Perhaps the most impressive gains have been in hurricane forecasting.
Just 25 years ago, when the National Hurricane Center tried to predict where a hurricane would hit three days in advance of landfall, it missed by an average of 350 miles. If Hurricane Isaac, which made its unpredictable path through the Gulf of Mexico last month, had occurred in the late 1980s, the center might have projected landfall anywhere from Houston to Tallahassee, canceling untold thousands of business deals, flights and picnics in between — and damaging its reputation when the hurricane zeroed in hundreds of miles away. Now the average miss is only about 100 miles.
Why are weather forecasters succeeding when other predictors fail? It’s because long ago they came to accept the imperfections in their knowledge. That helped them understand that even the most sophisticated computers, combing through seemingly limitless data, are painfully ill equipped to predict something as dynamic as weather all by themselves. So as fields like economics began relying more on Big Data, meteorologists recognized that data on its own isn’t enough.