Can We Predict Extreme Weather Events with Historical Weather Data?

1. Can We Predict Extreme Weather Events with Historical Weather Data?

2. How to forecast the weather on any day of the year using historical weather data? The historical weather data prediction can be relied upon when we need to know what the weather will be like in the coming weeks. Standard weather predictions are based on computer models that predict how the weather will change in the next few weeks. What happens, however, if you aren't interested in the coming weeks? Perhaps you're organizing a wedding, a vacation, or an outdoor event and need to know what the historical weather data will be like at a specific area and date in the future. In that instance, we utilize historical weather measurements gathered over a long period to assist us in predicting the weather we would likely encounter. What kind of statistical weather data are we looking for? We need to discover the typical weather patterns for that location while developing statistical weather data-based forecasts. For example, we'd want to say the average high and low temperatures and the likelihood of rain. This, however, does not provide us with the complete picture. If I'm arranging a vacation, I'll need to understand the average weather or the worst and best scenarios. It's fascinating to learn about previous weather 'normals,' but we also need to understand how likely the weather will be much hotter or colder than average. We need to see the big picture - what is regular weather, is this the worst weather that might happen, or what is the best weather? And what are the chances of more extreme weather occurring? Obtaining weather data from the past The NWS provides historical weather data in various forms for others to acquire and use. Some of these are the National Climatic Data Center (NCDC), NOAA Environmental Modeling System, and the Cooperative Observer Program (COOP). These groups are regarded as some of the most historically important historical weather data sources. The National Center for Environmental Information, for example, is a federal agency that collects and protects environmental data using observational records from throughout the world. They also study various atmospheric phenomena, such as climate change and variability.

3. The Environmental Modeling System of the National Oceanic and Atmospheric Administration (NOAA) is another source (EMC). To forecast the weather, EMC uses a variety of models. A mathematical analysis that uses historical data is one type; other forms include numerical forecasting models that can be used for short-term projections and the Cooperative Observer Program (COOP), which has over 12,000 reporting sites in North America and Hawaii alone, is another. Raw data, on the other hand, is insufficient. To make forecasts, the data must be evaluated and interpreted. Utilities must give statistics meaning and context before applying them to their specific infrastructure area. Creating predictive models Predictive models are constructed by "teaching" the model what has occurred in a certain location using available data. This historical weather data information is extrapolated to create models for predicting future situations. Models for a variety of scenarios will be constructed using machine learning. When faced with a severe weather disaster, these situations will help you make judgments. It will influence the type of mutual aid you implement, the locations where workers are deployed, and how you distribute your resources. This is where meteorologists use historical weather data from previous weather events to forecast future weather patterns. Because the data set is tiny, the model will learn from only a few real-life instances. On the other hand, what if that year contained characteristics that were out of the ordinary for your area? What if, for example, that year had a lot of rain, and you're predicting the weather for next month? If the model hasn't learned that those circumstances were out of the ordinary, it will make assumptions based on what was previously out of the ordinary in your location. This implies you can't predict how much rain would fall during specific months or what is considered "normal" for your area. You'd then make judgments based on a faulty data set, perhaps jeopardizing people’s lives. As a result, the more information you have, the more precise your forecasts will be.

4. Ongoing refinements The work of predicting the weather or developing these models cannot be completed in a single sitting. Rather, it is a continuous process that necessitates regular refinement. The models can learn and be more accurate over time as you continue to add fresh data. Organizations must compare this new data to historical weather data to establish what is normal, atypical, or even the beginning of a new trend. As a result, you'll want to make sure your weather forecast model continues to add data as it learns and improves. Using historical data It is becoming increasingly vital to develop more efficient means of dealing with power disruptions. Power outages due to weather-related occurrences have grown by 67 percent in the United States since 2020. Many utilities acknowledge the importance of advanced weather analytics, but many are hesitant to invest in the technology. That hesitancy and reluctance are frequently due to three factors: •Prior experience in poor data collecting. •A lack of seamlessly with existing systems; and •A lack of skill set required to interpret raw data. Storm Impact Analytics offers unrivaled customer service and historical weather data. Furthermore, our customers have faith in our forecasts and the assistance they get from your team of expert meteorologists. In addition, Storm Impact Analytics makes forecasts based on your specific utility. This aim necessitates having information on your assets, infrastructure age, and vegetation management.

5. References https://www.dtn.com/historical-weather-data-and-predicting-future-events/ https://www.popsci.com/story/environment/underestimating-extreme-weather-climate- change/ https://www.eumetsat.int/science-blog/forecasting-extreme-weather-now-and-future https://tu-freiberg.de/en/department-52-media-relations/how-can-we-better-predict-extreme- weather-in-the-mediterranean-a-few-w https://www.climateforesight.eu/cities-coasts/predicting-extreme-weather-events/