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

#artificialintelligence

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.


Weather forecasts to get a lot more accurate as NOAA unveils new '4D' maps

Daily Mail - Science & tech

Weather forecasts are about to get a lot more accurate thanks to a massive new upgrade to the digital models used by the NOAA. Its radical new upgrade works in 4D, taking into account how weather systems evolve on a 3D spatial grid over time, with time now becoming the fourth dimension. Forecasters say this dramatically improves the accuracy of their forecasts, and allows them to see hourly forecasts for the next five days. NOAA forecasters say the major overhaul of their system dramatically improves the accuracy of their forecasts, and allows them to see hourly forecasts for the next five days. The new model is run four times a day with each update forecasting out to 16 days.


Do I need a brolly? Google uses AI to try to improve two-hour rain forecasts

The Guardian

Weather forecasts are notoriously bad at predicting the chances of impending rain โ€“ as anyone who has been drenched after leaving the house without an umbrella can testify. Now, scientists at Google DeepMind have developed an artificial intelligence-based forecasting system which they claim can more accurately predict the likelihood of rain within the next two hours than existing systems. Today's weather forecasts are largely driven by powerful numerical weather prediction (NWP) systems, which use equations that describe the movement of fluids in the atmosphere to predict the likelihood of rain and other types of weather. "These models are really amazing from six hours up to about two weeks in terms of weather prediction, but there is area โ€“ especially around zero to two hours โ€“ in which the models perform particularly poorly," said Suman Ravuri, a staff research scientist at DeepMind in London and co-lead of the project. "Precipitation nowcasting" is an attempt to fill this blind spot.


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 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.