Computer Guidance for Tropical Forecasting

This is a sample lesson page from the Certificate of Achievement in Weather Forecasting offered by the Penn State Department of Meteorology. Any questions about this program can be directed to: Steve Seman


By the end of this section, you should be able to interpret basic computer guidance used by tropical forecasters, and discern between global models and those specifically designed for tropical cyclone forecasting. You should also be able to interpret simple ensemble forecast plots of storm track.


Although we covered an "old-school" approach for short-term tropical cyclone track forecasts on the previous page, we have many sophisticated tools for predicting the track and intensity of tropical cyclones. Indeed, the advent of computer model guidance revolutionized weather forecasting, and tropical forecasting is no exception. Thanks to developments in computer guidance, reasonably accurate forecasts for tracks of tropical cyclones are now the norm several days in advance.

You're already familiar with how computer models work and what some of their main flaws are from your previous studies, and you should be familiar with some commonly used computer models and forecast variables used for forecasting in the middle latitudes. That basic knowledge is still applicable to the tropics, but because tropical cyclones operate differently than mid-latitude cyclones, tropical forecasters need to evaluate some different forecast variables than mid-latitude forecasters do.

Before we get into some of those new forecast variables, let's start with addressing what computer models are commonly used for tropical forecasting. Since tropical cyclones are a global phenomena, forecasters often turn to "global models" (that is, models that have a domain covering the entire globe) to keep tabs on tropical cyclones in any basin. The flagship global model run in the United States is the GFS model, which you should already be familiar with. The U.S. Navy also runs a global model called the NAVGEM, which stands for NAVy Global Environmental Model. Other prominent global models are run in other countries, such as the United Kingdom (UKMET), Japan (JMA), Canada (CMC), and the model from the European Centre for Medium-Range Weather Forecasts (ECMWF). I should point out that freely available output from the ECMWF model on the Web is somewhat limited (ECMWF charges substantial fees for access to its full suite of guidance, which some private weather companies are willing to pay and offer to paying customers).

The GFS initialization for MSLP at 00Z on September 12, 2003 showed Tropical Storm Henri and Hurricane Isabel in the Atlantic
The Mean Sea-Level Pressure (MSLP) initialization of the GFS model over the Atlantic tropical basin at 00Z on September 12, 2003. Tropical Storm Henri and Hurricane Isabel are indicated by areas of relatively low sea-level pressure.
Credit: Florida State University

For an example of what a tropical cyclone looks like in the broad domain of one of these models, check out the initialization of the GFS model from 00Z on September 12, 2003 (above). The "footprints" of Hurricane Isabel and Tropical Storm Henri appear as regions of relatively low sea-level pressure. The global models are now run at sufficiently high resolution to also provide more detailed looks at the core of a storm and its wind field, although they were not originally designed to do so. Other models, however, have been developed specifically to provide more detailed guidance of an existing tropical cyclone.

One model developed specifically for tropical-cyclone forecasting is the HMON (Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic) model, which became operational in August, 2017. The reference to "ocean coupled" in the name of the model refers to the fact that changes in the ocean and atmosphere respond to each other in the model, which is not the case in global models. Ocean coupling in a model can be a big advantage because as you'll learn later, strong hurricanes can dramatically alter the characteristics of the ocean beneath them. "Non-hydrostatic" essentially means that the model can simulate small-scale vertical accelerations associated with convection. The HMON replaced the GHM (GFDL Hurricane Model, developed at the Geophysical Fluid Dynamics Laboratory in Princeton, New Jersey), which had been developed in the early 1990s.

Another model that was specifically developed for tropical cyclone forecasting is the HWRF (Hurricane Weather Research and Forecasting) model, which became operational in 2007. The HWRF is not a global model, but it is run in all tropical basins. Like the HMON model, the HWRF provides detailed forecasts of storm track and intensity, and is capable of predicting small-scale structures within a storm (although there's no guarantee that the details will be accurate!). As an example of the detail provided by the forecasts, check out the 15-hour forecast (below) of composite radar reflectivity and mean sea-level pressure for Hurricane Matthew, valid at 03Z on October 7, 2016 as it approached the Florida coast.

15-hour forecast of composite radar reflectivity and MSLP for Hurricane Matthew
The HWRF forecast for composite radar reflectivity and mean sea-level pressure in Hurricane Matthew, initialized at 12Z on October 6, 2016, and valid at 03Z on October 7, 2016. Note the great detail of the HWRF's depiction of the core of Matthew, and its predicted central pressure of 922 mb.
Credit: Levi Cowan  /

The core of Hurricane Matthew was depicted with great detail as it approached the east coast of Florida, and the HWRF predicted a central pressure of 922 mb. But, as I just mentioned, while the HWRF and HMON can make highly-detailed predictions, there's no guarantee that they'll be accurate (Matthew's actual central pressure around the time this prog was valid was about 937 mb).

The implementation of the HWRF was ground-breaking because it was first operational model to be able to assimilate Doppler radar data collected during aircraft reconnaissance flights into tropical cyclones in its initialization. Research continues on the model's performance, and the HWRF model continues to evolve and improve. Going forward, the HWRF is designed to serve as the operational backbone at NCEP for predicting the structure and evolution of tropical cyclones.

With many modeling options available, it's important to remember from your previous studies that forecasters look for consensus among the models and diligently comb over real-time observations that might offer clues about which model has more of a handle on a particular weather system. The same approach rings true for predicting tropical cyclones. If you're curious about where you can access model guidance from the global models and other models specifically created for predicting tropical cyclones, I'll have some links to data sources later on the page. But, for now, I want to talk about the value of ensemble forecasts in tropical forecasting.


As you know, computer guidance is fallible, and often, various models have differing solutions. Indeed, check out the average cyclone forecast track errors of various computer models. Given that no models are perfect, and their solutions are often different, do forecasters have any tools at their disposal for helping them navigate the sea of uncertainty? Ensemble forecasts, to the rescue! Ensemble forecasting embraces the tendency toward differing forecast solutions by allowing forecasters to see a range of possible forecast outcomes, which allows forecasters to gauge uncertainty.

You've already been exposed to the basics of ensemble forecasting, but allow me to quickly review. Recall from your previous studies that the data used to initialize a computer model is always imperfect (we're nowhere close to being able to perfectly measure variables in the atmosphere everywhere at all times). So, the model initialization always contains errors. Ensemble forecasts are created by slightly altering the initial conditions fed into the model and / or altering the model physics (recall that a model's ability to mimic the atmosphere is not quite perfect). Each slight altering of the initial conditions or model physics generates an ensemble member. When there's very little spread in the solutions from all ensemble members, confidence in the operational model solution is high, but when lots of spread amongst the individual member solutions, confidence is lower.

How can slightly altering the model's initial conditions lead to different solutions and allow us to gauge uncertainty? The details are beyond the scope of this course, but allow me to invoke a metaphor. Imagine that the tweaks to the initial conditions is akin to "tickling" the virtual atmosphere to see if you get any reaction. If you get a noticeable reaction (a set of very different solutions for a specific forecast area), you know you've found a "sensitive" area, where minor errors in the model initialization can create rapidly growing forecast errors, which cause lots of forecast uncertainty. If you tickle the atmosphere and don't get much reaction (ensemble member solutions look very similar), then the forecast isn't particularly sensitive to small errors in initialization, and we can have more confidence in the solutions. For example, check out the GFS ensemble track forecast for Hurricane Rita initialized at 12Z on September 19, 2005 below.

GFS ensemble track forecast for Hurricane Rita, initialized at 12Z on September 19, 2005
The GFS Ensemble track forecast for Hurricane Rita, initialized at 12Z on September 19, 2005, showed good agreement among the ensemble members in the short term, but less agreement after 24 hours.
Credit: NCAR Tropical Cyclone Guidance Project

The most striking message from this GFS ensemble forecast for Hurricane Rita is that confidence in the track forecast was pretty high within the first 24 hours (little spread in the solutions), but confidence steadily lowered with increasing forecast time (it's simply the nature of the beast that errors associated with computer guidance grow with increasing time). Another type of ensemble approach is to plot track forecasts from completely different models in a similar fashion. As an example, check out this corresponding plot showing predicted tracks of Hurricane Rita from a myriad of models run at 12Z on September 19, 2005. These models also suggested quite a bit of "scatter" in their predictions for Rita's landfall, suggesting that at the time these models were run, Rita's landfall location was highly uncertain.

We can take a similar ensemble approach with tropical cyclone intensity forecasts, as demonstrated by this plot of intensity forecasts for Hurricane Rita initialized at 12Z on September 19, 2005. The bottom line with respect to ensemble forecasts (both ensembles from the same model or completely different models) is that they help forecasters gauge the uncertainty and see the range of possibilities in a given forecast situation. That's very helpful information, and it's much better to take into account the range of possibilities as opposed to locking in on a couple of operational model runs.

If you're looking for more information and background on the variety of computer guidance available to tropical forecasters (including some of the individual models displayed on these ensemble plots), check out the Explore Further section below. In the meantime, I want to give you a list of resources on the Web where you can access computer guidance for tropical forecasters.

Resources on the Web

In addition to the PSU Tropical e-Wall (link above), you may want to bookmark the following Web sites if you want to keep an eye on the computer guidance used by tropical forecasters:

Yes, there's a wide variety of model guidance available to tropical forecasters, and you're now prepared to access guidance from a variety of models. But, because tropical cyclones operate differently than mid-latitude cyclones, some unique forecast variables are of interest to tropical forecasters. We'll take a look at these variables next by taking a tour of a rich online resource for tropical forecasters -- the Penn State Tropical e-Wall.

Explore Further...

This section focused on the major global and regional models that forecasters use to predict tropical cyclones, but as the ensemble forecast graphics above suggest, many more models are used by tropical forecasters. The details of all the models are far beyond the scope of the course, but I wanted to give you some additional resources if you're interested in reading up on some of the additional guidance available.

For starters, the National Hurricane Center provides a comprehensive overview of the available guidance. It's not hard to see from the table that there are a lot of models. However, some of the "models" are merely blends of other model guidance in an effort to create a consensus forecast or other type of ensemble product. You may also be interested to note that some tropical guidance has a statistical component, like the Model Output Statistics (MOS) that you've learned about in your previous studies. Specifically, the Statistical Hurricane Intensity Prediction Scheme (SHIPS) and its variations use predictors from climatology, persistence, the atmosphere and ocean to estimate changes in the maximum sustained surface winds of tropical cyclones.

Finally, if you're looking for a guide to the specific models used in the TCGP images shown above, check out this handy guide to the plots. Enjoy!