How reliable is your weather forecast?
The public may argue that some meteorologists simply throw darts at a board, but the truth is that a massive amount of scientific research goes into weather forecasting – and the science has improved. There is still much to learn, as forecasting for our chaotic atmosphere is difficult, but reliability has improved dramatically over the past decades in both the short- and long-term forecasts.
Over recent decades, increased computing power has led to improvements in short-range forecasting (forecasts from one to 14 days out). Traditionally, these forecasts are constructed by meteorologists analyzing current weather conditions and computer forecast model output from several physics-based models. These models ingest weather observations from around the world and run through a series of equations that simulate the processes in our complex atmosphere.
Forecasts at shorter lead times show the best accuracy. According to the National Oceanic and Atmospheric Administration (NOAA), a five-day forecast is accurate about 80% of the time. Beyond this time frame, forecasts degrade at an accelerating rate; NOAA estimates that a 10-day forecast is right half of the time. Accuracy drops off considerably using these physics-based models because the smallest errors in the initial observed weather conditions fed into the computer propagate through time.
Another limitation is the gaps in the weather observation network. A perfect forecast would require observations at every single location on Earth, not just at the surface, but throughout the layers of the atmosphere.
Additionally, mathematical equations used to resolve the complex interactions in our chaotic atmosphere are only a good approximation, not a perfect simulation, of how the atmosphere operates. These limitations snowball into larger errors in forecasts over five days.
For long-range forecasts, physics-based models do not work. Think of a droplet of water in the ocean as a storm and the rings that propagate out from the drop as the front. This interaction is easily predicted; that’s why physics-based models work in the short-term. In the long term, we have millions of drops interacting both at the surface and up to 50,000 feet above. If one of those drops is stronger/weaker than the model interprets, then everything changes. Different methods are used to forecast for the longer range.
One method is the use of climate cycles and their teleconnections to weather patterns across the globe. The most recognizable cycles are El Niño and La Niña, which are positive/negative phases of the El Niño Southern Oscillation, respectively. Using climate cycles alone, however, can result in a hit-or-miss forecast.
Historical weather is a building block for long-range forecasts. If we don’t know what’s happened in the past, how can we predict the future? With historical data, statistics can be incorporated to analyze temperature and precipitation trends and generate probabilities of certain weather events. For example, Weathertrends360 analysis has shown that a warm March in New York is frequently followed by a much colder, wetter, and snowier March the following year. This method of using a statistical approach to long-range forecasting is unique.
Predicting the future is difficult. Science and mathematics have made it possible to forecast the weather with great success.
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