Imagine you’re a farmer in the northern United States. It’s early spring, and nighttime temperatures are just starting to rise above freezing. You need to fertilize your newly-planted crops, but you also know that at freezing temperatures, the fertilizer will kill your crops. The weather forecast out of the closest town, which is 50 miles away, predicts that temperatures will stay above freezing for the next few days, and so you decide to go ahead and fertilize. But that night, temperatures in some parts of your fields dip below freezing. Over a quarter of your crops die.
Unfortunately, this is a common situation, especially when weather data comes from farther away. Now, researchers at Microsoft have developed a framework called DeepMC that can very accurately predict local weather, and could be used by farmers, renewable energy producers, and others. Microsoft researchers presented a study on the framework and its application at the Association of Computing Machinery‘s Conference on Knowledge Discovery and Data Mining in August.
DeepMC uses machine learning and artificial intelligence to localize predications related to weather and climate. It combines two different sources of data: One from on-site sensors, and the other from standard local weather forecasting data. DeepMC gets that data directly from application programming interfaces (APIs) that come from sources like the National Oceanic and Atmospheric Administration, Dark Sky, and the National Weather Service.
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