We all depend on the weather forecast for finalizing the various aspects, from our apparel choice to our journey decision. Hence the accuracy of the predictions is a vital criterion for the legitimacy of the weather forecast.
Most meteorological services use powerful computers to populate the weather forecasting data. Millions of calculations get processed through huge machines to feed inputs for the equations. The results of these equations are what helps to predict rainfall, wind, temperature, and any other climate change.
Even the fastest computers on earth, at times, become overworked due to the accuracy and speed that such forecasts demand. Researchers and scientists are continually working to develop solutions to aid in increasing the accuracy of such forecasts. Artificial intelligence is fast gaining popularity, and this article tries to find out its implications.
The Path Ahead
Experts predict that the future can see the adoption of a radically different approach. In recent times, Microsoft Research in collaboration with the University of Washington carried out studies relating to artificial intelligence for analyzing and predicting future events.
The above study shows the utmost importance of artificial intelligence for analyzing past weather data to put forth a forecast. It depicts that the AI-enabled weather forecasting systems are superior in accuracy and efficacy vis-a-vis today’s technology.
This new set of futuristic tools help provide a global weather model that considers past forty years of weather data as a base for its predictions. This logic of the new model helps to differentiate it from the current one that relies on detailed physics calculations.
The speed with which a simple data-based AI model can reproduce simulations for year weather around the world is faster compared to the traditional models. A paper published in the Journal of Advances in Modeling Earth Systems suggests that the model repeats simple and similar steps from one forecast to the next.
Experts suggest that a glorified version of pattern recognition is available with the help of machine learning systems. The system tries to identify typical patterns and tries to find out how it evolves. The data from the last 40 years is guiding the forecasting systems to make informed decisions about the future weather.
Although this new model is at a development stage and is trying to get a grasp of the forecasting methodologies, the accuracy levels are not at par with the top traditional forecasting models. The computing power that the AI model uses is about 7000 times less than today’s models. It ultimately leads to a faster result.
Many meteorological services are providing solutions to different industries to have weather forecasts integrated into their systems, applications, and software. Such API integration helps the various functions to make use of validated weather data for free. You can try Tomorrow’s weather API while checking options for this job.
Whenever there is a technological advancement or improvement in any sector, it generally helps to hasten the process or make it efficacious. The benefits outweigh the cost of any transition from a traditional system to a modern one.
The faster speed of the AI-enabled forecasting system will facilitate the forecasting centers to numerous models with a different starting point. This technique of ensemble forecasting helps to have a weather forecast for a range of expected outcomes for a particular event. For example, it can predict where the next cyclone or thunderstorm can take place.
Experts suggest that the efficiency of this approach is of prime importance. It identifies itself to be superior compared to the existing models in terms of dealing with predictability concerns. It achieves the same by having a model in place that is swift to take care of large data functions at one go.
Scientists tested the model under various circumstances and checked its efficacy by putting it through conditions like predicting
– A standard variable in weather forecasting,
– The global height of the 500 hectopascal pressure,
– Every 12 hours for a year.
There are various benchmark tests which the experts deem to be necessary for a data-driven weather forecast. The AI-enabled forecasting system scores high in these forecasting tests developed for three-day forecasts.
Therefore, as you can see, the AI-enabled forecasting systems are superior to the traditional ones in terms of both efficacy and accuracy. They can compete with the existing operational forecasting methods but will need more time to become mainstream.
The system will become more robust in generating better forecasts and prove to be a viable alternative in the future.