For a probabilistic forecast to be useful, it should be well-calibrated: it should know when it may be wrong and have confidence when it is likely to be right. This is a crucial aspect of the quality of the forecast distribution, allowing a decision-maker to hedge their choices in proport...
Scientists have been looking for better ways to make weather forecasts more accurate. Despite the maturity of ensemble numerical weather prediction (NWP), the resulting forecasts are still, more often than not, under-dispersed. As such, forecast calibration tools have become popular. Among those too...
Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. ...
Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has led to state-of-the-art ML models that have superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)’s high-resolution forecast (HRES), which is w...
伏羲气象大模型 FuXi: A cascade machine learning forecasting system for 15-day global weather forecast 全文翻译 伏羲是由复旦大学主力研发,Shanghai AI Lab (上海人工智能实验室)创新孵化研究院推出,采用了一种级联的模型架构,可以提供15天的全球预报,具有6小时的时间分辨率和0.25°的空间分辨率的气象预报大模型,...
Thus, humans developed computer forecast models – complex computer programs that run on the world’s fastest supercomputers and solve the intricate fundamental equations of motion, atmospheric dynamics, fluid mechanics, etc. that can convert current observations of the atmosphere – which also have inh...
In a talk with Huawei Editor-in-Chief Gavin Allen, Professor Pappenberger says machine learning has a huge advantage over traditional forecasting methods – but adds that the flawless weather forecast remains an impossible dream. This is a modal window. ...
Topics Climate and Sustainability Machine Learning Lab Brazil Overview Sub-Seasonal to Seasonal (S2S) climate prediction has long been a gap in operational weather forecasts. The S2S timescale varies from two weeks to an entire season, although some have recently used the term more broadly to ...
Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. ...
For example, as in any weather forecast, short-term AI-powered predictions (up to a few days) tend to be more accurate than long-term ones (weeks or months). And because AI tools often rely on finding patterns in historical data, it remains difficult for them to predict rare or extreme...