BEIJING, March 9 (Xinhua) -- A global team of researchers has made strides in refining weather forecasting methods using machine learning. Scientists have been looking for better ways to make weather forecasts more accurate. Despite the maturity of ensemble numerical weather prediction (NWP), the ...
Recent advances in machine learning (ML)-based weather prediction (MLWP) have been shown to provide greater accuracy and efficiency than NWP for non-probabilistic forecasts2,3,13,14,15,16,17,18. Rather than forecasting a single weather trajectory, or a distribution of trajectories, these methods...
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. ...
FuXi: A cascade machine learning forecasting system for 15-day global weather forecastarxiv.org/abs/2306.12873 模型、数据获取渠道(百度网盘):https://pan.baidu.com/s/1PDeb-nwUprYtu9AKGnWnNw?pwd=fuxi#list/path=%2F 模型、数据获取渠道(Zenodo):https://zenodo.org/records/10401602 (有些地方会...
National Weather Service forecasters are using some of these tools to better assess the likelihood of hazardous weather on a given day. Researchers also are embedding machine learning within numerical weather prediction models to speed up tasks that can be ...
Hourly and daily forecast options. Learn More Learn More Learn More Hyper-local Weather Forecasts WRF model forecasts can be customized and fine-tuned around on-site weather stations. Ability to capture local terrain effects that publicly available data misses. Data Feed Options CSV • XML ...
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 ...
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 ...
These models prove to be relatively accurate with short-term weather forecasts and continue to improve, becoming more accurate with medium- and long-range forecasts. Recently, there have been attempts to use Deep learning technology to produce weather forecasts. Deep learning is the primary ...
"While (conventional weather models) and deep learning models already share a lot of similarities in how they function, they are two different tools that serve different purposes, and we can make use of both," he says. The field of AI research is evolving so rapidly that Wimmers says it ...