Researchers correct recurring errors in weather forecasts weeks in advance
A Nature paper published on October 14 describes a method called Probabilistic Bias Correction. It learns the recurring errors in existing weather forecasts. In tests of an operational system, researchers report improvements across 98% of precipitation targets, 92% of temperature targets, and 91% of pressure targets. Those figures show how many targets improved, not the percentage increase in accuracy.
Verified 12:14 AM PDT · 2 original sources
The main tests cover weekly conditions for days 19 to 25 and days 26 to 32. They use a grid resolution of 1.5 degrees. The results do not promise precise local weather six weeks ahead. Microsoft says Kenyan forecasters are testing the method. Its commercial six-week service remains in private preview. The paper first appeared as a preprint in April. Wednesday marked its journal publication and Microsoft’s account of the deployment.
Local performance and farmers’ decisions would show whether better probability forecasts reduce losses. Benchmark gains alone do not establish that result.
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