Understanding Tropical Cyclone Forecasting: A Guide for Everyday People

Tropical cyclones, known as hurricanes in the Atlantic and typhoons in the western Pacific, are powerful storms that can cause significant damage and loss of life. Understanding how they form and are forecasted can help us better prepare and respond to their threats. Here, we'll explore the key factors involved in forecasting tropical cyclones, the computer models used, why forecasts can change over time, and some valuable resources available online that you can use to track these storms and to learn more about them.

Factors in Forecasting Track and Intensity

Several factors influence the forecasting of tropical cyclone tracks and intensity.

First, we must consider the atmosphere itself. The atmosphere is essentially an ocean made of air - with currents (wind) that are generated by heating and cooling, creating high and low pressure areas that constantly move around. The movement of these areas can have a significant impact on the track of a tropical cyclone, and yet their movement, while predictable to a degree, cannot be forecasted with complete accuracy. Generally, high pressure areas will tend to deflect or block a storm, while low pressure areas will tend to attract it as they rotate counterclockwise. The Coriolis effect, imparted by the rotation of the earth, will tend to draw storms poleward, with stronger storms and their larger wind fields being more susceptible to the effect and smaller, weaker storms less so. Recurvature - a storm curving north and then northeast - is driven mainly by the large-scale steering flow: as a storm reaches the western edge of the subtropical ridge of high pressure, it can be caught by the mid-latitude westerlies. In the Atlantic this often keeps recurving storms out at sea and away from land.

Sea Surface Temperatures (SSTs) play a crucial role as warm ocean waters (above 26.5°C or 80°F) provide the energy needed for cyclones to form and intensify. Meteorologists monitor SSTs to predict potential areas of cyclone formation and the intensity of existing storms. Another commonly used tool is the SST Anomaly analysis, which depicts temperature variations from historical averages. This shows where water is abnormally warmer or cooler than normal for a given time period. In 2024, for example, the Atlantic ran exceptionally warm for the time of year - which the SST Anomaly product made quite clear.

Wind shear, which refers to the change in wind speed and direction with height, is another significant factor. High wind shear can disrupt the structure of a storm, inhibiting its development and weakening existing storms, while low shear environments are more conducive to cyclone formation and intensification.

When storms transit over land, they lose their energy source, leading to weakening. Predicting the landfall and subsequent weakening of a storm is crucial to determine the impact for a given area. Additionally, Saharan dust, composed of dry, dusty air from the Sahara Desert, can suppress cyclone formation by stabilizing the atmosphere and reducing moisture. The presence of Saharan dust is monitored to assess its potential impact on cyclone development.

Climate phenomena like El Niño and La Niña also influence global weather patterns. El Niño tends to increase wind shear in the Atlantic, reducing hurricane activity, while La Niña decreases shear, enhancing activity. Seasonal forecasts consider the current phase of El Niño or La Niña to predict overall cyclone activity.

Computer Models and Their Role

Computer models are indispensable tools for forecasting tropical cyclones. There are two main types: global models and regional models. Global models, such as the Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF, the "Euro"), provide a broad overview of weather patterns worldwide. Regional models, like the Hurricane Analysis and Forecast System (HAFS, run as HAFS-A and HAFS-B), focus on specific storms at high resolution. HAFS became NOAA's primary next-generation regional model in 2023-2024, replacing the older HWRF and HMON models (which still run in a frozen "legacy" mode because they remain competitive for intensity).

These models rely on data input from various sources, including satellites, weather balloons, and dropsondes—instruments dropped from aircraft into storms to measure temperature, humidity, and wind speed. This data is crucial for creating an initial snapshot of the atmosphere. The models then use mathematical equations to simulate the atmosphere's behavior over time, providing predictions of storm track and intensity. They are regularly updated with new data to improve accuracy.

The Hurricane Analysis and Forecast System (HAFS) is a newer model designed to improve tropical cyclone prediction. HAFS integrates advanced data assimilation techniques and higher resolution modeling to provide more accurate forecasts of cyclone intensity and track. This model aims to better capture the rapid intensification of storms and offers more detailed insights into their potential impacts and has done a good job of predicting rapid intensification, aptly demonstrated by correctly predicting Hurricane Ian's rapid intensification between Cuba and Florida.

Model Ensembles and Spaghetti Plots

Model ensembles are collections of forecasts produced by slightly varying the initial conditions or model parameters. By analyzing these multiple runs, meteorologists can assess the range of possible outcomes and the uncertainty in the forecast. Spaghetti plots are visual representations of these ensembles, showing the different possible tracks a storm could take. Each line represents a different model run, and when many lines cluster together, confidence in that forecast increases.

Interpreting spaghetti plots involves looking for consensus among the model runs. While outliers can provide interesting insights, relying on them is risky. Instead, forecasts that show a convergence of multiple models are generally more reliable. This approach helps in making informed decisions and preparing for potential impacts.

The Dvorak Technique

The Dvorak Technique is a method of estimating tropical cyclone intensity using infrared and visible satellite imagery. By analyzing the cloud patterns and features of a disturbance, meteorologists can estimate wind speeds and pressure levels. This technique has been vital in providing estimates when direct measurements are unavailable, especially over vast oceanic regions.

Why Forecasts Change Over Time

Forecasts change over time due to the dynamic nature of the atmosphere and the continuous influx of new data. Meteorologists use a combination of model outputs, satellite imagery, radar data, and their experience to derive the most accurate forecast possible. As a storm evolves, the conditions influencing it—such as wind shear, sea surface temperatures, and interactions with other weather systems—can change rapidly. These changes are reflected in updated forecasts.

New data from satellites, weather balloons, and dropsondes is continually fed into the models, providing more accurate initial conditions for the simulations. As models are rerun with the latest data, they may produce different predictions, leading to changes in the official forecast. Additionally, the inherent uncertainty in forecasting complex weather systems means that small variations in initial conditions can lead to significant differences in the forecast outcomes.

Meteorologists interpret all available data and use their expertise to reconcile differences between model outputs. They consider the strengths and weaknesses of different models and weigh the consensus of multiple models to produce the most reliable forecast. This iterative process ensures that the forecast evolves as new information becomes available, enhancing accuracy and reliability.

Useful Websites for Tropical Cyclone Information

Several websites provide valuable data and forecasts for the public to view information about and track tropical cyclones:

1. Hurricanes.gov

2. TropicalTidbits.com

3. WeatherNerds.org

To Sum Up

Forecasting tropical cyclones is a complex task involving numerous factors and sophisticated computer models. While advancements in technology and data collection have improved our ability to predict these storms, challenges remain, especially in the early stages of cyclone development. By understanding the factors involved, the forecasting techniques, and the reasons for forecast changes, we can better appreciate the efforts of meteorologists in protecting lives and property from these powerful natural phenomena. Utilizing reliable sources like hurricanes.gov, tropicaltidbits.com, and weathernerds.org can further enhance your understanding of - and preparedness for - tropical cyclones.

Suggested Resources

NHC on Youtube:
https://www.youtube.com/@nwsnhc

Wikipedia Article on Models:
https://en.wikipedia.org/wiki/Tropical_cyclone_forecast_model

The New HAFS Model:
https://www.noaa.gov/news-release/noaa-launches-new-hurricane-forecast-model-as-atlantic-season-starts-strong

Models/Spaghetti Models:
https://www.foxweather.com/learn/forecasting-hurricane-spaghetti-plot

Sea Surface Temperature:
https://oceanservice.noaa.gov/facts/sea-surface-temperature.html