Use cases: Machine Learning Solutions
Meteorologists collect large amounts of data from weather stations, radars, and satellites, and use this data to predict future weather events with complex numerical models. While the field has advanced greatly, some events are challenging to calculate due to the high number of factors at play. The key to an efficient solution is Machine Learning. Based on historical data, Machine Learning models can learn very complex patterns and yield insight that cannot be achieved with traditional models.
Automating cloud classification and cloud coverage reporting by satellite imagery analysis.
Satellite data is readily available and despite its complexity, Machine Learning models can quickly analyze it. A Computer Vision model can readily learn to classify different cloud types and perform a continuous monitoring of cloud coverage with a real-time warning system.
Automatically retrieving weather information from websites and news articles.
Staying up to date on online news articles would require a lot of time and expertise from a human meteorologist. A Natural Language Processing system can quickly access and analyze large quantities of textual data from the internet, and summarize the key information a human expert can use for further processing.
Measuring sea levels from satellite imagery
The above mentioned satellite data can also be used to monitor large areas of water bodies and measure the sea levels in real-time. By notifying a human expert from any unexpected events, they can avert catastrophes and minimize losses.