On January 26, New York City Mayor Bill de Blasio declared a “winter weather state of emergency” and gravely announced he was shutting the city’s streets, schools and parks ahead of a predicted blizzard of historic proportions.
Only the blizzard didn’t materialize. A National Weather Service meteorologist issued a public apology on Twitter early January 27, saying the storm was a “big forecast miss.”
A study from Tel Aviv University provides some fresh insight into how it can be that despite modern technology, weather forecasting is still not an exact science – leading to embarrassment and unnecessary expenditures for cities like New York, or on the other hand leading to inconvenience and even tragedy because of unexpected inclement weather.
The research was published recently in the journal Land by a team led by geosciences Prof. Pinhas Alpert – the same expert who in 1976 headed an Israel Air Force forecasting unit that provided intelligence critical to the success of the raid to free hostages in Entebbe, Uganda.
“Considering my background in forecasting, weather prediction fallacies bothered me for a long time,” said Alpert. “Since joining TAU in 1982, I have been looking for a way to quantify the dominant factors that cause errors in forecasting. Until now, there has been no comprehensive analysis of these factors. They have been studied separately, but not in combination. I decided to quantify and prioritize the dominant factors for different regions, and provide this valuable information to the world scientific community.”
Why they get it wrong
Using multi-regression-based statistics on data collected between 1979 and 1993 from tens of thousands of forecast points, Alpert and his team quantified, for the first time, both manmade and natural causes of weather-prediction bloopers in Europe, North Africa, the Mediterranean, Asia, and East Asia.
The researchers found that sudden land-use changes, topography, particles in the atmosphere and population density are the dominant factors messing up the accuracy of weather forecasts.
“For example, when Israel’s national water pipeline crossed the northern Negev in June 1964, it changed the lay of the land,” said Alpert. “After a relatively short period of time, the desert was blooming, affecting the generation of clouds, precipitation, and temperature extremes.
“It is difficult for forecasters to incorporate changes like this. In effect, this single land-cover change altered the entire local climate over the Northern Negev, and existing forecast models had difficulty accommodating this, leading to erroneous predictions.”
The researchers are monitoring their model’s ability to accurately predict monthly weather conditions in different regions over 15 years. And they created a table of “factor prioritization” — gold, silver, and bronze labels — to identify dominant and less dominant factors for different regions in the world.
For example, they found that in the eastern Mediterranean, particles in the atmosphere were the most important cause of forecast fallacies, followed by land-cover change. Overall, topography was pinpointed as the most influential factor affecting weather around the world.
“The only tool the weather forecaster has is his model, and the only choice he or she has is to look at different models, each of which has strengths and weaknesses,” Alpert explained.
“Several hundred research groups are trying to improve forecasting models all the time. These groups also seek to improve predictions of climate change and global warming. Our study provides them with information about the right topics of research to address for each region.”
He is continuing to investigate factors that damage the quality of forecasts, hoping to devise new methods of improving weather and climate models.