NIWA: The week it snowed everywhere

NIWA: The week it snowed everywhere. “NIWA and Microsoft Corp. are teaming up to make artificial intelligence handwriting recognition more accurate and efficient in a project that will support climate research. The project aims to develop better training sets for handwriting recognition technology that will ‘read’ old weather logs. The first step is to use weather information recorded during a week in July 1939 when it snowed all over New Zealand, including at Cape Reinga.”

Phys .org: Recreating Earth through code

Phys .org: Recreating Earth through code. “The first Earth System Model developed and based in Africa are creating one of the most reliable and most detailed modulations of climate change. What does it take to recreate Earth? A couple of thousands of line of code, throw in some data from all the weather stations around the world, and a supercomputer.”

EurekAlert: Using AI to predict where and when lightning will strike

EurekAlert: Using AI to predict where and when lightning will strike. “At EPFL’s School of Engineering, researchers in the Electromagnetic Compatibility Laboratory, led by Farhad Rachidi, have developed a simple and inexpensive system that can predict when lightning will strike to the nearest 10 to 30 minutes, within a 30-kilometer radius. The system uses a combination of standard meteorological data and artificial intelligence.”

Phys .org: Deep learning application able to predict El Niño events up to 18 months in advance

Phys .org: Deep learning application able to predict El Niño events up to 18 months in advance. “A trio of researchers from Chonnam National University, Nanjing University of Information Science and Technology and the Chinese Academy of Sciences has found that a deep learning convolutional neural network was able to accurately predict El Niño events up to 18 months in advance.”

EOS: Finding Faces in Hailstorms

EOS: Finding Faces in Hailstorms. “Hail can be among the most damaging of severe weather phenomena, but predicting whether a passing thunderstorm might start spitting pea-sized (or golf ball–sized) hailstones is notoriously difficult. A new approach using machine learning techniques related to facial recognition technology is giving meteorologists a new tool for mapping how various components of a storm might add up to dangerous hail conditions.”

Chasing storm data: machine learning looks for useful data in U.S. thunderstorm reports (Iowa State University)

Iowa State University: Chasing storm data: machine learning looks for useful data in U.S. thunderstorm reports. “When [Bill] Gallus heard campus colleagues from Iowa State’s Theoretical and Applied Data Science research group talk about machine learning, he thought the technology’s data analysis capabilities could help him study and analyze the Storm Reports database. Maybe the computers could find relationships or connections in the reports that could lead to new forecasting tools? Well, not so fast, said scientists at the National Oceanic and Atmospheric Administration (NOAA).”

St. Louis Public Radio: A New Tool Can Help Mississippi River Cities Plan For Future Floods

St. Louis Public Radio: A New Tool Can Help Mississippi River Cities Plan For Future Floods. “The Mississippi River Cities and Towns Initiative (MRCTI) and the U.S. Department of the Interior created an electronic portal in response to this year’s near-record flooding. The MRCTI Imagery and Information Viewer aggregates maps, weather forecasts and up-to-date data on floods and droughts — all information necessary for cities to better plan for natural disasters.” The tool contains historical water level data as well.