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In our previous blog in this three-part series, we examined how weather information is becoming this decade’s geolocation. In this installation, we will look at specific kinds of weather data and how they can be used to help organizations improve decisions.
- Long-term climate — regional conditions have significant influence on the siting of key infrastructure and assets. Organizations must understand these long-term patterns and their impact on operating costs over the lifetime of the assets before they can make the best siting decisions.
- Local weather trends — beyond long-range patterns, these trends support planning budgets and resources in preparation for local and seasonal weather over the coming weeks and months. Knowing that the next winter will have lower than average temperatures or that the summer will be exceptionally dry can help business and organizations to repurpose heating costs, humidifying assets, or asset locations. In another example, if the impending hurricane season is expected to be stronger than normal, utilities can prepare by recruiting storm recovery crews in advance.
- Current ambient weather — current, location-based weather observations — such as temperature, cloud cover, and humidity — are the most popular data sets with organizations. This information is used to support real-time decisions and optimize operations, such as aiding heating and cooling decisions.
- Future anticipated weather — this is the second most popular data set for organizations. Knowing what will happen at a location weather-wise has tremendous economic and social value. Use can include day-ahead forecasts in the energy markets or 15-day forecasts to help predict load consumption for building owners. This data can be directly or indirectly linked to billions of dollars in transactions across numerous markets worldwide.
- Severe weather — the nature of the weather also matters to many organizations. For example, for utility operations, the forecast of fair weather allows managers to predictably modify the behavior of a system if it is required. However, if the impending weather is severe in nature, they must make important decisions to ensure minimum risk to life and property. Severe weather can cause massive economic damage, as shown in the past decade with events like Hurricane Sandy, Hurricane Katrina, and numerous large-scale tornado outbreaks. The social impact of these events is also significant with the destruction and rebuilding of communities and local businesses. So while the weather cannot be controlled, early prediction and improved operational decisions can make a significant difference in the outcome.
In our final blog in this three-part series, we’ll take a look at the evolution of weather intelligence and how it can improve efficiencies and operations in the future.
