Wildfires are becoming an increasingly pressing global challenge, threatening lives, ecosystems, and industries. At MetSwift, we understand the critical importance of staying ahead of these risks, and that’s why we are thrilled to announce the launch of the fully predictive element of our Global Wildfire Model, now available in Version 3.3 of MetSwift’s Advanced Long-Range (ALR) platform.
This first-of-its-kind enhancement not only builds on the success of our initial global model but also marks a major step forward in providing precise, actionable insights to help businesses, organisations, and governments tackle the growing threat of wildfires.
The Global Wildfire Model: A Quick Overview
MetSwift’s Global Wildfire Model has always focused on a critical question: when will an existing wildfire escalate to a scale of at least 10 square kilometres? By using advanced machine learning and integrating wildfire data with global weather, topography, and land cover information, the model delivers highly accurate climatological risk estimates, showing how risk varies across the globe as the weather develops throughout the year.
However, wildfire escalation is often influenced by unpredictable human factors like carelessness or deliberate actions. That’s why our model focuses on the predictable portion of the risk—environmental conditions conducive to wildfire escalation—giving you the ability to better anticipate and mitigate potential impacts.
What’s New: The Predictive Edge
With the release of Version 3.3, we are introducing a predictive element that transforms how wildfire risks are forecasted. Powered by the Claros model, this enhancement allows us to:
• Incorporate Teleconnection Science: Large-scale climate drivers, such as El Niño or La Niña, are now factored into predictions, providing deeper insights into the global dynamics influencing wildfire risk.
• Use Historically Weighted Data by Relevance: Our model dynamically assesses historical data to ensure forecasts are based on trends and patterns that are most relevant to the period of interest.
• Integrate Powerful Weather Predictions: All this adds up to our being able to use our powerful predictive weather model as a basis to show how wildfire risks will differ to the historical norms, throughout a 24-month predictive horizon.
This predictive capability offers unparalleled accuracy, helping users manage risks more effectively and make proactive decisions that could save lives and resources. It also gives you the ability to reference the coming 1- 24 months in comparison to our simulated global climatology.
MetSwift’s Predictive Global Wildfire Model showing wildfire risk for Los Angeles in August 2024. Risk for the month sits at 0.40%, however there is greater risk (0.845%) at the start of the month.
Users can view the Advanced Predicted Risk or the Standard Climatological Risk to compare the year in question to our simulated climatology, giving clearer resolution year on year.
How Does This Benefit You?
The predictive Global Wildfire Model is designed to empower industries most affected by wildfires, including:
• Insurance: Accurately assess and price wildfire-related risks.
• Reforestation and Carbon Reduction Projects: Protect investments in sustainable development.
• Energy: Safeguard infrastructure in wildfire-prone areas.
By integrating the model into the ALR platform, we ensure that this cutting-edge technology is both accessible and actionable.
2025 Predictions: A Regional Breakdown
In his detailed exploration, James Peacock, our Head Meteorologist, highlighted some of the key predictions for 2025 in the contiguous United States. Below are a few standout examples of regions and time periods where risks are expected to surge:
1. June – South-Eastern US
• Increased risks in oak and pine-oak forests stretching from New England to western Tennessee and Arkansas.
• Notable anomalies in southern Florida’s herbaceous wetlands, tied to drier-than-average conditions.
2. August – South-Western US
• Elevated risks in the shrublands north of Las Vegas and west of Provo, driven by below-average rainfall and extreme heat.
• Significant increases along California’s Pacific coast as wildfire season enters its peak.
3. November – Rocky Mountains Region
• Central Montana, southern Idaho, Wyoming, Utah, and Colorado face heightened risks due to high winds and unseasonably warm temperatures.
These probabilistic forecasts offer a clear call to action for increased vigilance and preparedness in these regions during key periods.
James will be looking to the rest of the world in his next blog, identifying global wildfire risks that could impact the rest of 2025.
Preparedness: The Key to Mitigating Wildfire Impacts
As James Peacock wisely pointed out, the socioeconomic impact of a wildfire depends heavily on the areas affected. Tragically, early 2025 fires in Southern California have already resulted in over 25 deaths and significant damage to thousands of structures.
However, the insights provided by the predictive wildfire model are a crucial step toward mitigating such impacts in the future. With advanced foresight into risk patterns, MetSwift users can implement contingencies and deploy resources more effectively, protecting lives and property.
A Safer, More Resilient Future
At MetSwift, we believe that understanding risk is the first step toward resilience. The launch of the predictive Global Wildfire Model and the enhancements in Version 3.3 of the ALR platform reflect our commitment to delivering actionable, science-driven insights to address some of the most pressing challenges of our time.
To learn more about these updates or how MetSwift can support your organisation, explore our platform or contact our team today.
info@metswift.com
