January 5, 2025
The Limitations of Long-Range Forecasting
Computer weather models have revolutionised meteorology, providing detailed forecasts that guide decisions across industries. However, once we move beyond the 6-7 day range, their accuracy diminishes significantly. Why does this happen? And why do extreme weather events, such as major tropical cyclones, floods and extreme heat, often appear in these forecasts but fail to materialise?
Chaos in the Atmosphere: The Butterfly Effect
The Domino Effect of Small Changes
A slight miscalculation in the location of a pressure system or temperature gradient can snowball into a dramatically different outcome, such as a tropical cyclone forming where none will actually develop, or a forecasted storm dissipating before reaching its potential.
Thunderstorms: A Classic Example of Chaos
Thunderstorms are an excellent example of how atmospheric chaos manifests. An unexpected morning storm, for instance, can significantly alter the potential for storms later in the day by cooling the atmosphere or creating stabilising outflow boundaries. Conversely, a lack of anticipated early convection can allow for stronger instability, leading to a much more intense storm outbreak.
A Real-world Illustration: March 3, 2022
March 3, 2022, provides a classic example of the chaos inherent in forecasting. An early morning storm swept across Brisbane and the surrounding areas, reducing the instability that had been expected to fuel severe storms later in the day. What might have been a high-impact storm event was significantly reduced in intensity—showcasing how small, unanticipated changes can ripple through the atmosphere with dramatic downstream effects.
Significant hailstorms occurred in the morning but did not generate significant impacts to Brisbane. Source: Weatherwatch HailTracker
Why Weather Models Seem to Love Extreme Events
Ever feel like weather forecast models have a knack for predicting extreme events that never quite happen? From tropical cyclones and flood events to severe storms that fizzle out, it can sometimes seem like the models are biased towards forecasting the dramatic. A year rarely passes without a city like Brisbane being "in the path" of a tropical cyclone that never materialises.
The Challenge of Extreme Event Forecasting
The truth is, extreme weather events require a near-perfect alignment of numerous factors. At longer forecast ranges—beyond 6-7 days—getting all these elements right becomes exponentially more difficult.
Take tropical cyclones as an example. While ocean temperatures above 27°C are necessary for cyclone formation, they are far from the only requirement. Tropical Cyclones also need:
A disturbance to initiate the system.
Moist air in the mid-levels of the atmosphere.
A delicate balance of wind shear (weak winds with sufficient outflow in the upper atmosphere)
This last factor, wind shear, is especially tricky. There's a fine line between supportive shear, which enhances the cyclone's outflow and development, and destructive shear, which can tear the system apart. Even minor errors in modelling these variables can lead to forecasts that overestimate or misjudge the potential intensity of a system.
Thunderstorms: A Delicate Balancing Act
Thunderstorms present similar challenges. The development of severe storms often hinges on factors like:
The strength of the atmospheric cap. Too strong, and storms can't form; too weak, and storms may occur too early, reducing their severity.
The timing and interaction of seabreezes or outflow boundaries from other storms.
Forecasting these elements accurately at a local scale is nearly impossible for global weather models. These models rely on assumptions and approximations to save computational power, often sacrificing the finer details critical to storm prediction.
The Day 6-7 "Cliff"
By day 6-7, models rely more heavily on mathematical equations rather than actual observed data. This reliance results in a forecast that is increasingly probabilistic, meaning forecasts beyond days 6-7 are best viewed as a range of possibilities rather than a precise prediction.
This is why meteorologists stress the importance of ensemble forecasts, which combine multiple model runs to assess the likelihood of different scenarios. When a single model run shows a tropical cyclone but the ensemble disagrees, it’s often a sign that the event is unlikely. Similarly, it's important to use a variety of models (which can be viewed as another type of ensemble). Even long-range extreme outcomes are more likely to occur if multiple forecast models indicate a similar scenario
Using multiple forecast models are similar to using ensemble forecasts allowing you to view multiple outcomes at a glance which can gauge in confidence of outcomes. Source: MetCentre.
What Does This Mean for Forecast Users?
For businesses and individuals relying on long-term forecasts, here’s how to approach them wisely:
Treat anything beyond 5-7 days as guidance, not a guarantee. For example, a tropical cyclone impact forecast 7-10 days out should merely be interpretted as there's the increased probability of one developing, not that it will develop.
Focus on trends rather than specifics. Is the model showing a general increase in storminess? Or is it a one-off extreme event? Rather than interpreting conditions as an impending flood, interpret them as a wetter signal.
Consult with meteorologists who can combine their extensive experience with model data with outputs and highlight the most likely scenarios.
The Role of Expertise in the Era of Automation
While models are indispensable, they’re only tools. Skilled meteorologists combine these outputs with their knowledge of atmospheric processes to provide context, especially when models go astray. Meteorologists can also provide a running assessment of which models are currently performing well and lean outcomes towards these. That’s where teams like Weatherwatch make the difference—ensuring that you get the most reliable and actionable weather intelligence available and removing the stress of uncertainty from your business.
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