2015 HFIP R&D Activities Summary:

Recent Results and Operational Implementation

May 2016

HFIP Technical Report: HFIP2016-1

Global coverage of HWRF in all basins

NOAA NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

United States Department of Commerce
Image on cover page illustrates operational HWRF global coverage

on a particular day and is used with permission from the

Environmental Modeling Center, 2016
2015 HFIP R&D Activities Summary:

Recent Results and Operational Implementation

S. Gopalakrishnan6, F. Toepfer11, R. Gall15, F. Marks6, E. N. Rappaport9, V. Tallapragada5, S. 

Forsythe-Newell13, A. Aksoy15, J. W. Bao4, M. Bender19, L. Bernardet4, J. Cione1, M. Biswas7, J. 

Cangialosi9, M. DeMaria9, M.Morin19, J. Doyle10, J. L. Franklin9, S. Goldenberg6, George 

Halliwell1, C. Holt18, S. Jason5, H. S. Kim5, P. Kucera7, N. Lett12, P. McCaslin4, A. Mehra5, M.

Mills13, J. Moskaitis10, S. Abarca5, J. Sippel5, S. Trahan5, H. Tolman5, R. Torn14, X. Wang16, J. 

Whitaker4, D. A. Zelinsky9, F. Zhang17, X. Zhang15, Z. Zhang5.

May 2016

 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)

 4Earth System Research Laboratory (ESRL), OAR/NOAA

 5 Environmental Modeling Center (EMC), NCEP/NOAA

6 Hurricane Research Division (HRD), AOML/NOAA

 7 National Center 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

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

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

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

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

3. The HFIP Model Systems...................................................................................................................2

a. The Regional Model................................................................................................................... 2

b. Initialization and Data Assimilation Systems.......................................................................... 3

c. The HWRF Community Code Repository and User Support............................................... 5

4. Meeting the HFIP Goals.....................................................................................................................6

a. The HFIP Baseline..................................................................................................................... 6

b. Meeting the Track Goals........................................................................................................... 8

c. Reaching the Intensity Goals.................................................................................................... 8

5. Operational Hurricane Guidance Improvements............................................................................9

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

b. A Note on Global Ensemble Forecast System for Track Guidance .................................... 10

c. Intensity Guidance: Hurricane WRF (HWRF) .................................................................... 11

1) Atlantic Basin ...........................................................................................................................................11

2) East Pacific (EPAC) Basin.......................................................................................................................14

d. HWRF Improvements in RI/RW Predictions in Atlantic and Eastern North Pacific 

Basins 14

6. HWRF performance in other global basins....................................................................................16

7. Important Stream 2 Results.............................................................................................................18

8. Data Assimilation Developments.....................................................................................................22

9. Physics Developments.......................................................................................................................24

10. Post Processing of Model Output ....................................................................................................25

11. NOAA Opportunity Announcement ...............................................................................................28

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

HFIP-NGGPS goals..................................................................................................................................30

References ................................................................................................................................................32

Appendix A: Model Acronyms................................................................................................................34
List of Tables

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

Table 2. Specifics of the HWRF and other regional model used by HFIP in 2015 ........................ 2

Table 3. Automated Tropical Cyclone Forecasting System (ATCF) ID Descriptions.................. 19

Table 4. HFIP Supported Projects from 2012-2016 with some R2O results. .............................. 28

List of Figures

Figure 1: HFIP Track and Intensity Error Baseline and Goals....................................................... 7

Figure 2: 2015 Seasonal track forecast skills over (a) Atlantic and (b) East Pacific basins........... 9

Figure 3: 2014-2015 Seasonal track forecast skills over (a) Atlantic and (b) East Pacific basins 10

Figure 4: Ensemble predictions from 20150930 12Z ECMWF and GFS ensembles cycles........ 11

Figure 5: 2011-2015 HWRF Intensity Forecast Improvement for the Atlantic Basin. ................ 11

Figure 6: 2015 Hurricane season intensity forecast skills for the Atlantic Basin......................... 12

Figure 7: Evolution of intensity from various HWRF forecast cycles. ........................................ 13

Figure 8: 2015 Hurricane season intensity forecast skills for the East Pacific Basin................... 14

Figure 9: HWRF predicted 24-hour maximum 1-m wind speed changes (knots)........................ 15

Figure 10: 2015 Seasonal track and intensity forecast skills for the West Pacific Basin. ............ 16

Figure 11: 2015 Seasonal track and intensity forecast skills for the Central Pacific Basin.......... 17

Figure 12: 2015 Seasonal track and intensity forecast skills for the Northern Indian Ocean....... 17

Figure 13: Multi-Model 2015 Season Mean Track, Intensity errors and bias.............................. 19

Figure 14: HWRF basin-scale example of far-field TC track forecast improvement................... 21

Figure 15: 1 km. Height Radar Wind and Pressure for HRD Wind and Hybrids......................... 23

Figure 16: Operational HWRF vs. the New Hybrid System during Edouard (2014)................... 23

Figure 17: Observed and HWRF 2015 model simulated 10 m wind............................................ 24

Figure 18: HWRF vs. Observed Wind Profiles. ........................................................................... 25

Figure 19: 5-Year sample SHIPS & LGEM models (2014 versions of each model, 2010-2014) 26

Figure 20: Time of Arrival Prototype. .......................................................................................... 27

Figure 21: Screenshot of the HFIP products webpage.................................................................. 27

Figure 22: Experimental Global HWRF System in the NMMB NEMS Framework. .................. 31
i

Executive Summary

This report describes the activities and results of the Hurricane Forecast Improvement Project1

(HFIP) in 2015. It should be generally noted that 2015, like 2014, was not a representative season 

for the Atlantic due to very low tropical cyclone (TC) activity and not many rapid intensification 

(RI) events. The major developmental focus in 2015 was on the Hurricane Weather Research and 

Forecasting2(HWRF) regional model and regional ensembles for track and intensity predictions. 

In 2015, HFIP was 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, its models, and results from both the operational model development (Stream-1) and 

experimental model development (Stream-2).

Stream 1.0 Results and Accomplishments

 HWRF implementation consisted of increased horizontal resolution from 27/9/3 km to 

18/6/2 km across all domains, continued improvement of the Nest-Tracking-Algorithm, 

advanced vortex initialization, and improved products.

 HFIP improved HWRF intensity forecasts from 2014 (Fig. 5). In fact, HWRF was the 

best intensity forecast guidance model in 2015 for the North Atlantic Basin (Fig. 6).

 HWRF is currently being run operationally in all TC basins, i.e., North Atlantic (NATL), 

East Pacific (EPAC), Central Pacific (CPAC), North West Pacific (WPAC), and North 

and South Indian (IO), and Southern Pacific (SP) oceans. HWRF continues to evolve as 

a unique TC forecast guidance tool using all ocean basins (See cover page image), and in 

fact, HWRF track prediction skills were as good as the Global Forecast System (GFS) 

over the WPAC in 2015 (Fig. 10). The expansion of HWRF for all global TCs ensures 

serving forecasters at Joint Typhoon Warning Center (JTWC), other NWS interests in the 

Pacific and Indian Ocean regions, and international TC forecast agencies get more 

accurate real-time operational forecast guidance for as many as 7 TCs from HWRF at any 

given time.

 HWRF showed some significant promise for detecting RI, and the probability of

detection (POD) for RI forecasts (increase of >30 kts./24 hrs. intensity) as well as the 

false alarm rate (FAR) showed improvements over previous years (Fig. 9)

 However, some TCs such as TC Erika (issues with initialization and over-intensification), 

TC Joaquin (problems with initialization, prediction of shear and multi-scale interactions 

and poor track forecast) and TC Patricia (issues related to model resolution, initialization,

under forecast of RI and peak intensity) demonstrated the need for further improvements 

in numerical guidance (Fig. 7 and Fig. 9).

 

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ii

 The GFS, which serves as the backbone for track advancements, continues to progress 

under parallel development at EMC. The model provides excellent guidance superior to 

most other models, is comparable to the European Centre for Medium-range Weather 

Forecasts model (ECMWF) guidance, and exceeds the 5-year HFIP goal out to 4-day lead 

time.

Stream 2.0 Results and Accomplishments

 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) model (11 members) were run on the HFIP Jet System in real-time where 

COAMPS-TC & HWRF control consensus and the ensemble mean outperformed singlemodel counterparts in deterministic validation.

 The Basin-Scale HWRF was transitioned to the Development Testbed Center3(DTC) and 

is evolving to provide a unique capability for the community. Some of the results from 

this system are starting to demonstrate the need for developing high-resolution moving 

nests supporting the Next Generation Global Prediction System (NGGPS).

 The Advanced Circulation (ADCIRC) storm-surge forecast system for oceanic, coastal 

and estuarine waters was run in semi-real time using 20 member ensembles from HWRF.

 Stream 2.0 and other HFIP sponsored/run models in real time were displayed on the 

HFIP website where new products and improvements to existing products are made.

Future configuration of the Hurricane Forecast System

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

guidance system is described in Table 1 below.

Table 1. Future Numerical Model Hurricane Forecast Guidance System

Component Specifications

Global model ensemble with 

Hybrid Data Assimilation

20 members at 10-20 km

Multiple moving nests to 2-3 

km horizontal resolution 

within the global model 

Telescopic nests, one for each hurricane, using all available 

aircraft and satellite data in the inner core and near environment 

of hurricane.

Additional models to make a 

multi-model ensemble 

(possibly run as a global model 

with internal nests).

Multi-model (at least two – e.g. HWRF/HNMMB, COAMPSTC)

Statistical Post Processing

Logistics Growth Equation Model (LGEM), Statistical 

Hurricane Intensity Prediction System (SHIPS), Statistical 

Prediction of Intensity from a Consensus Ensemble (SPICE), 

and others.

 

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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 is shortened somewhat from previous years but 

some of the same material is repeated for reference. For more background information the 

reader is referred to earlier reports available at: http://www.hfip.org/documents/reports2.php.

Acronyms are defined in the Appendix.

2. The Hurricane Forecast Improvement Project

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

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

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

(for 2019) are:

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

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

 Increase the probability of detection (POD)

4

for RI5to 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).

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

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

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

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

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

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

accommodated by 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 

 

4

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

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

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

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

forecasted RI that did not occur).

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

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

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.

3. The HFIP Model Systems

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

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

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

for at least the next five years to a resolution no finer than about 8-10 km, which is inadequate to 

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

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

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

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

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

a. The Regional Model

Although HWRF and GFDL are primary regional models under Stream 1, both COAMPS as 

well as Advanced Research WRF (ARW) are run under Stream 2. The COAMPSTC/HWRF/GFDL combined 41 member ensemble is used to demonstrate the value of multimodel ensembles in TC tracks and intensity predictions. A 41-member ensemble-system 

consisting of 10 perturbed members from COAMPS-TC, 20 perturbed members from HWRF

and 11 perturbed members from GFDL were run for the 2015 season. Similarly, the Penn State 

University group ran ARW- Ensemble Kalman Filter (EnKF) and HWRF-EnKF systems for 

demonstration of improved data assimilation (DA) techniques for TC intensity forecasting. 

Specifications of regional models used by HFIP in 2015 are shown in Table 2.

Table 2. Specifics of the HWRF and other regional model used by HFIP in 2015

Models

Domains / 

Horizontal 

Resolution 

(km)

Vertic

al 

Levels

Core

Cumulus 

Parameterizat

ion

Microph

ysics PBL Land Surface

Radiatio

n

Initial 

and 

Bounda

ry 

Conditi

ons

Initialization SST

HWRF (OPS)

3

18/6/2

(6/2 

following 

the storm)

61

NMM

Simplified 

Arakawa 

Schubert for 

18/6 nests

FerrierAligo

GFS 

NonLocal 

PBL

Noah RRTMG GFS

Hybrid

GSI-EnKF 

with vortex 

initialization

MPIPOM

HWRF in 

non-NWS 

basins 

(WP/SH/SL/I

O)

3

18/6/2

(6/2 

following 

the storm)

43

NMM

Simplified 

Arakawa 

Schubert for 

18/6 nests

FerrierAligo

GFS 

NonLocal 

PBL

Noah RRTMG GFS vortex 

initialization

GFS 

(static)
3

b. Initialization and Data Assimilation Systems

It is believed improved initial state for TC models should have significant positive impacts on 

track, intensity and structure predictions. A number of approaches are used to create the initial 

state for the regional models in the HFIP EFS:

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

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

applications, and is widely used by many modeling systems across NOAA and other agencies 

Models

Domains / 

Horizontal 

Resolution 

(km)

Vertic

al 

Levels

Core

Cumulus 

Parameterizat

ion

Microph

ysics PBL Land Surface

Radiatio

n

Initial 

and 

Bounda

ry 

Conditi

ons

Initialization SST

GFDL (WP, 

HFIP version)

3

55/18/6

(18/6 

following 

the storm)

42

GFDL

Simplified 

Arakawa 

Schubert

Ferrier

GFS 

NonLocal 

PBL

GFDL 

Slab 

Model

Schwarz

kopf-Fels 

(LW) / 

LacisHansen 

(SW)

GFS

GFDL 

Synthetic 

Bogus 

Vortex

MPIPOM

GFDL (Ens)

11 members

3

55/18/6

(18/6 

following 

the storm)

42

GFDL

Simplified 

Arakawa 

Schubert

Ferrier

GFS 

NonLocal 

PBL

GFDL 

Slab 

Model

Schwarz

kopf-Fels 

(LW) / 

LacisHansen 

(SW)

GFS.

GFDL 

Synthetic 

Bogus 

Vortex with 

inner core 

perturbation

MPIPOM

HYCOMCoupled 

HWRF

3

27/9/3

(9/3 

following 

the storm)

61

NMM

Simplified 

Arakawa 

Schubert

Ferrier

GFS 

NonLocal 

PBL

GFDL 

Slab 

Model

GFDL

Scheme GFS Vortex 

initialization

3D 

HYCOM

HWRFHRD/EMC

Basin Scale

3

27/9/3

(9/3 

following 

each 

storm)

61

NMM

Simplified 

Arakawa 

Schubert

Ferrier

GFS 

NonLocal 

PBL

GFDL 

Slab 

Model

GFDL

Scheme GFS Vortex initialization

GFS 

(static)

HWRF-HRD

(HEDAS)

2

9/3

(3km 

following 

the storm)

61

NMM

Simplified 

Arakawa 

Schubert

Ferrier

GFS 

NonLocal 

PBL

GFDL 

Slab 

Model

GFDL 

Scheme GFS

EnKF;

1-hour 

cycling;

stormrelative obs 

processing

GFS 

(static)

AHW 

(NCAR) 15-

member 

ensembles

3

36/12/4

36

ARW

Tiedtke (36/12 

km only) WSM6 YSU NOAH LSM

RRTMG 

(LW+S

W)

GFS 

(BC 

only)

96-member 

DART 

EnKF 

method in a 

6-hour 

cycling mode

Pollard 1-D 

Column 

Ocean

COAMPSTC©

(HFIP 

version)

3

45/15/5

(15/5 km 

following 

the 

storm)

40

COA

MPS

Kain Fritsch 

on 45 and 15 

km meshes

Explicit 

microph

ysics (5 

class 

bulk 

scheme)

Navy 1.5 

Order 

Closure

Slab 

with the 

NOAH 

LSM as 

an 

option

Fu-Liou GFS

Balanced 

vortex 

initialization

(4D-VAR, 

EnKF 

options)

NCODA 

with 

parametric 

SST (1D)

COAMPSTC©

(OPS)

3

45/15/5

(15/5 km 

after the 

storm)

40

COA

MPS

Kain Fritsch 

on 45 and 15 

km meshes

Explicit 

microph

ysics (5 

class 

bulk 

scheme)

Navy 1.5 

Order 

Closure

Slab 

with the 

NOAH 

LSM as 

an 

option

Fu-Liou NAVG

EM

Balanced 

vortex 

initialization

(4D-VAR, 

EnKF 

options)

NCODA 

with 

parametric 

SST (1D)
4

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

is used by NCEP GFS in operations since 2006.

2. Global Forecast System (GFS): Starting in 2012, GSI was transitioned to Hybrid EnsembleVariational DA System (HEVDAS). HEVDAS is a combination of the GSI 3D-VAR and an 

ensemble-based system to define the background error matrix.

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

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

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

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

vortex cycled from the previous six-hour forecast, from a parent global model, or defined 

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

and intensity of the GFS vortex are modified based on real-time observations. In the HWRF 

system, the tropical cyclone vortex is generally cycled from the HWRF previous six hour 

forecast, and the vortex is relocated based on the observed position. The hybrid GSI-EnKF 

DA system uses the modified vortex and ambient fields as a first guess for assimilating data 

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

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

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

the global analysis fields from the Global Data Assimilation System6(GDAS) onto the 

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

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

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

and 4) addition of observation data outside of the hurricane area using hybrid GSI. The flowdependent portion of the background error covariance comes from a 6-h. HWRF ensembles

(self-cycled since 2015) when tail Doppler radar (TDR) observations are available, and when 

TDR is unavailable it uses the 6-h. GDAS ensemble. The DA system is capable of ingesting 

inner core data to optimize the vortex initialization.

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

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

Previously a 3D-VAR system, it was upgraded in September 2009 to NAVDAS-Accelerated 

Representer (AR), a four-dimensional variational (4D-VAR) approach (Daley and Barker, 

2001).

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

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

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

HFIP models (e.g., AHW, HFIP GFS ensembles, Pennsylvania State University (PSU) etc., 

see Tables 4 and 5 above) are using the EnKF approach for DA. The Penn State group led by 

Professor Fuqing Zhang uses such an approach in both ARW and HWRF systems. The 

Hurricane Research Division (HRD)/AOML developed a variant of the EnKF based DA 

system using the HWRF model, known as the Hurricane Ensemble Data Assimilation System

(HEDAS) as noted by Aksoy et al, (2012).

 

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c. The HWRF Community Code Repository and User Support

During 2009-2015, both the Environmental Modeling Center7(EMC) and the Developmental 

Testbed Center (DTC) worked to update the operational version of HWRF from version 2.0 to 

the current community version of HWRF, version 3.7a (Bernardet et al, 2015; Tallapragada et al, 

2016). This 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. This was one of the 

initial goals of the WRF program, and is supported by HFIP for developing a repository for a 

community-based hurricane modeling system which ensures the same code base can be used for 

research and in operations. Support provided by the DTC in 2015 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 an 

experimental version of HWRF called the “basin-scale” HWRF that was created at AOML/HRD 

in collaboration with NCEP/EMC under the support of NOAA’s HFIP. This research system can 

support any number of high-resolution movable nests centered on TCs in either the Atlantic or 

East 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).

 

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4. Meeting the HFIP Goals

a. The HFIP Baseline

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

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

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

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

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

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

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

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

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

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

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

The baseline errors in Fig. 1 are also compared to the errors of the same cases for the 

climatology and persistence model (CLIPER5) supporting track and the Decay Statistical 

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

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

therefore are useful in assessing the inherent difficulty in a set of forecasts. When a track or 

intensity model error is normalized by the CLIPER5 or Decay-SHIFOR5 error, the normalization 

yields a measure of the model’s skill.

Because a sample of cases from, 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, represented in blue and labeled on the right side of 

the graph. Skill in the figure is the percentage improvement over the Decay-SHIFOR5 and 

CLIPER5 forecasts for the same cases. Note the skill baseline and goals for intensity at all lead 

times are roughly constant with the baseline representing a 10% improvement over DecaySHIFOR5 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) DecaySHIFOR5 can fail to adequately account for forecast difficulty in some circumstances. A 

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

Decay-SHIFOR intensity forecast errors (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.
7

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

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

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

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

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

solid, HFIP goals dashed), the goals are expressed as the percentage improvement over the CLIPER5 errors 

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

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 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).
8

b. Meeting the Track Goals

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

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

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

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

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

A version of this advanced DA went operational in the GFS model in May 2012. Looking at a 2-

year sample for the Atlantic basin (Fig. 3a) we’re near the 5-year HFIP goal, at least through 72 

hrs. In the East Pacific basin (Fig. 3b), for the 2-year sample, OFCL is well above the 5-year

goal and seemingly within reach of the 10-year goal. However, TCs like 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. It is also expected 

that the Next Generation Global Prediction System (NGGPS) may be able to provide some 

accelerated progress in reaching the HFIP 10-year goal. Toward this end, there is a gradual 

transitioning of HWRF efforts to focus on hurricane forecast guidance within NGGPS. HRD 

and NWS are working to transition hurricane multiple moving 1-3 km high resolution nest 

capability within the NGGPS model that could be used for any TC within the global model (see 

Section 12 for details). 

c. Reaching the Intensity Goals

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

eventually with global models that have moveable nests with a horizontal resolution finer than 3 

km covering the hurricane’s inner core. Some significant progress was made with the regional 

HWRF system meeting the 5-year HFIP intensity goal. In general, the operational HWRF model 

has started showing its potential for improved intensity forecasts, producing comparable and 

sometimes superior results versus statistical models and NHC official forecasts; as demonstrated 

through a large set of retrospective forecasts. In fact, on average HWRF produced the best 

intensity guidance of any single model for 2015 covering the Atlantic basin (Fig. 5). Results 

from HWRF model for intensity forecasts are presented in this report. In addition, early results 

suggest that output from individual HFIP models can be used in statistical models such as the 

Statistical Hurricane Intensity Prediction System (SHIPS), (DeMaria and Kaplan, 1994; NHC 

2009) or Logistics Growth Equation Model (LGEM) (DeMaria, 2009; NHC 2009) to further 

increase the skill of the intensity forecasts. The eventual goal is to create regional models that

will be able to interact within the global model. More specifically, there would be one set of 

nests for each hurricane in the global model thereby accomplishing both track and intensity 

forecast goals through a unified global-to-regional scale modeling system. In fact, the basinscale HWRF, that was experimentally run under the Stream 2 activity this year was a step taken 

by HFIP towards the eventual creation of global nests.
9

5. Operational Hurricane Guidance Improvements

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

by National Centers for Environmental Prediction8(NCEP) reaches those goals. Since 2015 

represents the sixth year of the project, it is expected to see progress toward meeting HFIP 10-

year goals in both operational models and experimental models. In this section, emphasis is 

placed upon improvements in the hurricane forecasts from models that were fully operational in 

2015. This includes the GFS and the HWRF operational regional models.

a. Track Guidance

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

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

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

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

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

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

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

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

results into operations. In addition, the GFS underwent other improvements including improved 

physics and increased resolution. In 2014, the deterministic operational GFS was run at T574 

(~27 km) and the GFS ensemble (GEFS) at T254 (~60 km). In January 2015, the resolution of 

the GFS was increased to T1534 (~13 km) and in December 2015, GEFS resolution was

increased to T382 (~33 km).

Figure 2: 2015 Seasonal track forecast skills over (a) Atlantic and (b) East Pacific basins.

In the 2015 season, over the Atlantic basin (Fig. 2a, left) the European model (EMXI) was the 

best performer and the only one that beat the official forecast at 36 h and beyond. The GFSI 

(operational GFS model) was a fair to good performer (second best individual model) with skill 

just below the official forecasts and the consensus models followed by GFS ensemble mean 

 

8

http://www.ncep.noaa.gov/
10

(AEMI), HWFI and the UK meteorological office model (EGRI). Over the East Pacific Basin

(Fig. 2b, right), the FSU super ensemble (FSSE) was a strong performer, but not as good as 

TVCE (average of at least 2 of GFDI, HWFI, UKMI, GFSI, EMXI). The EMXI was the best 

Figure 3: 2014-2015 Seasonal track forecast skills over (a) Atlantic and (b) East Pacific basins

individual model, but less skill than the official forecasts and consensus models. GFS ensemble 

mean, GFSI, HWFI, and EGRI were the next best models. The Atlantic Hurricane Joaquin was a 

forecasting challenge and illustrated the need for additional research to address model failures. 

Nevertheless, looking at a 2-year sample for the Atlantic basin (Fig. 3a, left) to get a more 

representative result shows that we’re near the 5-year HFIP goal, at least through 72 hrs. HWRF 

is competitive but less skillful than the GFS. In the East Pacific basin (Fig. 3b, right), for the 2-

year sample, OFCL is well above the 5-year goal and seemingly within reach of the 10-year goal. 

HWRF and GFS were neck and neck, and individually have both reached the 5-year goal.

b. A Note on Global Ensemble Forecast System for Track Guidance

NCEP's Global Ensemble Forecast System (GEFS) was upgraded in December, 2015. The 

horizontal resolution is increased from about 55km to about 33km for the first 192 hours (8 days) 

of model integration, and from about 70km to about 55km between 192 hours to 384 hours of 

model integration. The new GEFS also increases the vertical resolution from 42 levels to 64 

levels throughout the model integration. The ensemble initialization method is modified by 

replacing the Bred Vector with the Ensemble Transform and Rescaling (BV-ETR) scheme in 

conjunction with the Ensemble Karman Filter (EnKF) scheme. The 6-hour forecasts of the 80 

EnKF ensemble members of the Hybrid Data Assimilation system, from the previous cycle, are 

used to initialize the ensemble perturbations. This change unifies NCEP's global ensemble 

systems in data assimilation and forecast and will lead to reduction in computational resources. 

The Stochastic Total Tendency Perturbation (STTP) scheme is also modified by turning off the 

perturbations in surface pressure and improving the rescaling algorithm. In the 2015 season,

over both Atlantic and East Pacific basins, the AEMI (GEFS with 6-hour interpolation) 

performed very similar to GFS in terms of track prediction skill (Fig. 2a and 2b). Fig. 4, for 

instance, shows one cycle of Hurricane Joaquin (including TC Ida) from the ECMWF ensemble 

system and GEFS. The deterministic track forecast from ECMWF was much more skillful than 

its ensemble members. The ECMWF ensemble had large spread and deterministic forecasts 
11

were outliers. Whereas the deterministic forecast from GFS had taken the storm west. The 

GEFS produced less spread but more meaningful ensembles for this cycle than the deterministic 

forecast. In either models, there was a large divergence between deterministic and ensemble 

forecasts; illustrating the need for research on the spread of ensemble members and divergence 

between ensemble and deterministic forecasts for track predictions.

Figure 4: Ensemble predictions from 20150930 12Z ECMWF and GFS ensembles cycles.

Ensemble predictions from 20150930 12Z cycle (a) ECMWF and (b) GFS ensembles illustrate the need for 

research on the spread of ensemble members and divergence between ensemble and deterministic forecasts

for track predictions.

c. Intensity Guidance: Hurricane WRF (HWRF)

1) Atlantic Basin

Figure 5: 2011-2015 HWRF Intensity Forecast Improvement for the Atlantic Basin.

HWRF Intensity Forecast Improvement for the Atlantic Basin Improvements from the 2011 to 2015 are 

shown as forecast improvements in intensity error over the time. Seasons for which models were run are 

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

evolution in accuracy and advancement toward the HFIP 10-year goal is shown by the ellipse over the 108 h.

to 120 h. forecast period.
12

Fig. 5 portrays the progress of HWRF in forecasting intensity relative to HFIP goals. 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, in terms of error related to the five-year goal, the 2014 version of HWRF 

met or exceeded that goal beyond 72 hours. This illustrates that accelerated progress is being 

made in meeting the 10-year goal. Consistent with Fig. 5, the HWRF was the most skillful 

model in the 2015 Atlantic season between 36-96 hours (Fig. 6). However, another striking 

feature in Fig. 5 is the consistently very low skill early in the forecast (purple ellipse). This is 

related to the ongoing problems with initialization of the models. This problem is not unique to 

HWRF (Fig. 6). All dynamical models seem to suffer from a common initialization problem, i.e. 

the inability of the model to represent the initial state of the atmosphere accurately due to 

incompleteness in observations to provide all variables at all model levels for accurate 

initialization. The initialization of dynamical models continues to be a high priority for HFIP. 

Keep in mind however, that statistical models as well as the “early” (aka interpolated) versions 

of dynamical models’, show better results in the first 24 hours since the initial times are adjusted 

to the operational values. The results shown here are for the “late” (aka non-interpolated) 

versions and therefore the forecast errors at early lead times are generally larger than with the 

“early” versions.

Fig. 6 shows the intensity results for the 2015 Atlantic season. HWRF was the best performer 

for 2015. In the 2014 season, the statistical model DSHP was the best performer. Whereas in 

the 2013 season, HWRF outperformed statistical models DSHP and LGEM. HWRF for 2015 

beats these statistical models by at least a maximum of 10-15% between 36 and 96 hours. It 

should be noted that, since 2011, the HWRF model proved to be able to either outperform or at 

least to be comparable to the statistical model. Until 2011, none of the dynamical models have 

shown that kind of consistent skill. Tallapragada et al. (2014) attributed the improved forecasts 

to some important implementations: (a) higher-horizontal-resolution nest that better resolves 

convection and represents terrain effects; (b) PBL and surface physics for the higher resolution 

nest; and (c) improved representation of the initial conditions in the higher resolution nest. All 

of this may be credited to HFIP Streams 1 and 2 developments. Nevertheless, tropical cyclones 

like Erika (2015) and Patricia (2015) continue to pose challenges to intensity forecasting. 

Figure 6: 2015 Hurricane season intensity forecast skills for the Atlantic 

Basin. 
13

Sustained HFIP research and development are necessary for further improvements of rapid 

intensification and decay forecasts in these events. Erica was a classic case of a false alarm, i.e., 

storms will dissipate whereas models keep intensifying them. HWRF captured some of RI 

during Patricia’s intensification, but with intensity errors at 48 hrs. that still exceeded 40 kts.

 

Figure 7: Evolution of intensity from various HWRF forecast cycles.

This figure depicts the intensity evolution derived from various forecast cycles for (a) Erika (an example of 

false RI in 2015) and (b) Patricia (case of under predictions). The black dotted line shows the best estimates 

of 10-m wind speed.
14

2) East Pacific (EPAC) Basin

Figure 8: 2015 Hurricane season intensity forecast skills for the East Pacific Basin.

Fig. 8 provides the intensity forecast skill plot of the Eastern North Pacific basin for the 2015 

hurricane season. The OFCL was at or well above the 5-year goal, as was some of the guidance.

In this basin both the statistical models, DSHP and LGEM, performed well and so did the 

HWRF. The HWRF intensity forecast had large negative bias in the Eastern North Pacific that 

was likely due to use of the climatology of ocean temperature profile at initial time. Work is ongoing under HFIP.

d. HWRF Improvements in RI/RW Predictions in Atlantic and Eastern 

North Pacific Basins

Improving RI/RW forecasts is one of the highest priorities for HFIP and was recognized as the 

most challenging aspect of TC research. Much of the lack of improvement in the RI forecast 

skill is rooted in our lack of understanding on when and how RI occurs in different 

environmental conditions and the historic inability of dynamical models to adequately predict not 

only the convection in the hurricane core, but also the large scale environmental factors such as 

shear and moisture that produce an RI event. The impressive intensity forecast performance 

from the new operational HWRF model demonstrated its improved ability in representing and 

forecasting RI. Verification of the probability of detection (POD) and the false alarm rate (FAR) 

of RI forecasts for Atlantic and E-Pacific basins during 2015, shown in Fig. 9, indicate further 

improvements in the POD for the 2015 HWRF model compared to the 2014 version.

Specifically, the POD index for RI forecasts (an increase >30 kt. intensity change in 24 hrs.) in 

the 2015 HWRF model is 44.4% compared to 4.8% in 2014 over the Atlantic basin. The FAR 

remained very low. Similarly, in the Eastern North Pacific, the POD went up from 1.2 in 2014 to 

5.1 without much increase to the FAR. Improving RI forecasts is one of the highest priorities for 

HFIP and was recognized as the most challenging aspect of TC research. Much of the lack of 

improvement in the RI forecast skill is rooted in our lack of understanding of when and how RI 
15

Figure 9: HWRF predicted 24-hour maximum 1-m wind speed changes (knots).

Real-time HWRF-predicted versus Observed 24-hour wind speed changes from 2014 version (left panels) and 

2015 version (right panels) for the Atlantic (upper panels) and East Pacific (lower panels) basins. Points 

within the rectangles in the upper right quadrants are correctly predicted RIs. The corresponding 

contingency tables are shown in the upper left quadrants. Note: data represents storms after reaching 35 kts.

occurs in different environmental conditions and the historic inability of dynamical models to 

adequately predict not only the convection in the hurricane core, but also the large scale 

environmental factors such as shear and moisture that produce an RI event. The intensity 

forecast performance from the new operational HWRF model demonstrated its improved ability 

in representing and forecasting RI. Verification of the probability of detection (POD) and the 

false alarm rate (FAR) of RI forecasts for Atlantic and E-Pacific basins during 2015, shown in 

Figure 9, indicate improvements in the POD for the 2015 HWRF model compared to the 2014 

version. Specifically, the POD index for RI forecasts (an increase >30 kt. intensity change in 24 

hrs.) in the 2015 HWRF model is 44.4% compared to 4.8% in 2014 over the Atlantic basin. The 

FAR decreased substantially, as well. Similarly, in the Eastern North Pacific, the POD went up 

from 1.2 in 2014 to 5.1, with a decrease in the FAR. Note that the data used to develop Fig. 9 

represents storms after reaching 35 knots. RI predictions made before maximum winds reached 

35 knots are not included in the figure or the POD and FAR value calculations.
16

6. HWRF performance in other global basins

The cover picture shows the global coverage of the operational HWRF system on a particular 

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

Indian Ocean. In 2014, HWRF runs were also extended into the southern Pacific and Indian 

Oceans. These runs were done in real time on the HFIP computers in Boulder, CO rather than 

operational computers. Forecasts were transmitted to the Joint Typhoon Warning Center9

(JTWC) where they were used extensively in their forecasts. The transition of HWRF from 

Stream 2 to operations in 2015 was another success story for HFIP. This section briefly 

discusses the performance of HWRF in the other global basins.

 

Figure 10: 2015 Seasonal track and intensity forecast skills for the West Pacific Basin.

This figure illustrates (a) Track and (b) Intensity forecast skills over the West Pacific basin in the 2015 

season.

Fig. 10 shows the performance for WPAC storms of HWRF compared to various other models. 

In this basin, both GFS and HWRF are top performers in terms of track until 72 hours followed 

by the NAVY models (NAVGEM and COAMPS-TC with NAVGEM boundary conditions). For 

intensity forecasting COAMPS-TC with GFS boundary conditions was the best, followed by 

HWRF and COAMPS-TC with NAVGEM boundary conditions. JTWC forecasters noted 

significant improvements between 2014 and 2015 HWRF forecasts for both track and intensity. 

However, it was also noted there was a positive bias for intensity forecasts. Lack of ocean 

coupling may be the reason for this bias. The HYCOM-HWRF coupled forecast will be run in 

the near future for that basin.

 

9

https://metoc.ndbc.noaa.gov/JTWC/
17

Figure 11: 2015 Seasonal track and intensity forecast skills for the Central Pacific Basin. 

This figure depicts: (a) Track and (b) Intensity forecast skills over the Central Pacific basin in the 2015 

season.

Fig. 11 shows the performance for Central Pacific storms of HWRF compared to various other 

models. In the Pacific basin, both HWRF and GFS performed as well as the official track 

forecast in terms of track error. Fig. 11 also shows the GFDL model has significantly less track 

error than official forecast results for 96-120 hours. HWRF intensity performance was better 

than official forecast performance at longer lead times between 48-120 hours. Nevertheless, 

LGEM was the best performing model with an intensity forecast error about half as large as the 

HWRF error. It also be noted that the number of samples in this basin were low.

Figure 12: 2015 Seasonal track and intensity forecast skills for the Northern Indian Ocean.

This figure illustrates (a) Track and (b) Intensity forecast skills over the NIO basin in the 2015 season.

Fig. 12 shows the performance for North Indian Ocean storms of HWRF compared to various 

other models. Beyond 48 hours, HWRF tracks had some large errors reported in this basin. 

Nevertheless, HWRF was the best performing model for intensity guidance having nearly 

equaled or beaten the official JTWC forecast from 0 through 72 hours. The COAMPS-TC run 

with GFS boundary conditions had a similar intensity performance within that time. Note that 

COAMPS-TC run with GFS boundary conditions showed improvement in track guidance over 

that of COAMPS-TC with NAVGEM boundary conditions. NHC is considering this model 

configuration as an experimental candidate for real-time delivery for the upcoming hurricane 

season.
18

7. Important Stream 2 Results

a. Regional Multi-Model Ensembles

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

the HWRF, COAMPS-TC and the GFDL Ensembles (described in more detail below).

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

additional physics perturbations:

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

 20 members plus 1 control, the operational HWRF

 Initial Conditions (IC)/Boundary Conditions (BC) Perturbations were from the GEFSand Ensemble Transform with the Rescaling (ETR) system to form ensemble members:

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

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

 Model Physics Perturbations (vortex scale):

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

o Stochastic Cd perturbation

Large-scale perturbations of the ensemble were derived from the GEFS to initially define each 

member. A stochastic convective triggers (initial wind and PBL height perturbations) were 

subsequently added within each member.

The COAMPS-TC ensemble was similar to that run in 2014, except that the size of the inner nest 

was increased to be consistent with the operational model, and the 2015 version of the model 

was used for the ensemble:

 27-, 9-, 3-km. horizontal grid spacing (increased the size of the inner nest to match 

control) and this resolution is higher than the current operational COAMPS-TC.

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

 No physics perturbations (2015 version of COAMPS-TC has a new Cd formulation)

 No data assimilation

 Control forecast:

o Initialized from the GFS analysis

o Vortex initialized with a Rankine vortex based on TC vitals

 Ensemble members IC’s perturbed about the control:

o Synoptic perturbations drawn from static covariance (WRFVARcv3) for initial/BCs

o Vortex IC’s based on perturbed TC vitals

GFDL Ensembles:

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

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

for the ensemble, see Table 3). Working with NHC forecasters, GFDL scientists constructed an 

ensemble by modifying various parameters in the initial conditions, sea surface temperatures and 

surface fluxes used by the model. The unbogussed forecasts start from the GFS without 

modification to the vortex from what was in the GFS initially.
19

Table 3. Automated Tropical Cyclone Forecasting System (ATCF) ID Descriptions.

Figure 13: Multi-Model 2015 Season Mean Track, Intensity errors and bias.

This figure shows the mean (a) track & (b) intensity errors and bias from the multi-model regional 

ensembles. Blue curves are from COAMPS-TC members, pink from HWRF members and green from the 

GFDL members.
20

Fig. 13 provides an overview of the mean track and intensity from HFIP ensembles from the 

three models. While there were only 122 cases reflecting not many TCs in the 2015 Atlantic

season, there were a much greater number of 2015 semi-real time cases run than in 2014. It was 

found that for individual model, ensemble mean of the tracks had accuracy similar to or 

somewhat better than the control. For intensity, the combination of COAMPS-TC and HWRF,

outperformed the two individual models and consensus of COAMPS-TC and HWRF controls 

had superior accuracy and bias w.r.t. COAMPS-TC and HWRF ensemble mean. The combined 

ensemble (either two or three model) spread is not large enough, particularly for intensity at 

earlier lead times. However, the ensemble distinguished between low-uncertainty and highuncertainty cases, for both track and intensity.

Based on the HFIP demo project, a one-day workshop was held at the annual HFIP meeting in 

Miami, Nov 17, 2015. Special attention was given to ensembles of high-resolution regional 

models (Navy, NCEP, and GFDL). Workshop topics included design and implementation of 

hurricane ensembles, ensemble post-processing techniques (weighted means, super-ensemble 

techniques), probabilistic forecasts, representation of uncertainty, use of ensembles in data 

assimilation, observation sensitivity experiments, and single model vs. multi-model ensembles 

etc. While NCEP operations continues its emphasis on the use of ensembles and HFIP also 

conducted a workshop dedicated to the subject five or so years ago, it should be noted that there 

is still a need for better, new ensemble techniques to improve deterministic forecasts. For the 

first time, this workshop covered state-of-the-art in the science and application of hurricane 

ensembles, and discussed the prioritized development of new applications for effective use of 

ensemble products for operational needs at NHC. The goal of this workshop was to encourage 

more community involvement and produce a set of recommendations for consideration by HFIP. 

Those findings are provided at: 

http://www.hfip.org/events/annual_meeting_nov_2015/presentations/Wed_0845_Torn_EnsWs_

Summary_HFIP_2015.pdf. Two key recommendations are:

 An urgent need for more investigation on how to improve model spread.

 Determine whether the deficiency in spread is conditional (i.e., only for hurricanes).

 Find ways to improve deterministic track and intensity forecasts and probabilistic genesis 

using high-resolution ensembles with better ways to display uncertainty information.

 Provide NHC with probabilistic intensity changes based on EPS guidance during the 

2016 season.

b. Basin Scale HWRF developments

HWRF has become a valuable global hurricane forecasting system. As mentioned earlier, the 

HFIP’s eventual goal is to create regional models that can be nested within and interact with the

global model. Specifically, high-resolution nests would be placed over each tropical cyclone in 

the global model, thereby accomplishing the track and intensity forecast goals through a unified 

global-to-regional scale modeling system. Although the operational HWRF system is showing 

exceptional skill in intensity forecasting, it should be noted that the current operational HWRF 

configuration is storm-centric and single-nested. 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 goal of next generation efforts. Thus, the basin-scale 

HWRF was created under HFIP with: 1) A large outer domain that covers approximately onefourth of the globe (eventually will cover the entire globe) and 2) Multiple moving multi-level 
21

nests at 1-3km. horizontal resolution to produce simultaneous tropical cyclone forecasts. The 

latter is especially important because tropical cyclones interact with the large-scale environment 

and with one another. The basin-scale HWRF system is a Stream 2 development and in 2015 it 

was transferred to the DTC.

Figure 14: HWRF basin-scale example of far-field TC track forecast improvement.

Upper Left: A schematic showing how multiple TCs are simulated in the operational HWRF. Lower Left: As 

in Upper Left, except for the basin-scale HWRF. Upper Right: Track forecast skill verification for full TC 

sample in the Atlantic basin from 2011-2013. Lower Right: As in Upper Right, except stratified for 2+ farfield TCs.
22

At the same time, research was conducted at AOML in 2015 to identify scenarios for which the 

basin-scale HWRF improves TC forecasts. It’s skillful when multiple TCs are active and far 

apart from one another (“far-field TCs”). Far-field TCs are defined as being more than 3500 km 

away another TC. In Figure 14, the red-dashed circle (centered on “TC 0”) has a radius of 3500 

km. and highlights two far-field TCs (“TC 2” and “TC 3”). Far-field TCs may be better 

represented in the basin-scale HWRF since they are simulated at high resolution (i.e., are nested) 

and are within the large outer domain (Fig. 14, lower left). On the other hand, the operational 

HWRF relies on the delivery of information from far-field TCs through the boundary conditions, 

where this information is distorted or lost (Fig. 14, upper left). For the full sample, track 

forecasts for the 2013 basin-scale HWRF and the 2014 operational HWRF exhibited similar skill 

and are both improvements over 2013 operational HWRF track forecasts (Fig. 14, upper right). 

The full sample was stratified so that only TC track forecasts with at least two far-field TCs were 

verified, which retained ~20% of the cases (Fig. 14, lower right). In this scenario, basin-scale 

HWRF track forecasts were 10-20% more skillful than operational HWRF track forecasts.

Further research will investigate this TC-environment teleconnection to discover how TC 

information is passed over such long distances.

8. Data Assimilation Developments

In order to improve high resolution analyses and TC predictions for the Hurricane Weather 

Research and Forecasting (HWRF) model uses the dual resolution hybrid ensemble Kalman filter 

(EnKF)-variational data assimilation (DA) system. The EnKF/DA system is continuously cycled 

and grid-point statistical interpolation based. In this ensemble-variational (EnVar) hybrid 

system, a newly developed directed moving nest strategy was adopted to solve the issue of nonoverlapped domains for cycled ensemble DA. In addition, both dual-resolution and FourDimensional ensemble-variational (4DEnVar) capabilities were implemented in the system. The 

performance of this system is investigated by conducting the end-to-end DA cycling and forecast 

experiments for hurricane Edouard (2014) and other cases during 2015 (Lu and Wang 2016). All 

operational observations in addition to the Tail Doppler Radar (TDR) data were assimilated. It 

was found that the dual resolution hybrid DA improves upon the coarser, single resolution hybrid 

DA; Vortex initialization and relocation in the control and relocation of the ensemble 

background on top of the DA improve the forecasts. Using 4DEnVar in the TDR-involved 

cycles improved the intensity forecasts for early lead times compared to 3DEnVar.
23

Figure 15: 1 km. Height Radar Wind and Pressure for HRD Wind and Hybrids.

In this figure, wind (shaded and vector) and pressure (contour) at 1km height for (a) HRD radar wind 

composite, (b) Hybrid, (c) Hybrid-279.

In Fig. 15 (left) the analyzed horizontal structure is shown after assimilating Doppler data from 

different experiments (valid at 12:00Z on Sep. 17th, 2014). During this time period, the storm 

was going through an eyewall replacement process while the temporal coverage of TDR data 

was very brief. The 3DEnVar analysis from “Hybrid”, Fig. 15 (middle) showed a spuriously 

strong wind maximum in the east side of the storm center. In comparison, the 4DEnVar analysis 

from “Hybrid-4DTDR”, Fig. 15 (right), greatly reduced the wind maximum and matched the 

HRD radar data analysis much better. This result indicated that 4DEnVar, with the capability to 

resolve the temporal evolution of the error covariances, was more effective than 3DEnVar in 

assimilating the inner core TDR data.

Figure 16: Operational HWRF vs. the New Hybrid System during Edouard (2014)

Left- An example of forecast from one cycle of Edouard by operational HWRF (red) and the new hybrid 

system (blue). Black dot is the best track position from NHC. Right -RMSE of Vmax forecast for the entire 

life cycle of Edouard (2014) by operational HWRF (red) and the new hybrid system (blue).
24

The experiment with Edouard (2014) also showed the potential of the new hybrid system, i.e., it 

improved intensity forecasts relative to the operational HWRF. For example, Fig. 16 shows that 

during the intensification period of Edouard (2014), the “spin-down” issue of the operational 

HWRF was largely alleviated. Improvement resulted from better analyzed structures of an 

intensifying storm. Research is on-going and tests will be conducted for more cases.

9. Physics Developments

In 2015, apart from routine model evaluations based on track and 10-m-wind estimates, TC 

structural metrics were used to identify model biases in addressing HWRF physics 

advancements. Interestingly, some of them came from DA efforts providing the synergies 

between various HFIP teams. Particular attention was being given to the reported large radius of 

maximum winds in Edouard (2014) and the mismatch of the vertical wind profiles between 

HWRF and those reported by observations.

Figure 17, shows for September 15th at 18:00Z the observed and simulated 10 m wind speed for 

Edouard (2014). HWRF 2015 model integrations are shown for the initial conditions, and 6Z, 

12Z, 18Z, 24Z, 30Z, 36Z hours. It was shown that a large bias in the radius of maximum winds 

existed in the initial conditions and that it progressively got smaller as the integrations 

progressed, with the radius of maximum winds being comparable with observations at about 30 

hours point of simulation. While in this case the large radius of maximum winds was shown to 

be an issue of model initialization and that the bias was ameliorated while model integration took

place, an EMC-HRD collaboration consisting of a multi-storm study, based on 152 Doppler 

radar storm observations was conducted. Primary findings were reported at the American 

Meteorological Society 32nd Hurricane and Tropical Meteorology Conference in 04/2016.

Figure 17: Observed and HWRF 2015 model simulated 10 m wind.

Valid time: 15th September 18:00Z. While all panels are valid at the same time, panels b-i represent HWRF 

integrations at b) initial conditions, c) 6 d) 12, e) 18, f) 24, g) 30, h) 36 and i) 42 forecast hour.
25

Observed and HWRF 2015 simulated vertical profiles of wind magnitude in the eyewall and the 

outer vortex for four different storm simulations are illustrated in Fig.18. This figure shows that 

HWRF tends to produce near-surface winds that are 10-20% too weak as compared to those in 

the upper part of the planetary boundary layer. While this is an issue of vortex initialization with 

large impacts on data assimilation (Tong et al. 2016), the bias persists during the model 

integration and should be addressed from the perspective of model physics. To address this 

issue, changes in the vertical structure of vertical diffusion and the surface exchange coefficient 

are being tested. Lowering the magnitude of the surface exchange of momentum shows some 

promise to ameliorate the problem (not shown) and thorough tests are being conducted at EMC 

to integrate such changes in HWRF 2016.

Figure 18: HWRF vs. Observed Wind Profiles.

Vertical wind profiles composited from observations (asterisks, Franklin et al. 2013) and from HWRF (at 

hour 48 of different simulated storms) both in the eyewall (pink lines) and in the outer vortex (blue lines).

10. Post Processing of Model Output

a. Post processing of model output

Post-processing of model output can be utilized to move closer to the HFIP goals in a couple of 

ways. Model diagnostics and verification are used to evaluate model upgrades, and to determine 

what proposed changes will help or hurt a modeling setup. Examples of these diagnostics are 

included in most of the sections above. Statistical post-processing can also be used to modify the 

forecast from a model or generate an independent forecast. Such statistical methods have 

historically been competitive with the best dynamical forecast models for tropical cyclone 

intensity prediction. In particular, the SHIPS, LGEM, and a blend of those applied to several 

dynamical models (SPICE/SPC3) have been improved over the past few years through HFIP and 

are among the top intensity models available to NHC forecasters. A probabilistic model called 

the Rapid Intensification Index (RII), provides beneficial information to forecasters about the 

likelihood of rapid intensification. Improvements to the statistical models in 2015 included a 

correction for the input model upper-level warm core, an improved historical database, and 

enhanced use of satellite data. Fig. 19 shows the improved performance of the 2015 SHIPS and 
26

LGEM models relative to the 2014 version of each model after the aforementioned upgrades 

were implemented. This new version was run in parallel in 2015 and will be implemented 

operationally for the 2016 season.

Figure 19: 5-Year sample SHIPS & LGEM models (2014 versions of each model, 2010-2014)

b. Dissemination and communication of model forecasts

Post processing is also used to improve understanding of current model forecasts. NHC 

forecasters use diagnostics to evaluate and compare forecasts from different models that may 

show very different forecasts for a storm. In addition, post-processing can be used to improve 

general public understanding of model forecasts. A perfect forecast of a hurricane provides little 

value if no one can act on that forecast.

Another statistical model used by the NHC is a wind speed probability model, which uses a 

Monte Carlo method to generate an ensemble of forecasts based on the current official forecast 

and historical forecast errors. This model was upgraded in 2015 with HFIP support, and 

provides emergency managers with a better way of visualizing the uncertainty of a forecast than 

the traditional “cone” graphic. To improve the use of this product NHC is developing a new 

product called the “time of arrival” graphic, which is intended to give emergency managers and 

the general public an indication of when storm preparations must be completed. Fig. 20 shows a 

prototype of this product that was demonstrated to several NHC partners.

HFIP continues to maintain a webpage for the purpose of demonstrating many of the 

experimental models discussed earlier in this report. This website is located at 

http://www.hfip.org/products/. A link is also available on the main HFIP website 

http://www.hfip.org. The products webpage allows forecasters at NHC to view experimental 

products side by side. It also allows modeling groups to compare their models, and is a very 

good demonstration tool for HFIP. Fig. 20 shows a screenshot from the hfip.org/products 

webpage. A sample of products available includes ensemble tracks and probabilities, 

deterministic model fields, and real-time experimental diagnostics.
27

Figure 20: Time of Arrival Prototype.

The area in grey indicates locations where tropical storm force winds are possible during the next 5 days, 

with the darkness of grey showing how soon the winds will arrive. Darker colors indicate a sooner arrival.

Figure 21: Screenshot of the HFIP products webpage.
28

11. NOAA Opportunity Announcement

The Table below provides the list of projects supported by HFIP during 2012-2016 and some 

R2O outcome:

Table 4. HFIP Supported Projects from 2012-2016 with some R2O results.

HFIP Round One (2012-14) Awards

PI Name PI Institution Project Title

Xuguang Wang & M. Xue

University of Oklahoma Improving High-Resolution Tropical Cyclone Prediction Using 

a Unified GSI-based Hybrid Ensemble-Variational Data 

Assimilation System for HWRF

T. Galarneau, 

T. Hamill & 

J. Whitaker (unfunded)

U Colorado - Boulder HFIP Using Global Forecast System Reforecasts to Generate 

Tropical Cyclone Forecast Products

Jun Zhang, D. Nolan, and 

S. Lorsolo

University of Miami Improving Sampling Strategies Through OSSEs for Optimal 

Assimilation of Airborne Doppler Radar Observations Using 

HRD's HEDAS

Xuejin Zhang, 

Kao-San Yeh

& Da-Lin Zhang

University of Miami Development of Multiple Moving Nests Within a Basin-Wide 

HWRF Modeling System

Aksoy, J. Chang & 

B. Klotz

University of Miami Investigation of HWRF Model Error Associated with SurfaceLayer and Boundary-Layer Parameterizations to Improve 

Vortex-Scale, Ensemble-Based Data Assimilation Using 

HEDAS

Fuqing Zhang, 

Y. Weng & X. Ge

The Pennsylvania State University Real-time convection-permitting ensemble analysis and 

prediction of Atlantic hurricanes through assimilating airborne, 

radar and satellite observations

Ryan Torn University of Albany Evaluating Hurricane Intensity Predictability using the 

Advanced Hurricane WRF

T. Krishnamurti Florida State University Further Reduction in Intensity Forecast Errors for Hurricane by 

Extension of the Correlation Based Consensus (CBC) Method

Da-Lin Zhang University of Maryland Improving Hurricane Intensity Forecasts with Consistent 

Resolutions

Robert Fovell,

K. L. Corbosiero, 

H. Su (JPL) & K-N Liou

UCLA Influence of cloud-radiative processes on tropical cyclone 

storm structure

Z. S. Haddad & 

S. Hristova-Veleva

UCLA Assimilation of precipitation observations into HWRFX 

without the pitfalls of microphysical representations

Isaac Ginis and 

R. Yablonsky

University of Rhode Island Advancing NOAA's HWRF Prediction System through New 

and Enhanced Physics of the Air-Sea-Wave Coupling
29

List of R2O outcomes from the above projects:

1. Enhanced “hybrid” DA system for HWRF assimilating tail-Doppler radar data

2. New and more efficient ocean model (MPIPOM-TC) coupled to HWRF showed improved 

track and intensity forecast skill

3. Improved radiative and PBL parameterizations

4. Perturbation in ensemble physics has different effects than perturbations in initial conditions 

or environment

5. “Far” environment can affect warm-core HWRF analysis

6. Intensity forecast uncertainty due to oceanic perturbations can be larger but lagging 

atmosphere-only

HFIP Round Two (2014-16) Awards

PI Name PI Institution Project Title

Xuguang Wang

University of 

Oklahoma

Advancing the assimilation of airborne hurricane observations using the GSIbased hybrid ensemble-variational data assimilation system for HWRF

Z. S. Haddad UCLA A holistic approach to represent the dependence of all-sky nearly-simultaneous 

radiances from microwave (LEO) to IR (geostationary) on atmospheric variables 

for assimilation into WRF

Jun Zhang

Hua Chen

University of 

Miami

Addressing Deficiencies in Forecasting Tropical Cyclone Rapid Intensification in 

HWRF 

Ryan Torn University of 

Albany

Assessing the Predictability of Tropical Cyclone Intensity using HWRF

Isaac Ginnis University of 

Rhode Island

Advancing NOAA's HWRF Prediction System through New and Enhanced 

Physics of the Air-Sea-Wave Coupling

Chris Rozoff The Board of 

Regents of the 

University of 

Wisconsin System

Probabilistic Prediction of Hurricane Intensity with an Analog Ensemble

Mike Montgomery Naval 

Postgraduate 

School 

(NRL MOU)

Improvement of short-term prediction of tropical cyclogenesis in the 0-5 day leadtime by incorporating and evaluating the HWRF-Genesis model within the 

marsupial framework and new Lagrangian flow technique

T. Krishnamurti Florida State 

University

Research Towards Improvement of Hurricane Intensity Forecasts using the Multimodel Superensemble and a Suite of Mesoscale Models

Hakim University of 

Washington

Intrinsic Hurricane Predictability

Zou U. of Maryland Improved Satellite Data Assimilation and Vortex Initialization for Hurricane 

Forecast Using HWRF

Pu University of 

Utah

Improving vortex initialization in HWRF multiple-level nested domains with GSI 

hybrid data assimilation

Ping Zhu Florida 

International 

University

Understanding the impact of sub-grid scale physics in HWRF on the predicted 

inner-core structure and intensity of tropical cyclones

Otkin University of 

Wisconsin System

Using synthetic satellite brightness temperature to evaluate the ability of HWRF 

parameterization schemes to accurately simulate clouds and moisture in the 

tropical cyclone environment 
30

7. Enthalpy and bulk drag coefficient ensemble perturbations are greater than those from 

microphysics

8. Increased vertical resolution implemented in 2014 HWRF improved track and intensity

9. Vortex “spin-down” in HWRF can be mitigated by Hybrid-DA or digital filter initialization 

(DFI) of analysis

10. Further improvements in vortex initialization and model clouds/moisture can be obtained 

using satellite retrieval products (microwave, sounders)

11. Further improved the HWRF air-sea-module

12. Low predictability (large σ) in HWRF is associated with conditions favoring intensification 

and small or asymmetric vortex when compared to analog runs

13. Scale-aware Cu-parameterization

14. Improved statistical techniques (e.g., corrected consensus), undergoing evaluation

12. Future Configuration of a Numerical Model Hurricane 

Forecast Guidance System to meet HFIP-NGGPS goals

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

technology are added to the system. This led to some significant improvements (Fig. 5). HWRF 

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

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

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

framework, the infrastructure that was adopted for other models at EMC. In addition, HWRF 

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

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

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

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

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

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

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

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

hurricanes rather than separately for each hurricane. In addition HRD in partnership with EMC, 

ESRL and other NOAA partners is contributing to NGGPS via the development of a 

"generalized nesting framework" called NGGNF. This framework is ESMF based and it consists 

of a stand-alone, dynamical-core independent, grid-shape- and projection-independent nesting 

framework developed within NEMS. NGGNF will allow multiple atmospheric models or 

multiple instantiations of the same model (one serving as a parent and another serving as a highresolution nest) to be coupled together for the purpose of nesting. The NGGNF internally 

provides all of the required horizontal interpolation, mass adjustment, and wind projection 

adjustment processes. NGGNF will provide a simple API that will enable atmospheric models to 

gain access to NGGNF's built-in nesting capabilities. The end goal is to provide the highresolution (1-3 km) atmospheric nesting capabilities, which may be critical for hurricane 

predictions within NGGPS, (this powerful NEMS capacity may be extended over land as well). 

One example of nesting within the global framework is shown in Fig. 22.
31

Figure 22: Experimental Global HWRF System in the NMMB NEMS Framework.

This figure is an example of the experimental global HWRF System in NMMB NEMS framework: A nest 

(eventually a moving nest) may be created on demand within the next generation global prediction system to 

track hurricanes.
32

References

Aksoy, A., S. Lorsolo, T. Vukicevic, K. J. Sellwood, S. D. Aberson, and F. Zhang, 2012: The 

HWRF Hurricane Ensemble Data Assimilation System (HEDAS) for High-Resolution Data: 

The Impact of Airborne Doppler Radar Observations in an OSSE. Mon. Wea. Rev.,

140(6):1843–1862, doi:10.1175/MWR-D-11-00212.1.

Bernardet, L., V. Tallapragada, S. Bao*, S. Trahan and Y. Kwon, Q. Liu, M. Tong, M. Biswas, 

T. Brown+, D. Stark and L. Carson, R. Yablonsky#, E. Uhlhorn and S. Gopalakrishnan, X. 

Zhang, T. Marchok, B. Kuo, R. Gall, 2014: Community Support and Transition of Research 

to Operations for the Hurricane Weather Research and Forecasting Model. Bulletin of the 

American Meteorological Society, 96, 953–960, doi: 10.1175/BAMS-D-13-00093.1.

Cangialosi, J. P., and J. L. Franklin, 2011: 2010 National Hurricane Center Forecast Verification 

Report. NOAA/NWS/NCEP/National Hurricane Center, 77. [Available online 

http://www.nhc.noaa.gov/verification/pdfs/Verification_2010.pdf]

Cohn, S. E., and D. F. Parrish, 1991: The Behavior of Forecast Error Covariances for a Kalman 

Filter in Two Dimensions. Mon. Wea. Rev., 119, 1757–1785, doi:10.1175/1520-

0493(1991)119<1757:TBOFEC>2.0.CO;2

Daley, R., and E. Barker, 2001: NAVDAS: Formulation and diagnostics. Mon. Wea. Rev., 

129(4), 869-883, doi:10.1175/1520-0493(2001)129<0869:NFAD>2.0.CO;2.

[http://www.dtic.mil/dtic/tr/fulltext/u2/a396883.pdf]

DeMaria, M., 2009: A Simplified Dynamical System for Tropical Cyclone Intensity Prediction. 

Mon. Wea. Rev., 137, 68–82, doi: http://dx.doi.org/10.1175/2008MWR2513.1

DeMaria, M., J. Kaplan, 1994: A Statistical Hurricane Intensity Prediction Scheme (SHIPS) for 

the Atlantic Basin. Wea. Forecasting, 9(2):209-220, doi: 10.1175/1520-

0434(1994)009<0209:ASHIPS>2.0.CO;2.

DTC, 2012: Community Grid point Statistical Interpolation GSI Documents and Publications. 

[Available online at http://www.dtcenter.org/com-GSI/users/docs/index.php]

Evensen, G., 1994: Sequential Data Assimilation with a Nonlinear Quasi-geostrophic Model 

Using Monte Carlo Methods to Forecast Error Statistics. J. Geophys. Res, 99(C5):10,143–

10,162; doi: 10.1029/94JC0057.

Franklin, J. L., 2003: GPS Dropwindsonde Wind profiles in Hurricanes and Their Operational 

Implications. Wea. Forecasting, 18, 32-44, doi: http://dx.doi.org/10.1175/1520-

0434(2003)018%3C0032:GDWPIH%3E2.0.CO;2
33

Goldenberg, S., S. Gopalakrishnan, V. Tallapragada, T. Quirino, F. Marks, S. Trahan, X. Zhang, 

and R. Atlas, 2015: The 2012 Triply-Nested, High-Resolution Operational Version of the 

Hurricane Weather Research and Forecasting System (HWRF): Track and Intensity Forecast 

Verifications. Wea. Forecasting, doi: 10.1175/WAF-D-14-00098.1, in press.

Houtekamer, P. L., and H. L. Mitchell, 1998: Data Assimilation Using an Ensemble Kalman 

Filter Technique. Mon. Wea. Rev., 126(3):796–811, doi: 10.1175/1520-

0493(1998)126<0796:DAUAEK>2.0.CO;2.

Keppenne, C.L., 2000: Data Assimilation into a Primitive-Equation Model with a Parallel 

Ensemble Kalman Filter. Mon. Wea. Rev., 128(6):1971-1981, doi: 10.1175/1520-

0493(2000)128<1971:DAIAPE>2.0.CO;2.

Lu, X., and X. Wang, 2016: Improving High-Resolution Tropical Cyclone Prediction using a 

GSI-Based, Cycled, Dual Resolution Hybrid Ensemble-Variational Data Assimilation 

System for HWRF: System Description and Experimental Results. Conference paper 

presented during the 32nd Conference on Hurricanes and Tropical Meteorology, College 

Park, MD. [Available online at 

https://ams.confex.com/ams/32Hurr/webprogram/Paper291452.html;

https://ams.confex.com/ams/32Hurr/webprogram/Session40399.html].

NHC, 2009: NHC Track and Intensity Models. [Available at 

http://www.nhc.noaa.gov/modelsummary.shtml.]

NOAA SAB, 2006: Majority Report: National Oceanic and Atmospheric Administration Science 

Advisory Board Hurricane Intensity Research Working Group. NOAA SAB HIRWG, 05 July, 

2006. [Available at http://www.sab.noaa.gov/Reports/HIRWG_final73.pdf]

Parrish, D.F. and J. C. Derber, 1992: The National Meteorological Center's Spectral StatisticalInterpolation Analysis System. Mon. Wea. Rev., 120(8):1747-1763, doi:10.1175/1520-

0493(1992)120<1747:TNMCSS>2.0.CO;2.

Tallapragada, V., L. Bernardet, M. K. Biswas, I. Ginis, Y. Kwon, Q. Liu, T. Marchok, D. 

Sheinin, B. Thomas, M. Tong, S. Trahan, W. Wang, R. Yablonsky, and X. Zhang, 2015: 

Hurricane Weather Research and Forecasting (HWRF) Model: 2015 Scientific 

Documentation. Developmental Testbed Center, 105. [Available at

http://www.dtcenter.org/HurrWRF/users/docs/scientific_documents/HWRF_v3.7a_SD.pdf]

Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, 2003: Ensemble 

Square Root Filters. Mon. Wea. Rev., 131(7):1485-1490, doi:10.1175/1520-

0493(2003)131<1485:ESRF>2.0.CO;2.
34

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 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.

3D-VAR: Three-Dimensional VARiational approach

4DEnVAR: Four-Dimensional Ensemble-VARiational

4D-VAR: Four-Dimensional Ensemble-VARiational

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

AEMI: GEFS with 6-hour interpolation.

AOML: Atlantic Oceanographic and Meteorology Laboratory

API: Advanced Weather Research and Forecasting Model

ARW: Pennsylvania State University Advanced Research WRF

CLIPER: Climate and Persistence model.

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

Cyclone model at the Fleet Numerical Meteorology and Oceanography 

Center.

DA: Data Assimilation

Decay-SHIFOR5: Decay Statistical Hurricane Intensity Forecast model.

DSHP: Decay SHIPS.

DTC: Developmental Testbed Center

ECMWF: European Centre for Medium-range Weather Forecasts model.
35

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

hour interpolation.

EMXI: ECMWF with 6-hour interpolation.

EnKF: Ensemble Kalman Filter

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

ESRL: Earth System Research Laboratory in Boulder, Colorado

ETR: Ensemble Transform with Rescaling system

FAR: False Alarm Rate

FSSE: Florida State University Super Ensemble.

GDAS: Global Data Assimilation System

GEFS: National Centers for Environmental Prediction Global Ensemble Forecast 

System.

GFDI: GFDL with 6-hour interpolation.

GFDL: Geophysical Fluid Dynamics Laboratory model.

GFNI: Navy version of GFDL with 6-hour interpolation.

GFS: Global Forecast System.

GFSI: GFS with 6-hour interpolation.

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

function of forecast time, with 6-hour interpolation.

GPMI: GFDL ensemble mean (note all members of the ensemble include 6-hour 

interpolation).

GTMI: Geophysical Fluid Dynamics Laboratory model’s regional and dynamic 

ensemble.

GSI: Grid-point Statistical Interpolation

HEVDAS: Hurricane Ensemble Data Assimilation System

HRD: Hurricane Research Division
36

HYCOM: HYbrid Coordinate Ocean Model

HWFI: HWRF with 6-hour interpolation.

HWRF: Hurricane WRF.

HWRFI: HWRF with 6-hour interpolation

JTWC: Joint Typhoon Warning Center

LGEM: Logistics Growth Equation Model.

NAVDAS: NRL Atmospheric Variational Data Assimilation System

NAVDAS-AR: NRL Atmospheric Variational Data Assimilation System-Accelerated 

Representer

NAVGEM: Fleet Numerical Meteorology and Oceanography Center Navy Global 

Environmental Model (replaced NOGAPS February, 2013).

NCEP: National Centers for Environmental Prediction

NEMS: NOAA Environmental Modeling System.

NGGNF: Next Generation Generalized Nesting Framework

NGGPS: Next Generation Global Prediction System

NGPI: NOGAPS with 6-hour interpolation.

NHC: National Hurricane Center

NMMB: NMM on the B-grid.

NRL: Naval Research Laboratory (U.S.)

OFCL: Official National Hurricane Center Forecast.

PBL: Planetary Boundary Layer

POD: Probability of Detection

RI: Rapid Intensification (an increase of 30 knots in 24 hours).

RII: Rapid Intensification Index
37

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

SAB: NOAA Science Advisory Board

SHIPS: Statistical Hurricane Intensity Prediction System.

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

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

with forecast lead time.

SPICE: Statistical Prediction of Intensity from a Consensus Ensemble.

STTP: Stochastic Total Tendency Perturbation scheme

TDR: Tail Doppler radar

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

Model Track Forecasts

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

hour interpolation.

UTC: Universal Time Coordinated, Greenwich Mean Time (GMT), or Zulu 

Time Zone (Z). There is no time difference between all of these time 

zones.

WRF: Weather Research and Forecasting model. It is a regional system with 

options for the dynamic core, physics, initialization, post processing and 

verification. Variations include the Hurricane WRF (HWRF), PSU 

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

(AHW).