Researchers in Switzerland have figured out how to use AI to predict when and where lightning will strike.
Researchers from École Polytechnique Fédérale de Lausanne used standard meteorological data and machine learning to build a simple system that can predict lightning strike to the nearest 10 to 30 minutes inside a radius of about 18.6 miles, according to an account in Popular Mechanics.
“We have used machine learning techniques to successfully hindcast nearby and distant lightning hazards by looking at single-site observations of meteorological parameters,” wrote the authors in a new paper published recently in the journal Climate and Atmospheric Science.
The researchers used data about past lightning strikes to build an algorithm that can make predictions about new lightning strikes, in a process called hindcasting, as opposed for forecasting. Estimates based on past events are fed into a model to see how well the output matches known results.
The team looked at four weather variables that typically lead to lightning: air pressure at station level, air temperature, relative humidity, and wind speed. The data came from 12 Swiss weather stations in urban and mountain environments between 2006 and 2017. After a learning phase, the model was said to make correct predictions 80% of the time.
The PhD student who originated the technique, Amirhossein Mostajabi, said the results would be straightforward to replicate elsewhere. “Current systems are slow and very complex, and they require expensive external data acquired by radar or satellite. Our method uses data that can be obtained from any weather station,” he said. “That means we can cover remote regions that are out of radar and satellite range and where communication networks are unavailable.”
AI Seen Capable of Improving Overall Weather Forecasting
AI could be the key to improved overall weather forecasting. Researchers with the National Oceanic and Atmospheric Administration (NOAA) and elsewhere are predicting that machine learning and other AI techniques will be able to supplement or replace major components of operating weather forecasting systems, according to an account in Earth & Space Science News. The fields of remote sensing and numerical weather prediction (NWP) are in a position to exploit the rapid advances in machine learning made in recent years.
The volume, diversity and capabilities of observations, especially satellite observations, have increased dramatically in recent years. The fundamental requirements for incorporating AI to improve weather forecasting include:
efficient and intelligent signal and image processing
quality control mechanisms
data fusion (combining diverse streams of observations)
mapping (approximating functions efficiently)
To be applied to diverse geophysical domains that include the atmosphere, ocean, biosphere, hydrosphere, and near-space environment, all the requirements would need to be addressed.
The volume of data being produced is overwhelming the ability of the system to absorb it. Global weather forecasting uses one to three percent of available satellite data, and the processing times for traditional approaches is called crippling. The Internet of Things will create even more huge volumes of environmental data, and smaller constellations of hundreds if not thousands of satellites may be launched as well.
New approaches with greater efficiency and accuracy will be needed to exploit these new resources. Trained machine learning systems based on advanced neural networks are efficient and easily implemented with modern scientific programming languages.
Several systems of NOAA are providing much of the satellite data. Satellites as part of NOAA’s Joint Polar Satellite System (JPSS) orbit approximately 500 miles (805 km) above Earth. They orbit the Earth from pole to pole up to 14 times a day. Every part of the planet is monitored twice a day, according to an account in Interesting Engineering. This provides enormous data sets about the Earth’s entire atmosphere, including clouds and ocean, at very high resolution. Meteorologists in theory would be able to use this information to predict long-term weather patterns.
In deep space, satellites operated by NOAA’s Deep Space Climate Observatory (DSCOVR) orbit one million miles (1,609,344 km) from Earth. These satellites provide space weather alerts and forecasts, monitor solar energy absorbed by the Earth every day, and record information about the ozone and aerosol levels in the Earth’s atmosphere.
Weather Forecasting is a Business Too
Companies are also investing more in weather prediction. IBM recently purchased The Weather Company and has combined its data with IBM’s Watson platform. This lead to the development of IBM’s Deep Thunder, which provides customers with hyper-local weather forecasts with .2 to 1.2-mile resolution.
Monsanto has also been investing in AI for weather forecasting, with agricultural weather predictions coming from its Monsanto Climate Corporation unit.
Source at AITrends