Osaka Metropolitan University researchers propose a new method to generate meteorological data that takes into account the interdependence of meteorological factors, such as temperature, humidity, and solar radiation.

Over a typical two-year rotation of corn and soybeans, 40% of nitrous oxide emissions are in the fertilizer-free soybean year, according to new Iowa State University research. Winter cover crops and planting soybeans earlier could reduce emissions of the potent greenhouse gas by one-third while increasing yields. 

In this week’s Physics of Fluids, researchers employ machine learning to more accurately model the boundary layer wind field of tropical cyclones. Conventional approaches to storm forecasting involve large numerical simulations run on supercomputers incorporating mountains of observational data, and they still often result in inaccurate or incomplete predictions. In contrast, the author’s machine learning algorithm is equipped with atmospheric physics equations that can produce more accurate results faster and with less data.

Concordia University researchers have created a method that uses artificial intelligence to more accurately predict short-term river discharge using historical data from two hydrometric stations on the Ottawa River along with other weather-based parameters. They built on an existing type of algorithm called group method of data handling, which constructs predictive models by sorting and combining data into groups. The models are computed in different combinations repeatedly until the best and most reliable data combination is identified.