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AI and machine learning are improving weather forecasts, but they won't replace human experts

AIHub

A century ago, English mathematician Lewis Fry Richardson proposed a startling idea for that time: constructing a systematic process based on math for predicting the weather. In his 1922 book, "Weather Prediction By Numerical Process," Richardson tried to write an equation that he could use to solve the dynamics of the atmosphere based on hand calculations. It didn't work because not enough was known about the science of the atmosphere at that time. "Perhaps some day in the dim future it will be possible to advance the computations faster than the weather advances and at a cost less than the saving to mankind due to the information gained. But that is a dream," Richardson concluded.


The Danger of Leaving Weather Prediction to AI

WIRED

Humans have tried to anticipate the climate's turns for millennia, using early lore--"red skies at night" is an optimistic sigil for weather-weary sailors that's actually associated with dry air and high pressure over an area--as well as observations taken from roofs, hand-drawn maps, and local rules of thumb. These guides to future weather predictions were based off years of observation and experience. Then, in the 1950, a group of mathematicians, meteorologists, and computer scientists--led by John von Neumann, a renowned mathematician who had assisted the Manhattan Project years earlier, and Jule Charney, an atmospheric physicist often considered the father of dynamic meteorology--tested the first computerized automatic forecast. Charney, with a team of five meteorologists, divided the United States into (by today's standards) fairly large parcels, each more than 700 kilometers in area. By running a basic algorithm that took the real-time pressure field in each discrete unit and prognosticated it forward over the course of a day, the team created four 24-hour atmospheric forecasts covering the entire country.


Game-changing weather satellite launched into orbit

Christian Science Monitor | Science

GOES-R, the most powerful weather satellite ever built, launched into orbit atop an Atlas V rocket Saturday evening from Cape Canaveral, Fla. The weather satellite is the first of a new generation of satellites operated by the National Oceanic and Atmospheric Administration (NOAA) that is expected to improve weather forecasting across the entire Western hemisphere. The satellite launched at 6:42 PM EST from Cape Canaveral Air Force station. It took about 12 minutes for the rocket to boost the high-tech piece of equipment into orbit. GOES-R, which stands for Geostationary Operational Environmental Satellite, is the 16th in the GOES series.


The trouble with weather apps

The Guardian

It was a tale of two storms. The first consisted of the rain and thunder forecast for Bournemouth by the BBC weather app on the Saturday spring bank holiday. The second came when the first failed to materialise and a tourism manager in the town complained that visitors who stayed away could have come after all and enjoyed sunshine and blue skies. This opportunity to rage at inaccurate forecasting, bash the BBC and highlight the grievances of small businesses did not go to waste. For the Sun, it was a "blunderstorm".


The AI forecaster: Machine learning takes on weather prediction

#artificialintelligence

According to a 2009 study, U.S. adults look at weather forecasts nearly 300 billion times a year. Reliable forecasts can predict hazardous weather―such as blizzards, hurricanes, and flash floods―as early as 9–10 days before the event. Estimates value these forecasts at $31.5 billion per year. Although weather prediction keeps improving year to year for shorter-term forecasts, forecast skill decreases in the 2-week to 2-month time frame. These longer-timescale forecasts can play a critical role for many sectors, including water conservation, energy demand, and disaster preparedness.