Are you planning to leave town a week or so from now? Chances are you’ll check a weather app to figure out what to pack. And you’ll probably wind up with the right outfits.
And this is because weather prediction has become more accurate than ever before. And we take it for granted.
But writer Andrew Blum says we need to give props to the curious inventors who’ve perfected incredibly complex systems of forecasting weather.
We normally think of weather prediction as “some smart meteorologist, almost always a man, sitting in some control center, looking at lots of screens, imagining what the weather is going to do. And I think that really takes the emphasis away from this incredible global system that's been constructed over the last 50 years, or depending on how you count, 150 years,” Blum said.
Andrew Blum’s last book, “Tubes: A Journey to the Center of the Internet,” was about the infrastructure of the world wide web. His new book “The Weather Machine” goes deep into the technical wizardry behind weather prediction, so that we can now know for example when a massive storm is brewing, long before the sky clouds over.
His journey introduces us to Vilhelm Bjerknes, the Norwegian physicist who first posited that weather patterns could be calculated. He tells us about the Jet Propulsion Laboratory's role in developing the satellites used to sense soil moisture from space. And we learn how John F. Kennedy helped launch the World Weather Watch to encourage global cooperation in forecasting.
Now the weather industry is becoming lucrative, with for-profit companies rushing in to do what only governments had previously been able to do. The Trump White House has sided with private forecasting operations over its own government agencies, Blum said, even nominating former AccuWeather chief Barry Myers to run the National Oceanic and Atmospheric Administration.
“The thing that concerns me is a more fundamental erosion of the basic cooperation, the exchange of observations, the exchange of forecasts, the 150-year tradition of weather forecasting,” Blum said.
The risk, he says, is that “you end up with a forecast for the haves and a forecast for the have-nots, that you have two separate systems; one using private data and private models, and one using ostensibly lesser public data and public models.”