i

2017 HFIP R&D Activities Summary:

Recent Results and Operational Implementation

May 2018

HFIP Technical Report: HFIP2018-1

NOAA NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

United States Department of Commerce
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Large image on cover page is of Hurricanes Katia (AL13, left), 

Irma (AL11, center)1 and Jose (AL12, right)

NOAA/GOES-13 and NASA/GSFC GOES 0545 Z Sep. 08, 2017

 1 Small image is NOAA/GOES-16 13Z (UTC) September 08, 2017. 
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2017 HFIP R&D Activities Summary:

Recent Results and Operational Implementation

S. Gopalakrishnan6, F. Toepfer11, S. Forsythe-Newell13, M. DeMaria9 F. Marks6, E. N. Rappaport9, V. 

Tallapragada5, J. Franklin9, S. Abarca5, A. Aksoy15, G. Alaka Jr.15, J. W. Bao4, M. Bender19, L. 

Bernardet4, S. Chen10, J. Cione1, M. Biswas7, M. Brennan9, J. Cangialosi9, M. Morin19, J. Doyle10, J. L. 

Franklin9, S. Goldenberg6, George Halliwell1, J. S. Hilderbrand20, R. Hodur10, C. Holt18, S. Jason5, H. 

Jin10, Y. Jin10, H. S. Kim5, W. Komaromi10, P. Kucera7, N. Kurkowski21, B. Kuo3, N. Lett12, P. McCaslin4, 

A. Mehra5, J. Moskaitis10, L. Nance3, K. Newman3, M. Onderlinde9, A. Penny9, A. Reinecke10, A. Simon9, 

J. Sippel5, S. Trahan5, H. Tolman5, R. Torn14, S. Upadhayay12, S. Wang10, X. Wang16, J. Whitaker4, D. A. 

Zelinsky9, F. Zhang17, X. Zhang15, Z. Zhang5 and Lin Zhu5.

May 2018

 1 Atlantic Oceanographic and Meteorological Laboratory (AOML), OAR/NOAA 2 Cooperative Institute for Research in the Environmental Sciences, University of Colorado 3 Development Testbed Center (DTC) 4 Earth System Research Laboratory (ESRL), OAR/NOAA 5 Environmental Modeling Center (EMC), NCEP/NOAA 6 Hurricane Research Division (HRD), AOML/NOAA 7 National Centers for Atmospheric Research (NCAR) 8 National Environmental Satellite Data Information Center NOAA 9 National Hurricane Center (NHC), NWS/NOAA 10 Naval Research Laboratory (NRL), Monterey 11 National Weather Service Science and Technology Integration (OSTI), NWS/NOAA 12 Science and Technology Corporation 13 Syneren Technologies Corporation 14 University at Albany, State University of New York 15 University of Miami, CIMAS/AOML 16 University of Oklahoma 17 Pennsylvania State University 18 Global Systems Division (GSD)/ESRL/OAR

19 Geophysical Fluid Dynamics Laboratory (GFDL), OAR/NOAA 

20 National Weather Service Office of Planning and Programming for Service Delivery (OPPSD), NWS/NOAA
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Table of Contents 

Executive Summary..................................................................................................................................vii

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

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

3. The HFIP Baseline for meeting the goals..........................................................................................2

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

a. State-of-the-Art: Operational HWRF System (Stream-1)..................................................... 5

b. HMON System (Stream-1)........................................................................................................ 6

5. The HWRF DA system .......................................................................................................................7

6. Community Involvement....................................................................................................................9

7. Operational Hurricane Guidance Improvements............................................................................9

a. Track Guidance ......................................................................................................................... 9

b. Intensity Guidance................................................................................................................... 10

c. Advancing Rapid Intensification Prediction......................................................................... 12

d. HWRF Performance in other Global Basins......................................................................... 16

8. Important Stream-2 Results.............................................................................................................17

a. Research Advances: Multiple, Storm-following, Two-way Interactive Telescoping Nests 

(Stream-2).............................................................................................................................................. 17

b. Unified Modeling System for Hurricane Forecasting (Stream-2)....................................... 18

c. Research Advances: Regional Ensembles (Stream-2).......................................................... 22

9. New Products, Tools, and Services at NHC....................................................................................23

a. Improvements to NHC Public Products................................................................................ 23

b. Improvements to Forecaster Guidance and Visualization................................................... 25

10. NOAA Announcement of Opportunity (AO) .................................................................................26

11. Hurricane Analysis and Forecast System (HAFS).........................................................................26

12. Future direction of the HFIP program ...........................................................................................28

References..................................................................................................................................................30

Appendix A: Model Acronyms................................................................................................................33
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List of Tables

Table 1. HFIP Supported Projects from 2016-2018. .................................................................... 26

Table 2. Meteorology and Model Acronyms................................................................................ 33

List of Figures

Figure 1: HFIP (a) Track and (b) Intensity Error Baseline and Goals. Baseline errors.................. 3

Figure 2: NHC (a) Track errors (1960-2017) & (b) Intensity errors (1970-2017) in the Atlantic . 4

Figure 3: HWRF improvements in forecast track and intensity error for the Atlantic Basin......... 5

Figure 4: HMON track & intensity skills relative to the GFDL model (2014-16 NATL basin).... 6

Figure 5: Diagram of new HWRF DA system with full covariance cycling provided by EnKF... 7

Figure 6: Performance of the research version of the HWRF DA system...................................... 7

Figure 7: Impact on HWRF Vmax and track in terms of skill, of reconnaissance on HWRF ....... 8

Figure 8: Track forecast skills for (a) AL and (b) EPAC basins. ................................................... 9

Figure 9: 2017 Intensity forecast skills for (a) AL and (b) EPAC basins..................................... 10

Figure 10: NHC Intensity aids for Hurricane Harvey 12Z 23 Aug 2017 & 12Z 24 Aug 2017.... 11

Figure 11: NHC and select model (a) intensity forecast errors for Irma & HWRF 10-m wind. .. 11

Figure 12: Best track for intensity, official & model intensity forecasts of intensity for Maria .. 12

Figure 13: NHC Atlantic intensity forecast errors for cases with and without RI at any time..... 12

Figure 14: POD and FAR of RI for NHC forecasts and guidance models (2017 AL sample)..... 13

Figure 15: Brier Skill Scores of the 4 components of the operations SHIPS RI .......................... 15

Figure 16: Brier Skill Scores-exp. DTOPS & 2018v of SHIPS-RII for AL forecasts 2015-2017 16

Figure 17: WPAC Track & Intensity skill and S. Hemisphere Track & Intensity errors............. 17

Figure 18: Basin-Scale configuration operational HWRF with multiple moving nests at 2-km.. 18

Figure 19: FV3 model Zoomed-in on the AL basin and 2-way interactive 3-km telescopic nest 19

Figure 20: Stream-2 models compared to Stream-1 operational models...................................... 19

Figure 21: Basin-Scale HWRF wind speed predictions of Harvey compared to best.................. 20

Figure 22: Track skills of Basin-Scale HWRF compared to HWRF 2017 and GFS cases.......... 20

Figure 23: Example of a basin-scale HWRF DA product from 12Z 19 Sep. 2017 ...................... 21

Figure 24: HWRF HMON-COAMPS multi-model ensembles for Irma (2017/09/08 cycle) ...... 22

Figure 25: New or enhanced products: Earliest Reasonable Arrival Time of Tropical................ 23

Figure 26: Storm Surge Watch / Warning – Max.water levels (ft) measured from tide gauges .. 24

Figure 27: Cartoon from SIP presentations showing how hi-res nests may be moved ................ 27
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Executive Summary

This report describes the activities and results of the Hurricane Forecast Improvement Project2

(HFIP) in 2017. It should be generally noted that 2017 unlike 2015 and 2014, was not a 

representative season for the Atlantic due to very high tropical cyclone (TC) activity and many 

rapid intensification (RI) events. A major developmental focus in 2017 was on the Hurricane 

Weather Research and Forecasting3 (HWRF) regional model and advancements of Data 

Assimilation Technique for HWRF. In 2017, HFIP remained organized around two Streams: 

Stream-1: Operational model development and Stream-2: HFIP experimental models which test 

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

for possible operational implementation. Stream-2 also tests techniques that cannot be tested on 

current operational computers due to size and time requirements but can be tested on HFIP’s High 

Performance Computing Center (HPC) in Boulder, CO (also referred to as Jet). The HFIP HPC 

research studies look ahead to possible future operational computational capability. This report 

outlines HFIP, how it is organized, its goals, models, and results from both the operational model 

development (Stream-1) and experimental model development (Stream-2). The following were the 

highlights in 2017:

1. 2017 had the highest number of major Atlantic hurricanes since 2005 season and perhaps the 

most challenging season for intensity predictions from high-resolution dynamical models

2. Operational HWRF was the best deterministic intensity forecast model in the ATL

a. outperformed statistical models based on climatology 

3. Improved Data Assimilation system in HWRF

a. Improved vortex initialization with new composite vortex, Assimilated HDOBS, and 

Fully cycled HWRF Ens. Hybrid DA for TDR data

b. HWRF predicts secondary eyewall formation 

4. Improvements in numerical guidance continuing to appear in NHC official forecast for track 

and intensity

5. New products and tools at NHC ready for Operations

a. Time of Arrival operational in 2018

b. “Be Ready By”

c. Storm Surge Watch and Warning

6. HMON first year in operations replaced legacy GFDL hurricane model

7. Continued development and improvements to the HFIP Corrected Consensus Approach 

(HCCA) model 

8. The basin scale HWRF demonstrated some superior track skill over the operational HWRF 

illustrating a viable pathway for hurricane moving nest in NGGPS 

9. Initial results of nested fvGFS demonstrated showed promise for track and intensity 

predictions

 2 http://www.hfip.org/

3 http://www.emc.ncep.noaa.gov/gc_wmb/vxt/HWRF/index.php
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1. Introduction

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

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

recent advances in the skill of operational hurricane forecast guidance. The first part of this 

report is very similar to previous versions of the annual report since it basically sets the 

background of the program. This year’s version has been shortened and focuses upon capturing 

state-of-the-art HFIP modeling and data assimilation techniques, accomplishments during 2017 

season and future plans. For more background information readers are referred to earlier reports 

available at: http://www.hfip.org/documents/reports2.php. Acronyms are defined in the 

Appendix.

2. The Hurricane Forecast Improvement Project

Twenty-seven named tropical storms and thirteen hurricanes crossed US coastlines from 2000-

2010. The Hurricane Forecast Improvement Project (HFIP) was established within NOAA, in 

June 2007, in response to particularly damaging hurricanes (e.g., Charley, 2004; Wilma, Katrina, 

Rita, 2005) in the first half of that decade. HFIP’s 5-year (for 2014) and 10-year goals (for 2019) 

are:

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

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

● Increase the probability of detection (POD)4 for RI5 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 RI change is the highestpriority forecast challenge identified by the National Hurricane Center).

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

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

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

to coordinate the hurricane research needed to significantly improve guidance for hurricane track, 

intensity, and storm surge forecasts and accelerate the transition from research to operations. HFIP 

focuses multi-organizational activities to research, develop, demonstrate and implement enhanced 

operational modeling capabilities, dramatically improving the numerical forecast guidance made 

available to the National Hurricane Center (NHC).

HFIP is organized along two lines of activities: Stream-1 and Stream-2. While Stream-1 works 

within presumed operational computing resource limitations, Stream-2 activities assume that 

resources will be found to greatly increase available computer power in operations above that 

 4 Probability of Detection (POD), is equal to the total number of correct rapid intensification (RI) forecasts divided 

by the total number of forecasts that should have indicated RI: number of correctly forecasted ÷ (correctly forecasted 

RI+ did not but should have forecasted RI). False Alarm Ratio (FAR) is equal to the total number of incorrect 

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

occur + forecasted RI that did not occur).

5 RI for hurricanes is defined as an increase in wind speed ≥30 knots in 24 hours. This goal for HFIP also applies to 

rapid weakening (RW) of a decrease of 25 knots in 24 hours.
2

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

advanced science, technology, and increased computing will lead to the desired increase in 

accuracy, and other improvements of forecast performance. Due to the level of computing 

necessary to perform such a demonstration is larger than can be accommodated by current 

operational computing resources, HFIP developed its own computing system at 

NOAA/OAR/ESRL in Boulder, Colorado.

A major component of Stream-2 (also known as the Demonstration Project) is an Experimental 

Forecast System (EFS) that HFIP runs each hurricane season. The purpose of the EFS is to 

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

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

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

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

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

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

and therefore, are not used by National Hurricane Center (NHC) forecasters to prepare their 

operational forecasts or warnings. Nevertheless, most of the operational HWRF advancements, 

including the high-resolution nests and appropriate physics originated from Stream-2 work.

3. The HFIP Baseline for meeting the goals

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

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

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

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

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

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

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

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

errors, (a) for tracks and (b) for intensity, of the consensus over the period 2006-2008 for the 

Atlantic basin6

, and the 5-year and 10-year error goals (black lines labeled on the left side of the 

graph).

Because a sample of cases from, for example, the 2013 season might have a different inherent 

level of difficulty from the baseline sample of 2006-2008 (e.g., as it had an unusually high or low 

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

forecast skill provides a more representative longer-term perspective. Fig. 1 shows the baseline 

errors and the 5- and 10-year goals as skill (blue lines labeled on the right side of the graph). 

Skill in the figure is the percentage improvement over the CLIPER5 (for track) and DecaySHIFOR5 (for intensity forecasts) for the same cases. Note the skill baseline and goals for 

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

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

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

Decay-SHIFOR5 can fail to adequately account for forecast difficulty in some circumstances. A 

 6 A separate set of baseline errors (not shown) was computed for the eastern North Pacific basin (Franklin, 2009, 

2010).
3

hurricane season that features extremely hostile environmental conditions will lead to very high 

Decay-SHIFOR intensity forecast errors (as climatology will be a poor forecast in such years), 

but relatively low dynamical model and NHC official forecast errors (as few storms will 

intensify rapidly, making life easy on both models and forecasters). This combination of baseline 

and model errors yields an unrealistic skill estimate.

Figure 1: HFIP (a) Track and (b) Intensity Error Baseline and Goals. Baseline errors (black lines) were

determined from an average of the top-flight operational models during the period 2006-2008. The HFIP 

expressed goals (dashed lines) are to reduce this error by 20% in 5 years and 50% within 10 years. 

Comparisons of forecasts over non-homogenous samples, however, are best done in terms of skill. To obtain 

the 5-year and 10-year HFIP goal in terms of skill (blue lines-baseline skill in solid, HFIP goals dashed), the 

goals are also expressed as the percentage improvement over the CLIPER5 errors (track) and DecaySHIFOR5 (intensity) of the baseline sample (see text).

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

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

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

dynamical models currently in use at tropical cyclone forecast centers, such as the GFS or the 

GFDL model (or “GFDL”), are considered “late” models because their results arrive too late to 

be used in the forecast for the current synoptic cycle. For example, the 12:00 Coordinated 

Universal Time or Zulu Time Zone (Z) GFDL run does not become available to forecasters until 

around 16:00Z, whereas the NHC official forecast based on the 12:00Z initialization must be 

issued by 15:00Z, one hour before the GFDL forecast can be viewed. It’s actually the older, 

06:00Z run of the GFDL model that would be used as input for the 15:00Z official NHC forecast, 

through a procedure developed to adjust the 06:00Z model run to match the actual storm location 

and intensity at 12:00Z. This procedure also adjusts the forecast position and intensity at some of 

the forecast times as well and then applies a smoother to the adjusted forecast. This adjustment,

called “interpolation” procedure, creates the 12:00Z “early” aid GFDI that can be used for the 

15:00Z NHC forecast. Model results so adjusted are denoted with an “I” (e.g., GFDI). The 

distinction between early and late models is important to assessing model performance, since late 

models have an advantage of more recent observations/analysis than their early counterparts. 

However, it is interesting to note that although the early version loses about 3-5% of the skill for 
4

track forecasts compared to the late version, the skill for intensity forecasts are virtually the same 

for late and early versions (Goldenberg et al, 2015).

4. The HFIP Model Systems

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

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

region over time. Figure 2a shows the steep-step improvements to track predictions since the 

60’s. Those advancements have come through developing improved dynamical global models 

(e.g. GFS), improved resolution and physics in those models and advancement of data 

assimilation (DA) techniques. Most of the GFS developments have been at NCEP. Nevertheless, 

one of the first efforts in HFIP was to improve the existing operational global models. Early in 

the program, it was shown that forecasts were improved, particularly in the tropics, by using a 

more advanced DA scheme than the one employed operationally at that time. A version of this 

advanced DA went operational in the GFS model in May 2012. However, TCs like Sandy (2012) 

and Joaquin (2015) continue to pose challenges to track forecasting. Sustained HFIP research 

and developments may be necessary for further improvements in tracks of these outlier events.

Figure 2: Official NHC (a) Track errors (1960-2017) and (b) Intensity errors (1970-2017) in the AL basin

It is generally assumed that the inner core must be resolved to see consistently accurate hurricane 

intensity forecasts (NOAA SAB, 2006). HFIP believes that the best approach to improving 

hurricane track and intensity forecasts involves the use of high-resolution global models with at 

least some run as an ensemble. However, global model and their ensembles are likely to be 

limited by computing capability for at least the next five years to a resolution no finer than about 

8-10 km, which is inadequate to resolve the inner core of a hurricane. Maximizing improvements 

in hurricane intensity forecasts will, therefore, require high-resolution regional models or global 

models with moveable high-resolution nests, perhaps also run as an ensemble. During the last 10 

years, the focus has been on improving intensity forecast, which has significantly lagged behind

track forecast for decades. For that purpose, regional models with (two-way interactive) moving 

nests capable of resolving the inner core structure of hurricanes are usually used for intensity 

predictions. These regional models are further (one-way) nested within the global models to 

provide seamless track and intensity predictions. The HWRF that was developed under HFIP is a 

good example. Fig. 2b shows little to no improvements in the accuracy of NHC’s official 

intensity forecast until the onset of HFIP in 2009.
5

It is expected that the Next Generation Global Prediction System (NGGPS) may be able to 

provide some accelerated progress in improving track and intensity predictions within one 

unified modeling system. The eventual goal here is to transition the multi-nested “Basin-Scale” 

type of nest (illustrated in Figure 18) to NGGPS (illustrated in Figure 19).

a. State-of-the-Art: Operational HWRF System (Stream-1)

One of the major accomplishments of HFIP is the development and the eventual transition to 

operations of the triple nested, high resolution, HWRF model. A joint development between 

NOAA research and operation, with significant support from the Developmental Testbed Center 

(DTC), UCAR, and the community, HWRF is now paving the way to improve operational 

intensity forecasts all over the globe, which otherwise have had little improvement in skill for the 

last two decades. HWRF is one of the top performing track prediction models as well. 

Improvements to model resolution (3 km in 2012, 2 km in 2015 and 1.5 km planned in 2018), 

physics and initial conditions enhanced with aircraft observations, all of which were coordinated 

under the HFIP have led to steep-step progress in improved numerical guidance. Figure 3 

illustrates progress of HWRF in forecasting track (Fig. 3a) and intensity (Fig. 3b) relative to 

HFIP goals. In fact, during the 2017 season, track skills of HWRF were better than the global 

model for most of the time (shown later). More importantly, there is a steady decrease of 

intensity error from 2011 to the present by 15% to 20% per year; although some of the samples 

(years over which models were run) shown in the figure are not homogeneous. In fact, research 

results that include 2017 retrospective samples, show HFIP has reached its 5-year intensity error 

goals. It should be noted that HWRF was the best intensity forecast guidance model in 2015 for 

the North Atlantic Basin and in East Pacific in 2016. In the 2017 season, the operational HWRF 

was again the best dynamical guidance hurricane model in Atlantic basin. The model is 

consistently performing better than statistical-dynamical models. However, intensity forecast 

error improvements seem to be leveling off in the Atlantic basin (Fig. 3b). Sustained HFIP 

research and developments, especially focused on forecast failures and RI events, is 

recommended for further improvements in intensity predictions. It is also expected that the 

development of the Hurricane Analysis and Forecasting System (HAFS) may be able to provide 

some accelerated progress in reaching the HFIP 10-year goal (section 11).

Figure 3: HWRF improvements in forecast (a) track and (b) intensity corrected for absolute error in the 

Atlantic Basin from 2007-2011 to 2017 under HFIP are shown. Seasons for which models were run are 

depicted on each line. Note that some samples (years) are not homogeneous between models.
6

based on climatology. Accelerated improvements to track and intensity forecasting with HWRF 

model demonstrates that a focused program such as HFIP is needed to further accelerate 

numerical guidance of hurricanes. 

b. HMON System (Stream-1)

Until 2016 the GFDL hurricane model was used as the second dynamical model along with 

HWRF for intensity guidance. The model provided the variety required for multi-model 

ensembles but was still a hydrostatic model. The objective for Hurricanes in a Multi-scale 

Ocean-coupled Non- hydrostatic (HMON)/Hurricane version of Non-hydrostatic Multi-scale 

Model on the Arakawa B Grid (HNMMB) development is to provide higher-resolution intensity 

forecast guidance to NHC along with HWRF (and to replace the legacy GFDL hurricane model). 

The HMON is a new hurricane model for NWS/NCEP operations. The HMON model was built 

using shared infrastructure with unified model development within the NOAA Environmental 

Modeling System (NEMS) and could be also coupled to other (ocean, wave, land, surge, 

inundation, etc.) models within the NEMS infrastructure. Developed initially as a collaborative

effort between NOAA’s Environmental Modeling Center (EMC) and Hurricane Research 

Division (HRD) as a component of High Impact Weather Prediction Project (HIWPP), HMON 

was originally referred to as HNMMB. HMON’s development has been supported by HFIP and 

NGGPS projects. The HMON model is based on the NMMB dynamic core which is currently 

being used in other NCEP operational systems namely, the NAM (North American Mesoscale 

Model) and the SREF (Short Range Ensemble Forecast) model. The NMMB dynamic core is 

much faster and more scalable than other contemporary dynamic core deployed for modeling 

hurricanes at NCEP. Use of NEMS also paves the way for future use of CCPP (Common 

Community Physics Package) style physics packages. Robust retrospective testing and 

evaluation in the past year have demonstrated that the HMON model has shown superior results 

over GFDL (Fig. 4). It was also found that the HMON model had different characteristics than 

HWRF for both North Atlantic and North Eastern Pacific basins, hence providing the diversity 

for operating multi-model ensembles for intensity predictions. HMON is not yet available to the 

community.

Figure 4: HMON (a) Track and (b) intensity skills relative to the GFDL model (2014-2016 NATL basin)
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5. The HWRF DA system

In earlier reports we have given an overview on the DA systems used in HFIP7. Supported by 

HFIP, a dramatically improved DA system was implemented in operational HWRF in 2017. We 

discuss these developments in this section.

Figure 5: Diagram illustrating the new HWRF DA system with full covariance cycling provided by EnKF

There was a major upgrade to HWRF-DA system functionality in 2017. Past versions of HWRF 

used error covariance provided by the NCEP global model, which is suboptimal for tropical 

cyclone applications in a GSI hybrid scheme. The 2017 version of HWRF introduced a fullycycled EnKF to provide error covariance for GSI, which is more accurate for tropical cyclones. 

The workflow for this system, which was developed in collaboration with the University of 

Oklahoma, is illustrated in Fig. 5.

The 2014 operational HWRF (red 

line) is compared with the 3D 

ensemble-variational (light blue) 

and 4D ensemble variational (dark 

blue) versions of the research 

system (Figure 6). Statistical 

significance is assessed in the bar 

graphs beneath the error plots, 

where values near 1 indicate the 

research system is significantly 

better than the operational HWRF. 

Sample size for each forecast hour 

is indicated at the bottom of 

Figures 6a & 6b.

 

7 http://www.hfip.org/documents/HFIP_AnnualReport_FY2016.pdf

Figure 6: Performance of the research version of the HWRF 

DA system (adapted from Lu et al, 2017)
8

Testing has shown that adopting the new DA strategy can significantly improve tropical cyclone 

intensity forecasts. Lu, et al. (2017) examined a research version of this DA system with the 

2014 HWRF implementation and found major improvements for the forecasts of Hurricane 

Edouard (2014). These improvements were in part due to a more accurate initial vortex structure, 

which allowed the model to better capture Edouard’s intensification phase. For Edouard, this 

resulted in a ~25% reduction in the maximum wind speed (Vmax) error as well as a ~35% 

reduction in minimum sea-level pressure (MSLP) error (Fig.6).

There were a number of other 2017 HWRF DA system upgrades. Among these were upgrades to 

the GSI code to be consistent with EMC GSI, and an increase of the Vmax threshold above 

which inner-core GSI increments are eliminated. Eliminating the increments is necessary for 

strong storms to reduce imbalance, though this need will decrease as the DA system and physics 

improve. Another noteworthy upgrade is that the composite storm for vortex initialization was 

updated.

Concomitant with the DA system upgrades, HWRF now makes much greater use of data than in 

the past, particularly within the inner core. Flight-level high-density reconnaissance data was 

added in H217, and data impact tests revealed that this data significantly improves HWRF 

forecasts and is at least partly responsible for much improved HWRF performance in 2017 

(Fig.’s 7a and 7b).

Figure 7: Impact on HWRF Vmax (left) and track (right), in terms of skill, of reconnaissance on HWRF

The improvements in data usage, particularly for the inner core, mean that inner core 

reconnaissance is becoming increasingly important for improving intensity forecasts. 
9

Retrospectives from 2017 storms revealed that reconnaissance improved HWRF intensity 

forecasts by about 10% through 48 hours (Fig. 7c) for select major hurricanes. Note that major 

hurricanes represent the most difficult cases for reconnaissance DA improvement in HWRF.

6. Community Involvement

Research to Operations (R2O) was one of the initial goals of the WRF program and is supported 

by HFIP in developing a repository for a community-based hurricane modeling system which 

ensures the same code base can be used for research and in operations. During 2009-2016, both 

the EMC and the DTC worked to update the operational version of HWRF from version 2.0 to 

the current community version of HWRF, version 3.9a. The 3.9a version makes the operational 

model completely compatible with codes in community repositories, allows researchers access to 

operational codes, and makes improvements in HWRF developed by the research community 

easily transferable into operations. At this time (2017) there are more than 1,300 registered 

HWRF users world-wide. Support provided by the DTC in 2015-2016 included two in-person 

HWRF tutorials; one at NCEP in College Park, MD and another at Nanjing University of 

Information Science and Technology (NUIST) in China. User support was expanded with the 

Stream-2 effort, Basin-Scale HWRF. This research system can support any number of highresolution movable nests centered on TCs in either the Atlantic or eastern North Pacific basin. 

Working with HRD, the DTC also supported the transition of this research version to the latest 

community repository, enabling users to access all advancements in the HWRF system including 

the end-to-end Basin-Scale configuration (excluding ocean coupling and data assimilation). A 

similar testbed activity is recommended for transitioning the proposed FV3 based hurricane 

forecasting system.

7. Operational Hurricane Guidance Improvements8

a. Track Guidance

Figure 8: Official track forecast skill in 2017 for the (a) Atlantic and (b) eastern Pacific basins.

2017 was the busiest year in the Atlantic basin since 2012, with 407 forecasts issued, well 

above the 1990-2017 mean of 319. Official Atlantic track forecasts (Fig. 8a) were very 

 8 Various acronyms used in this section are explained in Appendix A.

(a) (b)
10

skillful and close to the best-performing consensus aids (FSSE, HCCA, TVCA), among 

which there was little difference in performance. EMXI was the best dynamical model in 

2017, but it was not quite as good, on average, as the NHC official forecast nor the consensus 

aids, particularly at longer lead times. EGRI and UEMI were the next best models, followed 

by GFSI, HWFI, AEMI, and CMCI in the middle of the pack. Performing relatively poorly 

in 2017 were the NVGI, HMNI, and CTCI. 

In the eastern Pacific (Fig. 8b), the consensus aids HCCA and TVCA led the way with the 

highest skill. NHC official forecasts were very skillful and close to the performance of the 

best consensus aids. The best individual model was the EMXI. The GFSI, HWFI, EGRI, 

and UEMI were in the middle of the pack, followed by HMNI, CMCI and NVGI. Note that 

the HMNI, a new model for 2017, performed poorly in both basins.

Figure 9: Official intensity forecast skill in 2017 for the (a) Atlantic and (b) eastern Pacific basins.

b. Intensity Guidance 

Intensity forecast verifications for the 2017 season are shown in Figure 9. In the Atlantic basin 

(Fig. 9a), official forecasts were skillful at all forecast periods but were less skillful than the 

consensus models (IVCN, HCCA, and FSSE) at most forecast periods. FSSE was the best model 

out to 72 h It should be noted that the season had the highest number of major hurricanes since 

2005, posing a particular challenge to the high-resolution dynamical models. Even so, HWFI 

was the best individual model for intensity forecasting in 2017. The GFDL replacement, HMNI, 

was not as skillful as HWFI but was more skillful than the statistical aids. DSHP and LGEM 

trailed. CTCI was a poor performer at the earlier leads, but showed better skill at longer leads, 

beating all other aids at 96 h. The global models GFSI and EMXI showed little skill. In the 

eastern Pacific (Fig. 9b), official intensity forecasts performed comparably to the best consensus 

aids (IVCN and HCCA). HWFI was a very strong performer through 48 h, but performed very 

poorly at the longer leads. DSHP and LGEM were moderately skillful throughout the 5-day 

forecast period. As in the Atlantic, the global models GFSI and EMXI were not skillful.

For Hurricane Harvey, there was significant intensity forecast inconsistency, especially in the 

early stages. For example, the intensification rate in the early part of the forecasts in Fig. 10a for 

most of the guidance was only about half of that in Fig. 10b. In addition, the rapid intensification 

(RI) that occurred beginning at 1200 UTC August 23, 2017 (Fig. 10a) was not forecast by the 

(a) (b)
11

guidance. Once the RI was well underway, the models were more successful in anticipating 

future RI events (e.g., Fig. 10b), and were very successful in all cycles after 1200 UTC on 

August 24, 2017 (Fig. 10b). 

Figure 10: NHC intensity aids (knots, colored lines) for Harvey at (a) 1200 UTC 23 August 2017, and (b) 1200 

UTC 24 August 2017. Verifying intensity is shown in white.

Figure 11: NHC and selected model (a) Intensity forecast errors for Hurricane Irma, 30 Aug. to 12 Sep. 2017 

and (b) HWRF 10-m wind for all forecast cycles compared with observations for the same case.

For Hurricane Irma, the intensity forecast errors from HWRF and COAMPS-TC were lower than 

those from the statistical aids, and also lower than the official forecast errors (Fig. 11a), an 

example of the progress that dynamical intensity models have made in recent years. That said, 

much work remains to be done. For example, several later cycles of the HWRF showed that the 

model could not replicate the actual intensification that occurred (e.g., the first few cycles in Fig. 

11b). In addition, spin-down issues remain a problem (the inability of a model to maintain an 

initially very strong vortex, as illustrated by the sequence of cycles in Fig. 11b when Irma was at 

an intensity of 160 kt). These two issues are active areas of HFIP research.
12

Figure 12: Best track for intensity (solid black), official (dashed black) and model (solid colors) intensity 

forecasts of intensity for Hurricane Maria from 0600 UTC 18 September 2017.

Predicting RI in TCs continues to be problematic. In addition to the Harvey example shown 

above, RI was also missed in 2017’s Hurricane Maria (Fig. 12). The next section discusses this 

problem in greater detail.

c. Advancing Rapid Intensification Prediction

The 2017 Atlantic hurricane season was very active, with 17 named storms and 6 major 

hurricanes (more than double the long-term average for major hurricanes). There were 43 

occurrences of RI (a 24-h period with a 30 kt or greater increase in the maximum wind) in the 

Atlantic in 2017, corresponding to about 12% of all 24-hr intensity changes. For comparison, the 

long-term average occurrence rate in the Atlantic is only about 6%. Intensity forecasts during RI 

are especially challenging because the guidance models rarely handle them well. Figure 13 

shows that the average NHC official 

intensity forecast errors are much higher 

when at least one 24-h period with RI 

occurs during the forecast interval than 

when RI does not occur.

NHC uses several deterministic intensity 

guidance models, including the operational 

HWRF and the Navy’s COAMPS-TC 

dynamical models, and the D-SHIPS and 

LGEM statistical models. A consensus of 

the available guidance models (IVCN) is 

also available. IVCN typically has lower 

mean intensity errors than any of its 

individual components. The operational 

HMON forecasts, which were available 

beginning in the latter part of July, were 

Figure 13: NHC official Atlantic basin intensity 

forecast errors during 2017, for cases with (red) and 

without (blue) at least one period of RI at any time 

during the forecast interval.
13

not included in the IVCN consensus in 2017 but will be part of IVCN in 2018.

Figure 14a shows the probability of detection (POD) and false alarm rate (FAR) of RI in the 0–

24-h forecast period, for the 2017 NHC official forecasts and the early versions of the models 

mentioned above. The figure shows that the statistical and consensus techniques have low PODs, 

which is not surprising since statistical models predict typical behavior and RI is by definition 

not typical. The HWFI and HMNI models had higher PODs, but also higher FARs. These results 

show that none of the deterministic intensity models provided very accurate RI forecast 

guidance, with the most-aggressive model (HMON) still only detecting about 25% of the RI 

cases.

 

Figure 14: POD and FAR of RI for NHC official forecasts and the statistical and dynamical guidance models 

(2017 Atlantic sample) for the 0-24 h forecast period (a), and for the dynamical models for the 24-48 h, 48-72 

h, 72-96 h and 96-120 h forecast periods (b).The COAMPS-TC shown here is the version run with GFS 

boundary conditions. HWRF, COAMPS-TC and HMON models are the “early” versions available to the 

NHC forecasters. 

(a)

(b)
14

Figure 14b shows the POD and FAR for the subsequent forecast intervals for the three dynamical 

models; the NHC official forecast and statistical model results are not included since their POD 

and FAR values were zero for all of these later intervals. The CTI shows some improvement at 

24-48 h and 48-72 h relative to the 0-24 h interval, with larger PODs and FARs comparable to or 

less than the PODs. All of the models show very poor performance for the 72-96 h and 96-120 h 

forecast intervals. 

Because of the inability of deterministic models to reliably predict RI, several probabilistic RI 

models have been developed over the past decade using statistical post-processing techniques. 

For example, the operational SHIPS model includes a rapid intensification index (RII) 

component, which estimates the probability that a TC will undergo RI. The SHIPS-RII predicts 

both the conventional RI definition (30 kt or more in the next 24 h) and several other intensitychange thresholds over time periods ranging from 12 to 72 h. The SHIPS-RII uses storm 

environment predictors such as vertical shear, ocean heat content, and parameters from infrared 

satellite imagery as input to several model formulations: a Bayesian method (RIOB), a 

discriminant analysis algorithm (RIOD), and a logistic regression (RIOL). Consensus 

probabilities from the three methods are also produced (RIOC). 

Probabilistic forecasts are often evaluated using the Brier Score (BS), the sum of the squares of 

the difference between the forecasted probability and verifying probability (either 0 or 100% for 

non-RI and RI cases, respectively), divided by the sample size. BS is then converted to a Brier 

Skill Score (BSS) by calculating the percent reduction in BS relative to a forecast where the 

forecast probability is set to the long-term climatological value. For example, a BSS value of 

20% indicates that a model had a BS 20% lower (better) than the BS from the climatological 

forecast. Such a forecast would be said to be skillful relative to climatology.

Results for the various versions of SHIPS-RI for the 2017 season are shown in Figure 15, for 

three different defined periods of RI. The figure shows that SHIPS-RII was skillful for most 

thresholds and model formulations, with the exception being the Bayesian model for the two 

longer RI periods. A BSS can also be calculated for deterministic models by assigning an RI 

forecast probability of 100% if the model intensity change forecast meets the RI definition. This 

calculation was performed for the 30 kt/24 h RI threshold for HWRF, HMON and COAMPS-TC 

for the 2017 cases, and the BSS values were 2.7%, 5.1% and -3.2%, respectively. This result 

indicates that in 2017 the statistical post-processing methods were more skillful than the 

deterministic dynamical models for RI forecasts.
15

Figure 15: The Brier Skill Scores of the four components of the operational SHIPS Rapid Intensification

Index (SHIPS-RII) for the 2017 Atlantic forecasts for three RI definitions. RIOB, RIOD, RIOL and RIOC 

represent the Bayesian, Discriminant analysis, Logistic Regression and Consensus models, respectively.

The HFIP Ensemble Products Tiger Team evaluated a number of new probabilistic RI products 

during the 2017 season. These products included RI probabilities estimated from the HWRF and 

COAMPS-TC ensembles, a hurricane analog ensemble (AnEn) method that matches the 

deterministic HWRF run with 20 previous HWRF forecasts, and a deterministic to probabilistic 

statistical (DTOPS) method that uses the HWRF, D-SHIPS, LGEM, GFS and ECWMF model 

intensity-change forecasts as input to a logistic regression model. The operational SHIPS-RII 

models were also included in the real time tests. Results showed that the HWRF and COAMPSTC ensemble RI probability estimates were much less skillful and reliable than those from the 

statistical post-processing methods (AnEn, DTOPS and the SHIPS-RII). This comparison 

indicates that the dynamical model ensemble systems need to be improved before they can be 

used as guidance for the NHC official RI intensity forecasts. 

Based on the encouraging results with the statistical post-processing models from the 2017 tiger 

team experiment, improvements were made to DTOPS and the SHIPS-RII. To evaluate the new 

versions, retrospective forecasts were made for the Atlantic cases from 2015-2017 in preparation 

for the 2018 season. Figure 16 shows the BSS values for these models from the retrospective 

runs for the three RI periods. These results show that the statistical-post processing models have 

potential value as guidance for NHC forecasters. It is also interesting to note that DTOPS 

provides skillful probabilistic RI forecasts even though the models used as input do not 

themselves have skill. Essentially the logistic regression serves as a bias correction, and is 

capable of generating high RI probabilities when the input models all forecast intensification but 

none individually predict RI.

Although the fairly low POD and unfavorable FAR to POD ratios for the dynamical models in 

Fig. 14 may seem discouraging, some progress has in fact been made. To evaluate progress, the 

POD and FAR statistics for the 30 kt/24 h RI definition were calculated for the HWRF and 

GFDL models for the 2008-2016 Atlantic sample. For HWRF, the POD and FAR were 4.7% and 
16

53.3%, respectively, and for the GFDL they were 2.0% and 66.7%, respectively. These are much 

worse than the more current dynamical model results from Fig. 14, suggesting that we are 

entering an era where the dynamical models can provide useful guidance for rapid 

intensification. The output from these models can be used as input to statistical post-processing 

algorithms to further increase their RI forecast skill. These improvements, along with better 

dynamical model ensembles, will continue under HFIP.

Figure 16: Brier Skill Scores of the experimental DTOPS model and 2018 version of the SHIPS-RII for 

retrospective Atlantic forecasts from 2015-2017.

d. HWRF Performance in other Global Basins

During the 2017 season in the western North Pacific basin, the Joint Typhoon Warning Center 

(JTWC) official track forecast proved to be the most skillful for virtually all forecast periods 

(Fig. 17a). Among the guidance models, the HWRF and GFS models continued to be top 

performers in track skill. The COAMPS-TC/GFS track skill was competitive with the HWRF 

and GFS from 12-48 h, after which it began to trail the other models. The NAVGEM was 

slightly better than COAMPS-TC/NVGM.

For intensity in the western North Pacific (Fig. 17b), the JTWC produced the most skillful 

forecasts for nearly all forecast cycles and were especially skillful at the earliest and longest lead 

times. (Note that the skill is computed relative to the ST50 Statistical Intensity model.) 

COAMPS-TC/NVGM, COAMPS-TC/GFS, and the operational HWRF were comparable with 

one another from 12-30 h. Beyond 30 h, the COAMPS-TC/GFS was the best-performing model, 

although it was essentially matched by HWRF at 120 h. COAMPS-TC/GFS even slightly 

bested the official JTWC forecast at 48 h. RI remains a challenge and a top forecast 

improvement priority for JTWC, as nearly 50% of western North Pacific TCs reaching tropical 

storm strength or greater will have at least one period of RI.
17

Figure 17: Western north Pacific track (a) and intensity (b) skill, combined southern hemisphere (SH) track 

(c) and intensity errors (d).

For combined southern hemisphere (SH) track forecasts (Fig. 17c), GFS and HWRF models 

provided the lowest errors in 2017. The model errors were tightly clustered out to 24 h, although 

after that time the COAMPS-TC/NVGM performance lagged behind that of the other models.

For the SH intensity models (Fig. 17d), the guidance errors of the various models were tightly 

clustered through 24 h, at which point the COAMPS-TC/NVGM lagged behind, demonstrating 

the importance of the choice of global model used to drive the initial and boundary conditions of 

the regional model. Overall, the GFDN and CTCX provided the best intensity guidance, with 

HWRF competitive with them except at 120 h. It should be noted that intensity errors are still 

very large in the SH and increase very quickly with time, suggesting a need for more research 

and case studies.

8. Important Stream-2 Results

a. Research Advances: Multiple, Storm-following, Two-way Interactive 

Telescoping Nests (Stream-2)

Although the operational HWRF system is showing improved skill in intensity forecasting, it 

should be noted that the current operational HWRF configuration is storm-centric and singlenested. This is not ideal for representing multi-scale interactions or for TC genesis forecast 

applications and is greatly limited in improving forecast skill beyond five days, which is a major 
18

goal of next-generation efforts. Also, the current operational configuration poses a lot of 

challenge for advancing data assimilation techniques and downstream applications at and after 

landfall. All these elements may be an issue for further advances to the hurricane forecasting 

system. Thus, a Basin-Scale HWRF was created under HFIP with: 1) A large outer domain that 

covers approximately one-fourth of the globe (eventually will cover the entire globe) and 2) 

Multiple moving multi-level nests at 1-3 km. horizontal resolution to produce simultaneous 

tropical cyclone forecast. The latter is especially important because tropical cyclones interact 

with the large-scale environment and with one another (see Fig. 18).

Figure 18: Basin-Scale configuration of 2017 operational HWRF with multiple moving nests at 2-km 

resolution covering Hurricanes Irma, Jose and Katia. This configuration was run under Stream-2 for the 

entire 2017 season. The above picture shows one cycle initialized on 06Z on 08 September 2017. This capacity 

was developed by HFIP.

b. Unified Modeling System for Hurricane Forecasting (Stream-2)

In late 2016, GFDL’s Finite Volume Cubed Sphere (FV3) model was selected as the next 

dynamical core for NOAA’s Global Forecasting System (https://www.gfdl.noaa.gov/fv3/). FV3 

is a fully non-hydrostatic model and will replace the operational Global Spectral Core at NCEP 

next year. The first version will be run at a horizontal resolution of about 13 km. Nevertheless, 

two kinds of downscaling are possible with this model. (i) Grid stretching and (ii) Telescopic 

nesting (https://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-11-00201.1). The GFDL FV3 

dynamical core with GFS physics (fvGFS) was used to perform near real-time forecasts of 

tropical cyclone track, structure, and intensity out to 132 hours during the 2017 Atlantic 

hurricane season. The model domain covered the entire Atlantic basin with a horizontal 

resolution of 3 km (see Fig. 19), and forecasts were run from early August through late October. 

The runs analyzed in this report cover most of the significant storms in the Atlantic 2017 

hurricane season, including Harvey, Irma, and Maria.
19

Figure 19: FV3 model (a) Zoomed-in over the AL basin and (b) Two-way interactive 3-km telescopic nest

covering the AL basin.

Both advanced versions of 2017 HWRF called the Basin-Scale HWRF and FV3 global model 

(nested down to 3 Km horizontal resolution in the AL basin) was run real-time on NOAA’s jet 

system. For the first-time, tail-doppler data was assimilated in the inner nest of the Basin-Scale 

HWRF similar to the operational HWRF system.

Figure 20: Stream-2 models (Basin-scale HWRF and 3km-FV3) compared to Stream-1 operational models

(HWRF & HMON) track error for (a) 2017 AL season and Hurricanes (b) Harvey, (c) Irma and (d) Maria.

Figure 20 illustrates track errors from the Stream-2 models compared to the operational models. 

Overall (Fig 20a), both Stream-2 models were competitive with the operational model. 

Significant storm-to-storm variability between high resolution models, but nested fvGFS was the 

best track performer for entire season (Fig. 20a). Basin-Scale HWRF performed extremely well 

for Harvey (Fig. 20b).
20

Figure 21: Basin-Scale HWRF (a) Wind speed predictions over several cycles of Harvey compared to best

estimates, (b) Rainfall predictions compared to stage 1 observations for Harvey, (c) Track predictions over 

several cycles of Irma.

The Basin-scale HWRF was the best NOAA model for Hurricane Harvey. Apart from near 

perfect track predictions (evident from Fig. 20b), the Basin-scale HWRF accurately captured the 

RI during several forecast cycles (Fig 21a). Perfect track forecast yielded very good rainfall 

predictions that compared well with observations (Fig 21b). Fig. 21c shows the track predictions 

for Hurricane Irma from the Basin-scale HWRF. The model was one of the earliest NOAA 

models to get the shift of tracks towards the west of Florida (Fig. 21c).

Figure 22: (a) Track skills of Basin-Scale HWRF compared to HWRF 2017 and (b) GFS cases for which

there was at least one more tropical cyclone in the Basin-Scale domain

In 2016, the Basin-Scale HWRF’s track performance exceeded those from Operational HWRF 

by about 15%. During the 2017 AL season, the gains were more modest, about a maximum of 

6%. However, Basin-Scale was much better the GFS especially at longer lead-times (Fig. 22a). 

More importantly, when stratified by cases in which at least one additional storm may be 

captured by the Basin-Scale HWRF (Fig. 22b), the skill -vs- operational HWRF was as high as 

15% at longer lead time and the performance of the Basin-Scale HWRF was as good as the GFS 

for these samples demonstrating that the multi-level, two-way-interactive moving nest 

framework will help build a pathway for seamless prediction of tropical cyclones in the Next 
21

Generation Global Prediction System (NGGPS). Basin-Scale HWRF can serve as an effective 

bridge between the current operational HWRF system and these next-generation global model 

efforts at NCEP” (Alaka et al., 2017).

Figure 23: Example of a basin-scale HWRF DA product from 12 UTC 19 September 2017. Dotted lines

indicate domain boundaries; solid red lines are MSLP contours every 5 hPa; colored shading shows 850 hPa 

vertical vorticity analysis increments every 0.8 x 10-4 s-1, blue letters indicate the location of a tropical lowpressure system (LO), tropical disturbance (DB), tropical depression (TD), Hurricane Jose (H), and Major 

Hurricane Maria (M).

Under Stream-2 efforts, in addition to the deterministic Basin-Scale HWRF runs, during the 

2017 season, scientists at AOML designed a continuously cycled ensemble DA and forecast 

system for the “basin-scale” HWRF model. The experimental system assimilates all available 

conventional and satellite measurements over a fixed HWRF parent domain, which spans both 

Atlantic and eastern Pacific hurricane basins. The new system leverages off the software 

infrastructure developed for the operational HWRF model, including the community GSI DA 

package, thus providing a valuable testbed for implementing research ideas into operations. At 

the same time, the experimental DA system avoids heuristic practices adopted for the operational 

HWRF system, such as relying on GFS for environmental initial conditions, avoiding the online 

calculation of satellite radiance biases (needed for their assimilation), and performing vortex 

relocation. Advancements made to community DA software, to ensemble calibration, to model 

physics development, and in the design and quality control of observing systems can transition 

seamlessly into other models, such as operational HWRF and future FV3 prediction systems. In 

addition, the basin-scale system provides an efficient framework for satellite DA research and 

development, which requires fully cycling DA over an extensive domain for accurate 

measurement and model bias estimation. The basin-scale DA system operated for more than 

three months in 2017 as part of a NOAA HFIP real-time demo project. Figure 23 illustrates the 

DA system operating for one cycle of the trial period. In this figure, 850 hPa vorticity increments 

(shading) indicate where the largest wind correction occurs over the domain—namely, in the 
22

vicinity of tropical and extratropical weather systems. In addition to providing the first 

successful application of the HWRF model in a fully-cycling DA schedule, the demo yielded a 

probabilistic multi-basin analysis of a highly active portion of the hurricane season, which is 

currently used at AOML for a retrospective model and DA development. The project also 

produced large samples of short-range forecast errors (relative to observations), which are 

currently used at AOML to calibrate ensembles and investigate biases in the HWRF model, 

which can be accurately quantified through season-term sequential DA. Development of a fullycycled regional system for HWRF will set the baseline for the Hurricane Forecast and Analysis 

System in future.

c. Research Advances: Regional Ensembles (Stream-2)

An important HFIP Stream-2 activity is the regional multi-model ensemble developments. Since 

2014, the HFIP began testing a multi-model regional ensemble. Three model ensembles are used: 

the HWRF, Navy’s COAMPS-TC and the GFDL (now HMON). A 41-member, multi-regional 

model ensemble system consisting of HWRF (20 members), Coupled Ocean/Atmosphere 

Mesoscale Prediction System-Tropical Cyclone (COAMPS-TC) model (10 members) and 

Geophysical Fluid Dynamics Laboratory (GFDL) or HMON model (11 members) were run on 

the HFIP Jet System in real-time. However, regional ensemble developments have generally 

lagged behind deterministic models in the first phase of HFIP. Spread or lack in spread of 

ensemble tracks continues to be an elusive forecasting problem. Often the mean track for 

ensemble members diverges significantly from observations as well as a deterministic forecast 

for both global as well as the regional models. The other issue is the divergence between 

ensemble and deterministic systems: for instance, HWRF ensembles lack the DA and (ocean) 

coupling advances that are a part of the operational HWRF. In 2017, HWRF ensembles run 

under Stream 2 continued to lag behind the deterministic operational HWRF. In fact, operational 

HWRF outperformed the means from ensembles both for track and intensity predictions. 

Nevertheless, there were a few interesting cycles that provided some useful information to 

researchers. The HWRF HMON-COAMPS multi-model ensembles for Hurricane Irma (12Z 

cycle 2017/09/08; Fig. 24a) and HWRF ensemble for Harvey (12Z cycle 2017/08/22; Fig. 24b) 

are worth noting.

Figure 24: The HWRF-HMON-COAMPS multi-model ensembles for (a) Irma (12Z cycle 2017/09/08)

and (b) HWRF ensemble for Harvey (12Z cycle 2017/08/22).
23

9. New Products, Tools, and Services at NHC

Figure 25: Examples of new or enhanced products in 2017: (a) Earliest Reasonable Arrival Time of TropicalStorm-Force winds and 5-day cumulative probability of Tropical-Storm-Force winds, (b) Earliest Reasonable 

Arrival Time of Tropical-Storm-Force winds media briefing graphic: “Be Ready By”, (c) 5-Day forecast 

track, cone of uncertainty, watches/warnings and initial wind radii; and (d) Interactive graphic overlays of 

watches/warnings, forecast track, initial wind radii and 5-day cumulative wind speed probabilities.

a. Improvements to NHC Public Products

The HFIP Post-Processing and Verification (PPAV) team effort in 2017 focused on new public 

product development (Fig. 25 a-d), improvements to NHC’s Guidance Suite, Wind Speed 

Probability (WSP) model, and model diagnostics. Significant progress was made in each of these 

areas. A new experimental product depicting the earliest reasonable and most likely arrival time 

of tropical-storm-force winds (Fig. 25a) was publicly available on the NHC website. Another 

version of this product intended for media and other NWS partner briefings was developed for 

use at NHC (Fig. 25b). These products were created based on feedback from emergency 

managers that indicated that probabilistic information on the arrival of 34 kt. winds would also 

be very useful for mitigation planning and were used extensively for decision support and media 

briefing during the 2017 Hurricane Season. The public versions of the products are scheduled to 

migrate to operational status in 2018.
24

Two further improvements to the wind speed probabilities were investigated in 2017. A method 

for computing tracks based on dynamical, rather than statistical, guidance was tested and found 

to be more skillful. In addition, the modification of the tropical cyclone wind field over land was 

improved, to better account for the decrease of winds over land areas. The new method for 

representing land interactions will result in a more realistic representation of the wind speed 

probabilities over land when implemented operationally in 2018.

The existing NHC graphical product suite was also upgraded, allowing NHC to produce all 

graphical products on a common map for the first time. This upgrade allowed NHC to overlay 

the initial wind radii over the cone of uncertainty to provide a visual indication of storm size 

(Fig. 25c). NHC’s GIS product suite was also updated and a number of new GIS products were 

available in 2017, including wind radii km files. The GIS upgrades also led to improvements in 

NHC’s interactive graphical products, allowing users to overlay information such as storm surge 

watches and warnings with the initial wind radii or wind speed probabilities (Fig. 25d).

Figure 26: Storm Surge Watch / Warning - Maximum water levels (feet) measured from tide gauges 

along the coasts of Texas and Louisiana during Hurricane Harvey and areas covered by storm surge 

warnings (magenta) and watches (lavender)

Beginning with the 2017 hurricane season, NHC issued storm surge watches and warnings to 

highlight areas along the Gulf and Atlantic coasts of the continental United States that have a 

significant risk of life-threatening inundation from an ongoing or potential tropical cyclone, a 

subtropical cyclone, or a post-tropical cyclone. The storm surge watch / warning areas are 

determined by a collaborative process between NHC and local NWS Weather Forecast Offices 

(WFOs). The primary objective guidance is the P-Surge, an ensemble-based probabilistic system 

driven by the SLOSH model, the latest NHC official tropical cyclone forecast, and the typical 

historical errors associated with NHC forecasts. Forecaster confidence, continuity from advisory 
25

to advisory, and other subjective factors help determine the areas placed under a watch or 

warning (Fig. 26).

b. Improvements to Forecaster Guidance and Visualization 

Another major milestone in 2017 was the continued development of the HFIP Corrected 

Consensus Approach (HCCA) model. The HCCA technique creates an unequally weighted 

consensus forecast from many independent models based on historical performance and biases;

and was among the best operational guidance available to NHC forecasters in 2017 (Fig’s. 8 and 

9). Accomplishments in 2017 included conducting input model sensitivity experiments, 

identifying and correcting for input model outlier forecasts, and extending the track forecast to 

seven days on an experimental basis.

In addition to the aforementioned improvements to the statistical rapid intensification guidance, 

improvements were also made to the statistical-dynamical models SHIPS (DSHP) and LGEM. 

Data from the new GOES-16 satellite was tested in preparation of that satellite becoming the 

operational GOES-East satellite in 2018. The developmental database was expanded to include 

2016 for the Atlantic and eastern North Pacific; and expanded back for other global basins. 

Finally, several updates were applied to the Statistical Prediction of Intensity from a Consensus 

Ensemble (SPICE) model, including beginning ingest of the HMON model and experimenting 

with the COAMPS-TC and NAVGEM models.

The PPAV team also developed and maintained several web-based tools for visualizing real-time 

and historical model forecasts. The real-time HFIP products web page (www.hfip.org/products) 

was available to the community throughout the 2017 hurricane season, and new guidance from 

the Rapid Intensification Tiger Team was added to that site. A new interface for displaying track, 

intensity, and size forecasts was also developed (www.hfip.org/nhc-display). This system allows 

users to see satellite or aircraft-based analyses of tropical cyclone location, strength, and size in 

real-time, in addition to NHC and model forecasts. A localized version of this tool is scheduled 

for implementation at NHC in 2018 that will be used for situational awareness and research 

purposes.
26

10. NOAA Announcement of Opportunity (AO)

The following Table 1 provides a list of projects supported by HFIP during 2016-2018 and some 

R2O outcome:

Table 1. HFIP Supported Projects from 2016-2018.

HFIP Collaborative Awards Round III (2016-2018)

PI Name PI Institution Project Title

Xuguang Wang University of Oklahoma 

(UofOK)

Further Advancement of HWRF SelfConsistent Ensemble-variational Hybrid 

DA System to Improve High Resolution 

Hurricane VI

Chanh Kieu Indiana University (IU)

Characteristics of HWRF Hurricane 

Intensity Error Growth and Predictability 

Limit

Ryan Torn University of Albany 

(SUNY)

Evaluating Methods of Parameterizing 

Model Error in the HWRF Ensemble 

Prediction System

Ping Zhu Florida International 

University (FIU)

Improving HWRF's ability to Predict 

Rapid Change in TC Intensity Governed 

by Internal Physical Processes 

Christopher Rozoff University of Wisconsin 

(UofWI)

Probabilistic Prediction of TC Track, 

Intensity, and Structure with an Analog 

Ensemble

11. Hurricane Analysis and Forecast System (HAFS)

HAFS will be a multi-scale model and data assimilation package capable of providing 

probabilistic guidance. HAFS will provide an operational analysis and forecast out to seven days, 

with reliable and skillful guidance on TC track and intensity (including rapid intensification), 

storm size, genesis, storm surge, rainfall and tornadoes associated with TCs. One of the biggest 

successes achieved during the first phase of HFIP was the creation of the high-resolution HWRF 

system. The HWRF, storm following, nested grid modeling system was designed to operate at a 

horizontal resolution of 2 km or less desired for capturing tropical cyclone inner core processes 

as well as the interactions with the large-scale processes, proven to be critical for improving 

track, intensity, rainfall and size predictions. It may be noted that HWRF has been providing 

only track and intensity guidance to forecasters until now. This system, with further 
27

advancements in ensembles, data assimilation techniques and better use of hurricane 

observations, will be the starting point for the first version of HAFS. 

HAFS will evolve into advanced analysis and forecast system for cutting-edge research on 

modeling, physics, data assimilation, and coupling to earth-system components for highresolution TC predictions within the Next Generation Global Prediction System 

(NGGPS)/Strategic Implementation Plan (SIP) objectives of the Unified Forecast System (UFS; 

https://www.weather.gov/sti/stimodeling_nggps_implementation).

In late 2016, GFDL’s Finite Volume Cubed Sphere (FV3) model was selected as the next 

dynamical core for NOAA’s Global Forecasting System (i.e., fvGFS; see Fig. 27 for moving 

high-resolution nests). Although the FV3 model itself is fully tested and cloud resolving, certain 

aspects of its nesting capability prevent its use in HAFS. Hurricane forecast applications require 

storm following, telescopic nests at about 1-2 km resolution that can be located anywhere in the 

globe and should be capable of following the tropical storms for several days. In addition, twoway interactive nests are essential for improving the accuracy of forecasts. AOML in joint 

partnership with GFDL and EMC, will be working on these developments during next 2-3 years 

to transition advances in HWRF to FV3-HAFS.

Figure 27: Cartoon from SIP presentations showing how high-resolution nests may be moved seamlessly

within the 6 faces of the FV3 cube sphere grid. For example, nest in position A and B crosses the edge of face 

1 and face 6. The nest will stay on one projection. The feedback and downscale at the leading edge of the 

moving nest will be on the interchangeable equivalent projections between face 1 and face 6 in this instance. 

The design will guarantee the physical equivalence in the finite volume framework on different cubic faces.
28

12. Future direction of the HFIP program

HFIP’s approach is designed to accelerate the implementation of promising technologies and 

techniques from the research community into operations. That approach has resulted in a 20% 

reduction in both storm track and intensity numerical guidance. Additionally, to better 

communicate risks to the public and emergency management community, HFIP invested in 

social science research contributed to the development and implementation of storm surge 

watch/warning products and services.

Tracking the progress, and lack thereof, of numerical storm prediction guidance during HFIP 

shed light on the development areas that deliver the most significant results (e.g., data 

assimilation, improvements to physics parameterization schemes), areas into which HFIP focus

should be extended (e.g., storm size, rainfall), and the need for more meaningful ways to 

measure progress for high-impact events (e.g., rapid intensity changes). Forecast challenges 

during the 2017 hurricane season underscored the address issues such as rapid intensity change, 

genesis, storm size, and rainfall, as demonstrated by Hurricane Harvey. Hurricane Irma 

highlighted a need for improvements in forecasting storm size and track. Hurricane Maria, which 

devastated Dominica and Puerto Rico, again pointed to a need to better predict rapid intensity 

change, as did Hurricanes Matthew (2016), Joaquin (2015), and Patricia (2015).

To improve TC forecasting with the goal of developing and extending accurate TC forecasts and 

warnings in order to reduce loss of life, injury, and damage to the economy, the next generation 

of HFIP will focus on:

1. improving the prediction of rapid intensification and track of TCs;

2. improving the forecast and communication of surges from TCs; and

3. incorporating risk communication research to create more effective watch and warning 

products.

A new strategic plan details the specific research, development, and technology transfer activities 

associated with HFIP’s next generation of science and R2O challenges. Some key challenges 

include: reducing HWRF forecast guidance errors associated with rapid intensification; reducing 

global modeling guidance errors to extend forecasts to 7 days with accuracy; improving 

utilization of high-resolution ensembles for model initialization and in product generation; better 

utilization of satellite observations in cloudy regions surrounding tropical cyclones; and 

identifying observations required to improve intensity forecasts.

In order to address the three primary focus areas outlined above, HFIP has developed a set of 

specific goals and metrics to improve the accuracy and reliability of TC forecasts and warnings 

and increase the confidence in those forecasts to enhance mitigation and preparedness decisions 

by emergency management officials at all levels of government and by individuals.

Improved model guidance for TC formation, track, intensity and size will be essential to address 

all three areas. Basic TC forecast parameters will be improved, including the formation time and 

location, position, maximum wind (i.e., intensity), and storm size. Estimates of the uncertainty of 

those parameters will also be enhanced, enabling better risk communication to end users through 

accurate probabilistic information (i.e., information that considers the likelihood, or probability, 

that an event will occur). Rapid intensification remains an especially important and challenging 
29

forecast problem. Specific goals and metrics are defined for the prediction of the basic TC 

forecast parameters, new extended range forecasts, rapid intensification, and TC formation.

The next generation of HFIP will build upon the original goals of the project through the 

following specific goals and metrics:

1. Reduce forecast guidance errors, including during rapid intensification, by 50 percent 

from 2017;

2. Produce 7-day forecast guidance as good as the 2017 5-day forecast guidance;

3. Improve guidance on pre-formation disturbances, including genesis timing, and track and 

intensity forecasts, by 20 percent from 2017; and

4. Improve hazard guidance and risk communication, based on social and behavioral 

science, to modernize the TC product suite (products, information, and services) for 

actionable lead-times for storm surge and all other threats.

Improved hazard guidance will derive from development of a hurricane analysis and forecast 

system producing ensemble guidance enabling probabilistic hazard products and improved track, 

intensity, and storm size predictions before formation and throughout the storm’s life cycle. 

Using social and behavioral science research, HFIP will design a more effective TC product suite 

to better communicate risk and apply it to all current TC hazards products. Success will depend 

on availability of sufficient resources for development, R2O, and infrastructure (including highperformance computing), continuing engagement with the larger academic community in 

addressing the challenges and goals.
30

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Appendix A: Model Acronyms

The following is a list of acronyms used to identify models in this document. Many of the 

acronyms follow the four-character naming convention in the Automated Tropical Cyclone 

Forecasting (ATCF) system. For example, 6 hour “early” (aka “interpolated”) forecasts from 

“late” models (see Section 4a) are adjusted so that the previous 6 hour forecast matches the 

conditions at the beginning of the current forecast. Forecasts of those future conditions are 

denoted with an “I” at the end (12 hour interpolations are denoted with a “2”).

Other conventions occasionally used in the model naming include the acronym “A” to denote 

advanced version, “D” to denote the addition of inland decay, “E” to denote ensemble, “H” to 

denote hurricane, “R” to denote research, “S” to denote statistical, “T” to denote track, “V” to 

denote Variable (ensemble of at least 2, for example), and beginning with an “I” to denote 

intensity.

Table 2. Meteorology and Model Acronyms

ACRONYM: DESCRIPTION:

AEMI: GEFS with 6 hour interpolation (AEMI)

AHW: National Center for Atmospheric Research Advanced Hurricane WRF.

AOML: Atlantic Oceanographic and Meteorology Laboratory (AOML)

AVNI: GFS with 6 hour interpolation (AVNI).

CLIPER: Climate and Persistence model (CLIPER)

CMC: Canadian Meteorological Centre model.

CMCI: CMC with 6 hour interpolation.

COAMPS-TC: Coupled Ocean/Atmosphere Mesoscale Prediction System-Tropical Cyclone 

(COAMPS-TC) at FNMOC

Decay-SHIFOR5: Decay Statistical Hurricane Intensity Forecast model (Decay-SHIFOR5)

DSHP: Decay SHIPS (DSHP)

DTC: Developmental Testbed Center (DTC)

ECMWF: European Centre for Medium-range Weather Forecasts model (ECMWF)

EGRI: UKMO model, subjective tracker, with 6 hour interpolation (EGRI)

EM: Equally-weighted Ensemble Mean for models used in MMSE.

EMXI: ECMWF with 6 hour interpolation.

EnKF: Ensemble Kalman Filter (EnKF).

EFS: Experimental Forecast System (HFIP Stream-2, demonstration project)

FAR: False Alarm Rate (FAR)

Far-Field: Tropical cyclones that are more than 3,500 km apart (Far-Field)

FSSE: Florida State University Super-Ensemble (FSSE)

FV3: NOAA’s Finite Volume Cubed-Sphere Dynamical Core (FV3)

GEFS: NCEP’s Global Ensemble Forecast System (GEFS)

GFDI: GFDL with 6 hour interpolation (GFDI).

GFS: Global Forecast System (GFS)

GFSI: Early GFS with 6 hour interpolation (GFSI)

GHMI: GFDL adjusted using a variable intensity offset correction that is a function of 

forecast time, with 6 hour interpolation (GHMI).

GSI: Grid-point Statistical Interpolation (GSI)

H3WI: Hurricane Research Division’s regional, dynamic and deterministic ensemble 

(H3WI)

HCCA: HFIP Corrected Consensus Approach (HCCA)

HDOBS: High Density Observations (HDOBS) from aircraft

HFIP: Hurricane Forecast Improvement Project (HFIP)
34

ACRONYM: DESCRIPTION:

HMON: Hurricanes in a Multi-scale Ocean coupled Non-hydrostatic (HMON) model or 

Hurricane Non-hydrostatic Multi-scale Model on B-grid (NMMB)

HNMMB: Hurricane Non-hydrostatic Multi-scale Model on B-grid (HNMMB)

HWHI: Basin-scale HWRF with 6 hour interpolation (HWHI)

HWMI: HWRF Ensemble Mean Forecast Interpolated Ahead 6 hour. (HWMI)

HWFI: Hurricane Weather Research & Forecasting (HWRF) with 6 hour interpolation 

(HWFI)

HWRF: Hurricane WRF (HWRF)

IV15: Intensity forecast ensemble including 2012 Stream-1.5 forecasts.

LGEM: Logistics Growth Equation Model (LGEM)

MAE: Mean Absolute Error

MMSE: FSU Multi-Model Ensemble (variably weighted mean).

NAVGEM: Fleet Numerical Meteorology and Oceanography Center Navy Global 

Environmental Model (NAVGEM), replaced NOGAPS February 2013.

NEMS:

NGGPS:

NOAA Environmental Modeling System (NEMS)

Next Generation Global Prediction System (NGGPS), for more information about 

the model see https://www.weather.gov/sti/stimodeling_nggps

NGPI: NOGAPS with 6 hour interpolation (NGPI)

NGXI: Experimental NOGAPS with 6 hour interpolation (NGXI)

NMM: Environmental Modeling Center Non-hydrostatic Mesoscale Model.

NMMB: NMM on the B-grid (NMMB)

NOGAPS: Fleet Numerical Meteorology and Oceanography Center Navy Operational 

Global Atmospheric Prediction System (replaced by NAVGEM February 2013)

OFCL: Official National Hurricane Center Forecast (OFCL)

RI: Rapid Intensification RI), An increase in 30 Knots/24 hours

RW: Rapid weakening (a decrease of 25 knots in 24 hours).

SHIPS: Statistical Hurricane Intensity Prediction System (SHIPS)

SPC3: Six-member weighted SPICE ensemble using output from GFS, HWRF, and 

GFDL as input for DSHP and LGEM. The ensemble weights vary with forecast 

lead time

SPICE: Statistical Prediction of Intensity from a Consensus Ensemble (SPICE)

SPIN-UP: Slang terminology for vortex acceleration and/or initialization 

SPIN-DOWN: Slang terminology for vortex deceleration and/or termination

TAB: Trajectory And Beta (TAB) model for trajectory track using GFS input 

TVCA: Track Variable Consensus of at least two of AVNI, EGRI, EMXI, NGPI, GHMI, 

HWFI forecasts

TVCE: Variable Consensus (TVCE) of AVNI, EGRI, EMXI, NGPI, GHMI, GFNI, 

HWFI Model Track Forecasts

TVCI: Variable Consensus (TVCE) of AVNI, EGRI, EMXI, NGPI, GHMI, GFNI, 

HWFI Model Track Forecasts (6 hour interpolation)

TVCN: National Hurricane Center Track Variable Consensus (TVCN)

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

interpolation (UKMI)

UW4I: University of Wisconsin’s Non-hydrostatic Modeling System (4 km)

UWNI: UW-NMS with 6 hour interpolation (UWNI)

UW-NMS: University of Wisconsin Non-hydrostatic Modeling System.

WRF: Weather Research & Forecasting (WRF) regional model w/options for the 

dynamic core, physics, initialization, post processing and verification. Variations: 

Hurricane WRF (HWRF), PSU Advanced Research WRF (ARW) and NCAR 

Advanced Hurricane WRF (AHW)