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| !       | Technical Summary of the National Hurricane     | ![       |
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a.  **Introduction**

"Forecast model" is a generic term that refers to any objective tool
used to generate a prediction of a future event, such as the state of
the atmosphere. Generation of such forecasts is usually created through
mathematical computations. The National Hurricane Center (NHC) utilizes
many models in their preparation of the official track and intensity
forecasts. The most commonly used models at NHC are summarized in Table
1.

Forecast models range from fairly simple methods, which can be run in a
few seconds on an ordinary computer, to those that require a number of
hours on a supercomputer. Dynamical models, also known as "numerical
models" use high speed computers to solve the physical equations of
motion governing the atmosphere. Statistical models, in contrast, do not
explicitly consider the physics of the atmosphere but instead are based
on historical relationships between storm behavior and storm-specific
details such as location and date. Statistical-dynamical models use both
dynamical and statistical techniques by making a forecast based on
establishing historical relationships between storm behavior and
atmospheric variables provided by dynamical models. Trajectory models
move a tropical cyclone (TC) along based on the prevailing flow derived
from a separate dynamical model. Ensemble or consensus techniques are
not true forecast models *per se*, but rather involve combinations of
forecasts from multiple models. The following sections provide more
detailed description of the types of modeling systems and a description
of the more commonly used individual models used at NHC.

**b. Early versus Late Models**

Forecast models are characterized as either *early* or *late*, depending
on whether they are available to the forecaster during the contemporary
forecast cycle. For example, consider the 1200 UTC (12Z) forecast cycle,
which begins with the 12Z synoptic time and ends with the release of an
official forecast at 15Z. The 12Z run of the NWS/Global Forecast System
(GFS) model is not complete and available to the forecaster until about
16Z, an hour after the forecast is released - thus the 12Z GFS would be
considered a "late" model since it could not be used to prepare the 12Z
official forecast. Conversely, the BAM models are generally available
within a few minutes of the time they are initialized; therefore they
are termed "early" models. Model timeliness is listed in Table 1.

Due to their complexity, dynamical models are generally, if not always,
late models. Fortunately, a technique exists to take the latest
available run of a late model and adjust its forecast to apply to the
current synoptic time and initial conditions. In the example above,
forecast data for hours 6-126 from the previous (06Z) run of the GFS
would be smoothed and then adjusted, or shifted, so that the 6-h
forecast (valid at 12Z) would match the observed 12Z position and
intensity of the TC. The adjustment process creates an "early" version
of the GFS model that becomes part of the most current available
guidance for the 12Z forecast cycle. The adjusted versions of the late
models are known, largely for historical reasons, as "interpolated"
models.

c.  **Interpreting Forecast Models**

It is important to note that forecast models are complex, each with
their own sets of strengths and weaknesses. Interpretation of forecast
model output is often aided by professional training and years of
experience. On average, NHC official forecasts usually have smaller
errors than any of the individual models
(<http://www.nhc.noaa.gov/verification/verify3.shtml>). A given NHC
forecast never relies solely on any one individual model (i.e. "model of
the day" or "best model"), but rather reflects consideration of all
available guidance as well as forecaster experience. Therefore, users
should consult the official forecast products issued by the NHC and
local National Weather Service Forecast Offices rather than simply
looking at output from the forecast models themselves. Users should also
be aware that uncertainty exists in every forecast issued by the NHC,
and proper interpretation of the NHC forecast must incorporate this
uncertainty. NHC forecasters typically discuss forecast uncertainty in
the Tropical Cyclone Discussion (TCD) product. NHC also provides
probabilistic forecasts which can also be used to evaluate forecast
uncertainty (<http://www.nhc.noaa.gov/aboutnhcprobs.shtml>). Finally,
NHC provides detailed information on the verification of its past
forecasts with a yearly verification report
(<http://www.nhc.noaa.gov/verification/verify3.shtml>).

**d. Statistical Models**

Statistical models are based on established relationships between
storm-specific information, such as location and time of year, and the
behavior of historical storms. While these models provided key forecast
guidance in past decades, today these models are most often used as
benchmarks of skill against which other more sophisticated and accurate
models, such as dynamical models, are compared. Models that perform
worse than a simple statistical model are considered "unskillful" and
models that perform better than statistical models are considered
"skillful". Due to their simplicity, statistical models are among the
quickest to run and are typically available to forecasters within
minutes of initialization.

*Climatology and Persistence Model (CLIPER5)*

CLIPER5 is a statistical track model originally developed in 1972 and in
1998 was extended to provide forecasts out to 120 h. As the name
implies, the CLIPER5 model is based on climatology and persistence. The
model predictors, chosen using multiple regression, are the current and
past movement during the previous 12- and 24-hour period, the direction
of motion, current latitude and longitude, date, and initial intensity. 
The CLIPER5 model is now used primarily as a benchmark for evaluating
forecast skill of other models and the official NHC forecast rather than
as a forecast aid.  Forecasts having errors larger than CLIPER5 are not
considered skillful.

*Statistical Hurricane Intensity Forecast (SHIFOR5)*

SHIFOR5 is a simple statistical intensity model that uses climatology
and persistence as predictors.  

*Decay-SHIFOR5*

Decay-SHIFOR5 is the SHIFOR5 with an inland decay component included to
account for the effects of land in the rate of intensity decay when TCs
encounter land. Decay-SHIFOR5 is most often used as a benchmark for
evaluating forecast skill of other models and the official NHC intensity
forecast.

**e. Statistical-Dynamical Models**

*NHC91/NHC98 Models*

The NHC98 (Atlantic) and NHC91 (east Pacific) models are referred to as
statistical-dynamical models because they reflect statistical
relationships between storm behavior and predictors obtained from
dynamical model forecasts, such as the deep-layer-mean GFS geopotential
heights fields (averaged from 1000 to 100 mb).    

*Statistical Hurricane Intensity Prediction Scheme (SHIPS)*

The SHIPS model is a statistical-dynamical intensity model that bases
its forecasts on statistical relationships between storm behavior and
predictors obtained from dynamical model forecasts. Due to the use of
dynamical predictors in addition to climatology and persistence, the
average intensity errors from SHIPS are typically 10%-15% less than
those from SHIFOR5. In addition, SHIPS has historically outperformed
most of the dynamical models, including the GFS, which provides the
dynamical input to SHIPS. SHIPS has traditionally been one of the more
skillful sources of intensity guidance for the NHC. However, the GFDL
model has recently become more competitive with the SHIPS. Additionally,
consensus intensity techniques have emerged in the last few years as
skillful intensity prediction tools.

SHIPS is based on standard multiple regression techniques. The
predictors for SHIPS include climatology and persistence, atmospheric
environmental parameters (e.g., vertical shear, stability, etc.), and
oceanic input such as sea surface temperature (SST) and upper-oceanic
heat content. The developmental data from which the regression equations
are derived include open ocean tropical cyclones from 1982 through the
present. Each year the regression equations are re-derived based upon
the inclusion of the previous year's data. That is, the equations used
in the 2007 SHIPS are based on developmental data from 1982 through
2006. Therefore, the weighting of the predictors can change from year to
year. The predictors found to be most statistically significant are
currently: the difference between the current intensity and the
estimated maximum potential intensity (MPI), vertical wind shear,
persistence, and the upper-tropospheric temperature. SHIPS also includes
predictors from satellite data such as the strength and symmetry of
convection as measured from infrared satellite imagery and the heat
content of the upper ocean determined from satellite altimetry
observations.

*Decay-SHIPS*

Decay-SHIPS is the SHIPS with an inland decay component included. Since
land interactions result in weakening, the Decay-SHIPS will typically
provide more accurate TC intensity forecasts when TCs encounter or
interact with land. Over open waters with no land interactions, the
intensity forecasts from Decay SHIPS and SHIPS will be identical.

*Logistic Growth Equation Model Summary (LGEM)*

LGEM is a statistical intensity forecast model that utilizes the same
input as SHIPS but in the framework of a simplified dynamical prediction
system, instead of a multiple regression. The evolution of the maximum
wind (i.e., intensity) is determined by a logistic growth equation which
constrains the solution to lie between zero and the maximum potential
intensity (MPI), where the MPI is estimated from an empirical
relationship with sea surface temperature (SST). The evolution of the
maximum wind depends on the growth rate coefficient, which is estimated
from a subset of the input to the SHIPS model. No satellite input is
currently included in the LGEM forecast. An important difference from
SHIPS is that the longer range forecast depends more strongly on the
environmental parameters at the later times (SST, vertical shear, etc).
Most of the SHIPS predictors are averaged over the entire forecast
period, while most of the LGEM predictors are averaged only over the 24
hours prior to the forecast valid time. The MPI in the LGEM prediction
is the instantaneous value, rather than the forecast period average used
in SHIPS. This difference makes the LGEM prediction more sensitive to
environmental changes at the end of the forecast period, but also makes
the prediction more sensitive to track forecast errors. Another
difference from SHIPS is that because the LGEM forecast is based on a
time stepping procedure, the forecast can better represent intensity
changes of storms that move from water to land and then back over water.

**f. Dynamical Models**

Dynamical models are the most complex and most computationally expensive
numerical models utilized by the NHC. Dynamical models make forecasts by
solving the physical equations that govern the atmosphere, using a
variety of numerical methods and initial conditions based on available
observations. Since observations are not taken at every location around
the world, the model initialization can at times vary tremendously from
the atmosphere, and this is one of the primary sources of uncertainty
and forecast errors within dynamical models. Errors in the initial state
of a model tend to grow with time during the actual model forecast;
therefore small initial errors can become very large several days into
the forecast. It is largely for this reason that forecasts become
increasingly inaccurate in time.

*U.S. National Weather Service Global Forecast System (GFS)*

The term "GFS" technically refers to all code that supports the
production of the National Weather Service (NWS) global model suite of
products, including the global data assimilation system (GDAS). The GFS
model itself is a global spectral model truncated at total wave number
T382 (equivalent to about 35-km horizontal grid spacing) with 64
vertical levels. This resolution is maintained through 180 hours of the
forecast. Thereafter, the GFS is truncated to wave number T190
(equivalent to about 80-km grid spacing) with 64 vertical levels out to
384 hours. The GFS employs a hybrid sigma-pressure vertical coordinate
system, a simplified Arakawa-Schubert (SAS) convective parameterization
scheme, and a first-order closure method to represent the planetary
boundary layer (PBL). All GFS runs obtain their initial conditions from
a three-Dimensional Variational (3-D VAR) Gridpoint Statistical
Interpolation (GSI), which is updated continuously throughout the day.
Rather than inserting data corresponding to an artificial TC vortex
("bogusing"), the GFS relocates the globally-analyzed TC vortex in the
first-guess field to the official NHC position. An assimilation of the
available (real) data is then performed to create the initial state. The
globally analyzed vortex is, however, often an incomplete representation
of the true TC structure. For this reason, the GFS is typically more
suited to producing track and outer wind structure forecasts. Developed
and maintained by the Environmental Modeling Center (EMC) of the
National Centers for Environmental Prediction (NCEP), the GFS is run
four times per day (00 UTC, 06 UTC, 12 UTC, and 18 UTC) out to 384
hours.

*Limited Area Sine Transform Barotropic (LBAR) Model*

Compared to the GFS, LBAR is a simple two-dimensional dynamical track
prediction model. It solves the shallow-water wave equations initialized
with vertically averaged (850-200 hPa) winds and heights from the GFS
global model. An idealized symmetric vortex and a constant wind vector
(equal to the initial storm motion vector) are added to the GFS global
model analysis to represent the storm circulation. The model equations
are solved using a spectral sine transform technique over an area near
the hurricane. The lateral boundary conditions are obtained from the GFS
global model forecast. LBAR includes no horizontal gradients in
temperature (and as a consequence, no vertical wind shear), making the
LBAR a relatively poor performer beyond 1-2 days or outside of the deep
tropics.

Vigh, J., S.R. Fulton, M. DeMaria, and W.H. Schubert, 2003: Evaluation
of a multigrid method in a barotropic track forecast model. *Mon. Wea.
Rev.,* **131,** 1629-1636.

*Canadian Meteorological Centre (CMC) Global Environmental Multiscale
Model (GEM)*

The CMC's GEM is a hydrostatic global grid point model laid on a
latitude/longitude coordinate system with 0.3 latitude-0.45 longitude
(approximately 33 km at 49 degrees latitude) horizontal grid spacing and
58 vertical eta levels. The eta vertical coordinate system used in the
CMC's GEM depicts the bottom atmospheric layer within each grid box as a
flat step. The CMC's GEM employs an advanced and computationally
expensive four-dimensional data assimilation scheme (4-D Var) where
temporal variations in the initial data are included. The condensation
package includes the Kain-Fritsch scheme for deep moist convection and
Kuo Transient scheme for shallow convection. The Bougeault and Lacarrere
(1989) mixing length is used for vertical diffusion due to atmospheric
turbulence. The CMC's GEM is run through 144 hours at 12 UTC, 240 hours
at 00 UTC, and 360 hours on Saturdays. The CMC's GEM has limited ability
to provide useful intensity forecasts.

<http://www.smc-msc.ec.gc.ca/cmc/op_systems/recent_e.html>

*European Center for Medium-range Weather Forecasting (ECMWF) Model*

Developed and maintained by an international organization supported by
28 European member states, the ECMWF model is the most sophisticated and
computationally expensive of all the global models currently used by
NHC.  Due to model complexity/resolution, data assimilation, and
operational requirements of the member states, the ECMWF model run is
among the latest arriving of all available dynamical model guidance. The
ECMWF model is a hydrostatic spectral model where the linear terms are
triangularly truncated to 799 waves (the nonlinear terms are calculated
at a coarser resolution) with 91 vertical levels (TL799L91). This
corresponds to a horizontal grid spacing of about 25 km. The ECMWF model
employs a hybrid vertical coordinate system which is terrain following
in the boundary layer (sigma) and becomes purely isobaric (pressure)
near the tropopause. The ECMWF was the first modeling center to use
four-dimensional (4-D VAR) data initialization which allows better
assimilation of off-time (non-synoptic) observations, particularly from
satellite data.  The ECMWF system provides forecasts out to 240 hours
(10 days) twice daily. Beyond the good medium-range tropical cyclone
track prediction skill of the ECMWF model, its high spatial resolution
has shown potential for useful intensity forecasting.

<http://www.ecmwf.int/products/forecasts/guide/>

*Navy Operational Global Atmospheric Prediction System (NOGAPS)*

The NOGAPS model is a global spectral model with triangular truncation
at 239 waves (approximately 55 km horizontal grid spacing) with 30
vertical levels (T239L30). The NOGAPS uses a hybrid sigma-pressure
vertical coordinate system. This configuration results in approximately
six terrain-following sigma levels below 850 mb and the remaining 24
levels occurring above 850 mb at near-pressure surfaces. The NOGAPS time
step is five minutes, but is reduced if necessary to prevent numerical
instability associated with fast-moving weather features. The NOGAPS
model uses a 3-D VAR analysis scheme. The model is run out to 180 hours
at each of the synoptic times (00Z, 06Z, 12Z, 18Z). The NOGAPS model
utilizes the Emanuel convective parameterization scheme with
non-precipitating convective mixing based on the Tiedtke method. Like
other global models, the NOGAPS model cannot provide very skillful
intensity forecasts but can provide skillful track forecasts.

<http://www.meted.ucar.edu/nwp/pcu2/nogaps/index.htm>

*NWS Geophysical Fluid Dynamics Model (GFDL) Hurricane Model*

The GFDL Hurricane model is a limited-area, grid-point model that was
designed specifically for TC prediction. The GFDL model consists of a
triply-nested grid configuration. The outer grid spans 75° x 75° at 1/2°
horizontal resolution (approximately 30 km). The middle grid spans 11° x
11° at 1/6° horizontal resolution (approximately 15 km). The inner grid
domain size spans 5° x 5° at 1/12° horizontal resolution (approximately
7.5 km). This high resolution, compared to other dynamical models,
allows the GFDL to resolve relatively small scale features within a
tropical cyclone such as the eye and eyewall. Still, even the GFDL is
not able to fully resolve the highly complex structure of a tropical
cyclone. The GFDL uses a sigma vertical coordinate system with 42
vertical levels. It employs a simplified Arakawa-Schubert convective
parameterization scheme and non-local PBL scheme. The large-scale
condensation scheme contains the Ferrier micro-physics package, and the
air-sea momentum flux parameterization is designed to account for strong
wind conditions to give a better estimate of surface winds. The GFDL is
coupled with a high-resolution version of the Princeton Ocean Model
(POM), which allows tropical cyclone-induced ocean modification, such as
sea-surface temperature cooling, and begins to account for the feedback
of the modified ocean on the tropical cyclone. In the Atlantic, the POM
is configured at 1/6° horizontal resolution with 23 vertical sigma
levels. For the initialization, the GFDL replaces the GFS TC vortex with
an axisymmetric vortex spun up in a separate model simulation. The
axisymmetric vortex model utilizes TC specifications as provided by NHC
forecasters. Since the horizontal resolution of the GFDL is sufficiently
high to simulate *some* of the inner core tropical cyclone structure,
the GFDL model has up to now been the only purely dynamical model that
can provide both skillful intensity and track forecasts
(<http://www.nhc.noaa.gov/verification/verify3.shtml>). The GFDL is run
for up to four TCs every six hours out to 126 hours as requested by the
NHC and the Central Pacific Hurricane Center.

Bender, M.A., I.  Ginis, R. Tuleya, B. Thomas, and T. Marchok, 2007:\
The operational GFDL coupled hurricane-ocean prediction system and
summary of its performance, 2007. *Mon. Wea. Rev*. (in press).

*Hurricane Weather Research and Forecasting Model (HWRF)*

The Hurricane Weather Research and Forecast (HWRF) model was developed
by the National Centers for Environmental Prediction (NCEP)
Environmental Modeling Center and implemented operationally in 2007. The
non-hydrostatic HWRF model solves the equations that predict vertical
air motions, allowing the model to explicitly represent the mesoscale
vertical motions that exist in the inner core of TCs. The HWRF model
uses a nested grid system with horizontal grid spacing of 27 km (9 km)
on the outer (inner) domain and 42 sigma vertical levels. The GSI 3DVAR
data assimilation scheme uses a first guess vortex based on the 6-hour
forecast from the previous HWRF run to produce an initial representation
of the tropical cyclone that matches intensity and structure parameters
provided by NHC forecasters. The HWRF is coupled to the Princeton Ocean
Model (POM) in the Atlantic basin to better represent the interaction of
the atmosphere and ocean in the hurricane environment, an important
factor in tropical cyclone intensity prediction. While the HWRF
currently has a configuration and physics package similar to that used
in the GFDL model, the HWRF will ultimately evolve into a more advanced
hurricane prediction system. Future versions of the model will employ
enhanced data assimilation techniques (especially within the cyclone's
inner core), higher resolution, and more sophisticated physics.
Specifically, the HWRF model will make use of airborne and land-based
Doppler radar observations to better represent the initial cyclone
structure, which is expected to provide further advancements in the
prediction of track, intensity, and rainfall. While the HWRF model will
eventually replace the GFDL model, they are currently both being run and
evaluated in parallel during the 2007 season.

*United Kingdom Meteorological Office (UKMET) Model*

The UKMET model is a non-hydrostatic global model that utilizes an
Arakawa C-grid resulting in an east-west horizontal grid spacing of 0.5°
longitude and a north-south grid spacing of 0.4° latitude. This
configuration equates to an approximate horizontal grid spacing of 40 km
in the mid-latitudes. The UKMET utilizes a hybrid vertical coordinate
system with 50 vertical levels. The UKMET model is run twice daily at
0000Z and 1200Z producing forecasts out to 144 hours. The model's 4-D
Var data assimilation scheme utilizes observations taken within 3 hours
of the forecast start time. That is, the 1200Z run uses observations
made between 0900Z and 1500Z. Accordingly, the actual forecast
computations for the 0000Z and 1200Z runs are started at approximately
0245Z and 1545Z respectively. Intermediate runs initialized around the
0600Z and 1800Z data cycles are run at approximately 1300Z and 0100Z,
but only produce forecasts to 48 hours. In 2002, the U.K. Met Office
introduced a completely new formulation of the UKMET model. The upgrades
included new formulation of the model\'s dynamical core, the fundamental
equations, and physical parametrizations. The physics package was again
modified in 2005. The UKMET typically provides useful tropical cyclone
track forecasts but has limited ability to produce valuable intensity
forecasts.

<http://www.metoffice.gov.uk/research/nwp/publications/nwp_gazette/jun06/um_config.html>

***g. Ensemble Models***

Ensemble forecasts are derived from a combination of forecasts from
multiple models, commonly called a consensus forecast, or from an
ensemble of forecasts from a single model. Consensus models are not true
forecast models *per se*, but represent combinations or averages of
forecasts from other models. The simplest way to form a consensus
forecast is to average the output from each prediction model, e.g., one
computes the mean of each model's predicted latitudes and longitudes of
the TC center at some forecast time. At the NHC the most commonly-used
consensus forecasts are GUNA, CONU, and FSSE, which are described below.
On average, consensus forecasts are typically more accurate than the
predictions from the individual models they are comprised of. The
variation or spread of individual model forecasts can provide a measure
of forecast uncertainty.

Single-model ensembles are multiple predictions from the same starting
time for a given model, using different initial conditions. This is a
way to account for the uncertainties in the initial state of the
atmosphere for a prediction model. A simple average of all of the
ensemble forecasts (ensemble mean) often produces a more skillful
forecast than any single ensemble run, since errors associated with the
individual forecasts tend to cancel each other. However the ensemble
mean often smoothes out the finer-scale details associated with the
individual ensemble member forecasts. In some cases, the ensemble runs
of a given model are made at relatively coarse resolution compared to
its parent model.

*GUNS*

GUNS is a simple track consensus calculated by averaging the track
guidance provided by the GFDI (interpolated GFDL), UKMI (interpolated
UKMET) and NGPI (interpolated NOGAPS) models. All three member models
must be available to compute the GUNS consensus.

*GUNA*

GUNA is a simple track consensus calculated by averaging the track
guidance provided by the GFDI (interpolated GFDL), UKMI (interpolated
UKMET), NGPI (interpolated NOGAPS), and GFSI (interpolated GFS) models.
All four member models must be available to compute GUNA.

*CGUN*

CGUN is a version of GUNA that is corrected for model biases. The biases
are derived statistically, based on parameters known at the start of the
forecast, such as model spread, initial intensity, location, etc.

*CONU*

CONU is a simple track consensus calculated by averaging the track
guidance provided by the GFDI (interpolated GFDL), UKMI (interpolated
UKMET), NGPI (interpolated NOGAPS), GFNI (interpolated GFDN model) and
GFSI (interpolated GFS) models. CONU requires at least two of the five
member models be present.

*CCON*

CCON is a version of CONU that is corrected for model biases. The biases
are derived statistically, based on parameters known at the start of the
forecast, such as model spread, initial intensity, location, etc.

*ICON*

ICON is a simple intensity model consensus computed as the average of
the Decay-SHIPS and GHMI (adjusted GFDI forecast) intensity output.

*Florida State University Super Ensemble (FSSE)*

The Florida State University Superensemble (FSSE) is a weighted
multi-model consensus that uses both dynamical models and the previous
official NHC forecast as the basis of its prediction.  Rather than
simply averaging the member models, the FSSE assigns different weights
to each member model in an attempt to account for the biases of each
individual member model.  Individual model biases are computed based on
the past performance of each member model, and the weights are
determined using linear multiple regression during a training phase. The
training phase includes approximately 75 individual sets of past
forecasts from each of the member models.  This makes the FSSE somewhat
different from other models used at NHC, since the FSSE represents a
form of artificial intelligence, as it constantly is learning from the
past performance of the models that comprise it.  One limitation of the
superensemble technique occurs when the past performance of the member
models does not accurately represent their present performance, e.g., if
major changes are made to a member model between successive hurricane
seasons.  The FSSE technique is most accurate when no major changes are
made to any of the member models between the training phase and
operational forecast phase. The FSSE technique originated at Florida
State University.  NHC currently receives real-time FSSE output from a
version of the technique provided by Weather Predict, Inc.

*National Weather Service Global Ensemble Forecast System (GEFS)*

The GEFS is an ensemble prediction system based on the GFS model. It
consists of a low-resolution (T126L28, or approximately 100 km
horizontal grid spacing with 28 vertical levels) control run of the GFS,
and 20 ensemble members at the same resolution. Uncertainties in the
initial conditions are addressed by the use of so-called Ensemble
Transform Bred Vectors. These generate different variations, or
perturbations, in the initial states of each of the 20 member runs.
Vortex relocation of TCs is applied to each member initial state, i.e.,
the starting locations of TCs are assumed to be well-known and are
therefore identical in the initial states of all ensemble members. The
GEFS produces forecasts out to 16 days, four times per day. The mean of
the 20-member ensemble forecasts is typically used as forecast guidance,
however the individual ensemble runs can yield useful prognostic
information as well. For instance, the variability of TC forecast tracks
in the ensemble may provide insight on forecast uncertainty. It should
be noted however that, on average, track forecasts produced by the GEFS
have been somewhat less skillful than those produced by a multi-model
consensus forecast such as GUNA. The GEFS can also be used for guidance
on TC genesis. For instance if many, or all, ensemble members predict
the formation of a TC, the forecaster would consider more seriously the
prospect of cyclone development.

***h. Trajectory Models***

Trajectory models are much simpler than dynamical or statistical models
as they merely move a TC along a track based on the prevailing flow
derived from a separate dynamical model. While trajectory models utilize
information from dynamical models to represent the prevailing flow, they
do not allow the cyclone to interact with the surrounding atmosphere.
Another limitation associated with trajectory models is their reliance
on static levels in the atmosphere to represent the prevailing flow. To
account for the variation in the prevailing flow with height, multiple
versions of the same trajectory model based on varying depths are
typically employed.

*Beta and Advection Model (BAM)*

The Beta and Advection Model (BAM) refers to a class of simple
trajectory models that utilize vertically-averaged horizontal winds from
the GFS to compute the trajectories. These trajectories include a
correction term to account for the drift of the storm due to the
"beta-effect" caused by the earth's rotation. The BAM concept is based
upon the relationship between storm intensity/depth and steering levels.
Strong cyclones typically extend through the entire depth of the
troposphere and thus steered by deeper layer-averaged winds, while
weaker cyclones are steered by shallower layer-average winds.
Accordingly, the BAM is run in three versions corresponding to the
different depths used in the trajectory calculation: BAM shallow
(850-700 hPa), BAM medium (850-400 hPa), and BAM deep (850-200 hPa),
known as BAMS, BAMM and BAMD, respectively. The performance of the BAM
is strongly dependent on the dynamical input from the GFS. A divergence
of the three versions of the BAM indicates varying steering flow within
the parent GFS model. Hence, spread among the three versions of the BAM
also serves as a rough proxy for vertical shear as well as the
complexity and uncertainty in the track forecast.

**i. Acknowledgements**

This paper updates the original NHC model overview document written by
Dr. Mark DeMaria in 1997. Information presented in this paper is also
based upon the "Model Fundamentals" training module developed by the
Cooperative Program for Operational Meteorology, Education and Training
(COMET^®^) of the University Corporation for Atmospheric Research (UCAR)
under cooperative agreement(s) with the National Oceanic and Atmospheric
Administration (NOAA). Dr. Jack Beven, James Franklin, Dr. Richard
Pasch, Dr. Michael Brennan, Dr. Mark DeMaria, Dr. James Goerss, Dr. Mark
Iredell, Dr. Morris Bender, Dr. Issac Ginis, Dr. Mike Fiorino, Dr. Ed
Rappaport and Colin McAdie are acknowledged for their constructive
comments. Table 1 was provided by James Franklin.

+----------------+-------+---------------+-------------+-------------+
| Na             | ATCF  | Type          | Timeliness  | Parameters  |
| me/Description | ID    |               |             |             |
|                |       |               | (E/L)       |             |
+================+=======+===============+=============+=============+
| Official NHC   | OFCL  |               |             | Trk, Int    |
| forecast       |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| N              | GFDL  | Multi-layer   | L           | Trk, Int    |
| WS/Geophysical |       | regional      |             |             |
| Fluid Dynamics |       | dynamical     |             |             |
| Laboratory     |       |               |             |             |
| (GFDL) model   |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| NWS/ Hurricane | HWRF  | Mutlti-layer  | L           | Trk, Int    |
| Weather        |       | regional      |             |             |
| Research and   |       | dynamical     |             |             |
| Forecasting    |       |               |             |             |
| Model (HWRF)   |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| NWS/Global     | GFSO  | Multi-layer   | L           | Trk, Int    |
| Forecast       |       | global        |             |             |
| System (GFS)   |       | dynamical     |             |             |
+----------------+-------+---------------+-------------+-------------+
| # Nationa      | AEMN  | Consensus     | L           | Trk, Int    |
| l Weather Serv |       |               |             |             |
| ice Global Ens |       |               |             |             |
| emble Forecast |       |               |             |             |
|  System (GEFS) |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| United Kingdom | UKM   | Multi-layer   | L           | Trk, Int    |
| Met Office     |       | global        |             |             |
| model (UKMET)  |       | dynamical     |             |             |
+----------------+-------+---------------+-------------+-------------+
| Navy           | NGPS  | Multi-layer   | L           | Trk, Int    |
| Operational    |       | global        |             |             |
| Global         |       | dynamical     |             |             |
| Prediction     |       |               |             |             |
| System         |       |               |             |             |
| (NOGAPS)       |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Navy version   | GFDN  | Multi-layer   | L           | Trk, Int    |
| of GFDL        |       | regional      |             |             |
|                |       | dynamical     |             |             |
+----------------+-------+---------------+-------------+-------------+
| Environment    | CMC   | Multi-level   | L           | Trk, Int    |
| Canada Global  |       | global        |             |             |
| Environmental  |       | dynamical     |             |             |
| Multiscale     |       |               |             |             |
| Model          |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| European       | EMX   | Multi-layer   | L           | Trk, Int    |
| Center for     |       | global        |             |             |
| Medium-range   |       | dynamical     |             |             |
| Weather        |       |               |             |             |
| Forecasting    |       |               |             |             |
| (ECMWF) Model  |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Beta and       | BAMS  | Single-layer  | E           | Trk         |
| advection      |       | trajectory    |             |             |
| model (shallow |       |               |             |             |
| layer)         |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Beta and       | BAMM  | Single-layer  | E           | Trk         |
| advection      |       | trajectory    |             |             |
| model (medium  |       |               |             |             |
| layer)         |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Beta and       | BAMD  | Single-layer  | E           | Trk         |
| advection      |       | trajectory    |             |             |
| model          |       |               |             |             |
|                |       |               |             |             |
| (deep layer)   |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Limited area   | LBAR  | Single-layer  | E           | Trk         |
| barotropic     |       | regional      |             |             |
| model          |       | dynamical     |             |             |
+----------------+-------+---------------+-------------+-------------+
| NHC98          | A98E  | Statisti      | E           | Trk         |
| (Atlantic)     |       | cal-dynamical |             |             |
+----------------+-------+---------------+-------------+-------------+
| NHC91          | P91E  | Statisti      | E           | Trk         |
| (Pacific)      |       | cal-dynamical |             |             |
+----------------+-------+---------------+-------------+-------------+
| CLIPER5        | CLP5  | Statistical   | E           | Trk         |
| (Climatology   |       | (baseline)    |             |             |
| and            |       |               |             |             |
| Persistence    |       |               |             |             |
| model)         |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| SHIFOR5        | SHF5  | Statistical   | E           | Int         |
| (Climatology   |       | (baseline)    |             |             |
| and            |       |               |             |             |
| Persistence    |       |               |             |             |
| model)         |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Decay-SHIFOR5  | DSF5  | Statistical   | E           | Int         |
| (Climatology   |       | (baseline)    |             |             |
| and            |       |               |             |             |
| Persistence    |       |               |             |             |
| model)         |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Statistical    | SHIP  | Statisti      | E           | Int         |
| Hurricane      |       | cal-dynamical |             |             |
| Intensity      |       |               |             |             |
| Prediction     |       |               |             |             |
| Scheme (SHIPS) |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| SHIPS with     | DSHP  | Statisti      | E           | Int         |
| inland decay   |       | cal-dynamical |             |             |
+----------------+-------+---------------+-------------+-------------+
| Previous cycle | OFCI  | Interpolated  | E           | Trk, Int    |
| OFCL, adjusted |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Previous cycle | GFDI  | Interpola     | E           | Trk, Int    |
| GFDL, adjusted |       | ted-dynamical |             |             |
+----------------+-------+---------------+-------------+-------------+
| Previous cycle | GHMI  | Interpola     | E           | Trk, Int    |
| GFDL, adjusted |       | ted-dynamical |             |             |
| using a        |       |               |             |             |
| variable       |       |               |             |             |
| intensity      |       |               |             |             |
| offset         |       |               |             |             |
| correction     |       |               |             |             |
| that is a      |       |               |             |             |
| function of    |       |               |             |             |
| forecast time. |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Previous cycle | HWFI  | Interpola     | E           | Trk, Int    |
| HWRF, adjusted |       | ted-dynamical |             |             |
+----------------+-------+---------------+-------------+-------------+
| Previous cycle | GFSI  | Interpola     | E           | Trk, Int    |
| GFS, adjusted  |       | ted-dynamical |             |             |
+----------------+-------+---------------+-------------+-------------+
| Previous cycle | UKMI  | Interpola     | E           | Trk, Int    |
| UKM, adjusted  |       | ted-dynamical |             |             |
+----------------+-------+---------------+-------------+-------------+
| Previous cycle | NGPI  | Interpola     | E           | Trk, Int    |
| NGPS, adjusted |       | ted-dynamical |             |             |
+----------------+-------+---------------+-------------+-------------+
| Previous cycle | GFNI  | Interpola     | E           | Trk, Int    |
| GFDN, adjusted |       | ted-dynamical |             |             |
+----------------+-------+---------------+-------------+-------------+
| Previous cycle | EMXI  | Interpola     | E           | Trk, Int    |
| EMX, adjusted  |       | ted-dynamical |             |             |
+----------------+-------+---------------+-------------+-------------+
| Average of     | GUNA  | Consensus     | E           | Trk         |
| GFDI, UKMI,    |       |               |             |             |
| NGPI, and GFSI |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Version of     | CGUN  | Corrected     | E           | Trk         |
| GUNA corrected |       | consensus     |             |             |
| for model      |       |               |             |             |
| biases         |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Previous cycle | AEMI  | Consensus     | E           | Trk, Int    |
| AEMN, adjusted |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Average of at  | CONU  | Consensus     | E           | Trk         |
| least 2 of     |       |               |             |             |
| GFDI, UKMI,    |       |               |             |             |
| NGPI, GFSI,    |       |               |             |             |
| and GFNI       |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Version of     | CCON  | Corrected     | E           | Trk         |
| CONU corrected |       | consensus     |             |             |
| for model      |       |               |             |             |
| biases         |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| Average of     | ICON  | Consensus     | E           | Int         |
| GHMI and DSHP  |       |               |             |             |
+----------------+-------+---------------+-------------+-------------+
| FSU            | FSSE  | Corrected     | E           | Trk, Int    |
| Super-ensemble |       | consensus     |             |             |
+----------------+-------+---------------+-------------+-------------+

Table 1. Summary of the mostly commonly used National Hurricane Center
track and intensity models. "E" refers to early and "L" refers to late
in the timeliness column. "Trk" refers to track and "Int" refers to
intensity the parameters forecast column.