Articles tagged with Weather Forecasting
Research shows that pressure measurements from smartphones can enhance hailstorm forecasts, outperforming traditional weather stations in some cases. The study aims to integrate smartphone data into forecast models for better early warnings and local-scale hazard capture.
A new study establishes a fundamental limit of weather prediction at 129 days under ideal conditions, where errors smaller than today's forecasts may behave. This finding offers forecasters a new perspective and a tangible target for future advancements.
Researchers identified a recurring setup of atmospheric conditions that lead to prolonged periods of intense rainfall. The study found that existing operational weather forecasts can capture these large-scale patterns, paving the way for more accurate predictions of high-impact southwest monsoon events.
Researchers developed a method to predict which hurricanes are likely to intensify rapidly, providing local officials with crucial time to make lifesaving preparations. The new technique uses statistical analysis of past storms and sea surface temperatures to calculate the probability of rapid intensification.
Researchers analyzed the 2025 Texas Hill Country floods, which killed at least 135 people, including 28 at Camp Mystic. Experimental models showed they could have predicted extreme flows with greater lead time.
A new study reveals that temperature changes in the Indian Ocean can significantly influence winter weather thousands of kilometers away in the Eastern Mediterranean, offering new opportunities to predict damaging dry spells months before they occur. Positive phases of the Indian Ocean Dipole are linked to large-scale shifts in atmosph...
A study found that forecast error types influence public emotion during disasters, with anxiety and worry being the most common emotions. The researchers suggest that communicating forecast uncertainty effectively could improve public trust and reduce emotional distress during future extreme weather events.
The University of Bath's Centre for Climate Adaptation & Environment Research has joined the STRIVE mission, a NASA Earth System Explorer project aiming to improve weather forecasting and climate modeling. The team will focus on understanding atmospheric waves and their impact on energy transport.
A new study reveals that climate models reproduce extreme heat but struggle to capture the gradual atmospheric buildup leading up to it. The researchers propose a process-based framework for evaluating climate models to improve prediction capabilities and enhance preparedness for extreme heat events.
Researchers at Arizona State University have developed an online tool called Cool Routes that uses real-time weather forecasts and shade data to find the coolest paths for walkers. The tool has been shown to reduce heat exposure by up to 4.5 degrees, making hot-weather walking less of an ordeal.
Researchers identified two weather patterns associated with an increased risk of headaches in the Northeast US. The study found that approaching cold fronts and high-pressure systems, such as the Bermuda High, can trigger new-onset headaches in people with episodic migraines.
A new AI-powered translation program developed by the National Weather Service (NWS) translates weather forecasts into multiple languages, including Spanish, Chinese, Vietnamese, Samoan, and French. The program aims to provide life-saving information to over 68 million US residents who do not speak English at home.
Researchers from Virginia Tech found that airborne particles have an outer alkaline shell, contradicting the traditional view of uniform droplets. This discovery has significant implications for understanding air quality and climate change.
Researchers found that small-scale thunderstorms strengthen over cities, while cold frontal storms weaken their rainfall intensity. Urban heat and roughness disrupt the lower part of storm systems, leading to varying effects on different types of storms.
Researchers at FSU have discovered a way to forecast winter weather patterns up to six months in advance by analyzing the stratospheric polar vortex. This new approach uses climate patterns like El Niño-Southern Oscillation to predict the vortex's behavior, allowing for more accurate forecasts of extreme weather events.
Recent studies investigate the relationship between weather conditions and extreme events like heat-related mortality, tropical cyclone hazards, and changes in precipitation patterns. Researchers also explore new modeling methods to predict visually appealing atmospheric phenomena like 'ornamental twilight'.
A high-resolution model developed by University of Pennsylvania researchers shows that river levels once expected every 50 years now occur every few years due to climate change. The 1-in-100-year threshold marks a tipping point when flooding spreads rapidly across the urban landscape.
Researchers from UNIGE and KIT found that traditional numerical models remain more reliable for predicting extreme phenomena than AI-based forecasts. The study highlights the limitations of AI weather models in extrapolating beyond their training domain, making them less effective for record-breaking events.
A UCLA-led study reveals that airborne desert dust traps about twice as much heat as previously estimated, leading to improved climate and weather models. This increase in precision will help forecasters predict regional temperature shifts, rainfall patterns, and atmospheric motions.
New research from the American Meteorological Society explores AI forecasting limits, the impact of climate change on hurricane trends, and simplifies the heat index. The studies find that machine learning can outperform numerical physics-based models in certain conditions, but not universally.
Researchers from Colorado State University found that tiny particles bubbling up from melting sea ice in the Arctic sky can create clouds, providing a platform for water vapor to freeze onto. This discovery sheds light on why Arctic clouds behave differently and could help improve weather modeling and climate change predictions.
The study found that complex terrain, particularly orographic gravity wave drag, significantly affects the location and intensity of extreme precipitation. Without this effect, the low-level mesoscale vortex moves westward, leading to decreased rainfall intensity and a northwestward shift of the rainfall center.
Researchers have identified fungal proteins that can catalyze ice formation at high subzero temperatures, making them a potential alternative to toxic silver iodide in cloud seeding. The discovery could lead to safer weather modification and improved food preservation techniques.
New research published in Weather assesses the energy consumption of AI models and finds that they consume considerable energy during training, but offset this by rapid forecasting ability. AI data-driven models are estimated to consume at least 21 times less energy than traditional models over one-year usage.
Researchers developed Zephyrus, an AI agent capable of analyzing and answering questions in natural language about weather and climate data. The agent can handle language-based queries, translating them into code and generating plain language answers.
Researchers found interactions between soil moisture patterns and wind in the lowest atmosphere influence where storms develop, providing clues to forecasters about location. The study aims to improve certainty about upcoming storms on hot days and save lives, livestock, and property.
A new study finds that U.S. tornado activity is shifting southeastward due to the relationship between extratropical cyclones and low-pressure systems. Additionally, global warming has led to a fourfold increase in tropical cyclone rapid slowdown events in coastal areas.
Researchers discovered that only observing the flow down to a specific scale is enough to reconstruct the full motion of fluid in two-dimensional turbulence, unlike three-dimensional systems. This finding has significant implications for modeling and prediction in atmospheric and ocean circulation.
Researchers at Newcastle University and the UK Met Office identified a three-layered atmospheric structure associated with heavy rainfall developing within minutes. The findings help explain record-breaking rainfall and flash floods in the UAE and Oman in April 2024, and may aid future improvements in identifying risk.
A recent study has clarified the record-late monsoon onset in Australia, defining its critical impact on water security and cattle industry. The researchers found that local wind conditions over Darwin prevented the upper-level winds from switching to easterlies.
Researchers at the University of Kansas have developed a tornado-forecast system called Warn-on-Forecast System (WoFS) that can predict tornado formation up to an hour before it occurs. The system uses high-resolution simulations and gauges uncertainties, providing guidance on severe weather threats.
Research from the University of Kansas finds that cuts in sulfur emissions from oceangoing vessels have led to a reduction in lightning stroke density along heavily trafficked shipping routes in the Bay of Bengal and South China Sea. The drop in sulfates from ships can cause fewer cloud condensation nuclei, resulting in weaker convecti...
Researchers improve extreme precipitation forecast accuracy using a novel CNOP-I scheme in a convection-allowing ensemble prediction system. The new method captures nonlinear evolution characteristics of weather systems, leading to more accurate predictions and reduced uncertainty.
Researchers Min Chen and Liangchen Guo developed a new system using Fengyun-4B satellite data and regional numerical forecast model RMAPS-ST to improve ultra-short-term solar irradiance forecasting. The system achieved significant reductions in forecast errors, particularly in warm seasons and short-term forecasts.
Recent research reveals that volcanic eruptions can cause unpredictable weather patterns, including volcanic rain, dust storms, and unstable Antarctic air. The study also found that the Antarctic surface atmosphere has become less stable and more prone to gravity waves since the 1950s.
A team of researchers used innovative approaches, including public data and machine learning models, to create more accurate predictions of coastal erosion and infrastructure damage. The project aims to improve storm preparedness and timeliness.
The 106th Annual Meeting of the American Meteorological Society will focus on the intersection of technology and human decision-making in weather, water, and climate sciences. The meeting will feature keynote presentations, Presidential Sessions, and named symposia highlighting key Earth science topics.
Over half of global coastal settlements have retreated from the coast over the past 30 years in response to intensifying climate hazards. Coastal regions in Africa and Oceania lead the retreat, while poorer regions often expand shoreline settlements due to economic needs.
Researchers investigate how adjacent cities exacerbate each other's heat island effects. A study also highlights the need for improved hindcasting infrastructure in climate science. Additionally, a campaign monitors pinned clouds over industrial sources of heat and finds that trees are more reliable against heat stress than buildings.
Researchers at the University of Utah have developed a new model to predict the snow-to-liquid ratio, which varies widely in the Western United States. By training a random forest model on high-quality data from 14 mountain sites, they were able to explain nearly half of the variability in snow density compared to existing methods.
Researchers at UAlbany are exploring the use of stable water isotopes to improve weather and climate forecasts. By analyzing differences in isotope masses, scientists can track moisture movement and understand related processes.
A new study by Purdue University researchers reveals that hurricanes grow in size faster when traveling over locally warm waters, which can help improve daily forecasting of hurricane size and impacts. This discovery has the potential to better model hurricane size in long-term risk models used by industry.
The UK's new weather-health alert system has been deemed successful in supporting vulnerable groups, but minor confusion remains around its color-coded warnings. Researchers identified areas for improvement, including clearer yellow alert scales and simplified alerts for rapid use on the ground.
A new study developed a flood-forecasting AI that can be tuned for any country, reducing errors in national flood prediction programming. The hybrid model combining the AI with the National Water Model was four to six times more accurate, improving forecast accuracy and potential economic impacts of floods.
A new study predicts an increase in western US wildfires sparked by lightning strikes, with 98% of the region seeing more risk days by 2060. The western US is expected to see a significant rise in lightning days, with areas like Oregon and Idaho experiencing up to 12 more days per summer.
The Madden-Julian Oscillation's speed and intensity are influenced by atmospheric stability, which affects regional convection and tropical cyclones. Uneven ocean warming changes MJO behavior, impacting rainfall patterns and climate forecasts.
Researchers from the University of Maryland tracked lightning storms in real-time using NASA's TEMPO instrument, detecting nitrogen oxide emissions that affect climate and air quality. The study reveals how lightning can produce pollutants that travel long distances, influencing air quality far from the original storm.
A new model incorporates rainfall and storm surge, leading causes of hurricane deaths. The proposed scale aims to improve evacuation decisions by providing a more accurate picture of the storm's severity.
New research from Purdue University reveals how moisture influences atmospheric blocking, a phenomenon that often drives heat waves, droughts, cold outbreaks and floods. The study introduces a new mechanism showing that moisture-induced diabatic heating strengthens ridge blocks but weakens dipole blocks.
A new AI system developed by the University of Miami Rosenstiel School can automatically identify and track tropical easterly waves, separating them from other tropical wind patterns. The model combines historical observations with reanalysis data to produce accurate real-time forecasts.
Researchers improved WRF model precipitation simulation by optimizing Grell-Freitas cumulus scheme and Turbulent Orographic Form Drag scheme. This approach reduced overestimation of precipitation across the Tibetan Plateau.
The China Meteorological Administration has demonstrated significant improvements in both track and intensity prediction accuracy for tropical cyclones. The study finds that track forecast errors have reduced, particularly at longer lead times, while intensity forecasts have also shown advancements.
Researchers developed a 3-km resolution model that achieved unprecedented accuracy, reducing track errors to below 100 km over a 120-hour forecast period. The innovative variable mesh refinement strategy balances computational demands with accuracy while targeting key weather systems influencing typhoon motion.
Researchers at ETH Zurich found that mineral dust particles can trigger freezing of cloud droplets, particularly important in northern regions where clouds form below freezing temperatures. This process affects sunlight reflection and precipitation generation, with major implications for climate models.
The ERIC system combines doorbell cameras and AI to analyze rainfall estimation and automatically adjusts irrigation schedules for more precise water use. Researchers estimate users can save up to $29/month in utility costs and 9,000 gallons of water per month with the innovative irrigation system.
Recent research from American Meteorological Society journals predicts a new record for lightning and a decrease in hurricane frequency. Additionally, fire forecasts have been updated to better manage water resources in Colorado's Gunnison River Basin.
Tiny droplets of sea spray can affect hurricane intensity, but measuring their concentration and size is challenging. Researchers at the University of Texas at Dallas are using lab experiments and simulations to develop a machine learning model that incorporates these factors.
A new framework developed by Northwestern University and UCLA scientists integrates various water-related processes with a machine-learning model to predict landslide threats. The framework identifies three main pathways leading to landslides: intense rainfall, rain on already saturated soils, and melting snow or ice.
Researchers developed a novel deep learning-based framework that improves five-day regional weather forecasting accuracy, even with limited data. The method achieved significant improvements in temperature, precipitation, and wind speed forecasts, outperforming mainstream global AI models.
A study found that declining aerosols are up to 2.5 times more influential than greenhouse gases in driving changes in heatwave occurrence in populated areas. Higher levels of aerosols suppressed heatwave exposure by reflecting the sun's rays, but this trend is now reversing due to clean air policies.