The weather is so unpredictable. You never know what the weather will be like next week, or even tomorrow for that matter.
For most of your life, not having a reliable weather forecast is relatively low-risk. For example, you may not have an umbrella when you need it, but you can still carry on with your day relatively unscathed.
However, as someone who works in the utility industry, the risks are much higher. For example, lengthy power outages in a particular area can lead to severe and widespread damage when there is no power for weeks after a big storm.
We need data from different periods to make better weather forecasts for the future and provide more reliable information.
With Storm Impact Analytics, we analyze historical data to create actionable insights. As the weather becomes more unpredictable and weather events such as hurricanes or tornadoes increase, having a reliable prediction can save lives and mitigate property damage.
This article will dive into the role that historical data plays in predicting future events and how you can have reliable weather information that you can trust.
What does historical weather data include?
Historical weather data is any type of data that has been recorded by the National Weather Service (NWS). The NWS uses a variety of sources to record and collect information.
The two main types of historical weather data include observational records and model forecasts. Observational records provide measurements taken at different times, dates, or locations, whereas model forecasts use that data to create scenarios for future weather conditions.
Why do you need historical data?
Traditionally, utilities would rely upon the historical information gained by long-term staff. However, that is no longer a viable option.
First, many of those long-term employees are aging and retiring. Therefore, having access to that information may not be an option.
Finally, an additional issue is that memory is not a reliable source of future weather predictions. Historical weather knowledge lives in the moment – you must combine it with a continued understanding of the planet and climate change.
Obtaining historical weather data
The NWS has a variety of ways that they provide historical data for others to access and use. The National Climatic Data Center (NCDC), the NOAA Environmental Modeling System, and the Cooperative Observer Program (COOP) are a few of these. These organizations are considered some of the primary historical weather data sources.
The NCDC, for example, is a federal agency that collects and preserves environmental information through the use of observational records from around the world. They also research various atmospheric phenomena, including climate variability and change.
Another source would be NOAA’s Environmental Modeling System (EMC). EMC uses different types of models to help forecast the weather. One type is a statistical model that uses historical data, but other types include numerical forecasting models, which can be used for short-term forecasts.
The Cooperative Observer Program (COOP) would also be an essential source of information since they have over 12,000 reporting stations in North America and Hawaii alone.
However, raw data is not enough. That data must be analyzed and interpreted to make forecasts. Utilities need to give meaning and context to the numbers and apply it to their specific infrastructure and service area.
Creating predictive models
Predictive models are created using available data to “teach” the model what has happened in a specific location. This data is extrapolated into that models can be built to predict future conditions. Using machine learning, models will be created that cover a range of scenarios.
Those scenarios will guide your decisions when facing a severe weather event. It will affect what kind of mutual assistance you put in place, where you deploy crews, and how you allocate your resources.
This is where the historical data about previous weather events becomes essential for meteorologists to predict future weather patterns. A small data set means that the model will learn based on relatively few real-life scenarios. However, what if that year had variables that are not typical for your area.
For example, what if that year had significant rainfall, and you are now predicting next month’s weather. If the model has not learned that those conditions were not typical, it would be making assumptions based on what was atypical in your area previously.
This means there is no way to know how much rain will fall during certain months or what would be considered “typical” for your area. You would then make assumptions based upon a poor data set that could put lives in danger!
Therefore, the more vast information that is available, the more accurate your predictions will be.
Predicting the weather and creating these models is not a “one and done” type of task. Instead, it is an ongoing process that requires constant refinement.
As you continue to input new data, the models will learn and get more accurate over time. Organizations must access historical weather information to compare to this new data and determine what is typical, atypical, or the start of a new trend.
Therefore, you want to ensure that your weather forecasting system continues to add data into the system to learn and become more accurate.
Using historical data
Developing more efficient methods of handling power outages is becoming more and more important. Since 2020, power outages in the United States caused by weather-related events have increased by 67%.
Many utilities recognize advanced weather analytics’s role; however, they are reluctant to invest in this technology.
That hesitancy or reluctance usually stems from three areas:
- previous experience with poor data collection;
- a lack of integration into existing systems; and
- not having the skill set needed to analyze the raw data.
Storm Impact Analytics provides unparalleled support and unmatched historical weather data. Additionally, our customers have confidence in relying upon our predictions and the support they receive from our team of experienced meteorologists.
Additionally, Storm Impact Analytics creates predictions with your unique utility in mind. This goal means having data on your assets, the age of your infrastructure, and information on your vegetation management.
All of this together creates a clear picture that will help you respond to the storm. Learn more about how Storm Impact Analytics can use your utility’s historical data and create a better future.