The last time you bothered to check weather forecast updates was for perhaps when you were planning for the next weekend getaway with your friends or a trip to Hawaii. Then you either typed the same in your search engine or listened to the local news weather report or called an old pal to enquire. But weather forecast updates might mean different for business people, government and farmers. It helps them prepare more meticulously in response to the possible natural disaster that might occur. It can help farmers make decisions about farming, like planting and harvesting. Even airline companies will benefit in charting the routes in advance or decide about the need to cancel the flights in case of a storm or make an emergency landing. But imagine how pissed off everyone can get when a meteorologist’s predictions come wrong.
Well, the traditional models of weather forecasting are based on statistical measures of numeric models, and it does not give answers in binary. The data collected can be from deep space satellites (e.g., NOAA’s Deep Space Climate Observatory (DSCOVR)), weather balloons, radar systems, nowcasting weather warnings and environmental analytics and sometimes from IoT based sensors. But with ever-increasing data set, changing atmospheric conditions, the accuracy of the predictions may fluctuate, especially for more extended periods. This is where AI can prove an asset in augmenting the accuracy and reliability of the weather forecasting while reducing the workload on the human forecasters.
The AI predictions are primarily based on Machine Learning algorithms. By processing more complex data in a shorter span of time using linear regression principles, now meteorologists can make predictions with improved accuracy and thus saves lives and money. Machine learning can abet with other forecasts as well, including temperature, wave height, and precipitation. One such popular model is the Numerical Weather Prediction (NWP). The model studies and manipulates vast data sets relayed from weather satellites, relay stations, and radiosondes to deliver short-term weather forecasts or long-term climate predictions. Other AI techniques for weather predictions are Artificial Neural Network, Ensemble Neural Network, Backpropagation Network, Radial Basis Function Network, General Regression Neural Network, Genetic Algorithm, Multilayer Perceptron, Fuzzy clustering.
A researcher team at NOAA found that “applying AI techniques along with a physical understanding of the environment can significantly improve the prediction skill for multiple types of high-impact weather.” This type of weather includes events like severe thunderstorms, tornadoes, and hurricanes. Last year another team of researchers at Penn State, AccuWeather, Inc., and the University of Almería in Spain designed a computer model that can detect the movement of “comma-shaped” clouds in the satellite images. These clouds are hard to be noticed and are associated with cyclone formations. This model can forecast results with a 99 percent accuracy, at an average of 40 seconds per prediction.
