2009 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, Y. Kwon 3, W. 

Lapenta3, S. Lord3, T. Marchok6, F. Marks1, L. Nance7, E. Rappaport9, C. Reynolds10, N. 

Surgi3, V.Tallapragada3, H. Tolman3, G. Vandenberghe

3

, Y. Weng12 J. Whittaker4, F. 

Zhang12, M. Zupanski2

27 April 2010

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

2009 HFIP R and D Activities Summary: ..........................................................................5

Accomplishments, Lessons Learned, and Challenges .......................................................5

1. Background on HFIP................................................................................................5

2. High-Resolution Ensemble Approach ......................................................................7

3. The HFIP Model Systems..........................................................................................8

3.1. The Global Models:....................................................................................................8

3.2. The Regional Models:................................................................................................9

3.3. Initialization systems: ..............................................................................................10

4. The HFIP Baseline ..................................................................................................11

5. Stream 1 FY09 Activities at NCEP/EMC................................................................13

5.1. The Global Data Assimilation and Forecast System (GDAS/GFS).......................13

5.2. The Global Ensemble Forecast System (GEFS).....................................................15

5.3. Hurricane Weather and Research Forecast (HWRF) Model ................................16

5.4. Real Time Ocean Forecast system (RTOFS)..........................................................18

5.5 Wave Modeling (WAVEWATCH III) .................................................................... 19

6. Stream 1 FY09 Activities at HRD............................................................................21

7. Stream 2 activities: Results from FY08 and FY09 Demonstration System ...........22

7.1. The 2008 Hurricane season.....................................................................................22

7.2. The 2009 hurricane season .....................................................................................22

8. Encouraging Results from FY09.............................................................................32

9. Lessons Learned from FY09 HFIP Activities.........................................................45

10. Challenges for HFIP beyond FY09.........................................................................52

11. HFIP Progress Toward its Performance Goals......................................................54

12. List of HFIP supported publications and presentations.........................................56

13. Appendix A List of HFIP teams.............................................................................57

14. Appendix B Organization Acronyms......................................................................60
3

2009 Hurricane Forecast Improvement Program (HFIP) R & D Activities 

Summary: Accomplishments, Lessons Learned, and Challenges

Executive Summary

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

Program in 2009. It is organized around three themes, Encouraging results from 2009 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:

Encouraging Results

Advanced data assimilation systems (EnKF, 4DVAR) appear to improve global 

forecasts over the current operational data assimilation system.

High resolution global and regional ensemble systems are showing promise but 

require further testing and evaluation.

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

available computing resources. Higher resolution is definitely possible. 

There is some preliminary evidence that airborne radar data used to initialize the 

hurricane vortex in regional models can improve forecasts of track and intensity.

The Multi-Model regional ensemble showed promise.

Various physics parameterization schemes were examined using HWRFX and 

COAMPS. Great sensitivities of storm structure to variations in the model physics 

were noted, strongly suggesting possible routes for improving the physics package 

in the operational HWRF model.

Assimilation of pseudo sea-level pressure observations in GDAS improved track 

forecasts out to day 5. 

The FY10 NCEP GFS upgrade including increased horizontal resolution (35 km to 

27 km) and upgrades to shallow and deep convection physics and the PBL has a 

significant positive impact on track and intensity forecasts. 

The FY10 NCEP operational HWRF configuration including coupling to HyCOM 

using more realistic surface flux exchange coefficients has a significant positive 

impact on track and intensity forecasts.

Most components of the HWRF model are now publicly available and supported, 

and HWRF can be configured from community code repositories.
4

Lessons Learned

Challenges

A vast majority of model forecasts will be initialized for storms for which no 

aircraft data are available. In those cases we need to better utilize available 

satellite data particularly for the regional models.

For storms with aircraft data (particularly radar data) more use of that data is 

crucial for hurricane initialization especially for hurricanes near the coast. 

Development and tuning of physics packages for hurricane models at high 

resolution is critical. 

Advanced DA systems in both regional and global models appear to lead to

substantial forecast improvement. We need to get these advanced assimilation 

systems into operation as soon as possible.

For a limited sample, high resolution global and regional ensemble systems are 

showing promise but require further testing and evaluation.

We need to develop better products to convey ensemble information to forecasters.

We need to fully engage the whole hurricane community in improving the 

operational HWRF. A major step in this direction is the adoption of the HWRF 

community code by NCEP, so that research and operations use the same code base.

We need to emphasize coordination between the HFIP modeling, observations and 

evaluation components to determine observational requirements for the 

improvement of model physics packages.

In the body of the text we discuss each of the above lessons and challenges and our plans for 

addressing each.

Simply increasing the resolution of the regional models alone does not lead to 

improvements in model guidance.

The research community must do a better job conveying the value and use of 

ensemble information to the forecast community and in developing value added 

products from ensembles.

Model performance metrics for hurricanes must include more than just track and 

intensity metrics.

Initialization of Regional models is a major problem.

Both the regional and global models greatly over-predict tropical cyclo-genesis. 
5

2009 HFIP R & D Activities Summary:

Accomplishments, Lessons Learned, and Challenges

1. Background on HFIP

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

needed to significantly improve guidance for hurricane track, intensity, and storm surge 

forecasts. It also engages and aligns the inter-agency and larger scientific community 

efforts towards addressing the challenges posed to improve hurricane forecasts. The goals 

of the HFIP are to improve the accuracy and reliability of hurricane forecasts; to extend 

lead time for hurricane forecasts with increased certainty; and to increase confidence in 

hurricane forecasts. These efforts will require major investments in enhanced observational 

strategies, improved data assimilation, numerical model systems, and expanded forecast 

applications based on the high resolution and ensemble-based numerical prediction 

systems. 

The specific goals of the HFIP are to reduce the average errors of hurricane track and 

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

to 7 days. The benefits of HFIP will significantly improve NOAA’s forecast services 

through improved hurricane forecast science and technology. Forecasts of higher accuracy 

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

response, including savings of life and property. 

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

challenges associated with improving hurricane forecasts requires interaction with, and 

support of, the larger research and academic community. It is hypothesized that these very 

ambitious goals of the HFIP can only be met using high-resolution (~5-15 km) global 

atmospheric forecasting numerical models run as an ensemble in combination with regional 

models at even higher resolution (~1-5 km). Demonstrating this is very expensive 

computationally so HFIP requires access to resources currently only available at the few 

supercomputing centers in the country. Only by demonstrating the value of high resolution 

is there any opportunity to obtain such a computational resource for operational hurricane 

forecasts. 

For FY09 the HFIP program consisted of about $27 M with $6 M dedicated to enhancing 

computer capacity available to the Program, including $4.5 M for a dedicated machine with 

3500 processors located in Boulder, Colorado. The other $1.5 M was for development and 

testing and an additional 160 processors for the development machine at the National 

Centers for Environmental Prediction (NCEP) to facilitate transition of HFIP technology 

into National Weather Service (NWS) operations. About $10M of the $27M is part of the 

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

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

The remaining $11M was distributed to various NOAA laboratories and centers (Earth 

System Research Lab (ESRL), Geophysical Fluid Dynamics Laboratory (GFDL), National 
6

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

Center (NHC). Funding was also provided to the National Center for Atmospheric 

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

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

University, University of Arizona and University of Rhode Island. Finally, $1M was 

contributed to the National Oceanographic Partnership Program (NOPP), Announcement of 

Opportunity for competed proposals related to improving understanding and prediction of 

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

Naval Research (ONR). 

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

focused on various components of the hurricane forecast problem. In late 2009 we 

combined some of the teams and assigned co-leads to each team (as opposed to a single 

lead initially) to increase leadership across the various HFIP organizations. Current teams 

are listed in Appendix A with the team leads shown in bold type. For reference a list of 

acronyms for the various organizations is shown in Appendix B. These teams are made up 

of over 50 members drawn from the hurricane research, development and operational 

community. 

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

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

organized along two paths of development called Streams. Stream 1 assumes that the 

computing power available for operational hurricane forecast guidance will not exceed 

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

for several years by EMC. HFIP activities at the NOAA labs and centers will help 

accelerate this development. 

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

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

available computer power in operations above that planned for the next 5 years. The 

purpose of Stream 2, therefore, is to demonstrate that the application of advanced science 

and technology developed under the auspices of HFIP along with increased computing will 

lead to the expected increase in accuracy and other aspects of forecast performance. 

Because the level of computing necessary to perform such a demonstration is so large, the 

Program is applying to resources outside NOAA in addition to trying to increase internal 

computing for development. 

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

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

weaknesses of promising new technology. As a result of the Demo System testing, some 

components may be found to be of particular interest to the operational forecasters, and, if 

resources do not permit its implementation in the operational infrastructure, the Demo 

system for the following season will emphasize those components and will provide specific 

output that is made available to NHC forecasters for evaluation. We refer to this 

component of the Demo System as Stream 1.5. Table 1 outlines these various streams.
7

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

Stream 2 we are assuming that the best approach to improving the forecast hurricane track 

beyond 4 days is through the use of high resolution global models run as an ensemble. We 

describe below the logic behind this assumption. For improvements in forecast of 

hurricane intensity, especially in the 1 to 4 day time range, the best approach is likely to be 

high resolution regional models, also run as an ensemble. The global models are likely to 

be limited in resolution to about 10 km for at least the next 5 years, because of computer 

limitations, especially when they are run as an ensemble. Thus the only way to achieve the 

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

with regional models. It is generally assumed that the inner core must be resolved before 

we can expect to see consistently accurate hurricane intensity forecasts.

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

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

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

community. This support started in February 2010 with the DTC/EMC/MMM Joint 

Hurricane Workshop and WRF for Hurricanes Tutorial.

Table 1. 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. Seeks computing resources from major 

supercomputing centers for testing and evaluation. Emphasis is on 

high resolution global and regional models run as ensembles. It 

will include a demonstration system run in real time each summer 

to test and evaluate promising new technology.

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

forecaster defined. Components from Stream 2 that forecasters see 

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

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

2. 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. 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. Because the various forecasts diverge with time, 
8

emergency managers should be able to make more effective decisions when provided with 

ensemble guidance compared to being provided with a single forecast. 

High resolution is hypothesized to be necessary in these ensembles in order to adequately 

resolve the hurricane structure, 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 

begin to get structures in the forecast model which resemble actual hurricanes, resolutions 

of 5-15 km are necessary. Ideally, each ensemble member will have this resolution, and 

ideally 20-30 members are computed to provide adequate estimates of the uncertainty.

Beyond about three days, forecast guidance must come from global ensembles since the 

planetary-scale patterns interact with and influence the steering of the storm. After about 

three days, it has been shown 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. 

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

through forecasts from international competitors such as the European Centre for MediumRange Weather Forecasts (ECMWF). 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 this requires at least that the high-resolution 

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 highresolution 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 leads, to intensity forecasts at medium forecast leads. 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 3-5 km. We will show some 

preliminary results from HFIP activities that demonstrate the value of high resolution 

ensembles in the intensity forecast problem. Ideally, regional high-resolution ensembles 

are nested within high-resolution global ensembles, which provide an ensemble of lateral 

boundary conditions that describe the influence of global flow patterns. Also, it is 

preferable to use similar advanced data assimilation approaches and model physics.

3. The HFIP Model Systems

3.1. The Global Models:

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

experimental global model that can be run at various resolutions and uses initial conditions 
9

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). 

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

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

operational model run at the NOAA National Centers for Environmental Prediction 

(NCEP) and an experimental version of that model. 

NOGAPS—Refers to Navy Operational Global Atmospheric Prediction System. Currently 

a semi-Lagrangian version of NOGAPS is being developed, which will allow for efficient 

high-resolution forecasts.

3.2. The Regional Models:

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

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

NCAR and NMM—Non-hydrostatic Mesoscale Model, built by EMC) and several options 

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

systems.

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

and the NCEP GFS boundary layer and deep convection.

The Multi-Model Ensemble—The multi-model ensemble was organized by Florida State 

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

various models and their resolution are indicated in Table 5. Two of the members are the 

operational models, GFDL at 7.5 km and HWRF at 9 km. GFDL is the old operational 

model that is still being run in parallel with the current operational model HWRF. HWRF 

is constructed from the NMM core of the WRF, and both GFDL and HWRF models are 

coupled to the Princeton Ocean Model (POM) in the Atlantic Basin. A version of the 

HWRF was also run at 4 km resolution (HWR4) during the 2009 hurricane season. 

HWRF-x is an experimental version of the operational HWRF run by the Hurricane 

Research Division of OAR. It did not have an interactive ocean model associated with it 

but did have an interactive nest within it. HyHWRF is the HWRF model coupled to the 

Hybrid Coupled Ocean Model (HyCOM), and it was run in parallel for the 2009 hurricane 

season for the Atlantic Basin.

In addition to HWRF, two versions of the WRF ARW system were also run. The ARW 

system run by NCAR used a simplified one dimensional model of the ocean. It used two 

interactive nests within the outer regional model. FSU also ran a version of the ARW 

without an interactive ocean. 

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

regional prediction system that is being run operationally and has an interactive ocean.
10

The Penn State Regional Ensemble

This was another version of the WRF ARW system similar to the NCAR WRF ARW. It 

used a static interactive inner nest but no interactive ocean. It was run as a 30 member 

ensemble.

3.3. Initialization systems:

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

models in the experiments we describe here and below. The choices include:

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

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

Interpolation (GSI) initialization system that has run operationally for many years; it is 

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

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

system used to provide the initial conditions to the Navy global model. It has been a 

3D-VAR system but starting late September 2009, it was upgraded to NAVDAS-AR 

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

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

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

for a Kalman Filter. While this approach is still in the experimental stage in the U.S. 

(though operational in Canada), it has shown considerable promise. The system in 

FY09 HFIP used the GFS model to provide the ensemble forecasts for the EnKF data 

assimilation, although the FIM may also be used in FY10. 

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

combines aspects of the EnKF and 3D- or 4D-Var for example, using the ensemble of 

forecasts to estimate the covariances at the start of a 4D-Var assimilation window. This 

technology is under development at NOAA/NCEP/EMC and NOAA/OAR/ESRL. It 

will not be ready for testing in the 2010 season but may be available for subsequent 

seasons. This hybrid approach is likely to define the operational global data 

assimilation system for NOAA in the 5-year time frame.

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

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

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

The operational HWRF utilizes an advanced vortex initialization and assimilation cycle 

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

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

vortex from the global analysis; 3) add the HWRF vortex modified from the previous 

cycle’s 6-hour forecast (or use a synthetic bogus vortex for cold start); and 4) add satellite 

radiance and other observation data in the hurricane area (9 km inner domain). The major 

differences from the GFDL model initialization are steps 3) and 4).
11

4. The HFIP Baseline

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

(HEOB) as:

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

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

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

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

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

focus on rapid intensity change is the highest forecast challenge indentified by the 

NHC.

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

forecasts in 2003).

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

5 years. 

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

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

These HFIP Performance Goals Baselines were developed in a white paper authored by 

James Franklin dated 5 May 2009 and summarized here. 

For both the track and intensity goals, a consensus (equally-weighted average) of 

operational guidance models was utilized, evaluated for the Atlantic Basin over the period 

2006-2008. This 3-year average was determined to be feasible and adequate based upon 

the trend data presented above. That is, this shorter 3-year period is adequate to determine 

the HFIP Performance Goal Baselines because there has been a significant reduction in 

track error in recent years, and because increased tropical cyclone activity in the Atlantic 

Basin in the last few years allows for more stable statistics over this shorter time period. 

For track error, this consensus was a particularly good choice because the mean skill of the 

official NHC forecast is very close to that of the consensus. Consequently, a 20% 

improvement in any HFIP guidance over this baseline could reasonably be expected to 

translate to a 20% improvement in the official forecast. This would not be the case if an 

individual operational model were used as a baseline. 

The track baseline was a consensus of GFSI, GFDI, UKMI, NGPI, HWFI, GFNI, and 

EMXI, which were computed whenever at least one of the consensus members was present. 

This is essentially the membership of NHC’s current operational consensus model TVCN. 

Even though HWFI was only available in 2007-8, the recommendation was to include 2006 

as well. The additional year of data will provide a more representative assessment of the 

current state of the forecast guidance. Evaluation of this consensus over 2006-8 is given 

below (CONS). For comparison, climatology and persistence skill baseline errors are also 

shown (OCD5), as are the official forecast errors (OFCL). Forecast errors are in nautical 

miles.
12

Table 2. 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

For intensity, the consensus members were: GHMI, HWFI, DSHP, and LGEM (note that 

GHMI is the GFDL model), with the consensus computed whenever at least one of these 

models was present. This is the same set of models used in the operational intensity 

consensus ICON (except that ICON is not computed unless all the member models are 

present). Evaluation of this consensus over 2006-8 is given below, along with 

climatology/persistence and the official forecast. Forecast errors are in knots. The table 

shows that the intensity consensus is actually slightly better than the official forecast, at 

least beyond 24 h or so. In part, this is because this intensity consensus has only been 

operationally utilized for one year. NHC’s operational practice of only making incremental 

changes to the official forecast, from forecast to forecast, may also contribute.

Table 3. Proposed HFIP Intensity Performance Baseline (knots)

VT (h) N OFCL OCD5 CONS

0 820 1.9 2.2 2.2

12 745 7.2 8.3 7.7

24 667 10.4 11.5 10.1

36 590 12.6 14.2 11.7

48 522 14.6 16.1 13.7

72 415 17.0 17.8 16.0

96 316 17.5 19.3 16.6

120 250 19.0 19.3 17.0

When evaluating HFIP forecast products it will be important to consider differences in 

forecast difficulty between the cases used by HFIP and those of the 3-year baseline sample. 

In order to compute the ―true‖ percentage improvement represented by the HFIP products, 

the OCD5 skill baseline will be computed for the HFIP sample, and the HFIP errors will be 

normalized so that the OCD5 errors for the two samples are equivalent.

HFIP also has a goal to introduce a 7-day track forecast. In this case, a baseline is not 

required, however a metric is needed to judge whether a 7-day forecast can be introduced. 

The metric decided upon is that this metric would be for HFIP to produce guidance at least 
13

as good as the 5-day official forecast was, on average, when it was introduced in 2003. A 

linear fit to the official 5-day errors (ignoring varying sample sizes from year to year), 

evaluated at 2003, yields 314 nm. 

There are no official forecasts issued for rapid intensification (RI), unless it is inferred from 

the official intensity forecast values. However, the official forecast virtually never shows a 

24 h increase in intensity of 30 kts or more, so the performance of the official forecast 

would not make a good baseline. Hence, it was decided that it was better to use the 

existing model guidance. Both deterministic and probabilistic frameworks were used. The 

GFDL model provided a baseline for deterministic HFIP models, using metrics such as 

POD/FAR and related measures. For consistency with the baselines discussed above, these 

guidance models were evaluated over the same period 2006-8. 

In the future all results that refer to track or intensity errors will contain the baseline for 

reference.

5. Stream 1 FY09 Activities at NCEP/EMC

The NCEP operational modeling systems supported by or in part by HFIP include the GFS, 

GEFS, HWRF and RTOFS modeling systems. FY09 upgrades to these systems along with 

impacts on tropical system forecasts are provided below. 

5.1. The Global Data Assimilation and Forecast System (GDAS/GFS)

These include some major changes in the GFS system from which the HWRF and GFDL 

models get their initial and boundary conditions. 

There are two phases for GFS upgrades, one implemented in December 2009 and one to be 

implemented in June 2010. These changes are:

• GFS upgrades (Implemented Dec 2009) 

– Added tropical storm pseudo sea-level pressure observations

– Added NOAA19 hirs/4,AMSU-A, & MHS brightness temperature

observations

– Added EUMETSAT-9 atmospheric motion vectors

• GFS upgrades (Planned for May 2010) 

– Resolution increase (27 km from 35 km)

– Upgrade radiation to AER RRTM2

– Revised Gravity Wave Drag and Mountain Blocking

– Removal of negative water vapor with a positive-definite tracer transport 

scheme (enhances impact of satellite radiance data)

– Higher resolution hurricane relocation

– Major upgrades to shallow convection, PBL, deep convection with 

overshooting cloud tops (minimizes grid point storms)
14

Although a major change slated for June 2010 is an increase in resolution to 27 km from 35 

km, the improvement in GFS hurricane track error is primarily due to the physics changes 

35 km operational GFS physics

27 km operational GFS physics

27 km upgraded GFS physics

CLIPPER

35 km operational GFS physics

27 km operational GFS physics

27 km upgraded GFS physics

CLIPPER

35 km operational GFS physics

27 km operational GFS physics

27 km upgraded GFS physics

CLIPPER

Figure 1. Improvements in track forecasts in the GFS model as a result of resolution and physics upgrades 

out to 5 days. The blue line represents the operational model before the listed changes. The red line shows 

the impact of increased resolution. The green line shows the impact of increased resolution and upgrades to 

the physics. The black line is the benchmark track model CLIPER. Numbers in parentheses at the bottom of 

the figures indicate sample size.
15

in PBL and the deep and shallow convection. Figure 1 shows the track errors for the 

Atlantic and East Pacific for the resolution increase (red) and the inclusion of the improved 

physics (green). In both basins, the improved physics accounts for the majority of the 

forecast improvement. 

5.2. The Global Ensemble Forecast System (GEFS)

There was a major implementation for GEFS (Global Ensemble Forecast System) on 23 

February 2010. This upgrade mainly includes:

Increasing horizontal resolution to 70 km from 90 km

Using 8th order horizontal diffusion instead of 4th order, at all resolutions

Adding a stochastic perturbation scheme to account for random model errors

This upgrade is significantly improving the skill of uncertainty forecasts out to 16 days. 

There is a one-day extended skill (60% anomaly correlation) from current operation for the 

Northern Hemisphere 500 hPa geopotential height (see Figure 2). For tropical storm 

prediction, there is about a 25% improvement for ensemble mean track errors during the 

2009 hurricane season (see Figure 3 for selected 4 major storms during the 2009 season).

Figure 2. Northern Hemisphere 500hPa geopotential height anomaly correlation for the period of Figure 2. 

Northern Hemisphere Anomaly Correlation for 500 hPa Height, 08/01 – 09/30/2007. GFS (grey) is NCEP 

high resolution deterministic forecast at T382L64. GEFS (blue) is NCEP operational ensemble at T126L28. 

GEFS (pink) is NCEP off-line ensemble parallel (T190L64).
16

5.3. Hurricane Weather and Research Forecast (HWRF) Model

At the time of this writing, NCEP is in the process of concluding HWRF FY10 preimplementation testing to determine the final configuration for the 2010 hurricane season. 

The following HWRF upgrades have been systematically tested and evaluated by EMC and 

TPC since the fall of 2009 to support the December 2009 GFS implementation and the 

proposed GFS May 2010 upgrade:

– Upgrade to surface exchange coefficients (closer to observations from 

CBLAST field experiment and dropsonde data)

– Modifications to the vortex initialization procedure through use of 

conventional and satellite based observations in the inner nest (9 km 

domain)

– Inclusion of Gravity Wave Drag parameterization

– Coupling with HyCOM ocean model in the Atlantic Basin

Figure 3. Ensemble mean track errors for important cases of Bill, Jimena, Rick and Ida during the 2009 

hurricane season. Blue bars are for operation T126L28 GEFS forecast, and red bars are for retrospective 

experiments for planned new GEFS (T190L28).
17

The HWRF FY10 pre-implementation test plan consists of a series of systematic 

experiments requiring more than 600 runs of 2008-2009 Atlantic and East Pacific storms. 

The first step in defining the FY10 operational configuration was to define a new 

benchmark for the HWRF. The FY10 benchmark is identical to the operational HWRF 

used in 2008 and 2009 in every way except that there were several corrections made to 

rectify code errors in the treatment of downward shortwave radiation. Each of the proposed 

FY2010 upgrades listed above were tested in (and compared to) the FY10 benchmark and 

evaluated independently to ensure the upgrades had the expected impact on track and 

intensity forecasts. Then the individual upgrades were tested in combination using the 

operational 2008 GFS configuration, the December 2009 GDAS/GFS upgrade, and the 

planned June 2010 GDAS/GFS resolution increase and physics upgrade. 

Results from the pre-implementation testing are presented in Figures 4 and 5 for both the 

Atlantic and East Pacific Basins, respectively. In the Atlantic Basin the combination of all 

3 upgrades listed above results in a reduction in track error compared to the baseline 

throughout the 5-day forecast period. Analysis of the track error shows that Hurricane Bill 

(2009) track was not simulated well by the GFS May 2010 upgrade. Removal of Bill from 

the sample results in additional reduction of track error compared to the FY10 baseline (not 

shown). The proposed upgrades to both the HWRF and GFS had the highest percentage of 

superior track forecasts, the lowest average intensity error, and a significant reduction in 

the intensity bias for 72 hours and beyond. It is noted that the intensity bias is now 

negative from 12 to 48 hours. However, NCEP considers this to be a reasonable result for 

the 9 km horizontal resolution. It was found that the new enthalpy exchange coefficient 

obtained from the observational study of CBLAST, and the new drag coefficient obtained 

from the observational study of Powell et al (2003) had the largest positive impact on the 

reduction of intensity errors. In the East Pacific Basin the planned HWRF and GFS 

upgraded systems resulted in a significant (10% to 20%) reduction of track errors at all 

forecast times. The proposed upgrades to both the HWRF and GFS had the highest 

percentage of superior track forecasts, the lowest average intensity error, and a near-zero 

intensity bias for 72 hours and beyond.

At the time of this writing the final testing of the coupled HWRF-HYCOM system was not 

complete. Therefore, the results of those tests were not included in this report. However, 

in 2009 a parallel real-time run of HWRF coupled with HYCOM was conducted at NCEP 

along with a parallel HWRF-POM configuration that included corrections to the downward 

shortwave radiation and gravity wave drag parameterization for 7 tropical storms and 

hurricanes in the Atlantic Basin. Figure 6 shows that coupling with HYCOM resulted in a 

reduction of track and intensity errors for this limited sample compared to the parallel 

HWRF-POM system. 
18

5.4. Real Time Ocean Forecast system (RTOFS)

An initiative is also underway at NCEP to improve and maintain the Real-Time Ocean 

Forecasting System (RTOFS) in the Atlantic in support of the coupled HWRF-HYCOM 

system. The RTOFS has been significantly improved by removing spurious features in the 

Sargasso Sea. The model initialization will soon be upgraded by assimilating Jason-2 

altimeter data, which will be implemented operationally in May 2010. Experiments have 

been conducted to show positive impact of synthetic TS profiles from altimeter data on the 

ocean model mixed layer and deep water characteristics, with modest impact on hurricane 

forecasting. Work on a global RTOFS model is also underway as EMC’s Ocean Modeling 

Branch is adopting the 1/12 degree Navy HYCOM model for operational implementation 

to provide a real-time global backbone ocean modeling capability that can be used to 

provide boundary data for coupled hurricane models in the Pacific (or globally if so 

Figure 4. HWRF FY2010 pre-implementation test results for 2008-2009 Atlantic Basin. The 2010 baseline 

HWRF is identical to the operational HWRF and includes corrections to radiation calculations. The 2010 HWRF 

upgrade configuration builds off the 2010 HWRF baseline and includes assimilation of satellite data on the 9 km 

nest, Gravity Wave Drag parameterization and updated surface flux exchange coefficients.

HWRF Operational

2010 HWRF baseline

2010 HWRF upgrade with operational GFS

2010 HWRF upgrade with GFS upgrade

CLIPR

Average Track Errors (NM)

2008 and 2009 Atlantic Basin

NCEP FY10 Pre-Implementation Test HWRF Operational

2010 HWRF baseline

2010 HWRF upgrade with operational GFS

2010 HWRF upgrade with GFS upgrade

CLIPR

Average Track Errors –Frequency of Superior Performance (%)

2008 and 2009 Atlantic Basin

NCEP FY10 Pre-Implementation Test HWRF Operational

2010 HWRF baseline

2010 HWRF upgrade with operational GFS

2010 HWRF upgrade with GFS upgrade

SHF5

Average Intensity Errors (Kt)

2008 and 2009 Atlantic Basin

NCEP FY10 Pre-Implementation Test HWRF Operational

2010 HWRF baseline

2010 HWRF upgrade with operational GFS

2010 HWRF upgrade with GFS upgrade

SHF5

Intensity Bias (Kt)

2008 and 2009 Atlantic Basin

NCEP FY10 Pre-Implementation Test
19

required). This is on track for parallel testing at NCEP Central Operations (NCO) in

FY2010Q4.

For 2011, EMC will consider use of the NOAH Land Surface Model (LSM) with the 

HWRF forecast system. Several preliminary cases have been run which indicate a positive 

impact on track and rainfall prediction. 

5.5. Wave Modeling (WAVEWATCH III)

The HWRF-HYCOM-WAVEWATCH III coupled model has been built at NCEP, and URI 

is fitting in the advanced coupling approach developed previously with the GFDL-POMWAVEWATCH codes. This coupling includes using sea spray to improve and link 

various surface fluxes. OSIP 06-096 has directed NCEP to centrally maintain and develop

capabilities for running a site-specific version of the WAVEWATCH III wave model with 

applications at arbitrary U.S. coastal areas. Typically such models would be run at WFOs 

or NCEP partners. This project is now funded by HFIP through NOS HFIP storm surge 

funding, and in this context has been expanded to become a coupled relocatable wavesurge capability. As part of this project NOS is implementing ADCIRC inundation 

capability for the U.S. West Coast using NCO computers. The project is scheduled to begin 

HWRF Operational

2010 HWRF baseline

2010 HWRF upgrade with operational GFS

2010 HWRF upgrade with GFS upgrade

DSHP

Average Intensity Errors (Kt)

2008 and 2009 East Pacific Basin

NCEP FY10 Pre-Implementation Test HWRF Operational

2010 HWRF baseline

2010 HWRF upgrade with operational GFS

2010 HWRF upgrade with GFS upgrade

DSHP

Intensity Bias (Kt)

2008 and 2009 East Pacific Basin

NCEP FY10 Pre-Implementation Test HWRF Operational

2010 HWRF baseline

2010 HWRF upgrade with operational GFS

2010 HWRF upgrade with GFS upgrade

CLIPR

Average Track Errors –Frequency of Superior Performance (%)

2008 and 2009 East Pacific Basin

NCEP FY10 Pre-Implementation Test HWRF Operational

2010 HWRF baseline

2010 HWRF upgrade with operational GFS

2010 HWRF upgrade with GFS upgrade

CLIPR

Average Track Errors (NM)

2008 and 2009 East Pacific Basin

NCEP FY10 Pre-Implementation Test

Figure 5. HWRF FY2010 pre-implementation test results for 2008-2009 East Pacific Basin. The 2010 

baseline HWRF is identical to the operational HWRF and includes corrections to radiation calculations. The 

2010 HWRF upgrade configuration builds off the 2010 HWRF baseline and includes assimilation of satellite 

data on the 9 km nest, Gravity Wave Drag parameterization and updated surface flux exchange coefficients.
20

1 April 2010 and will be accelerated with additional NCEP manpower in the summer of 

2010.

Figure 6. Coupled HWRF-HYCOM results for 2009 tropical cyclones: (A) Mean track error and standard 

deviation; (B) mean intensity error and standard deviation. Number of samples is listed on x-axis, along 

with forecast hour. 

Operational HWRF-POM

Parallel HWRF-POM

Parallel HWRF-HYCOM

Operational HWRF-POM

Parallel HWRF-POM

Parallel HWRF-HYCOM
21

6. Stream 1 FY09 Activities at HRD

The Hurricane Research Division of NOAA conducts a yearly field program in hurricanes 

as a component of the HFIP program. Listed below are the components of that program 

conducted during the 2009 season.

• Participated in the Intensity Forecast Experiment (IFEX) 2009: WP-3D (17 

missions, all 1 P-3/double crew) and G-IV (14 missions) with HRD crews flew 

~200 h to gather data in Hurricanes Paloma (2008) and Bill, plus TS Ana & Danny

• Deployed 756 dropwindsondes and 238 AXBTs. 55 real-time TDR analyses 

transmitted to NHC and Doppler radial wind files transmitted to EMC. TDR superobservations transmitted in Paloma, Bill and Danny provided real-time assimilation 

of TDR data into ARW model. 60 real-time H*Wind surface analyses.

Figure 7a shows the wind field computed from the P3 tail radar data taken in Hurricane 

Bill. These data were transmitted in real time to Penn State and used to initialize their 

ARW ensemble model. Figure 7b shows the wind field analyzed by the Penn State EnKF 

system through assimilating the radar data. Figure 19, to be described later, shows the 

impact of this radar data on regional model forecasts.

Figure 7a. Wind observations retrieved from the NOAA P3 tail radar for Hurricane Bill 20 August 2009. 

Color coding for the winds is shown on the right in m/s.
22

7. Stream 2 activities: Results from FY08 and FY09 Demonstration 

System

7.1. The 2008 hurricane season

HFIP performed a series of experiments during the summer of 2008 which provided some 

preliminary results on a limited number of cases. These experiments were accomplished 

using the Texas Advanced Computing Center (TACC) computers in CY 2008. Because of 

the limited number of cases, the conclusions are only tentative. However, there is evidence 

that:

Global ensembles can provide improved guidance for forecasters at lead times of at 

least 5 days.

Introducing radar data taken by aircraft improves the initial state of the model, and 

hence, improves subsequent forecasts.

7.2. The 2009 hurricane season

During the 2009 hurricane season HFIP conducted the Stream 2 Demonstration program 

noted above. In 2009, we were not yet ready to implement Stream 1.5 (see Table 1) ) thus

the bulk of the work this last year focused on Stream 2 which included the near real-time 

demonstration system. There will be additional retrospective runs for the 2008 season to 

increase the statistical sample for the performance evaluation. 

Figure 7b. Initial wind fields at 850 mb in the ARW model run in real time using the radar data shown in the 

upper figure. Plots are for Bill at 00Z 20 August 2009. Color coding for the winds is in knots.
23

Table 4 summarizes the global models run during 2009. Table 5 is the same for the 

regional models.

Table 4: Global models run in the Demonstration System during 2009

30 km EnKF Data assimilation System was run during Aug. and Sept.

FIM Deterministic models run each day during Aug-Sep:

30 km (Initialized with GSI-3DVAR and EnKF).

15 km (Initialized with EnKF).

10 km (Initialized with EnKF).

Global Ensembles:

30 km FIM (initialized with EnKF) 20 members.

27 km GFS (Initialized with GSI-3DVAR) 5 members.

55 km NOGAPS (3DVAR then 4DVAR), 9 members

Table 5: Regional models run in the Demonstration System during 2009

Multi Model Ensemble (various initialization schemes):

(run for all storms—not all models present for all run times)

– HWRF 9km

– HWRF4 4km

– GFDL 7.5km

– HWRF-x 3km

– WRF/ARW/NCAR 1.3km

– WRF/ARW/FSU 4km

– COAMPS-TC 5km

–

Single model Ensemble (run for most storms) 

– WRF/ARW/PSU 4.5 km 30 members

a) Initialized with an EnKF system

b) Initialized with P3 radar data when available

In the following section we will describe some results from the Demo System. Later we will 

summarize lessons learned from the demo system and other HFIP work followed by a 

summary of challenges for HFIP learned from these lessons. 

During September and November 2009, NCEP set up experimental high resolution 

ensemble through the TACC high performance computer system. The configuration 

includes:
24

GFS 2008 version (T382L64)

Use T382L64 GSI analysis running ETR (Ensemble Transform with 

Rescaling) with cycling every 24 h (every 6 h for current operation) to 

generate 4 ensemble initial perturbations

Upgrade to T574L64 resolutions for integration

5 members (including control) forecast out to 168 h 

No stochastic perturbations

Period: 09/01 – 09/20/2009, 09/25-09/26/2009, 11/06-11/09/2009

Unfortunately, there were no strong storms during the period when the NCEP high 

resolution ensemble experiment was running. However, there were several cases where 

significant differences were found between the high resolution ensembles and the 

operational Global Ensemble Forecast System (GEFS) as shown in Figure 8. The high 

resolution ensemble successfully captured storm development reasonably well. Figure 9 

shows the statistics (small samples) of ensemble mean track errors from different 

resolutions. Note that there are only 5 members of T574L64 ensembles, and there is no 

significant difference from T190L28 ensembles with 20 members and well tuned model 

physics.

Preliminary results from the high resolution GEFS ensemble experiments, indicate that 

enhanced ensemble performance may require tuned initial perturbations, optimum cycling 

(every 6 h), and stochastic perturbations.

Figure 8. 24-hour 1000hPa wind forecast from 00UTC 20 September 2009 for hurricane Fred (07L). The top 6 

panels show the high resolution analysis (T382L64/GSI analysis, top left) and low resolution ensemble forecast 

(T126L28). The bottom 6 panels show the high resolution analysis (T382L64/GSI analysis, top left) and high 

resolution (T574L64) ensemble forecast.
25

In Figure 10 we show ensemble forecast tracks for two different times during Hurricane Bill. 

Note that in both cases shown, the observed track very closely follows a path down the 

middle of the ensemble though there is a spread in the track forecasts particularly as shown 

by the various tracks from the deterministic runs on 08/14/09. Note that a deterministic run 

is essentially a member of a virtual ensemble and so the deterministic runs can show a spread 

similar to the ensemble.

For the second time period, the spread of the tracks for all members of the ensemble and the 

deterministic models was very narrow indicating a high degree of confidence in the forecasts. 

The verification results were excellent for this case.

In Figure 11 the lower panel shows a period in the storm when the ensemble of forecast 

tracks were to the right of the observed track although perhaps another way of looking at the 

forecast is that the verifying track was near the left edge of the ensemble of forecast tracks. 

Figure 9. Ensemble mean track errors for the period of 09/01 – 09/20/2009. AEMN (red) is for operational

(T126L28 and 20 members). OEMN (blue) is for parallel (T190L28 and 20 members) implemented in February 

2010. GVMN (green) is for experiment (T574L64 and 5 members). AVNO (black) is for NCEP deterministic 

forecast at T382L64.
26

Figure 10. Forecast tracks by the FIM global model runs and FIM global ensemble. The upper figure is for an 

initial time of 0000 UTC 8/14/09 and the lower figure was initialized at 0000 UTC 8/20/09. Note that in the 

upper figure tracks for two storms are shown: AL02 was the disturbance that became tropical storm ANA and 

AL90 the disturbance that became Hurricane Bill. The longer tracks are those associated with Bill. In the lower 

figure only Bill is shown. The black track in both figures is the observed track for Bill. The white tracks are from 

the 20 member 30 km ensemble, magenta and green are 30 km deterministic runs with different initial conditions 

(EnKF vs. GFS), the yellow and red tracks are from the 15 km deterministic model with the different initial 

conditions and the blue line is from the 10 km deterministic run using the EnKF initial conditions. In all cases the 

forecast length was 168 h or 7 days.
27

The upper panel in Figure 11 shows each member of the 30 km ensemble run from the same 

initial time as the lower part of the figure and verifying 5 days later. Each ―postage stamp‖ is 

centered on the observed location of the hurricane. This is another way to look at the 

ensemble information and can be used to give some idea of the rate of intensification forecast 

by the various members. However, note that at 30 km resolution, the deep central pressure 

observed in hurricanes will not appear since the central region of the hurricane is not 

resolved. The track forecast information is also available in this presentation, but note that 

all forecasts are to the east and north of the observed location. This information is also 

available in the lower part of the figure. 

Figure 11. The lower figure is the same as for Figure 10 except it was initialized at 0000 UTC 08/16/09. 

The upper figure shows the 20 members of the 30 km ensemble initialized at 0000 UTC 08/16/09. The upper 

figure is a 120 (5 day) forecast verifying at 0000Z UTC 08/21/09. The contour field is sea level pressure 

every 4 mb and the forecast central pressure is indicated. The red dot on each figure is the verifying location 

at 0000 UTC 08/21/09.
28

The presentation in the upper part of Figure 11 also provides an indication of the probability 

of genesis. The hurricane that appears in the lower right in about half of the members was 

the one that eventually slowly and briefly intensified to Tropical Storm Danny. This 

ensemble would indicate about a 50% chance that the system following Bill would intensify 

into at least a tropical storm. So far we have not yet finished developing the software to 

verify the genesis statistics (that is underway) but a cursory examination of the results 

indicate that the global model used in these experiments is over active with respect to 

hurricane genesis. At this point that is true of almost all global and regional models. The 

reason is not known at this time but may be related to physics parameterization or the lack of 

an active ocean coupled to the atmosphere model, particularly with the global models.

Figure 12 shows two snapshots from the 10 km FIM global model started at the same time as 

the lower panel of Figure 10; the upper panel of Figure 12 is a 72 h (3 day) forecast and the 

lower panel is a 168 h (7 day forecast). These panels illustrate the global nature of the 10 km 

forecasts. The top panel shows Hurricane Bill off the East Coast of the U.S. and Typhoon 

Vamco in the central West Pacific. At the end of the forecast period, bottom panel, both 

storms had been absorbed into, respectively, the Icelandic Low and the Aleutian Low. The 

red lines on the figures show their tracks. To the east of Bill, the development of the next 

system that became Danny is apparent. These forecast indicated a very active genesis period 

in the Eastern Pacific with four tropical depressions or tropical storms active on 08/27/09 

according to the model. This verified to some extent in that during this forecast period three 

named storms developed and one, Jimena, became a category 4 hurricane. But generally it 

was noted that the FIM over-predicted genesis.

Figure 13 shows verification statistics for models run from the GSI initial state (the 

operational data assimilation system) and the EnKF system. These data assimilation 

systems were used to initialize the operational GFS and the FIM. Shown in the figure are 

the RMS error and anomaly correlation coefficient for 72 h forecasts with both models. 

Here we show verification of just one field: the tropical winds (30N-30S) at 250 mb. It 

turns out that this field showed one of the biggest differences between the models and is 

important in determining hurricane environment. Other fields such as the 250 mb wind in 

polar regions showed much less difference. 

Recall that smaller RMSE is better, and larger anomaly correlation is better. Note that when 

the EnKF initialization replaced the GSI initialization in both models there was a substantial 

improvement in the forecasts for both models. Comparing the models when the same 

initialization system is used for both, the FIM model was better than the GFS system. Note 

however, that the FIM was run at 15 km and the GFS at 30 km. It is not known at this time 

whether this improvement was due to the increased resolution of the FIM over the GFS or to 

differences in forecast models or initialization schemes. In addition the EnKF system 

introduced a vortex based on observations of the individual hurricanes which led to initial 

vortices that were initially considerably stronger than when the models were initialized by 

the GSI. We return to this issue in the ―Encouraging Results‖ section below. 
29

Figure 12. The above two plots are snapshots of the 10 wind speed and wind barbs from the 10 km global 

model initialized at 0000 UTC 082009, the same as the lower panel in Figure 10. The color shading shows 

the 10m wind speed in knots. Red lines are the forecast tracks from the initialization time. The upper figure 

is a 72 h (3 day forecast) for 0000 GMT 082309; the lower panel a 168 h (7 day) forecast for 0000 UTC 

08/27/09.
30

Figure 14 shows the impact of resolution in the FIM global model on intensity forecast bias. 

All three models were initialized with the 30 km EnKF system noted above, and use a vortex 

relocation process to initialize the vortex at the correct location so all start out with the same 

initial bias in part because the vortex is not well resolved in the 30 km model. With time the 

higher resolution models improve relative to the 30 km run with the 10 km model performing 

the best. The smaller biases at the longest lead times are partly because the weaker storms 

have decayed by then leaving the stronger and more easily forecast storms. There are also 

fewer cases in the statistics at the longer lead times which may bias the result. The increasing 

negative bias out to 72 h is because there are many weak storms in the 2009 sample, and 

these were poorly forecast but the sample size is very small.

Figure 13. Verification statistics for forecasts run from initial conditions specified by the GSI operational data 

assimilation system and the EnKF experimental data assimilation system. The statistics for a 72 h forecast of 

winds in the tropics at 250 mb are shown for the Global Forecast System (GFS) operational model run at 30 

km and the experimental FIM run at 15 km each using both sets of initial conditions. The legend on the upper 

right of each figure indicates the initialization system and model used for each curve. The number in 

parentheses in the legend shows the average skill for each configuration. Date is indicated along the horizontal 

axis using yymmdd at 0000 UTC. Shown are the RMS vector wind error (m/s, top panel) and Anomaly 

Correlation (%, lower panel). In the top panel, lower is better and in the bottom panel higher is better.
31

Figure 14. Intensity bias in the FIM global model for various lead times (hr) and resolutions of 30 km (blue), 15 

km (red) and 10 km (purple). The numbers labeled N along the bottom indicate the number of cases contained in 

the statistics. 

Figure 15. Comparison of track errors for the 15 km FIM global model (red bars) and the 30 km FIM 

global ensemble (blue bars) for all basins (Atlantic, East Pacific, Central Pacific and West pacific) for 

2009.
32

Figure 15 compares the track accuracy for the mean from the 20 member, 30 km ensemble 

with the error of the 15 km deterministic global model. Though the number of cases is still 

rather low, some promising improvements are suggested using the ensemble, especially at the 

longer lead times (however, note the small sample sizes for these lead times). HFIP plans to 

run the FIM global ensemble at higher resolution (15 km) where we expect to see still further 

improvement.

8. Encouraging Results from FY09

Table 6 gives a high level overview of some encouraging results from the HFIP work 

during FY09. 

Table 6 Encouraging Results from FY09 Demonstration System

Advanced data assimilation systems (EnKF, 4DVAR) appear to improve global forecasts 

over the current operational data assimilation system

This was a major result for FY09. The computing resources that HFIP was able to locate 

outside NOAA allowed an extensive test of the EnKF data assimilation system and a 

comparison with the current GSI data assimilation system. Figure 13 shows one example 

of this comparison. It shows comparisons of the EnKF system used in two of the models 

1. Advanced data assimilation systems (EnKF, 4DVAR) appear to improve global 

forecasts over the current operational data assimilation system.

2. High resolution global and regional ensemble systems are showing promise but 

require further testing and evaluation.

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

on available computing resources meaning higher resolution is definitely 

possible. 

4. The multi-model regional ensemble showed promise.

5. There is some preliminary evidence that airborne radar data used to initialize 

the hurricane vortex in regional models can improve forecasts of track and 

intensity.

6. Various physics parameterization schemes were examined using HWRFX and 

COAMPS. Great sensitivities of storm structure to variations in the model 

physics were noted, strongly suggesting possible routes for improving the 

physics package in the operational HWRF model.

7. The use of AXBT data assimilation also showed promise.
33

described briefly above, the FIM and the GFS, compared to the same models initialized 

with the GSI, the same system as is being used operationally. Shown are statistics for 

forecasts of the 250 mb tropical winds. The largest impacts were found in the tropics 

though positive impacts were noted elsewhere and for other quantities. For the GFS, the 

improvement in the forecast of the high-level tropical winds was about 10%.

Figure 16 compares performance of the GFS model initialized both with the GSI and EnKF 

with the emphasis on hurricane track. Out to 4 days in this comparison there is a steady 

improvement with forecast lead time with about a 20% improvement at 4 days. In this 

figure the same bogus vortex was used in the GSI as used in the EnKF. Day 5 was left off 

the figure because there were very few cases (6) at that lead time for the comparison. 

Figure 17 compares the NAVDAS 3DVAR system with the Navy’s new 4DVAR system. 

As with the EnKF system, 4DVAR shows improvement in track forecasts for all lead times 

and by Day 5 the improvement amounts to 18%.

Figure 16. Comparison of hurricane track forecast errors in the GFS when EnKF and GSI are used to 

initialize the model. Data are from 2009 for all basins.
34

0

50

100

150

200

250

300

350

24 48 72 96 120

AR

3DVAR

CONW

JTWC

NOGAPS 2009 Western North Pacific (17W-23W)

9/24 to 11/1: Homogeneous TC Forecast Error (nm)

97 83 71 55 47 Number of Forecasts

4DVAR

3DVAR

Consensus

JTWC

Improvements with 

4DVAR at 2-5 days.

Finally Figure 18 compares the performance of an ensemble forecast from the ECMWF 

with the performance of an ensemble forecast using the GFS at much higher resolution than 

is currently used in the GFS operational ensemble (at the same resolution as the 

deterministic GFS operational model). Here the EnKF data assimilation systems used to 

initialize the GFS ensemble. The ECMWF model is globally considered to be the current 

leader for global model forecasts. Using both higher resolution in the GFS and a more 

advanced data assimilation system (EnKF in this case) the skill of the GFS system can be 

made to match that of the ECMWF. It has not been established how much of this ability to 

match skills comes from the data assimilation system and how much comes from improved 

resolution though both are likely important. It is also not known which of the data 

assimilation systems, 4DVAR or EnKF, will prove better in the end but the results shown 

in Figure 18 suggest that either provide a significant improvement in hurricane track 

forecast skill. A hybrid that uses both may be the optimum approach to data assimilation 

although this hypothesis still needs to be tested.

Figure 17. Comparison of NOGAPS initialized with 4DVAR and 3DVAR and with the official JTWC 

forecast. Consensus is the mean of the 3DVAR and 4DVAR forecast
35

A regional EnKF (developed at CIRA) has been employed to produce situation-dependent 

background (SDB) error covariances for future use for anisotropic error covariance 

modeling at NCEP. The system employs 32 ensembles, and is used with HWRF (moving 

nest resolution 6.6 km) and with NCEP operational observations. The system directly 

employs HWRF and GSI infrastructure components through specially developed interface 

scripts. In this preliminary stage, mostly devoted towards developing and testing the new 

EnKF-HWRF system, the system was applied to Hurricane Gustav (2008). There are 

several conclusions that can be drawn: (i) moving nest can be successfully initialized from 

outer domain ensembles, (ii) EnKF-HWRF system creates error covariances that are 

smooth and dynamically consistent, and (iii) there is a great need for cloudy radiance 

assimilation. The problem of initializing the moving nest exists because the ensemble 

perturbations from the previous cycle cannot be used in the current cycle, as geographical 

domain of the nest has changed. We adopted a strategy to reinitialize the moving nest 

ensembles from outer domain ensembles (done automatically in HWRF), and our results 

confirm that this is a viable approach to EnKF assimilation of inner domain observations. 

In Figure 19 we illustrate the error covariance structure in the moving nest. 

Figure 18. The NCEP GEFS (Global Ensemble Forecast System) run at T 382 initialized with an EnKF 

system compared to the ECMWF ensemble at T399 initialized with 4D-VAR
36

(a) (b)

In Figure 20 we show the root-mean-squared (RMS) errors with respect to wind and 

satellite observations. The figure indicates anticipated reduction of the RMS errors due to 

assimilation, but also shows a general unavailability of satellite observations due to cloud 

clearing. 

(a) (b)

Figure 19. Error covariances from the first ensemble member at 850 hPa, valid 12UTC on 31 August 2008 for: 

(a) wind, and (b) specific humidity. Error covariance structure is smooth and dynamically realistic.

Figure 20. RMS errors with respect to observations for hurricane Gustav: (a) east-west wind component, 

and (b) AMSUB satellite radiance (cloud cleared). One can notice general improvement of RMS errors due 

to analysis, but also that very few satellite radiance observations are available (b).

Figure 20. RMS errors with respect to observations for Hurricane Gustav: (a) east-west wind component, and 

(b) AMSUB satellite radiance (cloud cleared). One can notice general improvement of RMS errors due to 

analysis, but also that very few satellite radiance observations are available..
37

High resolution global and regional ensemble systems are showing promise but require 

further testing and evaluation

For the global models we would expect that most of the improvement in hurricane forecasts 

would be for track. Because the hurricane is at best only marginally resolved in the global 

models, (since there are limitations that computational power places on that resolution) the 

intensity is usually under-predicted (see Figure 14). Furthermore, it is not surprising that as 

the resolution increases the intensity is less under-predicted, also shown in Figure 14. Thus 

for the global models the emphasis should be on track for the longer lead times (4-7 days) 

Figure 15 compares track forecasts of a 15 km deterministic forecast with the mean of a 21 

member ensemble both using the FIM and EnKF for initialization. Even though the 

deterministic model has twice the resolution of the ensemble (we cannot yet run the global 

ensemble at higher resolution) the track forecasts of the ensemble appear to be superior to 

the deterministic model.

Figures 21 and 22 illustrate how regional ensembles can be used in intensity forecasting 

and came from HFIP work in 2009. Figure 21 shows tracks from an ensemble of 30 

members using the ARW version of the WRF regional model. The color coding denotes 

tracks to the left (orange), middle (green) and right (blue) of the fan of tracks produced by 

the ensemble. Note that the observed track is to the left of the ensemble mean track (not 

shown but easily inferred). On the right hand side of Figure 22, traces of the maximum 

wind over the 4 days of the forecasts are shown and the same color coding as in Figure 21 

is used to relate intensity curves to the members falling to the left, middle and right of the 

forecast tracks.

7

2/4/2010 7

Figure 3

Figure 21. Track forecasts from a 30 member regional ensemble using the WRF ARW regional model for 

Typhoon Morokot from 6 September 2010 through 9 September 2010 in the Western Pacific. The orange 

color signifies the 1/3 of the storms that were near the left side of the ensemble of tracks, green the 1/3 near 

the middle and blue the 1.3 near the right at the 24 h forecast. The heavy orange line is a single deterministic 

run; the black line is the observed track.
38

Note from comparing Figures 21 and 22 that tracks on the left side of the ensemble fan 

tended to have intensities greater than storms on the right side. In fact note also that the 

observed track was located toward the left side of ensemble fan. We present this not as a 

proof of skill but rather as a way to present ensemble information to forecasters. As the 

ensemble will become available several (8-12) hours after the initial forecast time, 

subsequent information may be used by the forecaster to adjust the forecast towards the 

more likely simulations.

To continue the spirit of demonstrating a ways to present ensemble information, the left 

side of figure 22 shows the frequency of forecast intensities at three different times denoted 

A, B and C. A and B are at the 48 and 54 hour forecast lead times, respectively, and 

display a typical probability distribution, when there is skill, with the most probable being 

near the mode of the distribution. A and B are six hours apart and show a forecast intensity 

that is close to the observed though somewhat weaker. A comparison at times A and B 

suggest that at 48 hours the ensemble is forecasting a slight weakening. Its was in fact 

observed beginning a long period of slow weakening.

Plot C is for much later in the forecast, after 3 days, and the flat distribution suggests that 

the ensemble has lost predictability in intensity by then. We note below that one of the 

Figure 22. The same as Figure 21 except for the maximum wind over the four days of the forecast. The 

color coding is the same as in Figure 21 where orange are the intensity plots for tracks near the left edge of 

the ensemble of tracks, green are those near the center and blue those near the right. The observed wind 

maximum wind speeds are shown by the black line. The bar graphs on the left edge of the figure show the 

number of forecast maximum winds that fall in various bins of 5 m/s. They apply at the times A, B and C 

as noted in the right hand figure. 
39

challenges of HFIP will be to convince forecasters of the value of ensembles, how they can 

be used, and develop products that they will find useful. Figures 21 and 22 are presented as 

a way to begin the dialog between ensemble scientists and forecasters on how to present 

ensemble information. 

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

available computing resources meaning higher resolution is definitely possible. 

One of the assumptions of the HFIP program is that the use of high resolution ensembles 

both in the regional and global models will be a key to meeting its goals of a 20% 

improvement in 5 years and 50% in 10 years. Of concern is the computing capability 

necessary to run the high resolution models in ensembles, particularly the global models, 

using a more advanced data assimilation scheme such as 4DVAR or EnKF. An objective 

of HFIP is to eventually be able to run a global ensemble of at least 15 km with 20 

members. During the summer of 2009 summer we were able to run a 30 km ensemble 

using an EnKF data assimilation system. The 20 member ensemble was run once per day 

each day for 2 months. The data assimilation system ran continuously for the 2-month 

period 4 times per day. Both systems were run in real time on the Texas Advanced 

Computer Center computers.

While the 30 km resolution is still half of our ultimate goal for the global ensemble, it still 

is one of the highest resolution ensemble runs anywhere in the world in FY09. Thus with 

the computational resources available we were able to prove that at least a 30 km ensemble 

is possible though the currently available operational computing will not allow it. This 

provides a guide on the capabilities of future operational computing. 

There is some preliminary evidence that airborne radar data used to initialize the 

hurricane vortex in regional models can improve forecasts of track and intensity.

Radar data from the NOAA P3 aircraft tail radar have been gathered in hurricanes for many 

years and used for understanding the structure and dynamics of the inner core of the 

hurricane. Only recently have there been attempts to incorporate that data into the 

initialization of the hurricane vortex in models. As we will note below, one of the lessons 

learned from the FY09 hurricane season was that the current methods of initialization of 

regional models was likely a reason for poor model forecasts during the 2009 season. A 

characteristic of the FY09 hurricane season was that many of the storms experienced 

considerable shear during their life cycle. When the storms were initialized at a time shear 

was present, the intensity forecasts were typically poor and often continued to intensify in 

the model even though the actual storm decayed under the shear. While there are a number 

of possible reasons for this problem such as a problem with the physics parameterizations, 

the convective parameterization and initialization of the vortex are particularly likely 

causes. Many current initialization methods introduce a vortex that is mostly vertical 

whereas in sheared storms the vortex becomes tilted. 

One solution to the initialization of the vortex is to use high resolution data taken within the 

hurricane core such as radar data taken from aircraft. Figure 23 shows some tests of 

incorporating the radial wind from the aircraft radar using an EnKF approach to initialize 
40

the model. The example shown in Figure 23 suffers from there being a small number of 

cases available with radar data but it does indicate promise that the radar data can improve 

the regional model forecasts of intensity out to 48 h and track beyond.

Figure 23. Comparison of regional model forecasts using the Penn State WRF system with an inner nest of 

4.5 km and an EnKF data assimilation to include airborne radar data. The blue bars show intensity and track 

forecasts without the radial winds from the radar data but using all other available data, and the red bars show 

the results when the radial winds from the radar data are added. The colored lines show the number of 

samples used in the comparison.
41

Figure 24. The above figure shows the overall track and intensity errors from the Multi-Model 

Ensemble (Table 5) for the 2009 Atlantic season. The ARFS model (WRF/ARW/FSU in Table 5) 

performed reasonably well for both the track and intensity. The ensemble mean (ENSM) performed quite 

well for track where it ranked among second or third for most of the forecast hours among the 

component models. For the intensity errors the ensemble mean was not that good as compared to the 

track forecasts. It suffered from the fact that H3HW (HWRF-X) and HWR4 performed badly during the 

season. Among the models the GFDL, HWRF and COTC (COAMPS-TC, see Table 5) performed better 

compared to their counterparts. AHW1 is WRF/ARW/NCAR in Table 5. OFCL is the official forecast 

error for the 2009 season for comparison.
42

The Multi-Model regional ensemble run this summer showed promise.

In Table 5 the regional multi-model ensemble was outlined as part of the 2009 Demo 

system. Figure 24 shows the raw results from that experiment and Figure 28, the bias 

corrected results. The figures indicate that the ensemble provides useful statistics even in 

the raw data. For example the ensemble mean provides estimates of position error that are 

comparable to the official forecast. The same is true for the intensity. Note that the poorest 

performing model, the 4 km HWRF, was put together very quickly just before the 

hurricane season and had several, now known, serious problems. It probably should not be 

included in the statistics of the ensemble mean but we have included it for completeness. 

The FSU ARW model was only run on a few cases (28 compared to 58 overall cases) in 

2009 and is probably strongly biased toward better forecasts than the other models as a 

result. Still, the results show promise that, with adequate preparation of the component 

models and better statistical post processing, it can provide useful forecast guidance.

Various physics parameterization schemes were examined using HWRFX and COAMPS. 

Great sensitivities of storm structure to variations in the model physics were noted, 

strongly suggesting possible routes for improving the physics package in the operational 

HWRF model.

To help understand the response of the operational HWRF model to various physics 

permutations available in the WRF modeling framework, as well as to explore how to 

improve the operational HWRF model physics package systematically, an idealized 

initialization package has been implemented in the experimental version of the HWRF 

(HWRFX) model during FY2009. This package allows users of the HWRFX model to 

initialize the model with a weak axisymmetric vortex disturbance in various idealized 

tropical environment conditions that are favorable for the vortex disturbance to develop 

into a hurricane.

Sixteen physics sensitivity experiments with various permutations of physics options are

available within the WRF modeling framework. All of the experiments are run with a 9:3 

km nested grid configuration. They are all initialized on an f-plane located at 12.5°N with 

a prescribed axisymmetric vortex of the maximum surface tangential wind of 15 m/s and 

the radius of surface maximum wind of 90 km. The quiescent environment with the widely 

used Jordan sounding and a constant sea surface temperature of 29°C is prescribed for 

obtaining the initial mass and wind fields by solving the nonlinear balance equation for the 

prescribed vortex. 

Table 7 indicates the permutations of physics options used in the 6 highlighted experiments 

that are shown in Figures 1-2 for the 3-km grid. In Table 7, note that the physics 

configuration used in Experiment 2 is very similar to that in the operational HWRF model. 

Also note that the SAS scheme is turned on for the 3 km grid in order for the model to 

produce the storm intensity comparable with the theoretical prediction.
43

Table 7. Physics permutations used in 6 of the 16 sensitivity experiments. MYJ 

refers to the Mellor-Yamada-Janjic boundary layer scheme, while GFS refers to the 

GFS boundary layer scheme. SAS refers to the Simplified Arakawa-Schubert 

convective parameterization scheme. Option 1 for the radiation scheme refers to both 

the Dudhia shortwave scheme and the RRTM longwave scheme. The option 5 

microphysics scheme refers to the Ferrier scheme. D1 and D2 refers to 9 km and 3 

km grids respectively.

Experiment # Boundary 

Layer 

Scheme

Convective 

parameterization 

scheme (D1/D2)

Radiation 

Scheme

Microphysic

s scheme

Other

1 MYJ SAS/SAS 1 5 NA

2 GFS SAS/SAS 1 5 NA

3 GFS SAS/SAS 1 5

momentum 

and thermal 

diffusivity 

increase in 

ABL

4 GFS SAS/SAS 1 5

Momentum 

diffusivity 

increase in 

ABL only

5 GFS SAS/SAS 1 5

momentum 

and thermal 

diffusivity 

decrease in 

ABL

6 GFS SAS/SAS

1 5

Sea-spray 

modified 

surface drag
44

Figure 25 shows that while the maximum surface wind speed in Experiments 1, 2, 3, 4, and 

5 levels off after 60 h into the forecast, the minimum sea-level pressure continues to 

decrease, confirming the well-known problem of the wind-pressure relationship in the 

HWRF model. This problem is significantly improved when a different surface drag 

parameterization associated with the sea spray physics is used (Experiment 6). This 

illustrates that the surface drag is a key parameter that controls the wind-pressure 

relationship in an intensifying storm. The structural differences in the time-mean 

azimuthally averaged secondary circulation shown in Figure 29 from Experiments 1, 2, 3, 

4, and 5 indicate that in the HWRFX model, the ABL diffusivities above the surface layer 

control the size of the storm.

The use of AXBT data assimilation also showed promise.

EMC has conducted tests for AXBT data assimilation into RTOFS, and the preliminary 

results show an improvement in the simulation of the upper ocean structure. For example, 

Figure 26 shows model profiles with and without AXBT assimilation. The project proved 

to be successful in testing: 1) the NCO decoder and data format to Buffer ingest system; 2) 

the data pipeline set up in NCO in collaboration with EMC and HRD; and 3) the data 

assimilation algorithms in RTOFS.

Figure 25: Left panel is the maximum surface wind speed in ms-1and the right panel is the minimum 

sea level pressure in mb for 6 of the 16 sensitivity experiments highlighted in Table 7.
45

9. Lessons Learned from FY09 HFIP Activities

Above we noted a number of encouraging results from the FY09 program. There were also 

a number of lessons learned that will help us guide the program forward. In this section we 

outline those lessons. In the next section we will discuss the challenges posed by these 

lessons and our strategy to meet the challenges. Table 8 outlines the lessons learned.

Table 8. Lessons Learned from FY09 HFIP Activities

• There is a major problem with initialization of regional models.

• Both the regional and global models greatly over-predict genesis.

• Simply increasing the resolution of the regional models alone does not lead to 

improvements in model guidance.

• The research community is doing a poor job of conveying the value and use of 

ensemble information to the forecast community.

• Model performance metrics for hurricanes must include more than just track 

and intensity metrics.

Figure 26. Sampling locations for the pre-storm AXBT survey on 17 July 2009 (top) and model profiles 

with (multi-color representing hourly profiles) and without (green) assimilation, superimposed on data 

assimilated (blue) (bottom): current quality control employed in RTOFS accepted, during 3 days 

assimilation, 24 out of 57 profiles for the 1st day of assimilation, followed by 18 for the 2nd day and 12 for 

the 3rd day.
46

There is a major problem with initialization of regional models.

This became particularly apparent during the FY09 season where many of the storms were 

initialized at a time when vertical shear was strongly affecting the hurricane. For most of 

the models we ran as part of the multi-model ensemble, the storms in the model continued 

to intensify even though the observed storm decayed often to a remnant low in the forecast 

period. Figure 27 is a typical illustration of a forecast of a highly sheared storm, in this 

case Erika on 2 September 2009. Note that most of the models, including the operational 

models, HWRF (blue line) and GFDL (light blue line) forecast the hurricane to grow to at 

least a Category 2 storm in 120 h. The storm was a remnant low at that time.

Plots like this one from the multi-model ensemble stimulated much discussion within the 

HFIP group, speculating on the cause of the spurious intensification. It was generally 

thought that one problem, especially with the sheared storms, was the initialization of the 

regional model. Most of the models above used a process to ―move‖ the hurricane center 

and observed minimum pressure to the observed position either using a bogus vortex or 

moving the storm from the previous forecast to the observed position. Both methods 

generally result in a storm that is mostly vertical. In shear the storms start to tilt, 

sometimes strongly, and this is not handled well with current initialization systems. Of the 

one model that used a data assimilation system in the group shown above, the NCAR ARW 

model provided the best forecast in this case. In other cases it did not do as well. 

For the case shown in Figure 27, most of the models had a very rapid intensification after 

start up. In other cases the models took some time to spin up. This is also an indication of 

inaccurate initial conditions.

Figure 27. Forecasts of maximum wind from the various models used in the multi model ensemble color 

coded on the left for Hurricane Erika. Initialized 9 February 2009.
47

In additional to problems with the initial conditions, it was also thought that the convection 

parameterization may also be a factor in the spurious intensification. As parameterized, 

most convection in the models is vertical extending through deep layers. This orientation 

in association with a vortex that is erroneously vertical rather than tilted can result in the 

intensification observed in the models. We will outline our plans to address this and other 

lessons learned in the section on ―Challenges‖.

Both the regional and global models greatly over-predict genesis.

The over-prediction of genesis in the FIM global model was noted in Figure 12 for the 

Eastern Pacific. In FY09 we did not have tracker software available to identify and keep 

track of all storms generated by the various models. Our sense of over-genesis is based on 

observing forecast maps. A tracker is under development and we will quantify the genesis 

allowing us to compute the probability of detection (POD) and the false alarm rate (FAR) 

of genesis. We suspect that the POD is near 1 meaning we detect most cases of observed 

genesis, however, we also suspect we have a very high FAR. This result is generally 

expected for all current global and regional models. 

The problem with over-genesis is particularly important for forecasts that go out 5 to 7 

days. Many storms last less than that and so in order to predict accurately we need to be 

certain that the storms that are being forecast to form are going to form. In addition the 

presence of other spurious storms relatively near the actual storms in the model (such as the 

string of storms in Figure 12) can affect the track and intensity of the actual storm in the 

model through vortex/vortex interaction.

The cause of the over-prediction of genesis is thought to be related to problems with the 

physics packages used in the models. There have been some preliminary indications that 

the shallow convection parameterization may play a role in the over-genesis but it could 

also be related to surface fluxes and PBL parameterization.

Simply increasing the resolution of the regional models alone does not lead to 

improvements in model guidance.

In FY09, HFIP funded a project conducted by the DTC to examine whether simply 

increasing resolution in regional models would lead to a significant increase in the skill of 

the models. This was known as the High Resolution Hurricane (HRH) test. In these tests, 

5 groups around the country ran their models at two different resolutions (roughly 10 km 

and 5 km but that varied with model) over a total of 69 cases (a case is a single forecast for 

a single storm) from a variety of storms selected by forecasters at the NHC. Table 9 

summarizes the report filed by the DTC for the HRH test. 
48

Table 9. HFIP High-Resolution Hurricane Test 

Evaluation by the Developmental Testbed Center (DTC)

Ligia Bernardet, Louisa Nance et al:

• Runs for up to 69 cases at two or more horizontal grid spacings were submitted 

for evaluation of impact of resolution on track and intensity forecasts.

• Increased resolution did not substantially improve forecasts for any model.

• Modest improvement (a few lead times) were seen for HWRF-X (9 and 3 km) and 

AHW (13.5 and 1.5 km) in track and/or intensity. GFDL (9 and 6) showed no 

difference and COAMPS-TC (9 and 3) and UW-NM had some degraded tracks.

• May need better physics and/or initialization to realize benefits of higher 

resolution.

• Final Report is at: 

http://www.dtcenter.org/plots/hrh_test/HRH_Report_30Sept.pdf 

The main conclusion was that simply increasing the regional model resolution does not 

significantly improve the skill of the forecasts. Many say that this should not have been a 

surprise but it clearly indicates that we need to address other aspects of the models before 

or in parallel with increasing resolution. This includes both improving the initialization, as 

noted above, and carefully tuning the physics packages for the target resolution.

The research community is doing a poor job of conveying the value and use of ensemble 

information to the forecast community.

The research community is generally convinced that use of ensembles has been proven to 

provide better forecasts than a single forecast by a model, known as a deterministic 

forecast. In fact a deterministic forecast can be regarded as a member of a virtual ensemble 

and the fan of tracks shown for a typical ensemble forecast in Figures 10, 11 and 21, for 

example, suggests that the deterministic model forecast could be anywhere within the fan. 

Using statistics from the ensemble there are ways to predict which of the members of the 

ensemble has the highest priority of being what will be observed.

Still the forecasters doubt the value of information in an ensemble beyond the consensus of 

the various deterministic models that are currently used. The two communities, research 

and operations, need to work together to develop the products from the ensemble that will 

be most useful in preparing forecasts. This will include developing products beyond the 

ensemble mean, for which groups of single-model ensembles may prove beneficial.
49

Model performance metrics for hurricanes must include more than just track and intensity 

metrics.

Comparing the storm intensity and structure among the 6 experiments shown in Figures 25 

and 29 indicates that storms with different sizes and structural characteristics may share 

very similar intensities in terms of maximum surface wind speed and minimum sea-level 

pressure. Therefore we need metrics beyond the usual track and intensity metrics in order 

to diagnose reasons for poor model performance. These statistics should include measures 

of storm size and structure.

To further highlight the issue of dependence of storm size and structure on physics, Figure 

28 compares Hovemoller diagrams of the axisymmetric mean tangential winds from an 

idealized case using the Experiment 2 physics configuration and a run in which the surface 

roughness formulation is replaced with the conventional Channock formulation. It is obvious 

that the run using the Experiment 2 physics configuration shows a stronger inner core than 

the counterpart produced in the run using the same physics configuration but with the 

conventional Channock formulation. 

Of course the storm size and structure are also strongly dependent on grid resolution. 

Figure 30 compares the forecasted warm core development in an idealized run using the 

Experiment 2 physics configuration with that from a 9 km, single grid run using the same 

physics. As depicted by the temperature perturbation and e (in K and contours indicated 

by black color) in Figure 30, moisture and heat are carried to the middle and upper 

troposphere by thermal plumes in both the 3 km and 9 km runs, but the overall structural 

characteristics of the two runs are significantly different. All these results strongly suggest 

the need to verify the model storm structure for real-time event forecasts. More 

importantly, they suggest that track and intensity evaluation metrics are not sufficient for 

fundamental improvement of the operational HWRF model.

Figure 28: Hovemoller diagrams of the axisymmetric mean tangential winds from an idealized run using the 

Experiment 2 physics configuration (left panel) and a run (right panel) using the same physics configuration 

except that the surface roughness formulation is replaced with the conventional Channock formulation. The 

contour lines from the outer to the inner core region are respectively, 17.2 ms-1, 33 ms-1, 43 ms-1, 50 ms-1

and 59 ms-1representing the radius of gale forced winds and core of hurricane winds starting from minimal 

category of 1 to minimal category of 4. 
50

Experiment 1 Experiment 2

Experiment 3 Experiment 4

Experiment 5 Experiment 6

Figure 29: The 60-72 h mean azimuthally averaged secondary circulation (arrows), vertical velocity (color 

shaded contours) and radial winds (black contours mb for 6 of the 16 sensitivity experiments highlighted in 

Table 7.
51

3 km 3 km

Figure 30. East-west cross-sections of instantaneous temperature perturbations (white contours and 

color shaded) across the eye-wall region to depict the warm core development in an idealized run using 

the Experiment 2 physics configuration and a 9 km, single grid run using the same physics. Panels (a) 

and (b) are for the 9 km run valid at 9 h and 24 h into the run, and panels (c) and (d) are for Experiment

2 valid at the same times for the 3 km run. The vector indicates the direction of the vertical motion and 

the black contours are equivalent potential temperature. 
52

10. Challenges for HFIP beyond FY09

The lessons learned that have been outlined in the previous section suggest a number of 

challenges that must be addressed before HFIP can meet the goals it has set out for itself. 

These are discussed below.

A vast majority of model forecasts will be initialized for storms for which there is no 

aircraft data available.

Above we noted that a major problem identified in FY09 was initialization of the regional 

models. The most promising way to improve the initialization would be to use all available 

data in the vicinity of the hurricane, especially satellite data and use it within an advanced 

data assimilation system to provide a more accurate initialization of the vortex. For many 

years there has been a focus on using aircraft data. Unfortunately most of the times when 

we will want to initialize a regional model, there won’t be any aircraft data available. This 

is true for any storm in the Western Pacific, most of the storms in the Eastern Pacific, and 

storms in the Middle or Eastern Atlantic. In fact most of the observations by aircraft will 

be in storms close to the Atlantic and Gulf coasts of the U.S. Therefore, a strong emphasis 

on utilizing satellite data is needed because it will be the primary source of information on 

the storm inner core for the non-aircraft cases, and a major contributor to the analysis of the 

storm environment over the ocean in all cases. 

• We are engaging the JCSDA, AOML, CIRA to develop use of new satellite data 

sources for hurricane initialization.

• NRL and EMC are considering alternatives for simplified hurricane vortex 

initialization.

For storms with aircraft data (particularly radar data) more use of that data is crucial for 

hurricane initialization especially for hurricanes near the coast.

• A number of ongoing activities at EMC, AOML, PSU and other organizations

are being funded by HFIP

Development and tuning of physics packages for hurricane models at high resolution is 

critical. 

This was noted above related to the poor performance of the regional models in the sheared 

storms of 2009. Development, testing and evaluation of physics packages for hurricane 

models at high resolution is critical. 

GFS physics seems to work well down to 10-15 km but the operational 

version greatly over-predicted genesis in 2009

– Next GFS package (T574+physics) looks to be a significant 

improvement. 

– ESRL is experimenting with other physics packages for their FIM. 

We will continue to work with operational physics packages to 

– Develop appropriate sensitivity to vertical shear
53

– Apply improved understanding from carefully designed high resolution 

regional experiments

– Tune them at higher resolution and include a research effort to better 

understand the physical processes in hurricanes.

Plan a workshop to define a community wide effort on improving the 

operational physics packages—emphasis on 4-10 km resolutions.

Advanced DA systems in both regional and global models appear to lead to significant 

improvements.

HFIP, EMC, AOML and ESRL will focus on developing a hybrid 

technique that combines advantages of 4DVAR and EnKF

Can the transition to operations be accelerated?

For a limited sample, high resolution global and regional ensemble systems are showing 

promise but require further testing and evaluation

Main challenge will be to find adequate computing for running these 

operationally.

HFIP is funding several groups to further develop ensemble systems 

including improving initialization and model diversity.

We need to develop better products to convey ensemble information to forecasters.

HFIP will make this a priority this year.

A workshop is planned to define the community effort to improve 

ensemble products.

We need to fully engage the whole hurricane science community in improving the 

operational HWRF.

The HFIP program plan is tied closely to the EMC set of priorities for 

HWRF.

This will increase the resources that can be focused on improving the 

operational model.

HWRF has a focus of the expanding DTC capability. 

Research and operations need to use the same HWRF code base in order to 

facilitate transition of new developments to NCEP. For that, additional tests 

on the community HWRF configuration need to be conducted and, as soon 

as the community HWRF model has demonstrated skill, it needs to be 

implemented at NCEP.

We need to emphasize coordination between the HFIP modeling, observations and 

evaluation components to determine observational requirements for the improvement of 

model physics packages. 

Figure 2 shows that in the experiment where the physics configuration is similar to that 

used in the operational HWRF (Experiment 2), the depth of the ABL inflow is greater than 

in the experiment where the MYJ scheme is used (Experiment 1). Unfortunately, there are 

not sufficient observations to help judge whether or not this greater ABL inflow depth is 

realistic. We need to fully coordinate the HFIP modeling, observations and evaluation 
54

programs to determine observational requirements for the improvement of the physics 

package in hurricane models.

11. HFIP Progress Toward its Performance Goals.

Tables 2 and 3 define the baseline performance of model guidance that HFIP will use to 

measure progress toward its goals of increasing the accuracy of model guidance by 20% in 

5 years starting in 2009. In Figure 31 we show the performance of the GFS system run as 

an ensemble at T382, the current resolution of the operational GFS deterministic model and 

using the EnKF data assimilation system instead of the operational GSI system. The EnKF 

system was described earlier and other results shown from it.

The blue line shows the performance of the operational ensemble mean (GFS at T 126 

using the GSI data assimilation system. Note that the GFS at the higher resolution and 

using the advanced data assimilation system (EnKF) is better than the baseline beyond 

three days by about 10%. Thus using the current operational system we are able to reach 

about half of the stated HFIP goal for track at the longer lead times in one year. We don’t 

show a similar comparison with the intensity baseline for the global models since at 30 km 

the global models under predict intensity.

Figure 31. Forecast hurricane track errors for the GFS deterministic operational model (T382) run as a 

20 member ensemble (ensemble mean shown), red, plotted against the HFIP baseline for track that was 

defined in Table 2, black. The blue line is the error of the ensemble mean from the GFS T126 

operational ensemble. The data include all storms for all basins in 2009.
55

The blue line in Figure 31 shows the performance of the operational ensemble mean (GFS 

at T126 using the GSI data assimilation system. Note that the GFS at the higher resolution 

and using the advanced data assimilation system (EnKF) is better than the baseline beyond 

3 days by about 10%. Thus using the current operational system we are able to reach about 

half of the stated HFIP goal for track at the longer lead times in one year. We don’t show a 

similar comparison with the intensity baseline for the global models since at 30 km the 

global models under-predict intensity.

Figure 32. Same as Figure 24 but showing line graphs of the bias ensemble corrected ensemble mean 

(BCEM) track (upper panel) and intensity (lower panel) errors for the multi-model ensemble. Note that the 

errors for the individual models are not bias corrected. For this figure, only the 5 best (defined by overall 

error performance) models are included in the ensemble mean and are shown on the graphs.
56

The results for the regional models were summarized in Figure 24 and again in Figure 32. 

For the purposes of the discussion in this section just focus the data for the ensemble means 

(raw mean for Figure 24, bias corrected mean for 32) and on the baseline. In Figure 24 

those are the last two columns in each forecast hour group in the figure. The second 

column from the right hand side of each group in Figure 24 is the ensemble mean from the 

multi-model ensemble and the right column is the baseline defined in Tables 1 and 2. 

Figures 24 and 32 indicate that we have a ways to go with the regional models to reach the 

HFIP goals intensity and track. At the shorter lead times the bias corrected ensemble mean 

track error is comparable to the baseline but does not improve upon it. At the longer lead 

times it is worse. Figure 31 indicates that the reverse is true for the global models—they 

are better at longer lead times and worse at the shorter times. This provides some 

justification for earlier statements in this report that the focus of the regional models will be 

on the shorter lead times and the global models on the longer lead times.

Intensity errors are shown in the lower panel of Figures 24 and 32 and again just focus on 

the two rightmost columns of each forecast hour lead time. At all forecast lead times the 

ensemble mean errors are worse that the baseline so this indicates that we have made no 

progress in meeting the HFIP intensity goals this year. Note that the ARFS model was run 

on only half the number of cases as the other models in the ensemble and it is believed 

therefore that the ARFS model. is biased toward better forecasts (e.g., applied to easier to 

forecast storms). However, we point out that it gave forecast errors that were considerably 

lower than the baseline.

12. List of HFIP Supported Publications and Presentations.

This list is quite long and is available at: 

http://www.hfip.org/documents/presentations_publications.php
57

13. Appendix A: List of HFIP Teams

Bold face type denotes team leads.

1. Global Model/Physics Development Team 

Stan Benjamin (ESRL)

John Brown (ESRL) 

Kevin Yeh (AOML) 

Melinda Peng (NRL)

Shian-Jiann Lin (GFDL) 

Steve Lord (EMC) 

Jim Ridout (NRL) 

Jian-Wen Bao (ESRL)

John M. Ward (EMC) 

2. Regional Model/Physics Development Team

Morris Bender (GFDL)

Young Kwon (EMC)

Steve Lord (EMC) 

Sundararaman.G.Gopalakrishnan (AOML) 

Rich Hodur (NRL) 

Shaowu Bao (ESRL) 

Isaac Ginis (URI) 

Jim Doyle (NRL)

Robert Rogers (AOML) 

Jian-Wen Bao (ESRL)

Bob Tuleya (ODU) 

Ligia Bernardet (ESRL) 

Chris Davis (NESL) 

3. Ensemble Systems Development Team

Zoltan Toth (ESRL)

Carolyn Reynolds (NRL) 

Sim Aberson (HRD) 

Tom Hamill (ESRL) 

Jeff Whitaker (ESRL)

Fuqing Zhang (PSU) 

Yuejian Zhu (EMC) 

Jun Du (EMC) 

Mike Brennan (NHC) 

Mrinal K Biswas (FSU)

T. Krishnamurti (FSU) 

Teddy Holt (NRL) 
58

4. Data Assimilation/Vortex Initialization Team)

Jeff Whitaker (ESRL)

Bill Lapenta (EMC) 

Steve Lord (EMC)

Jim Doyle (NRL) 

Altug Aksoy (AOML)

Yuanfu Xie (ESRL) 

Milija Zupanski (CIRA)

Liyan Liu (EMC) 

Fuqing Zhang (PSU) 

Tomislava Vukicevic (AOML) 

5. Verification Team

Tim Marchok (GFDL)

Barb Brown (RAL) 

Jim Goerss (NRL) 

Mark DeMaria (NESDIS)

Robert Rogers (AOML) 

James Franklin (NHC) 

Vijay Tallapragada (EMC)

Michael Fiorino (ESRL)

Hao Jin (NRL) 

Ligia Bernardet (ESRL) 

Louisa Nance (RAL) 

Tony Eckel (OST ) 

6. Applications Development/Diagnostics Team

Mark DeMaria (NESDIS/STAR)

Ed Rappaport (NHC) 

Vijay Tallapragada (EMC) 

Yi Jin (NRL)

Buck Sampson (NRL) 

Robert Rogers (HRD) 

Barb Brown (RAL) 

Richard Pasch (NHC)

Michael Fiorino (ESRL) 

Louisa Nance (RAL) 

Bob Tuleya (Old Dominion Univ) 

Jim Hansen (NRL) 

John Knaff (NESDIS)

Sundararaman.G.Gopalakrishnan (AOML) 

T. Krishnamurti (FSU) 

Tony Eckel (OST ) 
59

7. Hurricane Observations Team 

Sim Aberson (AOML) 

Nick Shay (RSMAS) 

Jack Beven (NHC)

Naomi Surgi (EMC)

Mark DeMaria (NESDIS) 

Chris Fairall (ESRL) 

Isaac Ginis (URI) 

Peter Black (NRL)

Paul Chang (NESDIS)

Jim McFadden (AOC)

Tara Jensen (RAL) 

Thiago Quirino (AOML) 

Bob Atlas (AOML) 

Yuanfu Xei (ESRL)

George Halliwell (AOML) 

8. Coupled Ocean/Wave model Team 

Hendrik Tolman (EMC) 

George Halliwell (AOML) 

Isaac Ginis (URI)

Shaowu Bao (ESRL)

Sue Chen (NRL) 

Jian-Wen Bao (ESRL)

Chris Fairall (ESRL) 

Nick Shay (RSMAS)

Daniel Melendez (OAR) 
60

14. Appendix B: Organization Acronyms

AOC—Aircraft Operations Center, NOAA

AOML—Atlantic Oceanographic and Meteorological Laboratory, OAR/NOAA

EMC—Environmental Modeling Center, NCEP/NOAA

ESRL—Earth Sciences Research Laboratory, OAR/NOAA

FSU—Florida State University

GFDL—Geophysical Fluid Dynamics Laboratory, OAR/NOAA

NCAR—National Center for Atmospheric Research

NCEP—National Centers for Environmental Modeling, NWS/NOAA

NESDIS—National Environmental Satellite Data Information Service, NOAA

NHC—National Hurricane Center, NWS/NOAA

NOAA—National Oceanic and Atmospheric Administration

NRL—Naval Research Laboratory, Monterey

NWS—National Weather Service, NOAA

OAR—Ocean and Atmospheric Research, NOAA

ODU—Old Dominion University

OST—Office of Science and Technology, NWS/NOAA

RAL—Research Applications Laboratory, NCAR

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

URI—University of Rhode Island