It is clear that artificial intelligence and machine learning can be used to provide a great deal of insight into the weather. The work that has been done on applying machine learning to forecast high-impact weather has shown us some of what’s possible. The possibility of accurate weather prediction increases ten fold with AI as it computes a lot of data to detect patterns that help with nowcasting and future casting.
Research has indicated that artificial intelligence can deliver forecasts that are on par with those of conventional models with a lower level of computing. Early research has shown that artificial intelligence can be used to forecast rainfall as well as nowcasting weather, which predicts the weather within the next hour or two.
AI can be a very powerful tool to nowcast or forecast the weather with great accuracy as it models itself after the traditional means of forecasting through weather observation. The advantage AI has is that it can go through multiple datasets to observe real time data, collate insights, patterns and learn from historical context for accurate forecasts.
There a lot of volatile variables within the atmosphere, such as turbulent air, high-pressure systems, and so on, that have to be forecasted for different times within the next hour, within the next day, and within the next week.
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In order to forecast weather, meteorologists collect and process observations of temperature and pressure, assimilate those variables into numerical computer models, extrapolate the models into the future, and use the projections as a starting point for future iterations.
The weather sensors and data loggers on weather satellites, on the ground, and in the oceans around the world are providing a wealth of weather and climate data in real time using IoT around the globe. The amount of information in the world is far too vast and overwhelming to be analyzed and scanned for patterns even by humans or even traditional computer programs. It is a problem because if we are unable to make sense of this deluge of information, then we are not utilizing this data to our advantage.
Fortunately, with artificial intelligence systems, machine learning, neural networks, deep learning, and their pattern-recognition capabilities, this is something that can easily be accomplished. It is possible to feed massive amounts of data into the system so that it learns to detect lightening and tornadoes in a storm. This kind of pattern recognition can be applied to both weather and climate datasets in order to spot patterns that could lead to destructive hurricanes or brutal snowstorms in the future.
Better data collection can improve our understanding of underwritten patterns within the ecosystem of the datasets collected. The better data we collect the smarter our algorithms can be to detect patterns and give us deeper insights into the possibilities of what weather could be in the future.
Traditional means of sifting through this data and keeping in mind the historical context while doing so is impossible by traditional means of data sifting. But with AI, and current computing / processing it is possible to make those connections and deliver accurate predictions.
Rather than relying on the laws of physics, the latest artificial-intelligence techniques train neural networks on the massive amount of data that is available. By reviewing weather data from the past, these networks develop their own understanding of how conditions evolve instead of using brute-force computation to forecast weather based on current conditions. As artificial intelligence systems attempt to feed their insatiable appetite for data, satellite meteorology and atmospheric science offer an ideal training ground that can be used to feed their insatiable appetite.
The National Oceanic and Atmospheric Administration sponsored the first conference on artificial intelligence in 1986, and since then, basic artificial intelligence techniques have been applied to weather and climate. However, recent advances in deep learning and increased availability of computers capable of running them have enabled a rapid increase in research.
A combination of machine learning and forecasting may also prove crucial to accurate “nowcasting,” which is a method of predicting the future at a rapid rate that conventional methods do not be able to provide. A couple of researchers at Google Research recently found that deep neural networks can predict rainfall in the next eight hours better than other state-of-the-art traditional weather models, even if no physical laws are explicitly encoded into them.
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Contextual History Based Prediction
In order to make predictions using the newly developed global weather model, rather than relying on detailed physics calculations, the model looks at the weather data that has been collected over the last 40 years. Using a simple, data-driven artificial intelligence model, researchers are able to simulate the weather around the world much quicker and almost as effectively as traditional weather forecasts, by using similar repeated steps from forecast to forecast, with reduced computing power, and this is called iterative forecasting. This process is good to detect high-impact weather events that can be used to save lives.
Weather forecasters today use numerical weather prediction models to deduce current weather patterns based on observations collected from sources such as weather stations, weather balloons, weather stations and satellites. These numerical models use observations to calculate current weather conditions based on equations that are used to calculate movement of air, current pressure in the atmosphere..etc.
However, the smaller a weather event is, the harder it is for these models to predict. In addition, forecasters who have been in this profession for a while are remarkably proficient at combining the huge amount of weather information they have to consider every day. However, their memory and bandwidth are limited unlike AI that can compute multitude of data points to understand weather systems to make predictions.
A number of challenges can be solved through artificial intelligence and machine learning, and forecasters are now using these tools in a number of ways, including making predictions of extreme weather that can’t be predicted by current models.
The probability that it will snow to cause blizzard conditions will occur based on a series of decisions trees developed using a machine learning method called “random forests.” The process uses many decision trees to split data up into different categories and then forecast the likelihood of different outcomes.
Similarly, the same method has been applied to forecasting extreme weather events such as tornadoes, large hailstorms, and severe winds associated with thunderstorms in recent years. Using machine-generated weather forecasts, National Weather Service forecasters are able to better determine the chances of hazardous weather on a given day based on the likelihood of hazardous weather occurring.
It is also becoming increasingly common for researchers to embed machine learning into numerical weather prediction models in order to speed up tasks that can be computationally intensive, such as predicting how water vapor will become rain, snow, or hail, which can be extremely time consuming. Thus making modern weather forecasts faster to predict.
Eventually, machine learning models may replace traditional numerical models or current weather models entirely. These systems, in contrast to the traditional models, would instead process thousands of past weather maps and great amounts of weather information to figure out how the weather systems tend to behave instead of solving a set of complex physical equations. In the end, they would use current weather data to make weather predictions that are based on what they have learned from the past and may lead to accurate weather forecasts and predicting unprecedented weather phenomena.
École Polytechnique Fédérale de Lausanne, a prestigious engineering school in Switzerland, has developed an inexpensive system that can be used to predict when lightning will strike to the nearest 10 minutes or 30 minutes in advance. Published in the journal Climate and Atmospheric Science.
There is no doubt that artificial intelligence combined with machine learning, neural networks, deep learning, and high-performance computing will have a great impact on the quality of weather forecasting. In recent years, climate scientists and weather forecasters have been relying more and more on these tools to analyze massive amounts of data and determine patterns that could indicate severe weather or climate change, among other things. Predictive technologies combined with powerful algorithms will enhance weather forecasts significantly.
Advances in machine learning make a better promise for weather prediction and weather advances which can accurately predict heavy rain, inches of rain, rain accumulations, tracks of hurricanes, and other extreme weather conditions. We have come a long way from using primary tools to proliferation of tools augmented by vast amounts of data from various modeling earth systems, satellite images, and collaboration between scientists and their AI and machine learning experiments to predict the weather better.
Though there are major challenges to be perfect in predicting weather, we are slowly working away on smaller challenges in meteorology that deal with complex processes and human expertise and embedding them with AI and machine learning to add a sophisticated layer of computing and prediction.