Computer Guidance for Tropical Forecasting

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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).

The GFS forecast for Mean Sea-Level Pressure Anomaly valid at 18Z on August 14, 2023 (initialized at 12Z on August 14) showed four tropical cyclones across the Pacific Ocean.

Credit: Tropical Tidbits

For an example of what a tropical cyclone looks like in the broad domain of one of these models, check out the GFS forecast valid at 18Z on August 14, 2023 above (initialized six hours earlier). The "footprints" of four tropical cyclones (circled) are apparent as regions of relatively low sea-level pressure. From this image, we can get the idea that tropical cyclones are relatively small features in the scheme of things (certainly compared to the larger mid-latitude cyclones located at higher latitudes). Just a few decades ago, global models had resolutions that were so coarse that they weren't of much use in providing detailed looks at the core and wind field of a tropical cyclone, but resolution has increased so that global models can provide these details to some degree. Still, other models have been developed specifically to provide more detailed guidance for existing tropical cyclones.

NOAA's flagship model developed specifically for tropical-cyclone forecasting is the Hurricane Analysis and Forecast System (HAFS), which became operational in June, 2023. Some benefits of the HAFS include the fact that it is "ocean coupled," which means that changes in the ocean and atmosphere respond to each other in the model, which is not the case in most 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, which can then in turn alter the intensity of the storm.

The HAFS is also run at a relatively high resolution, with "nests" that follow individual storms along in time. Its high resolution means that it is capable of predicting small-scale structures within a storm. Of course, there's no guarantee that these small-scale details will be accurate for any given storm, but the ability to realistically simulate deep convective cells can be very helpful in simulating processes in the cores of tropical cyclones, which can improve intensity prediction, on average. As an example of the detail provided by these forecasts, check out the 6-hour forecast (below) of composite radar reflectivity and mean sea-level pressure for Super Typhoon Doksuri, valid at 06Z on July 25, 2023, as it approached northern Luzon in the Philippines.
 

The HAFS-A forecast for composite radar reflectivity and mean sea-level pressure in Super Typhoon Doksuri, initialized at 00Z on July 25, 2023, and valid at 06Z on on July 25. Note the great detail of the HAFS depiction of Doksuri's core, and its predicted central pressure of 917 mb.

Credit: Levi Cowan  / tropicaltidbits.com

The core of Doksuri was depicted with great detail as it approached northern Luzon, and the HAFS predicted a central pressure of 917 mb. But, as I just mentioned, while the HAFS can make highly-detailed predictions, there's no guarantee that they'll be accurate (the lowest estimated central pressure during Doksuri's life was 926 mb).

The HAFS is actually run in two configurations -- HAFS-A and HAFS-B (note that the forecast prog above is from the HAFS-A). While the HAFS is not a global model, the HAFS-A configuration is run in all tropical basins. The HAFS-B configuration is only run on tropical basins under the responsibility of the National Hurricane Center and the Central Pacific Hurricane Center. The HAFS-A and HAFS-B also have some differences in their ocean coupling schemes and how they simulate some small-scale physical processes. Furthermore, tropical cyclones in the HAFS-B domain that have Doppler radar and other data collected during aircraft reconnaisance flights have some extra initialization data compared to storms in other basins.

Lest you think that NOAA didn't run tropical-cyclone specific models until the HAFS debuted in 2023, there's actually a history of such models going back to the early 1990s with the GHM (Geophysical Fluid Dynamics Lab Hurricane Model). Earlier generations of tropical-cyclone specific models also consisted of the HMON (Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic model), which became operational in 2017, and the HWRF (Hurricane Weather Research and Forecasting) model, which became operational in 2007. The HWRF in particular was ground breaking because it was first operational model to be able to assimilate Doppler radar data collected during aircraft reconnaissance flights in its initialization. The HMON and the HWRF are still being run, but are planned to be phased out.

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.

Ensembles

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 exists among 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 ECMWF ensemble track forecast for Hurricane Ian initialized at 12Z on September 27, 2022 below.

The ECMWF ensemble track forecast for Hurricane Ian, initialized at 12Z on September 27, 2023, showed good agreement among the ensemble members in the short term, but less agreement after 24 hours.

Credit: weathernerds.org

The most striking message from this ECMWF ensemble forecast for Hurricane Ian 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 Ian from a myriad of models run at 12Z on September 27, 2022. These models also showed some "scatter" in their predictions for Ian's landfall along the west coast of Florida, suggesting that at the time these models were run, Ian'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 Ian initialized at 12Z on September 27, 2022. 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.

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.