3.1. High resolution ensemble approach

A single “deterministic” forecast by a particular numerical model has an inherent but unknown 

level of uncertainty; any two model forecasts starting from infinitesimally different initial states 

will grow differently with time, the amount of difference depending upon the weather situation. If 

the forecast is reproduced many times, each time introducing small initial differences, the result is 

called an ensemble, and the different model forecasts can potentially provide information on the 

confidence one should place in a particular forecast. In fact, a single deterministic model is 

inherently a single member of some ensemble and so would be part of the same uncertainty 

apparent in ensemble predictions. Note that ensembles can be created in several ways including 

changing initial conditions, using several models and altering some component of the model in 

some way such as the physics packages or output from the model’s physics package. Frequently, 

but not always, the highest probability is that the correct forecast is near the mean, median or mode 

of the ensemble, though other ensemble realizations have a finite probability of being correct

(Buizza 1997). Because the various forecasts diverge with time, emergency managers should be 

able to make more effective decisions when provided with ensemble guidance compared to being 

provided with a single forecast. Ensemble predictions can also be used by forecasters to analyze 

reasons for uncertainty and given more recent information available after ensemble initialization, 

make choices on how the forecast is going to go relative to the ensemble. For example if the 

uncertainty in hurricane track is being forced by movement (slow or fast) of a nearby mid latitude 

trough, more recent information on the speed of the trough than that available to the ensemble can 

help to discriminate between the various ensemble members as the more likely. High resolution is 

hypothesized to be necessary in these ensembles in order to adequately resolve the hurricane 

structure (HIRWG Report), for the hurricane can alter the flow in which it is embedded and, in 

turn, this altered flow will impact the hurricane track and so also its intensity. To even begin to get 

structures in a forecast model which resemble actual hurricanes, resolutions of 15-25 km are likely

necessary. Ideally, 20-30 members are computed to provide adequate estimates of the uncertainty 

with each ensemble member having a resolution of 15-25 km.

Beyond about three days, forecast guidance must come from global ensembles since the planetaryscale patterns interact with and influence the steering of the storm. After about three days, it has 

been shown (Reynolds et al. 2009, Hakim et al 2003, Langland et al. 2002, Palmer et al. 1998, 

Rabier et. al. 1996, Hoskins and Ambrizzi 1933, Chang, 1993) that the evolution of the 

atmospheric flow at a given location depends on atmospheric features distributed globally. 

Therefore, forecasts that extend out to 4-7 days require that the forecast models be global. 
9

The potential value of high-resolution global ensembles has been demonstrated in part through 

forecasts from the international community such as the European Centre for Medium-Range 

Weather Forecasts (ECMWF) (Buizza et al. 2005). However, there is still much to be learned 

about high-resolution global modeling. The best way for the U.S. to make progress is to run the 

ensembles over enough cases such that statistical significance of the computed skill of the 

forecasts can be determined. Generally for hurricane forecasts this requires at least that the highresolution ensemble be run over the most active few months of the hurricane season and every 

forecast period from genesis to decay (with 2 to 3 years of cases being even better at capturing the 

full range of tropical cyclone characteristics associated with inter-annual changes in environment, 

e.g., associated with El Nino events). This is an enormous computing challenge but it needs to be 

performed to demonstrate the value of the high-resolution forecast guidance over the guidance that 

is operationally available today. 

Much the same can be said for regional ensembles, but here the emphasis shifts from track 

forecasts at longer forecast lead times, to intensity forecasts at medium forecast lead times. Much 

of the control of the intensity of the storm is thought to reside in the dynamics of the inner core 

region of the hurricane. If this is true, then the inner core must be resolved to account for these 

dynamics requiring a resolution of at least 1-5 km. The regional high-resolution ensembles are 

nested within high-resolution global ensembles, which provide the lateral boundary conditions and 

at least a first guess of initial conditions to the regional model. 

3.2. The Global Models

The descriptions below are for the various individual global model components of the HFIP 

experimental system. Some, as noted later, are run as an ensemble.

The Flow-following finite-volume Icosahedral Model (FIM) is an experimental global model that 

can be run at various resolutions and uses initial conditions from a number of sources. It is 

currently using a fixed ocean underneath. It has been built by the NOAA Earth System Research 

Laboratory (ESRL) http://fim.noaa.gov/fimdocu_rb.pdf (Benjamin et. al. 2004).

There are two versions of the Global Forecast System (GFS) model currently running in the 

demonstration system. This includes a version of the current operational model run at the NOAA 

National Centers for Environmental Prediction (NCEP) and an experimental version of that model 

at ESRL. The main difference between the two versions is the initialization system: GSI for the 

operational model and EnKF for the ESRL version http://www.emc.ncep.noaa.gov/GFS/doc.php.

Currently a semi-Lagrangian version of the Navy Operational Global Atmospheric Prediction 

System (NOGAPS) is being developed, which will allow for efficient high-resolution forecasts 

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA247216&Location=U2&doc=GetTRDoc.pdf.

Some specifics of the global models are shown in Table 3. Note that GFS/GSI and NOGAPS are 

operational global models.
10

Table 3. Specifications of the HFIP Global Models

Models

Horizontal 

resolution

Vertical 

levels

Cumulus 

Parameterization

Microphysics PBL Land Surface Radiation Initialization

FIM 27 km 64 From 2010GFS -

Simplified Arakawa 

Schubert

Ferrier GFS NonLocal PBLNoah LSM GFDL/RRTM ESRL EnKF

GFS/EnKF 27 km 64 Simplified Arakawa 

Schubert

Ferrier GFS NonLocal PBLNoah LSM GFDL

scheme

ESRL EnKF

GFS/GSI 27 km 64 Simplified Arakawa 

Schubert

Ferrier GFS NonLocal PBLNoah LSM GFDL GSI

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

Fu-Liou

NAVDAS-AR

3.3. The Regional Models

The Weather Research and Forecasting (WRF) model is actually a modeling system that is 

comprised of many configurable components. The options for the dynamic core include the 

Advanced Research WRF (ARW) built by NCAR and the Non-hydrostatic Mesoscale Model

(NMM) built by EMC. The ARW system includes an option for a simplified one dimensional 

model of the ocean and two interactive nests within the outer regional model. There are also 

several options for physics as well as initialization, post processing and verification systems 

http://www.dtcenter.org/HurrWRF/users/docs/index.php.

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

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

coarse domain is 27 km resolution and covers a 75° x 75° region with 42 vertical layers. 

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

the NCEP GFS boundary layer and deep convection. An experimental version of HWRF being 

developed by EMC and AOML has a third inner nest with a resolution of 3 km.

The Penn State Regional Ensemble is another configuration of the ARW system. It uses a static 

interactive inner nest but no interactive ocean

http://hfip.psu.edu/realtime/AL2011/forecast_track.html (Zhang et. al. 2011).

The Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS-TC) is the Navy nextgeneration tropical cyclone model developed and run by NRL Monterey (Doyle et al. 2012). It is a 

version of their COAMPS regional prediction system that is being run operationally and has an 

interactive ocean http://www.nrlmry.navy.mil/coamps-web/web/tc. 

The Wisconsin Model was developed by Greg Tripoli of the University of Wisconsin and is being 

run as part of the HFIP Experimental Forecast System

http://cup.aos.wisc.edu/will/HFIP/config.html.

The GFDL Model development is being led by Tim Marchok. It was run as part of the HFIP 

Experimental Forecast System as a regional ensemble prediction system and the basic model is the 

same as the GFDL operational system. See below for description of the GFDL Ensemble. 
11

Specifics of the regional models are shown in Table 4. Note that GFDL (OPS) and HWRF (OPS) 

are the operational regional models.

Table 4: Specifications for the HFIP Regional models 

3.4. Evaluation of the Stream 1.5 Candidates

There are a variety of global, regional, and statistical models being developed as part of the HFIP 

effort. For the 2011 retrospective evaluation, there were eight modeling teams that submitted a 

variety of models and configurations. The evaluation was conducted using storms from the 

Atlantic and Eastern Pacific basins sampled from the 2008-2010 hurricane seasons. TCMT 

evaluated the model configurations and provided results and summary reports to NHC. A 

consistent methodology was applied for each evaluation. A flowchart for the evaluation is shown 

in Figure 1. In summary, if the candidate was a late model, an interpolator was applied to the 

forecasts. All the forecasts and operational baselines (Table 5) were run through the NHC 

Verification software to compute the errors. Afterwards, the forecasts errors were matched to 

provide a homogeneous sample. Pairwise differences were generated and error distribution 

properties were computed. Results were displayed in a variety of graphical summaries and 

statistically significant tables. The NHC decisions based on these analyses are provided in Table 

A.1. in the Appendix.

Models

Nesting /

Horizontal 

Resolution (km)

Vertical 

Levels

core

Cumulus 

Parameterization

MicrophysicsPBL Land Surface Radiation Initial and 

Boundary 

Conditions

Initialization

HWRF (OPS) 2

27/9

42

NMM

Simplified 

Arakawa Schubert

Ferrier GFS NonLocal PBLGFDL Slab Model GFDL

Scheme

GFS Bogus vortex 

plus GSI 

3DVAR

GFDL (OPS) 3

30/15/7.5

42

GFDL

Arakawa Schubert Ferrier GFS NonLocal PBLSlab Model Schwarzkopf-Fels 

Scheme

GFS GFDL 

Synthetic 

Bogus Vortex

HWRFHRD/EMC

27-9-3

3

27/9/3

42

NMM

Simplified 

Arakawa Schubert

Ferrier GFS NonLocal PBLGFDL Slab Model GFDL

Scheme

GFS Bogus vortex 

plus GSI 

3DVAR

HWRF-HRD

27-9

9

27/9

42

NMM

Simplified 

Arakawa Schubert

Ferrier GFS NonLocal PBLGFDL Slab Model GFDL

Scheme

GFS EnKF with 

aircraft data

NCAR ARW 3

12/4/1.3

36

ARW

New Kain Fritsch 

(12 km only)

WSM5 YSU 5-Layer Thermal 

Diffusion soil 

Model

RRTM 

(longwave) / 

Dudhia 

(shortwave)

GFS EnKF method 

in a 6-hour 

cycling mode

COAMPS-TC 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 3D-Var cycling 

data 

assimilation

with synthetic 

observations

Wisconsin ModelUW NMS

(3D enstrophy/

entropy/KE

conserving 

dynamics core)

3-4 

90/45/9 km

90/45/9/3 

km

52 Modified 

Emanuel

Explicit bulk 

microphysics 

(cloud/rain/pri

stine/

aggregate/grau

pel)

1.5 Order Closure WRF 

vegetation/la

nd 

surface/Andr

eas emulsion 

layer

RRTM GFS/GFDL

Penn State ARW 3

40.5/13.5/4.5 for 

ensemble forecast

1.5-km nest for 

control

35

ARW

Grell-Devenyi 

ensemble scheme

(40.5 km only)

WSM 6-class 

graupel 

scheme

YSU 5-layer thermal 

diffusion scheme

RRTM 

(longwave) / 

Dudhia 

(shortwave)

GFS EnKF with 

NOAA 

airborne radar
12

Figure 1: Flowchart showing the steps used in the retrospective evaluation of Stream 1.5 

candidates.

Table 5: Baseline comparisons for the Stream 1.5 evaluation

Operational Baselines Stream 1.5 configuration

Individual: 

Track – GFSI, GHMI, HWFI (HWRF only)

Intensity – LGEM, GHMI, HWFI (HWRF only)

Stream1.5

Variable Consensus -

Track

AL: GFSI, EGRI, GHMI, HWFI, GFNI

EP: GFSI, EGRI, GHMI, HWFI, GFNI,NGPI

Intensity

AL & EP: DSHP, LGEM, GHMI, HWFI

ARW, UM-NMS, COAMPS-TC, FIM:

Consensus + Stream 1.5

HWRF, GFDL, SPICE:

Consensus w/ Stream 1.5 

equivalent replacement

Average error of previous year’s top flight 

models

Track: GFSI, EGRI, GHMI

Intensity: GHMI, DSHP, LGEM

Stream 1.5
13

3.5. Initialization systems

The initial state for the regional models was generally produced by removing the vortex from the 

initial state and then inserting a new vortex often known as a bogus vortex. In addition, 

downscaling directly from a global model is also used for some models. Other models such as the 

Penn State model and the NCAR ARW model use an EnKF initialization system. 

The operational HWRF utilizes an advanced vortex initialization and assimilation cycle consisting 

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

(GFS) onto the operational HWRF model grid 2) remove the GFS vortex from the global analysis

3) add the HWRF vortex modified from the previous cycle’s 6-hour forecast (or use a synthetic 

bogus vortex for cold start) and 4) add satellite radiance and other observation data in the hurricane 

area (9 km inner domain). The major differences from the operational GFDL model initialization 

are steps 3) and 4).

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

described above. The choices included:

1) The current operational model, Global Forecast System (GFS), interpolated to the higher 

resolution grid. The GFS uses the Grid point Statistical Interpolation (GSI) data 

assimilation system that has run operationally for many years. It is a three-dimensional 

variational approach (3D-VAR) http://www.dtcenter.org/com-GSI/users/docs/index.php , 

(Parrish et. al. 2003, Parrish et.al. 2003, Wu et.al. 2002, Parrish, et al. 1992, Cohn and 

Parrish 1991).

2) The NRL Atmospheric Variational Data Assimilation System (NAVDAS) is used to 

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

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

representor), a four-dimensional variational approach (4D-VAR).

3) The Navy regional model, COAMPS-TC, makes use of synthetic observations based on the 

forecaster’s best estimate of the tropical cyclone intensity and structure. The vortex is 

relocated in the background fields and then the synthetic observations are blended with the 

other conventional observations (dropsonde, aircraft, radiosonde, satellite) using the 

NAVDAS 3D-VAR system. One of the unique aspects of COAMPS-TC is that cycling 

data assimilation is used on all the meshes. 

http://www.nrlmry.navy.mil/docs/_NAVDAS01.pdf (Daley and Barker, 2001).

4) The Ensemble Kalman Filter (EnKF) is also 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 et al., 1998, Houtekamer et. al. 

1998). While this approach is still in the experimental stage in the U.S. it has shown 

considerable promise (Hamill et. al., 2011). 

5) The Hybrid Variational-Ensemble Data Assimilation System (HVEDAS) combines aspects 

of the EnKF and 3D- or 4D-VAR using the ensemble of forecasts to estimate the 

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

development at NOAA/NCEP/EMC, NOAA/OAR/ESRL and NOAA/AOML/HRD. It is 

expected to be ready for testing in the 2012 season. This hybrid approach is likely to 

define the operational global data assimilation system for NOAA in the 5-year time-frame.
14

3.6. The HWRF community code repository 

During 2009-2011 both EMC and DTC worked to update the operational version of HWRF from 

version 2.0 to the current version of WRF (version 3.3). This makes every component of the 

HWRF operational model connected with the community codes, allowing researchers access to the 

operational codes and making improvements in HWRF developed by the research community 

easily transferable into operations. This was one of the initial goals of the WRF program and has 

now been fully implemented for the first time with the hurricane models. The process of 

transforming HWRF onto a community code, which involves the porting to various platforms, 

creation of several levels of testing including regression tests and consistency checks, and the use 

of the model by a much larger group, has resulted in a higher quality and more robust code.

Figure 2 shows schematically how the process of moving the operational model into the WRF 

repository was achieved. The version 3.3 codes were carefully compared to the original operations 

code until the two versions gave identical answers. Subsequent changes to the operational code 

will now be entered directly into the unified version.

Figure 2 Schematic of the process for incorporating operational capabilities of the HWRF 

atmospheric component onto the community code (Developmental Testbed Center, personal 

communication 2011). Note that new capabilities for the 2012 operational model are being 

added directly onto the community code, so that merges between code developed in separate 

repositories is no longer need. This process reduces labor costs and human errors.
15

4. Meeting the HFIP goals

4.1. The HFIP Baseline

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

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

Performance Goals Baselines were developed by James Franklin and others at the National 

Hurricane Center (NHC) and are provided in Table 6 (track performance) and Table 7 (intensity 

performance). The CONS is a consensus of operational models and is the primary baseline for 

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

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

baseline period.

Table 6. HFIP Track Performance Baseline (nautical miles)

VT 

(h)

N OFCL OCD5 CONS

0 818 7.4 7.7 7.8

12 741 29.4 44.5 30.0

24 663 49.6 93.3 49.8

36 586 69.9 150.9 69.5

48 518 91.2 212.2 89.6

72 411 135.0 317.2 132.0

96 313 173.0 396.5 175.2

120 247 218.6 473.0 221.9

Table 7. HFIP Intensity Performance Baseline (knots)

4.2. Meeting the Track goals

Earlier we noted that forecasts beyond a few days become a global problem meaning the influence 

on a forecast for a particular location is coming from systems far removed from the local forecast 

region. This is of course an important consideration for hurricane track prediction out to 7 days, a 

goal of HFIP. We will note below that global models generally outperform the regional models on 

track at all lead-times. Thus the HFIP strategy for meeting the track goals is to improve the global

models and run them as an ensemble at as high a resolution as possible. The regional models 

(which can resolve inner core processes) will focus on intensity forecasts. Generally, especially 

VT 

(h)

N OFCL OCD5 CONS

0 820 1.9 2.2 2.2

12 745 7.2 8.3 7.7

24 667 10.4 11.5 10.1

36 590 12.6 14.2 11.7

48 522 14.6 16.1 13.7

72 415 17.0 17.8 16.0

96 316 17.5 19.3 16.6

120 250 19.0 19.3 17.0
16

when run as an ensemble, the global models will not have sufficient resolution to resolve inner 

core structures and consequently will have a low bias in intensity anyway. Though this low bias 

can be removed from the forecasts, the global models still won’t resolve the inner core and so can 

only indicate trends in intensity due to interactions with the larger scales in the atmosphere and 

ocean. 

Perhaps it is not surprising that global models are more skillful with track than the regional models

since track is largely a result of large scale processes which the global models can resolve and 

which the regional models must take from the global models. Improving the global models is 

expected to lead to improved intensity forecasts in the regional models since intensity often 

depends on the track of the storm (Emanuel et al. 2004).

The influence track has on intensity is especially obvious when comparing the storm tracks 

interaction with or without land. However, even when the tracks remain well away from land this 

can be true. An example from one of the HFIP regional models (Penn State ARW in Table 4) 

(Zhang et al. 2011, Weng and Zhang 2011, Snyder and Zhang 2003) is shown in Figure 3. At the 

midpoint of the forecast period the various tracks from a 60 member ensemble are divided into 

three categories (the third on the left of the envelope denoted orange), the third in the middle 

(green) and the third on the right (blue) based on the forecast storm positions at 18Z 7 September. 

This same color coding is used to identify the various intensity plots from the ensemble members 

displayed in a spaghetti plot. Note that the simulated storms on the left (south) side of the track are 

consistently stronger than those on the right. The observed track was toward the south side of the 

envelope and indeed the observed winds were consistent with the higher intensity for tracks in the 

southern third.

FIG. 3. Track (left panel) and intensity (right panel) forecasts from a 60 member Penn State ARW 

regional ensemble for Typhoon Morokot from 00Z 5 through 00Z 9 September 2010 in the 

Western Pacific. The black curve is the Best Track (analysis of Japan Meteorology Administration 

based on observations). The heavy orange line is a single deterministic run initialized with GFSEnKF analysis at 00Z 05 September 2010. The orange curves signify the 1/3 ensemble members of 

the storm (20 members) that were located on the left side of the Best Track at 18Z 07 September 

2010, green curves show other 1/3 members near the Best Track and blue curves denote the other 

1/3 members located at the right of the Best Track at 18Z 07 September 2010 (F. Zhang 2010, 

personal communication).
17

One of the first efforts in HFIP was to improve the global models over what is currently being run 

operationally by NCEP since the global models generally provide better track forecasts. It was 

shown early in the program that using a more advanced data assimilation scheme than the one 

currently in use at NCEP (the operational Grid Statistical Interpolation (GSI) method) in just 

about any global model, including the current Global Forecast System (GFS), improved forecasts 

particularly in the tropics. Figure 4 compares forecasts of tropical winds at 250 mb in the GFS and 

FIM (see Table 3) using GSI with GFS and FIM using an Ensemble Kalman Filter (EnKF) 

approach.

Figure 4. Verification statistics for forecasts run from initial conditions specified by the GSI 

operational data assimilation system, the EnKF experimental data assimilation system and the 

experimental hybrid data assimilation system. The statistics are for a 72 h forecast for the deep 

layer mean wind (850, 700, 500,400, 300, 200 hPa) in the tropics for the Global Forecast 

System (GFS) operational model run at 30 km using the various assimilation systems for 

initialization. The legend on the lower right of the figure indicates the initialization system used 

for each curve. The number in parentheses in the legend shows the average the RMS vector 

wind error for each configuration. Date is indicated along the horizontal axis using yymmdd at 

0000 UTC. (Jeff Whitaker and Daryl Kleist, personal communication).

Note the GFS performs much better using the EnKF data assimilation approach as compared to 

GSI. All HFIP global models now use the EnKF system and most of the regional models will 

eventually switch over. Based on comparisons such as that in Figure 4, NCEP has embarked on a 

planned replacement of the current GSI data assimilation system with a hybrid system. This 

system combines the EnKF approach with the GSI approach and results for that system are shown 

in blue. The combined (hybrid system) clearly performs even better than the EnKF system alone.
18

We noted above that ensembles on average produce more skillful forecasts than any individual 

component of the ensemble run at the same resolution. Since one can consider a single 

deterministic run as just one component of some virtual ensemble, that deterministic run could be 

any member of that ensemble and so likely to have a higher average error than if the virtual 

ensemble had actually been run. Of course, single deterministic runs can outperform any ensemble 

if they use a superior model. 

Figure 5 shows the track errors for various regional and global models including the operational 

model (GFS GSI—AVNO) and the ECMWF high resolution deterministic model for the 2010 

hurricane season. Those with shades of purple are the regional models and the others are the 

global models. The official forecast errors are shown in orange. The green bar is the HFIP global

ensemble using the GFS global model (the same as the operational NCEP operational model) but 

using the EnKF system to initialize. Note that the ensemble was run at low resolution, T254, as 

compared to the operational, GFS T574, and the ECMWF T1299. The error statistics are shown 

relative to the HFIP baseline (Table 6) which is why the official forecasts are close to the zero line 

(the baseline was defined from the official model guidance in available in 2009). Anything below 

is worse than the baseline and anything above is better. 

Figure 5 Average track errors for various models in the Atlantic. All storms in 2010-2011 are 

included; a total of 36 storms. The number of cases at each forecast lead time is shown across 

the bottom of the figure. HFIP 20% goal refers to the 5 year goal of reducing track errors by 

20% relative to the HFIP baseline defined earlier( Mike Fiorino personal communication).

Note that even though the GFS/EnKF ensemble is the lowest resolution of any of the models 

shown, it still out performed all others in the figure including the very high resolution ECMWF for 

hurricane track. In fact the GFS ensemble reached the HFIP 5 year goal of a 20% improvement 

# of cases/forecast lead time (hr) % Improvement in Track 
19

over the baseline for most forecast lead times. Also the track forecasts from the regional models 

shown here are below the baseline which is typical of all the regional models.

The superior performance of the GFS with EnKF ensemble has to be due to the data assimilation 

system since the model is exactly the same as the operational GFS/GSI. The ensemble is run at 

lower resolution than the GFS/GSI (T574) so resolution isn’t the reason.

4.3. Reaching the Intensity Goals

HFIP expects that its intensity goals will be achieved through the use of high resolution regional 

models that can have resolution near the core of at least 3 km. This will allow those inner core 

processes that control intensity to be resolved. In addition, early results suggest that results from 

individual HFIP models can be used in statistical models to further increase the skill of the 

intensity forecasts.

In 2011, the suite of regional models that went into the HFIP regional ensemble are listed in Table 

4 and all were part of Stream 1.5. In addition GFDL, NCAR ARW, COAMPS-TC and the Penn 

State ARW were run as individual ensembles. The GFDL ensemble was constructed from the 

operational GFDL model using NHC recommendations for varying the initial conditions and 

convective parameterization. The GFDL ensemble also served as a way to test sensitivities of a 

regional model to various storm parameters provided by NHC to describe the storm. Figure 6 lists 

the various members of the GFDL ensemble. The other two models vary initial conditions to 

construct the ensemble.

Figure 6. Seventeen member GFDL ensemble: 16 perturbed members and a control forecast. 

ROCI stands for radius of outermost closed isobar. An unbogussed forecast means that the 

initial condition used the raw vortex from the GFS global operational model and not the GFDL 

model initialization process.
20

In Figure 7, we show results from the 2011 hurricane season for the various models that are

operational or are part of the HFIP stream 1.5. It includes all cases through the middle of 

September 2011 and the comparison is homogeneous. The two black lines in the diagram are for 

the official forecast (upper line) and for the HFIP baseline (lower line) that was described above. 

Note that all the lines above the official forecast are for the current operational models running at 

7-9 km resolution (GFDL and HWRF including the GFDL ensemble which is a variation of the

operational GFDL). The lines below the official forecast include the operational statistical models 

(DSHP = Decay Ships--Statistical Hurricane Intensity Prediction System, LGEM = Logistic Growth 

Equation Model) and the experimental stream 1.5 consensus statistical model, SPC3, that uses both 

DSHIP and LGEM using parameters defined from GFDL, HWRF and the operational GFS (see

section 4.4). Solid lines are various HFIP stream 1.5 dynamical models. Note that for the cases in 

Figure 6, COAMPS-TC and the SPC3 statistical model showed a 10-20% improvement over the 

HFIP baseline after about 36 hours into the forecast. Of the Stream 1.5 models, COAMPS-TC and 

SPC3 performed better than the current operational models. COAMPS-TC (COTI) was the only 

dynamical model to outperform the statistical guidance at 24 h and beyond. COAMPS-TC has 

intensity forecast skill that exceeds the HFIP baseline at forecast times of 48 h and beyond (by 

more than 20%). All the models still have start up issues and it is taking about 12-24 h hours for 

the rapid error growth to decrease. 

Figure 7. Intensity statistics from the Stream 1.5 models through mid-September. It is a 

homogeneous sample and shows absolute error. The dashed lines indicate statistical models 

and solid the dynamical model (from James Franklin, NHC). The various models are: OFCL = 

official forecast, DSHP = Decay Ships--Statistical Hurricane Intensity Prediction System, LGEM = 

Logistic Growth Equation Model, GHMI = operational GFDL model, HWFI = operational HWRF, 

SPC3 = Intensity consensus: 6 DSHP and LGEM with predictors from GFS/GFDL/HWRF, GPQI = 

GFDL Ensemble mean, G1Q1 = GFDL Ensemble member 1 (no bogus vortex), AHQI = NCAR Model, 

COQI = COAMPS TC and GFS initial conditions, UWQI = Univ of Wisc. Model

HFIP Baseline
21

It was demonstrated by Fuqing Zhang and his group using the Penn State configuration of the 

ARW model that data collected by the NOAA P3 tail Doppler radar data in hurricanes can 

significantly improve forecasts of intensity over forecasts when the tail Doppler radar data are not 

included (Figure 8). The EnKF data assimilation systems was used with the PSU model for this 

study.

Figure 8. Impact of P3 tail Doppler radar data on intensity statistics from the PSU ARW using 

an EnKF data assimilation system. All cases with radar data from 2004-2010 are included. 

Data are personal communication from Fuqing Zhang (PSU) 2011.

The radar data from the PSU study (above) was verified by the group at HRD (Aksoy et. al. 2012) 

using the HWRF model and HEDAS data assimilation. The results compared to the HFIP baseline

are shown in Figure 9. In the figure all cases for which radar was taken in storms from 2008-2010

are included. 

Note that for all forecast lead times beyond 36 hours inclusion of radar data improves the forecasts 

by 20% and as much as 40% for these cases. It is likely that this result is because the high 

resolution data near the core better defines the environmental flow within the hurricane and hence 

the impact out to 5 days. It avoids the use of filtering and bogusing to create initial conditions that 

are used in most regional hurricane models. Note also there is a problem in the early part of the 

forecast where the model is still adjusting to the initial conditions. Fixing this initial problem is a 

major current focus of the HFIP Program.
22

Figure 9. Impact of P3 tail Doppler radar data on intensity statistics from the HRD/EMC 27/9/3

HWRF using HEDAS and the PSU ARW using an EnKF data assimilation system. All cases 

with radar data from 2008-2010 are included. It is a homogeneous sample and shows error 

relative to the HFIP baseline (posivive means above the HFIP baseline). Blue bars are for the 

PSU Model and red for HWRF. Number of cases available at each forecast lead time shown 

across the top. Data are personal communication from Sim Aberson (HRD) and Fuqing Zhang 

(PSU) 2011.

The above results suggest that the newer regional models (COAMPS-TC, HWRF and the Penn 

State model) may provide perhaps a 10% improvement in intensity as compared to the HFIP 

baseline and adding the tail Doppler radar and other aircraft data, when available, may add an 

additional 20%-40%. This would meet the HFIP goal of 20% for those storms for which tail 

Doppler radar data is collected. For other storms HFIP is pursuing the use of high resolution 

satellite data near the hurricane. However, it should be noted for the small homogeneous sample, 

the models that assimilated radar observations did not have errors as low as COAMPS-TC, which 

did not assimilate radar data.

4.4. Statistical Post Processing of Model Output

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

We have already stated in section 4.3 that the statistical models (DSHIP, LGEM and SPC3)

performed among the best as predictors of hurricane intensity. In fact, SPC3 provides comparable 

improvement as the radar data described above when compared to the operational statistical 

models. SPC3 (Figure 7) is an “ensemble” of six members created by using HWRF, GFDL and 

GFS operational models each with DSHIPS and LGEM. Figure 10 is further indication of the 

impact of statistical models and shows a comparison between the current operational HWRF and 

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

12 24 36 48 60 72 84 96 108 120

All cases with Doppler data 2008-2011

HFIP 5 

year goal

AOML %Improvement in Intensity 

PSU

Forecast Lead Time (hr)
23

GFDL models by themselves and a combination of DSHIP/LGEM using parameters determined

from each of those two operational models.

Note that for both the GFDL and HWRF statistical models, using parameters from the respective 

operational model, gave an improvement of up to 20% over the parent operational model.

Figure 10. The impact of including the operational regional models (HWRF, GFDL) in the 

SHIPS statistical intensity model. All cases for 2008-2010 are included (Mark DeMaria, 

personal communication) 

It is likely statistical post processing along with model improvements, the use of ensembles, 

improved data assimilation and inclusion of more data such as aircraft and satellite data promises 

to be the key to meeting the HFIP goals. 

4.5. Reaching Rapid Intensification Goals

A major goal of HFIP is to improve the detection of rapid intensification (RI) and weakening 

decay (RW) with a high probability of detection and a low false alarm rate. At this point none of 

the HFIP dynamical models are capable of providing reliable forecasts of RI reliably in the first 36 

hours. The global models are not able to resolve the inner core processes that are likely to be very 

important in the RI process and all the regional models have serious spin up (and spin down) 

problems (Figures 7 and 9). In some cases during the spin up (or spin down) period, intensity 

changes can meet or exceed the RI limits (30 mb in 12 hours). Occasionally, one or more of the 

HFIP regional models can do very well capturing RI (Figure 11 for example) but that figure is the 

exception rather than the rule. The spin up (spin down) problem is related to model initialization 

that has to be resolved before progress can be achieved toward the RI goal, especially in the most 

important first 36 hours.
24

Figure 11. Wind speed forecasts from various models for hurricane Karl, 2010. The observed 

wind is shown as the black line. MME is the mean of HWRF, GFDL, and Experimental 

HWRF(Gopalakrishnan-personal communication)

The solution is likely to come from using data assimilation systems rather than some form of a 

bogus vortex initialization that is employed by most regional hurricane models. Additionally, 

advancements in physical parameterizations, particularly the microphysics and hurricane boundary 

layer representations, are needed. These issues are a current focus of the HFIP program. 

5. Recent Accomplishments

In this and the following two sections we will outline and describe results from the summer 2011 

demonstration program that indicate promising results beyond what is outlined in the previous 

sections, some lessons learned and remaining challenges.

Recent results include:

 The GFS model initialized with an EnKF Data assimilation system is showing significant 

ability to forecast tropical storm genesis out to 5 days or more

OBS Experimental HWRF

Operational HWRF

Operational GFDL

Experimental GFDL

Ensemble Mean

RI
25

 Track forecasting skill with the global GFS/EnKF system at or better than the skill of the 

best global models in the world with 20% improvement for most forecast lead times

 The impact of high resolution data near the hurricane center using aircraft data (including 

the tail Doppler radar data) noted last year using the Penn State ARW model has been 

confirmed using HWRF – 20 to 40% improvement in intensity forecast

 The initialization system for the operational HWRF has been greatly improved

 There have been dramatic improvements in physical parameterizations in HWRF

 A 3 km version of HWRF, to be operational in 2012, is showing significant skill 

improvement over the current operational 9 km version at 84 hours and beyond

 New advancements in the COAMPS-TC data assimilation, initialization and physical 

parameterizations resulted in a greatly improved intensity forecast skill in 2011. 

COAMPS-TC was the only dynamical model to outperform the statistical guidance and 

exceeded the HFIP intensity goal for forecast times at 48 hours and beyond

 Statistical post processing of model output from two different statistical model approaches 

give the best intensity forecasts as compared to current dynamical and statistical models

 New model output products are providing more useful information from the models, 

especially ensembles

 New diagnostic verification tools are providing more useful information for model 

evaluation.

5.1. Forecasting Tropical Storm Genesis Out to 5 Days or More.

The GFS/EnKF global system that HFIP ran during the 2011 demo season showed a remarkable 

ability to forecast hurricane at least out to 5 days and perhaps to 7. We have just completed a tracker 

system (by Tim Marchok at GFDL) so statistical confirmation of this result will be forthcoming but 

we present below some subjective evidence of genesis skill. We do note here that at least some of 

the global models besides the GFS/EnKF system show similar skill, particularly the ECMWF and 

the operational GFS (an example is shown later). The subjective evaluation of genesis skill for both 

the West Pacific and the Atlantic in 2011 suggests a high Probability of Detection (POD) and a low 

False Alarm Rate (FAR). 

The EnKF global system is outlined in Table 3 and some skill results are shown in Figures 4 and 5. 

It was run as a 20 member ensemble at a resolution of T254 or about 60km. Figure 12 shows a 

forecast starting at 1200Z 18 August, 2011 for hurricane Irene using the GFS/EnKF ensemble. 

The hurricane was named 0000Z on August 24 or 3 and a half days after the start of this forecast. 

Shown are the various ellipses of position determined by the ensemble that contain 80% of the 

members. The ellipses that elongate along the track indicate a predominant speed uncertainty and 

ellipses that elongate perpendicular to the track indicate a predominant position uncertainty left or 

right. The green track shown in the figure is the best track from NHC after the storm was named. 

The forecast was excellent though slightly to the left of the actual track after 7 days. The forecast 

was begun when NHC initially indicated that the wave that was to become Irene was listed as an 

investigation area for possible development.
26

Figure 12. Seven day ensemble forecast made by the GFS/EnKF global system starting 1200Z

August 18, 2011. This was a 22 member ensemble (including the ops GFS and a control version 

of the GFS/EnKF) and various details of the plot are noted on the figure.

Figure 13 shows two “postage stamp” presentations of forecasts from the various members of the 

GSF/EnKF ensemble. The top panel was initialized at 0600Z on August 16, two days before the 

wave was listed as an investigation area. By visually counting those members that were indicating 

a tropical storm after 7 days (17 out of 22) one could say that genesis of a tropical storm after a 

week was 77% based on the ensemble forecast and very near its observed position (between days 4 

and 5 on Figure 12). This forecast was two days before the wave that became Irene was identified 

as an investigation area and 5 days before it was named.

The second panel in Figure 13 shows the “postage stamps” from the forecast initiated at 1200Z 18 

August, 2011 (Figure 12). Again, by counting members with storms, all but 1 (95%) forecast a 

storm and half indicated that the storm would be strong (50%). At this time Irene was a hurricane
27

Figure 13. “Postage stamps” depicting surface pressure (mb) from each GFS/EnKF ensemble 

member at day 7 of the forecast. See text for further discussion. Upper panel for forecast 

initialized at 0600Z August 16, 2011, and lower panel initialized at 1200Z August 18, 2011.

This is just one example, for Irene, but we observed similar skill for most of the other storms in the 

Atlantic and West Pacific this season with the exception of a couple of storms that were very small 
28

when they formed (such as Jose). Remember that the resolution of the GFS/EnKF ensemble was 

about 60 km which cannot resolve storms that are essentially growing from a single thunderstorm. 

Figure 14 is a hypothetical product that could be generated from information from a global 

ensemble forecast. It is for August 16 near the time that the forecast in the upper panel of Figure 

13 was initialized. It is the official Graphical Tropical Weather Outlook generated by NHC 

several times a day. Figure 14 shows tropical storm Gert and a yellow ellipse that indicated an 

investigation area (an area where NHC was watching for tropical cyclone development). That area 

eventually became Harvey. The white ellipse off Africa is the wave that became Irene five days 

before Irene was named. The white circle is the area where Irene was expected to form from the 

forecast initiated near the time of Figure 12. This kind of ensemble information could easily be 

shown to forecasters and we show a real example in section 5.8.

Figure 14. Graphical Tropical Weather Outlook form NHC, 16 August, 2011. Hypothetical 

product for genesis shown by white ellipses. See figure for other notation.
29

5.2. Track forecasting skill

In section 4.2 we discussed the skill in the HFIP GFS global model in track forecasting. The skill 

seems to be coming from the EnKF DA assimilation system since the skill is considerably higher 

than the corresponding model, the operational GFS that uses the GSI data assimilation system. It 

has approximately the same skill as the ECMWF model which is widely viewed as the best model 

for hurricane track in the world. Note the track skill is near or exceeding the 20% improvement 

relative to the HFIP baseline.

Figure 15 shows additional evidence of this skill by comparing the GFS/EnKF system to four 

operational global ensembles including those for NCEP, ECMWF, CMC, and UKMO. The HFIP 

GFS/EnKF system provides more accurate track predictions as compared to all centers except 

ECMWF (which uses a 4DVAR DA system thought to be comparable to EnKF) where the two 

systems have similar skill.

Figure 15. Comparison of track forecasts from various operational center ensembles (NCEP, 

ECMWF, UKMO and CMC) blue curves and the GFS/EnKF system run by HFIP (red). Solid 

lines are for RMSE and dashed lines are for ensemble spread. Plots are homogeneous and 

error bars are shown for the operational models. Number of cases is indicated in parentheses.

Based on these results HFIP expects to meet its 5 year 20% goal by using more advanced data 

assimilation systems than GSI (or 3DVAR). This goal can be met using already existing global 

models at NCEP by changing to an EnKF or Hybrid (combined GSI and EnKF) system. The GFS 
30

system at NCEP when run with high resolution and a hybrid data assimilation system is more than 

adequate to meeting HFIP track goals

5.3. The Impact of High Resolution Data 

Last year we reported for the first time incorporation of tail Doppler radar data taken by the NOAA 

P3s provided dramatic improvement in forecasting hurricane intensity out to 5 days though with an 

adjustment period for the first day or so of the forecast where the skill was lower. This was

illustrated in Figure 9. The results for last year were from the PSU group using the WRF ARW 

model. That result was again confirmed by them using the storms from this year and in addition 

the PSU results were confirmed by results from AOML, blue bars in Figure 9. Red bars are the 

PSU result. Both show very similar results even though the two models are different (HWRF used 

by AOML and ARW used by PSU) and each used different versions of the EnKF data assimilation 

system

In addition, AOML used not only the tail Doppler radar data but all other data transmitted from the 

aircraft in real-time (including flight level, dropsonde, SMFR surface wind data) as well as 

dropsonde and flight level data collected by the Air Force. They tried experiments with all aircraft 

data including the radar data and all aircraft data excluding the radar data. Both gave similar 

results with strong improvement over not using any aircraft data. A 10% additional improvement 

was seen using radar data with other aircraft data as compared to all data excluding radar. This 

suggests that it is not only the radar but also the high spatial resolution data near the center that is 

making the difference. The radar improves on the other data because it is of higher spatial 

resolution.

It was somewhat of a surprise that the impact of the inner core data appears to last out to 5 days 

and perhaps beyond. Many would have expected that the impact of this data to have decayed with 

the predictability timescales of the convective processes in the core. However it is likely that, in 

addition to initializing at least some of the convective scales near the core, the data near the core is 

also better specifying the larger scale components that is part of the environment in the region near 

the hurricane and which have longer predictability than convective process and which determine

the evolution of the storm in structure and intensity as well as it’s track. 

We note here that these results stress the need to use initialization schemes that can better define 

the storm environment. Initialization schemes that use some sort of bogus initial vortex probably 

don’t do this well if at all. In addition it also stresses the need to find better ways to use available 

satellite information at higher resolution near the hurricane for those storms when there is no 

additional aircraft data. 

One limitation regarding using data from various sources in GSI is that the data has to be in 

PrepBUFR format. To facilitate the community in converting data to PrepBUFR format, HFIP and 

DTC have made available a website with extensive documentation, examples and tools.

5.4. A 3 km Version of HWRF Showing Significant Skill Improvement 

During the 2011 HFIP demo project, a parallel model to the operational HWRF was run. The 

operational model had two domains with grid resolutions of 27km and 9km respectively. The 

parallel model had three domains resolutions of 27km, 9km and 3km respectively. Figure 16

shows the error statistics from retrospective runs from 2008-2010 and compares the operational
31

HWRF, and GFDL and the SHIFOR5 statistical models to the 3 km HWRF. Note that the 3 km 

model was very similar to the operational HWRF but produced better intensity forecasts out to 84

hours as compared to GFDL and HWRF. Beyond 84 hours the performance was substantially 

better than the operational model and about the same as the GFDL operational model. The triply 

nested HWRF will be made operational for the 2012 hurricane season.

Figure 16. Absolute intensity errors for the 2008-2010 hurricane seasons for the 3 km HWRF 

(H3GP), operational HWRF (HWRF), operational GFDL model (GFDL) and SHIFOR5 

(SHF5) statistical model. The sample is homogeneous and the number of cases is outlines in 

parentheses. 

5.5. Initialization of the operational HWRF has been substantially 

improved

In past years we have noted that there are major problems with the initialization of all the 

dynamical models including the operational HWRF. One of the most dramatic problem was a 

tendency for HWRF to spin up weak storms and greatly spin down strong storms. This is 

illustrated in Figure 17 by the red bars which show the wind speed bias after 60 hours as a function 

of starting initial intensity. Note that the initialization scheme adjusts the initial intensity to match 

the initial observed intensity so the initial intensity bias is zero. Thus an increase by 60 hours 

represents a spin up and a decrease represents a spin down.

During this last year, the EMC HWRF team spent considerable effort on improving the 

initialization of the model including adding size parameters (from the NHC observations) in 

constructing the initial vortex. The result of this effort is shown in Figure 17 by the blue bars. 
32

Spin up vs. spin down has the same meaning as for the red bars but except for the very strongest 

storms (where the sample size is small) the sign of the blue bars is basically randomly distributed 

over the various storm strengths. The bias in the storms is greatly reduced over the 2010 version 

of the HWRF operational model.

Figure 17. Forecast wind speed bias after 60 hours for the HWRF operational models in 2010 

and 2011 as a function of initial wind speed.

5.6. Physical parameterizations in HWRF

An obvious statement is that there are three ways to improve models: better initialization, better 

model core and better model physics. It is generally felt in HFIP that improvement in the model 

core (the finite differencing, model grid etc.) gives only marginally improved skill but initialization 

(like discussed in the earlier sections) and model physics can make big differences.

The HWRF model team has also made various changes in the HWRF physics package and some of 

those changes are illustrated in Figure 18. The changes shown in the upper panels of the figure are 

related to the surface heat and momentum flux coefficients. The gray dots are that the model 

computed for various wind speeds and the color lines or dots are observed values. The model is 

now much closer to the observed values than in the past. The lower panels show the mixing 

coefficients in the PBL from the original GFS physics package used previously in HWRF and the 
33

modified scheme used this last summer in HWRF. Note the closer comparison with the observed 

values (colored).

Figure 18. Comparison of Cd (drag coefficient), Ck (surface heat flux coefficient) and Km 

(momentum mixing in the boundary layer) used in the high resolution HWRF 27-9-3. Gray 

dots are computed values from the model and color dots or lines show various observation data 

sets.

5.7. Statistical Models

In previous reports we demonstrated the skill of model output can be further improved using 

statistical post processing. Figures 19 and 20 give two separate examples of this.

Figure 19 compares skill of various models compared to SHIFOR5, a standard used by NHC for 

guidance comparison. GFDL and HWRF operational dynamical models are shown. Also shown 

is the Intensity Consensus (ICON) prediction. Of interest here is the SPC3 (Statistical Prediction 

of Intensity with a Consensus Ensemble (SPICE), 3 parent model version) an HFIP experimental 

statistical model. This is a combination of 6 different models, SHIPS using results from the 

operational GFDL, HWRF and GFS dynamical models and LGEM using output from the same 

three dynamical models giving an ensemble of 6 which is then averaged. In this diagram higher 

numbers are better. Beyond 36 hours the SPC3 exceeds the skill of all the guidance systems 

shown, including what is considered the best of the guidance, LGEM, especially at the longer lead 

times. The SPC3 system was an attempt to see if using model output data in the statistical models 

would give an improvement and that clearly is the case for intensity at the longer lead times. The 

goal for next year is to use additional models from the HFIP stream 1.5 models in the statistical 
34

models. Since those models have been better than HWRF and GFDL it is expected we will see an 

even greater increase in skill than is shown in Figure 19.

Figure 19. Intensity skill of various models relative to SHIFOR5. SHIFOR5 is an operational 

statistical and improvements relative to that model show increased skill relative to that model. 

Higher numbers are better. This type of comparison takes into account the relative difficulty of 

a particular forecast since that measure is contained in the SHIFOR5 statistics. SPC# is the 

HFIP Spice model that uses both DSHIPS and LGEM each with input parameters from the 

operational GFDL, HWRF and GFS for a total of 6 different statistical estimates. These are 

then averaged for the SPC3 prediction. ICON is the intensity consensus that NHC produces 

from those models it has available in real-time, LGEM and DSHP (Decay Ships) are two 

operational statistical models, HWFI and GHMI are the operational HWRF and GFDL models 

respectively.

Figure 20 shows another example of statistical post processing of model output. The system 

shown in Figure 20 was also shown last year and this year it includes even more models including 

both regional and global models. It is the FSU Multi-model Ensemble, the various models are 

indicated in the figure and both track and intensity are shown. The sample isn’t homogeneous but 

includes most cases from 2011. EM is the mean of the ensemble of all the models shown; in other 

words an equally weighted mean. MMEN is a weighted mean of the various model members 

(similar to the Correlation Based Consensus discussed last year) where the weights are determined 

based on historical performance of the various models. Like the SPC3 system shown in Figure 19, 

the multi model ensemble (MMEN) is the best performer of all the models shown in Figure 20 and 

is better than the HFIP baseline for Intensity at the longer lead times, up to 30% better.
35

These results from statistical post processing of the model emphasize that the best strategy to meet 

the HFIP goals is to use this technique to enhance the skill of the dynamical models that are the 

core of the HFIP strategy. It is expected that the skill of the post processing is dependent on 

individual model components that go into the statistical process. Hence, the HFIP strategy is to 

emphasize improvement in the dynamical models, both global and regional, along with 

development of the best strategies for post processing. In this section we have shown two different 

post processing strategies. This also emphasizes the value of ensembles in enhancing the skill of 

any component in the forecast guidance process.

Figure 20. Non homogeneous comparison of mean track (upper figure) and RMS intensity 

errors from various models compared to the weighted ensemble mean of all the models 

(MMEN—red bar). The various models are: AVNO—operational global model, NGPS—Navy 

global model, ECMW—ECMWF global model, FIMY—experimental FIM global model, 

AHW—NCAR hurricane regional model, COTC—Navy regional hurricane model, H3GP—

Experimental 3km HWRF, HWRF—operational HWRF, EM is the ensemble mean and 

baseline is the HFIP baseline.
36

5.8. New Model Output Products

It is not sufficient to simply increase the skill of the dynamical model and statistical model 

guidance but also to improve methods to display the information coming out of these systems to 

the hurricane forecasters. Ultimately it is the human forecasters who turn the guidance from all the 

data sources including various models described above into a forecast of the hurricane. The need 

for new ways to display the model output is especially critical for output from ensemble systems. 

In Figures 21-23, we display a few of the products that are being developed and used by the HFIP 

community.

Figure 21 shows a simulated water vapor image derived from the operational HWRF in the left 

hand panel. The right hand panel shows the observed image. This type of product is not new in 

forecasting but has just been implemented in the last year or so in the hurricane forecast system. 

The value of this type of product is both for forecasting and analysis of the model. For forecasting, 

it can reveal how the model is evolving the larger scale fields that influence the hurricane and 

hence whether the forecaster can put trust in the model results since it can be compared to the 

observed images that may have been taken after the model was initialized. It also can also be used 

to assess the initialization and spin-up processes in the model simulations. For example, in the left 

panel of the Figure 21, there are very few cold cloud tops near the center of synthetic image 

compared to the observed image in the right panel. These cold cloud tops appear in the synthetic 

images a few hours into the model integration. 

It can also be used for model diagnostic. For example it clearly revealed the evolution of a dry 

bias in earlier versions of HWRF.

Figure 21. Synthetic satellite water vapor (channel 3) image from the operational HWRF, left 

panel. The right panel shows the observed water vapor (channel 3) image.

Figure 22 provides a different way to display information from an ensemble. The upper part of the 

figure shows an example from the GFS/EnKF ensemble and depicts the cumulative probability of 

storm force winds for the storms/hurricanes being tracked by NHC. This example is for Ophelia 

and Philippe. For comparison the equivalent operational product from NHC is shown in the 
37

bottom part of the figure. In comparing these figures note that the color scales are very different 

and that the map backgrounds are also different. The probabilities in the current operational 

product are derived from climatology and hence show a wider distribution, lower probabilities and 

a probability that decreases with time along the storm track that may not be due to weakening of 

the storm. It drops because of reduced confidence with time. The probabilities in the upper panel 

are derived from the individual ensemble members so if all members at some point in the forecast 

period have storm force winds at a particular point then the probability is 100%. Hence the 

probabilities depend on the spread of the ensemble and not climatology probabilities. Thus the 

distribution of probabilities about the mean storm track is tighter and the probabilities much 

higher. Whether this adds value to a forecaster will depend on verification of the results illustrated 

in Figure 22 which has not been done yet.

Figure 22. Upper panel shows the cumulative probability over a 5 day period of tropical storm 

force winds as determined from the GFS/EnKF 20 member ensemble. Lower panel similar 

operational figure where probabilities are determined from historical error distributions. Note 

that the color scales in the two figures are different.
38

Figure 23 is an actual example of the type of product discussed earlier in section 5.1 (Forecasting 

Tropical Storm Genesis out to 5 Days or more) and illustrated schematically in Figure 14. The 

initial time of the forecast is prior to the development of Ophelia and Philippe in the Atlantic and 

Hillary in the East pacific. The left hand panel shows the tracks of storms from the NCEP 

ensemble derived from the tracker developed at GFDL. On the right the areas of expected tropical 

cyclogenesis during the first 24 hours of the forecast period are shown. Note that the three areas 

with the highest probability of genesis were regions where hurricanes Ophelia, Philippe and 

Hillary eventually formed and that the probability of genesis indicated in between 80% and 90%. 

The area near Cuba with a probability of genesis less that 40% did not develop into a storm. 

When Figure 23 is compared with Figure 14, it is very close to what was proposed in section 5.1.

Figure 23. An experimental tropical cyclogenesis product (right panel) derived from the GFDL 

tracker system (left panel) from the operational GFS high resolution deterministic model and 

the operational GEFS ensemble. In the right panel the colors denote probability of genesis 

within the first 24 hours of the forecast period and the gray lines show the ensemble. This is an 

actual figure to be compared with the conceptual figure shown in Figure 12.

5.9. COAMPS-TC Ensemble Forecasts

While research is ongoing to improve deterministic atmospheric forecasts through advancements 

to the forecast model and more accurate estimates of the initial state, simultaneously there has been 

interest in obtaining probabilistic information derived from ensemble forecasts. A new COAMPSTC ensemble system that is capable of providing probabilistic forecasts of TC track, intensity, and 

structure has been developed. This system makes use of the community-based Data Assimilation 

Research Testbed (DART) developed at the National Center for Atmospheric Research, which 
39

includes various options for Ensemble Kalman Filter (EnKF) data assimilation. The COAMPS-TC 

DART system constitutes a next generation data assimilation system for tropical cyclones that uses 

flow dependent statistics from the ensemble to assimilate observational information on the 

mesoscale. 

A real-time COAMPS-TC ensemble system was run in a demonstration mode as part of the HFIP 

Stream 2 tests in 2011 for the W. Atlantic, E. Pacific, and W. Pacific regions. The system 

comprised an 80-member COAMPS-TC cycling data assimilation ensemble on three nested grids 

with horizontal spacing of 45, 15, and 5 km. For each new warning message issued by either NHC

or JTWC, the COAMPS-TC ensemble was initialized by interpolating global forecast fields from 

the HFIP 80-member GFS-EnKF system to the three nested grids, which were centered on the 

storm. Six-hour forecasts were made four times daily to provide background estimates for the 

assimilation of observations from surface stations, ship data, radiosonde accents, cloud-track wind 

retrievals, and aircraft data with the DART EnKF. In addition to these conventional data sets, the 

NHC and JTWC TC position estimates were directly assimilated with the EnKF system. Undersampling issues in the data assimilation procedure associated with the finite ensemble size were 

controlled by limiting the spatial influence of observations with a static localization radius of 1000 

km, as well as applying a spatially and temporally varying inflation factor to the prior ensemble 

perturbations. To most effectively use the high-resolution capability of COAMPS-TC, a two-way 

interactive data assimilation procedure was implemented. In this algorithm, the innovations were 

defined using the highest resolution nest that contained the observation. These innovations were 

used to update the COAMPS-TC fields on all three grids. Furthermore, observations contained 

outside of a nest were allowed to update the fields within the nest.

Ten-member forecasts were performed twice daily to five days using the same three nested grid 

configuration as the data assimilation ensemble. The first 10 members of the data assimilation 

ensemble were used to define the forecast ensemble. Lateral boundary conditions for the forecast 

ensemble were drawn from the GFS-EnKF ensemble forecast. Examples of probabilistic products 

for Hurricane Irene are shown in Figure 24 for both track (top panel) and intensity (bottom panel). 

This is a real time forecast initialized at 1200 UTC 23 August, which is four days prior to landfall. 

The probabilistic track product shows the TC position from the individual ensemble members 

every 24 h and ellipses that encompass 1/3 and 2/3 of the ensemble member forecast positions. 

Note that the observed landfall location of the eye was within the ensemble distribution, although 

the ensemble mean landfall was approximately 12 h later than observed. The probabilistic 

intensity product (lower panel) shows a considerable spread among the members, particularly 

beyond 84 h, just prior to landfall. These products can be extremely valuable to assess the 

uncertainty in both track and intensity forecasts, and NRL is currently developing these 

capabilities and products further.
40

Figure 24. Probabilistic products from the COAMPS-TC ensemble for Hurricane Irene 

corresponding to the track (top panel) and hurricane intensity (bottom panel). This real time 

forecast was initialized at 1200 UTC 23 August, which is approximately four days prior to 

landfall. The probabilistic track product shows the TC position from the individual ensemble 

members every 24 h and ellipses that encompass 1/3 and 2/3 of the ensemble members. The 

intensity (knots) distribution is shown as a function of forecast lead time (hours) with the 

minimum value, maximum value and various quantiles of the ensemble distribution shown as 

denoted by the legend.

5.10. New Diagnostic Verification Tools

A variety of methods were used in the evaluation of forecast models this year, which provided 

useful information about the performance of a candidate model that goes beyond the mean or 

median performance as a function of lead. An example of the additional evaluation tools is shown 

in Figure 25. These plots show example rank histograms for difference performances. The top 

panel is an example when the candidate model is outperforming baseline used in comparison. The 

middle plot shows when the candidate model performs similar to the baseline (e.g. flat 

distribution). The bottom panel shows when the candidate model either performs better (rank 1) or 
41

worse (rank 4) compared to the baseline. This type of result examines the frequency of model 

performance and is useful for investigating how the candidate model is performing against the 

baseline.

Figure 25: Rank histogram plots showing examples when a candidate mode is performing well 

(top), equally (middle), or often better or worse (bottom) than the baseline.

New methods are being developed to evaluate track and intensity errors of ensemble forecast 

systems. Figure 26 shows an example of a “wind rose” plot of intensity and track errors evaluated 

for the GFDL ensemble forecast system. The plot on the left shows the frequency of intensity 

errors as a function of track errors. The results show that the track errors tend to be east of the 

analyzed best track. Intensity errors tended to be positive with most errors < 39 knots. A few 

larger errors > 39 knots were observed for track errors to the west of the best track. The right hand 
42

plot in Figure 26 shows that most errors were < 200 n mi with a small percentage exceeding 200 n 

mi.

 

Figure 26: Example “wind rose” plots showing the frequency of intensity errors as function of 

track error location (left) and frequency of direction and magnitude of track errors (right).

6. Lessons Learned

The HFIP has gotten to the point were the main development foci have been identified and so the 

list below is mostly a repeat from earlier reports.

 Initialization of the regional models has improved but it still remains a major problem. It 

will need to be solved before we can make much progress on forecasting rapid 

intensification

 Physics packages suitable for models with resolutions of 3 km need to be improved

6.1. Initialization of the Regional Models Remains a Major problem 

This is clearly shown in Figure 9 and has been pointed out many times in other annual reports. 

HFIP has made considerable progress by emphasizing ensemble data (or hybrid) data assimilation 

systems combined with the use of high resolution data near the hurricane center. NCEP is 

currently planning to implement hybrid data assimilation for their global model in the next year

and development has started at EMC on a hybrid DA system for HWRF.

6.2. Physics Packages 3 km Models need to be improved

There has been little effort to find the optimum physics package appropriate for models with 3 km 

resolution. This is especially true for the hurricane models because of the high winds. Progress is 

being made (Figure 18), but there needs to be a careful assessment of the various options for 

convection, PBL and microphysics followed by a careful tuning of the entire package so the 

components work well together. 
43

7. Challenges

 The biggest challenge to the results from HFIP will be for NOAA to identify the computer 

resources necessary to run the operational system that will be required to meet its goals

 The program needs a focus on developing appropriate physics packages for high resolution 

(3km) regional models

 Find ways to better use satellite data at resolutions appropriate for the hurricane

7.1. Computer Resources to run the HFIP Operational System 

Our biggest concern in HFIP is that the computer resources will not be available within the 

National Weather Service to run the forecast models and ensembles at the resolution necessary to 

meet the HFIP goals. Current projections of the computing power available through the next 

several years are well below what will be required to meet the HFIP goals. This is an issue not just 

for HFIP but for US operational forecasting in general. One possible solution might be to turn the 

u-jet/t-jet computers into an operational machine and use it to focus on a few critical models on 

which much of the rest of the forecast system relies, in particular the high resolution global 

ensembles. This could probably be accomplished with a relatively low additional cost.

7.2. Physics Packages for High Resolution (3km) Regional Models

In past years HFIP has noted that the biggest challenges facing the program is initialization of both 

the regional and global models. Ensemble techniques have clearly improved the initialization of 

the global models (Figures 4 and 5) and NCEP has set as a goal to introduce the hybrid data 

assimilation (which uses an ensemble approach as well as a 3DVAR approach) within the next 

year. A similar effort with regional models has shown similar promise and plans are underway to 

test hybrid data assimilation in the operational regional models within a year. Even using the more 

traditional methods for regional model initialization, significant progress has also been 

demonstrated for the operational HWRF (Figure 16) and COAMPS-TC (Figure 7). 

Since HFIP has been focused on the initialization problem it has put less emphasis on physical 

parameterizations. It is widely felt that along with initialization, a most promising area for 

improving model guidance will be improving the physical parameterization packages. Indeed, it is 

felt that better physics in combination with improved data assimilation techniques is a key part of 

improving model initialization in addition to the subsequent forecast. In particular a physics 

package appropriate for 3km models including surface flux, PBL, microphysics and convection 

representations need to be advanced for models at high resolution. 

HFIP sponsored a Regional Modeling Physics Workshop in 2011 where the research and 

operational scientists came together to discuss the main deficiencies and routes for improvement. 

Follow-up discussions led to the decision to focus in three main areas: planetary boundary layer, 

microphysics, and convective parameterization. Work is underway at the DTC to expand the 

HWRF capability to be connected with a variety of physics packages and to assess the HWRF 

performance with a variety of cumulus schemes.
44

7.3.Better use satellite data 

We noted above that high resolution data taken near the center of the hurricane by aircraft and 

incorporated into model initialization with an appropriate data assimilation system will greatly 

reduce intensity forecast errors out to several days. A problem with this result is that aircraft are 

only in the storm for perhaps 10% of all times models are initialized (but the majority when the 

storm is near land). We have some very preliminary results that suggest using satellite data 

(especially high resolution cloud track winds) provide a similar improvement. This result needs 

further development including consideration of other high resolution satellite data near the 

hurricane.

8. Final HFIP Numerical Model Hurricane Forecast System 

In the discussion above we showed evidence that it is likely that HFIP will meet or exceed its 5 

year intensity and track goals. It is less clear whether we can or cannot make the RI goals.

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

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

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

vicinity of the hurricane. Another major focus for HFIP is on improving satellite data assimilation 

in regional model initialization systems.

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

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

the end of the initial 5 years of HFIP.

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

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

processed with various statistical models. Resolution of these global models needs to be at least 20 

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

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

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

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

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

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

system might include:

Global model ensemble with Hybrid Data Assimilation

20 members at 20 km

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

Regional model ensemble

20 members at 3 km

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

Using all available aircraft and satellite data in core and near environment of hurricane
45

Statistical Post processing

Bias correction, LGEM, SHIPS

The ability to run this system will however require at least a 10-30 times increase in computer 

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

9. Concluding Remarks

By reaching out across the hurricane research, development and operational communities, HFIP 

has promoted the cooperative effort necessary to make rapid improvements in hurricane forecast 

numerical guidance. The focus on improving the data assimilation system in the global models 

and the use of ensembles from the global models is likely to lead to substantial improvements in 

hurricane track forecasts in operations in the near future. Use of high resolution regional models 

with advanced data assimilation systems such as EnKF or the hybrid system, and the use of 

airborne radar data and satellite data at the scales of the hurricane will likely lead to improved 

forecasts of intensity. An initial GSI-hybrid capability for HWRF is being developed by 

ESRL/EMC as an extension of the system used for the GFS. The DTC is working with ESRL, 

EMC, and various research partners to create a system that can be used by and built on to receive 

developments to the community. In intensity and track, HFIP expects to easily reach its five year 

goals of improving both by 20% over the HFIP baseline.
Appendix

Table A.1: 2011 Stream 1.5 Evaluations: Summary of NHC Decisions

46

Technique

Name

Model info Contacts Parameter Application at NHC Comments

AHWI NCAR/MMM –

SUNY

4-km AHW. 

Early version of 

AHW4. 

Chris Davis

Wei Wang

Sherrie 

Fredrick

Ryan Torn

Track - Display explicitly in 

ATCF

- Include in TV15*

3-4% improvement 

when added to Atlantic 

consensus w/EMXI 

from 24-96 h, and no 

degradations at other 

times. Track errors 

comparable to topflight models. Not 

tested in east Pacific.

AHWI NCAR/MMM –

SUNY

4-km AHW.

Early version of 

AHW4; GHMI 

interpolator. 

Chris Davis

Wei Wang

Sherrie 

Fredrick

Ryan Torn

Intensity - Display explicitly in 

ATCF

- Include in IV15 

3-9 % improvement 

when added to 

operational Atlantic 

consensus from 24-96 h 

w/o serious 

degradations at other 

times.

COTI NRL COAMPSTC.

Early version of 

COTC. 

Jim Doyle

Hao Jin

Track None Errors larger than topflight models. 

Improvements when 

added to consensus 

w/EMXI were > 3% 

only at 48-72 h in the 

Atlantic, and it 

degraded the east 

Pacific consensus 

w/EMXI.

COTI NRL COAMPSTC.

Early version of 

COTC; GHMI 

interpolator.

Jim Doyle

Hao Jin

Intensity - Include in IV15 Improved intensity 

consensus by 3-11% in 

both basins at most 

forecast times. 

A1PI PSU 1 km; TDR 

assimilated.

Early version of 

A1PS 

Fuqing 

Zhang

Yonghui 

Weng

Track/Intensity None No systematic 

improvement over 

A4PI

A4PI PSU 4.5 km; TDR 

assimilated.

Early version of 

A4PS 

Fuqing 

Zhang

Yonghui 

Weng

Track None Improved Atlantic 

consensus that did not 

have EMXI by 3-9%, 

but had virtually no 

impact on the 

consensus w/EMXI 

(the latter restricted to 

2008-10 only). Very 

small sample of 

forecasts.
47

A4PI PSU 4.5 km; TDR 

assimilated

Early version of 

A4PS; GHMI 

interpolator.

Fuqing 

Zhang

Yonghui 

Weng

Intensity - Include in IV15 

when radar data 

assimilated 

Small sample. Large 

SS improvements over 

consensus for 

land/water cases, but 

limiting to water almost 

completely eliminated 

the positive impact. 

For 2008-10 cases only, 

a sample that might be 

more representative of 

the expected 2011 

performance, about 9% 

improvement to 

operational fixed 

consensus for all cases, 

8% improvement for 

water only cases.

A4NI PSU 4.5 km; No 

TDR.

Early version of 

A4NR 

Fuqing 

Zhang

Yonghui 

Weng

Track None Inadequate sample for 

evaluation.

A4NI PSU 4.5 km; No 

TDR.

Early version of 

A4NR 

Fuqing 

Zhang

Yonghui 

Weng

Intensity None Inadequate sample for 

evaluation. Mostly 

positive impact on 

consensus for all cases 

but only 48 h SS. 

Mixed impact for water 

only cases. For 2008-

10 cases only, 9% 

improvement to 

operational fixed 

consensus for both 

water/land and water 

only.

FIMI GSD FIM;

Early version of 

FIMY 

Mike 

Fiorino

Track - Display explicitly

- Include in TV15

Lower errors than GFSI 

in both basins. 

Improved Atlantic track 

consensus by 3-7% 

from 24-120 h. 

Improved east Pacific 

consensus by up to 4% 

through 108 h.

FIMI GSD FIM;

Early version of 

FIMY 

Mike 

Fiorino

Intensity None Did not perform as well 

as GFDL; did not 

improve consensus.

UWNI U. Wisconsin 

UW-NMS

8 km

Early version of 

UWN8 

Will Lewis

Greg Tripoli

Track None Track errors larger than 

top-flight models. 

Improvements to the 

Atlantic variable track 

consensus w/o EMXI 

were only about 1-2% 

and mostly not SS. For 

NHC fixed consensus 

w/EMXI improvements 

were about 2-3%. 
48

UWNI U. Wisconsin 

UW-NMS

8 km

Early version of 

UWN8; GHMI 

interpolator. 

Will Lewis

Greg Tripoli

Intensity - Include in IV15 Erratic behavior 

wrt/top-flight models. 

SS improvements to 

intensity consensus.

SPC3 CSU-CIRA 

SPICE 6-member 

statistical 

consensus

Mark 

Demaria

Kate 

Musgrave

Intensity - Display explicitly SS improvements of up 

to 25% relative to 

DSHP, up to 12% over 

LGEM for the Atlantic. 

Improvements in 

eastern Pacific were 

much smaller but 

almost always positive. 

H3GI NCEP/EMCAOML/HRD;

HWRF 3 km

Early version of 

H3GP 

Vijay 

Tallapragada

Sam Trahan

Thiago 

Quirino

Track - Display explicitly Improvements of 3-

12% over operational 

HWRF in the Atlantic, 

making its errors 

comparable to the topflight models. Mixed 

impact in east Pacific. 

Replacing HWFI 

improved the variable 

consensus w/o EMXI. 

Slight (2%) 

improvements when 

added to fixed 

consensus w/EMXI, 

although made 

negligible contribution 

when other Stream 1.5 

candidates were also in 

the consensus. 

H3GI NCEP/EMCAOML/HRD;

HWRF 3 km

Early version of 

H3GP; GHMI 

interpolator. 

Vijay 

Tallapragada

Sam Trahan

Thiago 

Quirino

Intensity None Even with the 

improvements over the 

operational HWRF, still 

lags the better 

operational intensity 

guidance. No 

systematic positive 

impact on consensus: 

when added to 

DSHP/LGEM/GHMI 

consensus, slight 

improvements in 

Atlantic and slight 

degradations in east 

Pacific. When the 

more promising Stream 

1.5 candidates are also 

included in the 

consensus, H3GI had 

negligible impact.

GPMI GFDL Ensemble 

mean 

Early version of 

GPMN 

Tim 

Marchok

Morris 

Bender

Track - Display explicitly Better than GFDL 

control by 2-4%. 

Negligible impact on 

consensus.
49

GPMI GFDL Ensemble 

mean 

Early model 

version of ; GHMI 

interpolator.

Tim 

Marchok

Morris 

Bender

Intensity - Display explicitly Better than GFDL 

control by up to 6% 

through 72 h. Slight 

degradation at 96 h. 

No systematic impact 

on consensus.

G01I GFDL Ensemble 

member GP01

Early model 

version 

Tim 

Marchok

Morris

Bender

Track - Display explicitly Up to 10% better than 

the control GFDL 

(except 4% worse at 72 

h). Up to 3% 

improvement in 

consensus in place of 

control.

G01I GFDL Ensemble 

member GP01

Early model 

version; GHMI 

interpolator. 

Tim 

Marchok

Morris 

Bender

Intensity None Worse than control.

G01I-G16I GFDL Ensemble 

members GP01-

GP16

Early model 

versions

Tim 

Marchok

Morris 

Bender

Track - Display spread Spread and 

characteristics of 

regional model 

ensemble provides 

subjective information 

for forecasters.

G01I-G16I GFDL Ensemble 

members GP01-

GP16

Early model 

versions; GHMI 

interpolator. 

Tim 

Marchok

Morris 

Bender

Intensity - Display spread Spread and 

characteristics of 

regional model 

ensemble provides 

subjective information 

for forecasters.
50

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