Weather forecasting happens to be one of the most complicated challenges facing humanity. If solved, it would have some of the most far-reaching advantages. For sectors playing a fundamental role in daily life, like renewable energy and agriculture, weather forecasting is particularly essential.
The distinction between good and great weather forecasts amount to notable sums of money each year. With that being stated, artificial intelligence has turned out to become a beacon of hope for creating a world in which ideal forecasts happen to be a norm.
Challenges in Weather Forecasting
Financial planners, air traffic controllers, emergency agencies, and farmers across the world are just some of the types of people that seriously could benefit from great weather forecasting. Here are some of the challenges faced:
- Wrong weather forecasts especially happen to be damaging. It is because they exactly need to predict how much electricity is required to be produced from their wind turbines and put into the power grid 24 hours in advance. It is such a prediction on which the system operator depends when it comes to balance in the power grid.
- When power grids are not properly balanced, it leads to significant costs for power companies, thereby adding up to approximately 7% of the overall profit. Moreover, if the weather prediction is either too high or too low, people are likely to suffer from the economic consequences.
- Unlike gas, power cannot be saved in large quantities. Therefore, the ordered supply and demand need to balance regularly. When it comes to wind energy, both the supply and demand can be better predicted with stronger weather forecasting because of artificial intelligence. For example, if you understand precisely how much power the wind turbines are likely to produce for the next 48 hours, you can readily sell it for a higher price on the market. It makes detailed forecasts valuable.
Applications of AI to Weather Forecasting
Government organizations still happen to be the significant players in weather forecasting, both as a source of predictive models and raw that are utilized by private organizations. Recently, the National Oceanic and Atmospheric Administration of the United States has been utilizing machine learning for improving their weather forecasts.
A group of scientists from the NOAA discovered that applying artificially intelligent techniques along with physical knowledge of the environment can notably enhance the prediction skills concerning multiple kinds of high-impact weather. This includes events like hurricanes, tornadoes, and severe thunderstorms.
Artificially intelligent methods easily extend to predicting the parts directly when it comes to high-impact weather. Power generation from variable sources like energy consumption in an area, wind and solar energy, or airport arrival capacity also counts. Apart from that, one example to be highlighted signifies that machine learning can offer more accurate hailstorm forecasting.
Hailstorms cause billions of dollars worth of damage each year. Moreover, even the modest advancement in hail warning can lead to significant savings by getting people to safety and moving their cars.
Leveraging Artificial Intelligence to Manage Hail Forecasts
Lately, scientists have been utilizing a neural network as a portion of a weather model that analyzes storm factors like wind direction, updraft, speed, humidity, and temperatures at different altitudes. Apart from that, it helps them recognize patterns that signal whether or not a storm could produce hail. It might fall at a speed of up to 120 miles per hour, thereby annually causing about 8 to 10 billion in damages. This is where machine learning comes in.
By leveraging machine learning, scientists can estimate the possibility of hail storms in a particular area, a day in advance. Different pieces of data in massive datasets, when pieced collectively, can help indicate the advancement of a hailstorm. It is one of those systems that can predict whether or not the storm will be a small or a large one. To make such predictive systems operate successfully, scientists are required to organize, analyze and gather large amounts of weather information utilizing advanced artificial intelligence.
Amalgamation of Technology
Weather sensors on the ground, in oceans, and on satellites around the world happen to be providing a firehose concerning climate and weather data. It’s far too overwhelming and vast to be scanned and analyzed for patterns by traditional computer systems and humans. That is where the problem lies. It is because, without the capability to make sense of a huge amount of data, it happens to be a wasted opportunity. There is so much data that weather forecasters happen to be overwhelmed with the amount of data to sort through when it comes to making a decision.
They are already well past the particular point where there are so much data and information that humans struggle to understand the patterns in them. Pretty good visualization analysis tools can help you discover the ways to dig in. It is something scientists are doing with artificial intelligence systems, neural networks, deep learning, and machine learning. It is because of their tailor-made pattern recognition abilities.
With all that on the line, such systems can be fed notable amounts of information and analyze how to spot a storm that might produce tornadoes or lightning. It can witness patterns that likely happen to be leading to brutal snowstorms and hurricanes. This type of pattern recognition operates with both climate datasets as well as weather datasets.
The above-mentioned innovative methods of weather prediction already have proven to be both cheaper and faster in comparison to the conventional approach which requires hours and lots of expenses in computer hardware to process. By using artificial intelligence and machine learning, the same prediction can be done in milliseconds costing half the amount, which happens to be a massive advancement.
Companies and governments invest billions of dollars every year when it comes to weather forecasting. And, that too for good reason. Effectively, no sector of the economy exists which is not indirectly or directly impacted by the climate and weather.
The possible source of weather-related information will proceed to develop dramatically and the latest advances in machine learning happen to be making it possible for companies and government agencies to make better utilization of all the information. Though it never can truly be perfect, artificial intelligence can allow the weather forecasting practice to continue to enhance its accuracy along with its resolution.
Possibly the most interesting part about machine learning in the industry of weather forecasting signifies how it is being collectively combined with additional data regarding human behavior. We only have started to acknowledge all the subtle ways the climate impacts our choices. The more localized and refined our climate information gets the easier it is likely to be to discover distinct connections and patterns.
With that being stated, even the tiniest improvements in the industry of weather forecasting will provide companies with new beneficial pieces of information by discovering new correlations, thereby providing organizations with more lead time to take advantage of them.