A major breakthrough for longer term (2-6 weeks) forecasting and disaster planning usually referred to as the ‘predictability desert.’ The researchers introduce a "postprocessing layer" which uses AI to learn from a forecasting system’s mistakes based on historic data and shifts its forecasts. In the 1970s, MIT Professor Edward Lorenz came up with the idea of chaos theory to describe how tiny changes in initial weather conditions lead to completely different outcomes. “He came up with this theoretical two-week limit where beyond that, we just can’t predict weather because the noise in the system grows so much that it overwhelms any signal you start with...that had a strong influence on the field that may have held people back from exploring forecasting beyond the two-week range. Recently, the best weather forecasts come from either dynamical models, which numerically simulate how the atmosphere and oceans evolve over time, or artificial intelligence models, which learn to make predictions from historical weather data. AI models have shown improvements in predicting weather at short ranges, but they’re even worse than dynamical models at longer time ranges. MIT Research Scientist Judah Cohen, who is based in the MIT Department of Civil and Environmental Engineering, focuses on forecasting weather two to six weeks in advance, which is known as subseasonal forecasting. That range is referred to as the ‘predictability desert.’ While other forecasting systems do more simplistic error correction, the research team’s postprocessing layer uses AI to learn from a forecasting system’s mistakes based on historic data and shifts its forecasts. In that time range, forecasters use so-called probabilistic forecasts, where the likelihood of a range of possibilities if given. As more forecasts and data become available, the model adaptively improves its correction. In the @Nature paper, they howed their technique could be used to improve land forecasts in most regions across the globe. As an add-on wrapper, it can be used with any model. It even improves models that are already bias corrected using another AI algorithm. So it shows a robust improvement. And it’s open source and low cost. This could help decide when to fertilize around rain, when there’s a freeze coming to protect crops during winter harvests, or help avoid droughts.