What is seasonal and subseasonal forecasting?

By | 3 minute read | November 30, 2017

View of clouds from above

Advancements in computer weather models and significant increases in data volumes have propelled us to a place where providing reasonably accurate predictions and data analytics for extended time frames are possible. Seasonal and subseasonal forecasts deliver an easy-to-use, predictive analysis that anticipates market behavior and potential risks. By intelligently integrating seasonal and subseasonal forecasts, businesses can make better business decisions with actionable data.

What is seasonal forecasting?

So, what is seasonal forecasting? Seasonal forecasts predict weather anomalies at monthly intervals up to 7 months out. This sounds like a tough challenge, and in some cases creating 7-month forecasts is rather difficult. However, other times, such as during strong El Nino events, accuracy levels can be quite significant.

It is scientifically impossible to provide accurate daily forecasts one, two, or seven months in advance. Instead, seasonal forecasts offer guidance on large-scale weather patterns and whether a given location or region will more likely see above-normal or below-normal temperatures or precipitation over a month. Seasonal forecasts are updated twice per month on a global scale via API or, for North America, Europe, and Asia, via a comprehensive technical document that details the meteorological reasoning behind the forecast.

What is subseasonal forecasting?

Subseasonal forecasts are generated at weekly intervals up to 5 weeks out. Accuracy levels are similar to seasonal forecasts since there is a balance between predicting less far into the future (higher accuracy) and predicting at weekly rather than monthly intervals (lower accuracy).

Similar to the seasonal forecast, the sub-seasonal forecasts are available on a global scale via API or, for North America and Europe, via a technical document. Subseasonal forecasts are updated weekly.

How are these forecasts used across various industries?

Long-range forecasts can provide valuable insights for many industries. Four examples of industries that leverage this data are aviation, media, energy, and insurance. Let’s take a closer look at each:

1. Aviation

Airlines that are preparing for the upcoming winter will benefit from the forecast that the jet stream will be stronger or weaker than normal. They will be able to more accurately predict the amount of fuel necessary to make it through the winter months and can invest in the market as they see fit.

2. Media

Over the last decade, long-range predictions for the upcoming months have been established as a primary traffic-driver for both traditional and digital media. The public has always been interested in hearing from the experts on whether the upcoming winter will be cold or snowy or if next summer will be hot.

Since seasonal and subseasonal forecasts are applicable across various industries, it’s likely these advanced forecasting models will speak to a good portion of your audience.

3. Energy

Commodity traders are early adopters of long-range forecasting products and have leveraged these insights to benefit their book of business. Both subseasonal and seasonal forecasts, issued from trusted weather vendors, can have significant impacts on energy market prices in both the short and long term.

The natural gas and power markets are significant consumers of this forecast information. If a seasonal forecast shows a cold winter ahead, there will be increased demand for gas to heat residential and commercial buildings. Armed with this knowledge, market participants can establish an advantageous position in the market and significantly increase their profits

4. Insurance

Across the insurance industry, preparation is the name of the game. Subseasonal and seasonal forecasts are answer important insurance-related questions:

  1. How bad is the hurricane season going to be?
  2. Can forecasting improve your client relationships by alerting them of impending inclement weather?
  3. Will there be any inclement weather worth warning your customers about?
  4. How much property damage can we expect across different seasons on a global scale?

Accurate long-range forecasts allow insurance companies to wisely set rates with advanced data to back it up. This will result in a higher level of confidence in your plans.

Final thoughts

Seasonal and subseasonal forecasting have reach and influence across multiple industries. These advanced models will help you plan for market shifts that may affect profitability in the 3- to 5-week, 1- to 4-month, and 5- to 7-month time frames.

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