Ever
wonder why it’s so hard to predict where and when a hurricane will exactly make
landfall or how strong it will be? It’s not from a lack of information available; it’s actually from too much information available.
Hurricane
forecasters have many different computer models to aid in predicting a storm’s path and its intensity.
All of them are good, but they each take into account many variables and when
looking at a five day and beyond forecast, prediction can vary widely. Many times,
meteorologists will refer to these as the “spaghetti models” because when laid
out on a map, the storm paths resemble strings of spaghetti. Another cliché reference is the “cone of uncertainty”
taking into account paths from one extreme to the other.A
rule of thumb: the average error rate in forecasts for each 24 hour period is
about 75 miles in each direction as a storm gets closer to landfall. One such example: in the summer of 2004 as Hurricane Ivan bore down on the state of
Florida, four different computer models run on
September 9th, 2004 put the path of the storm everywhere from
Miami to the
Florida
panhandle. The storm eventually made landfall at about the Alabama/Florida
border, then literally split in two, ran up through northern states and made a
loop, hitting
Louisiana and
Texas a week later (Ivan was indeed a very
bizarre storm).
Before
we get to specific models, understand there are four different categories of models with a subset of three different types:
Model
Categories:
Dynamic: These models look at the current conditions in the atmosphere to
arrive at a conclusion for how intense a storm might be or where it will
go…
Statistical: These models ignore current atmospheric conditions and predict
movement based on interactions of past storms with other parameters…
Statistical-Dynamic: Kind of the best of both worlds combining what
storms done previously with current conditions. This however gets a little
tricky as too much data can create inaccurate forecasts…
Consensus: Alphabetically, this should come first, but it’s listed last because
consensus modeling looks at information from numerous models of a variety
of types to create its own forecast…
Model
Types:
Tracking: These models are used specifically to forecast the path of a
storm…
Intensity: These models are used specifically to forecast how strong or
weak a storm will be…
Hybrid: These models forecast both the track and intensity of a storm…
One more thing to know about computer models: if you look closely at the spaghetti
model graphics of your local forecast you’ll sometime see acronyms for some of
the models that end in the letter “I”. These plots are what are known as
“interpolated” models (as an example GFDI is the interpolated version of the
GFDL model). An interpolated model is something of a very educated guess used
to help forecasters adjust paths and intensities of hurricanes to catch up with
current conditions. Why would this be done? Usually it’s a time crunch issue: because
of the availability of information from a variety of resources (satellites, aircraft and other models), new conclusive data might not exist for up to half a day. Forecasters sometimes can't wait that long and need a
more up to date model run. Interpolated adjustments are up to six hours before
the next full analysis; if you happen to see a “2” behind a computer model
acronym that signifies an interpolated adjustment of between 6 and 12 hours
prior to the next full analysis.
The
six most popular hurricane computer models are listed below. All are
track/intensity hybrid types and fall into the dynamic category (acronyms in
parentheses are ATCF (Automated Tropical Cyclone Forecasting System)
identification codes). Below the chart is additional information about each
model; further down is even more information about other
models:
ECMWF (EMX)
GFDL (GFDL)
GFS (GFSO)
HWRF (HWRF)
NOGAPS (NGPS)
UKMET (UKM)
European Center for Medium Range Weather Forecasting Model
Geophysical Fluid
Dynamics Laboratory
Model
Global Forecast
System
(Previously known as AVN)
NWS Hurricane Weather
Research
Model
Navy Operational
Global Atmospheric Prediction
System
United
Kingdom Meteorological Office
Program
European Center for
Medium Range Weather Forecasting
National Weather Service (NOAA) (Created in Princeton)
National Centers
for Environmental Prediction
National Weather Service(NOAA) Environmental ModelingCenter
U.S.
Navy Fleet Numerical Meteorology & Oceanography Cntr
The MET Office &
Public Weather
Service
ShinfieldPark, Reading, England
Princeton,
NJ and California
Camp
Springs, Maryland
Camp
Springs, Maryland
Monterey,
California
Devon,
England
*ECMWF
(EMX) is a four dimensional model considered the preeminent medium range global forecast tool. ECMWF
is most effective in tracking the late development of a storm and is the most
complex and expensive computer program used in severe tropical weather forecasting.
The
EuropeanCenter for Medium Range Weather
Forecasting, home of ECMWF, is supported by 28 European countries and makes its
data available to the U.S. EPS is a low resolution version of ECMWF; EMXI is
the interpolated version of ECMWF...
*GFDL
(GFDL) was originally designed to forecast cyclones; it is considered one of the
most accurate early model predictors on Earth as it creates a three-dimensional
grid by combining information and data from multiple sources. GFDL is “nested”
within the GFS system but specifically focuses on the
Atlantic
and Pacific basins (detailed regional forecast model). GFDL is pretty accurate,
usually coming in first or second on computer model outcomes. GFDN is the
Navy’s version of GFDL (it is also sometimes referenced as NGFDL). While GFDL will not be developed any further past 2008 (HWRF
will eventually replace it) development of GFDN will continue for the
foreseeable future. Both GFDI and GHMI are interpolated versions of previous
cycle GFDL models; GFNI is the interpolated version for GFDN…
*GFS
(GFSO) measures storm variables at twenty-eight different levels in the
atmosphere and is a worldwide forecast computer model. Because it covers the
entire planet, GFS can forecast storms up to three weeks before development, but
doesn’t do as good a job plotting where any particular storm will end up. GFSI
is the interpolated adjusted model of a previous GFS cycle. Regarding the
Atlantic hurricane basin, forecasters like GFDL more than GFS as GFS “overdevelops”
a lot of tropical storms. Although it is considered a hybrid track/intensity
type, GFS usually does a better job of predicting the track of a storm when
compared to intensity. AVNO is the aviation component of GFS; AVNI is the interpolated version of AVNO…
*HWRF
(HWRF) launched in 2007 and as mentioned above is scheduled to eventually replace
GFDL. HWRF is superior to GFDL in predicting the track of a storm, but GFDL
still does a better job with intensity forecasting. HWRF is a three dimensional
real time Doppler based computer model collating information from a variety of
sources such as satellites, buoys and hurricane reconnaissance aircraft. Much
of NOAA’s forecasting in the coming years will hinge upon HWRF computer models.
HWRF is a specialized version of the WRF (Weather Research and Forecasting)
model. HWFI is the interpolated version of the previous HWRF cycle…
*NOGAPS
(NGPS) specifically wasn’t designed to forecast hurricanes, but it works pretty
well. NOGAPS uses upper air data to predict storm paths and the strength of a
storm. Although it is a track/intensity hybrid, NOGAPS functions much better as
a track only model…
*UKMET
(UKM) is also a four dimensional model very similar to the NOGAPS system. Like
NOGAPS, UKMET does a better job of predicting a storm’s track and not as good a
job forecasting intensity. UKMET does however have a separate model for predicting
intensity: EGRR. EGRI is the interpolated version of the previous EGRR cycle…
Other Dynamic Models:
BAM:Beta and Advection Model- Also run out of
Camp Springs,
Maryland
at the National Centers for Environmental Prediction, BAM goes hand in hand
with GFS by following the GFS trajectory and then categorizing results into
three areas: Shallow (BAMS), Medium (BAMM) and Deep (BAMD). The further away
from each other the BAM models land, the more complex a forecast for a storm
will probably turn out to be. BAM models are forecast track only and don’t
measure the intensity of systems…
CMC
GEM:Environment Canada Global
Environmental Multiscale Model- Well, that’s a mouthful, huh? The CMC GEM (an
acronym for the Canadian Meteorological Centre Growth Equation Model) is a four
dimensional program similar to both ECMWF and UKMET that forecasts both track
and intensity. The model, which only covers North America, has been through
some changes recently, one of the most major being an upgrade in June 2009. CMC
GEM is usually referred to as just CMC; CMCI is the interpolated version of the previous CMC cycle…
LBAR:Limited area BARotrophic Model-
The program looks at vertical winds by relying on upper level air
pressure. Upper level and lower level systems (high and low pressure) can push
storms on different tracks; LBAR outputs a two dimensional forecast for
predicting a hurricane path and like the BAM models does not measure intensity.
LBAR gets some of its raw data from the GFS model. Like NHC98 (below), LBAR has been surpassed by a number of newer more
accurate programs…
NHC98:NationalHurricaneCenter 1998 Model- This
NHC model, also referred to as A98E), relies on initial latitude and longitude
to forecast a storm’s progression and like BAM and LBAR does not measure
intensity. NHC98 uses the output of CLIPER to help in coming to its
conclusions. It was an update to NHC90 and is the sixth version of the series. As you can probably deduce from the number 98, this is a very, VERY old model and is not as reliable as some of the newer programs…
Statistical
Models:
CLIPER:CLImatology and PERsistence Model- This is
a tracking only model (no intensity forecasting) run by the
NationalHurricaneCenter and like SHIPS
(see below), uses climate history to produce a “trackcast”. CLIPER is a three
day statistical model; CLIPER5 is the five day derivative. CLIPER5 ignores
current atmospheric factors and therefore is used as a benchmark for measuring
the accuracy of other storm models (for more specific information on CLIPER5
baseline, see the James L. Franklin/NHC link below). Though CLIPER was very popular pre-1980, with today's more sophisticated computers and available resources, it has become somewhat of an antiquated forecast tool. Most TV meteorologists won't even show CLIPER in their forecasts, but you'll see it pop up a lot on websites...
SHIFOR:Statistical Hurricane Intensity FORcast- This is an intensity
only computer model (as opposed to a forecast tracking model) supplemented by
both SHIFOR5 and Decay-SHIFOR5. Like CLIPER, SHIFOR relies upon historical data
of similar storms to arrive at its conclusions and therefore is also used as a
benchmark against other intensity models. Decay-SHIFOR5 (DSHIFOR5) was
introduced in 2006 and factors in the decay of a storm when the system
interacts with a land mass. SHIFOR was a very accurate model but has recently
been surpassed by newer technology…
Statistical-Dynamic
Models:
LGEM:Logistic Growth Equation Model- This
intensity only program has the same raw data as SHIPS, but uses different
algorithms to arrive at conclusions. LGEM places more of an emphasis on the
changes to the environment for the 24 hours prior; SHIPS looks at environmental
changes over the course of the complete forecast period…
SHIPS: Statistical Hurricane Intensity Prediction Scheme- SHIPS
outshines most others in the area of predicting the storm’s intensity as it relies on climate history using predictors from the
GFS model. Like DSHIFOR5, the DSHP model (Decay SHIP) factors in what a land
mass will do to the intensity of the storm…
Consensus
Models:
CONU:An acronym for five interpolated models
that make up the consensus. For 2010, those models included AVNI, GFDI, GFNI, NGPI and
UKMI. With the CONU
program, as little as two of the five models can be plugged in to create a
forecast…
FSSE:Florida State University Super Ensemble-
There are actually two FSSE models: the first is a track only consensus
model combining data from the following five interpolated models and one
consensus model: GFDI, GFSI, GUNA (consensus of GFDI, UKMI, NGPI and GFSI),
OFCI and UKMI. The second part of FSSE is an intensity consensus model
combining data from three interpolated models (GFSI, OFCI and UKMI) one
statistical-dynamic model (DSHP) and one statistical model (SHIFOR5). FSSE was developed in 2005 with funding
from Weather Predict; its information is only available to subscribers (and
yes, the NHC does receive this information). One really interesting thing about
this model is that it constantly revaluates other models almost to the point
where it learns from their past mistakes. FSSE rewards and penalizes other
models by changing the weighted structure according to the accuracy of both
storm intensity and location from previous forecasts…
GEFS:National Weather Service Global
Ensemble Forecast System- Also known as AEMN (Automated Environmental Monitoring Network), this track/intensity
hybrid program is based on the GFS system but made up of twenty different
models. Although GEFS produces a forecast up to sixteen days in advance, it is
not as reliable as the ECMWF model. AEMI is the interpolated previous cycle
adjusted model of GEFS/AEMN…
GUNA:A consensus model combining data
from a number of interpolated models. For 2010, those models were AVNI, GFDI, NGPI and UKMI. The
acronym dates back to 1998 when the old GUNS consensus model (GFDL, UKMET and
NOGAPS) added the aviation model part of GFS (AVNO) to create GUNA. CGUN is a version
of GUNA corrected for individual model biases. Like most consensus models, individual models used for drawing conclusions can change from year-to-year. If you are paying attention, you'll notice the models in GUNA are the same as the models for CONU with the exception of GFNI in the CONU model…
ICON:A simple intensity only model
computing the average when all of the following models are present: DSHP, GHMI,
HWFI and LGEM. Individual models that make up the consensus of ICON can change
from year-to-year; the models listed above were in use for the 2010 hurricane season…
IVCN:Another simple intensity only
model computing the average when combining DSHP, GFNI, GHMI, HWFI and the LGEM
models. IVCN requires data from at least two of the five models to form a
consensus. Individual models that make up the consensus of IVCN can change from
year-to-year; the models listed above were in use for the 2010 hurricane season…
TCON:A consensus model combining
data from five interpolated models: EGRI, GFSI, GHMI, HWFI and NGPI. Individual
models that make up the consensus of TCON can from change year-to-year. TCCN is
a version of TCON corrected for model biases…
TVCN:A consensus model combining
data from the following interpolated models: EGRI, EMXI, GFSI, GFNI, GHMI, HWFI
and NGPI. TVCN doesn’t always use all seven of these models for a consensus,
but needs at least two for creating its quorum. Like ICON, IVCN and TCON,
individual models that make up the consensus of TVCN can change from
year-to-year. TVCC is a version of TVCN corrected for model biases…
Finally, there’s one more model to tell
you about that isn’t necessarily a storm model, but is tied to the effects of
severe tropical weather:
SLOSH:SeaLake and
Overland
Surges from Hurricanes Model. This computer model focuses strictly on a
hurricane’s storm surge, the rising of seawater as a hurricane hits land…