More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Storm chasers took this photo of a rotating wall cloud in Clovis, New Mexico, in May 2023. Breakthroughs, discoveries, and DIY tips sent six days a week. Flying cows, SUVs soaring through the air like toys, quaint towns that are virtually wiped off the map. Hollywood certainly makes the very real world of chasing tornadoes appear exciting on the big screen.

Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such forecasts enjoy unprecedented accuracy out to two weeks thanks to steady advances in physics-based dynamical models and data-driven artificial intelligence (AI) models. However, model skill drops precipitously at subseasonal timescales (2 - 6 weeks ahead), due to compounding errors and persistent biases. To counter this degradation, we introduce probabilistic bias correction (PBC), a machine learning framework that substantially reduces systematic error by learning to correct historical probabilistic forecasts. When applied to the leading dynamical and AI models from the European Centre for Medium-Range Weather Forecasts (ECMWF), PBC doubles the subseasonal skill of the AI Forecasting System and improves the skill of the operationally-debiased dynamical model for 91% of pressure, 92% of temperature, and 98% of precipitation targets. We designed PBC for operational deployment, and, in ECMWF's 2025 real-time forecasting competition, its global forecasts placed first for all weather variables and lead times, outperforming the dynamical models from six operational forecasting centers, an international dynamical multi-model ensemble, ECMWF's AI Forecasting System, and the forecasting systems of 34 teams worldwide. These probabilistic skill gains translate into more accurate prediction of extreme events and have the potential to improve agricultural planning, energy management, and disaster preparedness in vulnerable communities.

With the goal of making high-resolution forecasts of regional rainfall, precipitation nowcasting has become an important and fundamental technology underlying various public services ranging from rainstorm warnings to flight safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to outperform traditional optical flow based methods for precipitation nowcasting, suggesting that deep learning models have a huge potential for solving the problem. However, the convolutional recurrence structure in ConvLSTM-based models is location-invariant while natural motion and transformation (e.g., rotation) are location-variant in general. Furthermore, since deep-learning-based precipitation nowcasting is a newly emerging area, clear evaluation protocols have not yet been established. To address these problems, we propose both a new model and a benchmark for precipitation nowcasting. Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU) model that can actively learn the location-variant structure for recurrent connections. Besides, we provide a benchmark that includes a real-world large-scale dataset from the Hong Kong Observatory, a new training loss, and a comprehensive evaluation protocol to facilitate future research and gauge the state of the art.

Code is available at https://github.com/argenycw/FACL. Nevertheless, solely improving the model capability could not resolve this issue due to the high spatial randomness of the atmospheric dynamics.

The validation of global climate models is crucial to ensure the accuracy and efficacy of model output. We introduce the spherical convolutional Wasserstein distance to more comprehensively measure differences between climate models and reanalysis data.

Snowpack levels across a wide swath of western US states are among the lowest seen in decades, even as regulators struggle to negotiate water rights in the region. States across the western US are facing record low snowpack levels in the middle of the winter season. The snowpack crisis, which could mean a drier, more wildfire -prone summer, is coming as states are racing unsuccessfully against a deadline to agree on terms to share water in the Colorado River Basin, the source of water for 40 million people across seven states in the West. "Barring a genuinely miraculous turnaround" in the remainder of the winter, says Daniel Swain, a climate scientist at the University of California Agriculture and Natural Resources, the low snowpack "has the potential to worsen both the ecological and political crisis on the Colorado Basin, and then also produce really adverse wildfire conditions in some parts of the West." Data provided by the US Department of Agriculture show that as of February 12, snowpack was at less than half its normal level in areas across nine Western states--some of the lowest levels seen in decades.

Modern deep learning approaches haveshown promising results inmeteorological applications like precipitation nowcasting, synthetic radar generation, front detection and several others. Inorder toeffectively train and validate these complex algorithms, large and diverse datasets containing high-resolution imagery are required. Petabytes of weather data, such as from the Geostationary Environmental SatelliteSystem(GOES)andtheNext-Generation Radar(NEXRAD) system, are available to the public; however, the size and complexity of these datasets isahindrance todeveloping and training deep models.