August 1, 2023


One of the most important keys to successfully managing winter maintenance operations is to have the latest and most reliable weather forecasting system(s) at your disposal. But as anyone who has ever tried to plan for the weather knows, even the best tools can’t always get it right.

The earth’s atmosphere is a highly complex system with intricate interactions between air, water and energy. Small changes in any one part of the system can have far-reaching effects, making it challenging to accurately model weather patterns. Limits to observational data due to gaps in observing humidity, wind speed and atmospheric pressure over remote or oceanic regions can also limit the accuracy of predictions.

Weather forecasting involves mathematical models that simulate the behavior of the atmosphere, but weather systems can be uncertain and even chaotic. And while mathematical models have improved over time, they still face challenges in accurately representing all aspects of the atmosphere. Even small uncertainties can lead to significant divergence in the predicted outcomes, making long-range forecasts more challenging.

Despite all these challenges, advancements in technology, improved observational capabilities and ongoing research efforts continue to enhance weather prediction accuracy.

Successfully managing winter operations

Since we haven’t quite perfected predicting the weather, careful planning, preparation and efficient execution is key. Here are some key strategies to keep in mind:
  • Develop a winter operations plan: Your plan should outline the objectives, strategies and resources required for winter management in your area. This plan should also include protocols for snow and ice removal, equipment maintenance, staff assignments and communication procedures.
  • Ensure adequate equipment and resources: Make sure you have the necessary equipment and resources for effective winter management. Your equipment should be regularly maintained and inspected to ensure proper functioning all winter long.
  • Use monitoring and forecasting: Collaborate with local meteorological agencies or subscribe to weather alert services for accurate and timely information. This helps you anticipate the types of conditions you will be working with.
  • Prioritize safety: Safety should be the top priority during winter operations. Develop safety protocols for your staff, such as proper use of equipment, wearing appropriate personal protective equipment (PPE) and creating guidelines for pedestrian safety and safe driving.
  • Implement efficient snow and ice removal strategies: Prioritize cleaning major roadways, intersections and high traffic areas first, followed by secondary roads and parking lots. Use appropriate de-icing materials to minimize ice formation and enhance traction. Clear walkways and entrances to ensure safe passage for pedestrians.
  • Prioritize communication and coordination: Establish clear communication channels to keep staff, stakeholders and the public informed during winter operations. Maintain regular communication with your team, providing updates on weather conditions, operational plans and any changes in schedules. Use digital platforms, email or text messages to disseminate important information to employees, tenants or customers.
  • Commit to continuous improvement and evaluation: After each winter event, evaluate your operations and identify areas for improvement. Assess the effectiveness of your strategies, equipment performance, staff training and response time. Learn from past experiences to refine your winter management plan for future events.

Remember, effective winter management requires flexibility and adaptability to changing conditions. Regularly review and update your strategies based on feedback, technological advancements and industry best practices to ensure efficient operations and maximize safety during winter weather events.

Baldo & Santo Gucciardi are the founders of International Landscaping Inc. and EcoSolutions, located in Milton, Ont. ILI offers a full range of landscaping services, including winter maintenance, and actively seeks environmentally responsible methods to reduce salt use.

The evolution of weather forecasting

Advancements in data collection, technology and computational capabilities have evolved significantly over time — from the earliest known observations of the sky dating back thousands of years and the invention of the barometer, to machine learning applications of today.

Weather observations date back thousands of years, with ancient civilizations noting patterns in the sky and weather conditions. Early tools, including the barometer and thermometer, allowed for more quantitative measurements in the seventeenth and eighteenth centuries.

In the mid-nineteenth century, the invention of the telegraph enabled the rapid transmission of weather observations over long distances. This gave rise to national weather services which provided centralized forecasting and warnings. Numerical Weather Predictions (NWP) were developed in the mid-twentieth century and revolutionized weather forecasting. NWP involves using mathematical models to simulate the atmosphere’s behaviour based on initial conditions and physical laws. The rise of computers played a crucial role in solving the complex equations, allowing for more accurate and detailed predictions.

The launch of weather satellites in the late 1950s and early 1960s provided a global view of Earth’s atmosphere. Satellite data, including cloud patterns, temperature and moisture distribution significantly enhanced the understanding of weather systems and improved prediction accuracy. Also in the 1960s, the Doppler radar was introduced — enabling detailed observations of precipitation and wind patterns. This technology improved the detection and tracking of severe weather phenomena.

In the late twentieth century, data assimilation integrated diverse observational data into numerical models. This process helped reduce errors in initial conditions, improving the accuracy of forecasts.

Ensemble forecasting, which involved running multiple simulations with slightly varied initial conditions to capture uncertainties in predictions, was introduced in the 1990s.

In the years since, advances in computing power have enabled the development of high-resolution models that simulate atmospheric processes with more detail.

Most recently, machine learning techniques have been integrated into weather prediction processes. These methods allow for the analysis of large data sets, pattern recognition and the development of more accurate models.