Articles tagged with Weather Forecasting
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Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
Aardvark Weather, a fully AI-driven system, delivers accurate forecasts tens of times faster and using thousands of times less computing power than current systems. The new approach transforms weather forecasting, providing bespoke forecasts for specific industries or locations.
Researchers developed a numerical tool to quantify sunlight intensity and its influence on plant growth, enabling accurate predictions of sunlight patterns. The model can help farmers optimize greenhouse conditions and planting schedules, leading to improved crop yields.
The University of Texas at Arlington (UTA) has been awarded a $1 million NASA grant to develop safety systems for drones and unmanned flying vehicles. The project aims to create an adaptive safety assurance architecture through extensive simulations and experimental testing, making future advanced air mobility vehicles smarter and safer.
A new study shows that climate change affects short-term precipitation events lasting hours by significantly increasing their frequency, while longer-term rainfall events lasting days are influenced by global weather phenomena like El Niéo. This distinction is crucial for assessing the risk of flooding in different regions.
The PUNCH spacecraft will study the solar corona and track space weather events in three dimensions for the first time. The constellation includes four small suitcase-sized spacecraft that will provide a clear view of the Sun's outer atmosphere, allowing scientists to discern the exact trajectory and speed of coronal mass ejections.
Climate change drives large increases in electricity demand and costs in Texas due to extreme temperatures. Meanwhile, atmospheric rivers become more frequent, larger, and moister globally. Diagnostic studies also predict malaria outbreaks with five-month lead time using sea-surface temperature anomalies.
The US weather enterprise faces significant risks due to federal science funding cuts, which could lead to reduced weather forecasting accuracy and increased vulnerability to hazardous weather. The value of weather information to the US economy exceeds $100 billion annually.
Research in Beijing reveals that differences in wind fields and thermodynamic conditions hinder or enhance thunderstorm cluster movement, with cold pools acting like topographical features to strengthen convergence. The study analyzed a specific merger process using simulation data from the Weather Research and Forecasting Model.
A new study projects that tropical cyclone numbers in the Atlantic could double compared to 1970s levels over the next decade. The total energy of these storms is also predicted to increase dramatically, with storm energy rising to twice its 1970s levels in the North Atlantic.
A new study improves solar power forecasts by applying machine learning and post-processing techniques to weather models, revealing the time of day as a crucial factor for accuracy. The research suggests that including hourly data in algorithms can significantly enhance forecasting results.
Researchers investigated the impact of AI weather models' lateral boundary conditions on convective-scale ensemble forecasts, finding comparable performance to traditional models. Reducing the vertical resolution of these conditions led to inferior forecast results.
MIT researchers have developed a new approach to assess predictions with a spatial dimension, leading to more accurate forecasts in spatial prediction tasks. The new method provides better validations than classical methods, especially in cases where assumptions about data independence break down.
A study found that excessive Tibetan Plateau spring warming was the primary factor driving catastrophic June 2024 heavy rainfall in southern China. The research used the ESM to simulate extreme warm temperatures over the TP and heavy rainfall in S. China, reproducing approximately 55% of the observed anomaly.
A recent study has employed machine learning algorithms to improve the accuracy of flood season rainfall predictions. The findings show that combining climate system numerical models with ML-based correction methods results in substantial improvements, increasing prediction scores by up to 7.87%.
A new contrastive learning model has been developed to forecast cyclone rapid intensification (RI) with high accuracy, reducing false alarms by a factor of three compared to existing techniques. The model achieved an impressive 92.3% accuracy when tested on data from the Northwest Pacific between 2020 and 2021.
Researchers analyzed tropical storm-related precipitation to understand its impact on local water resources and provide insights into climate predictions. The study aims to help communities prepare for extreme storms and manage water resources before and after the storms.
Researchers have identified a critical link between tropical ocean temperatures and rainfall patterns in the Middle East, shedding light on the complexities of forecasting seasonal weather. The study found that positive phases of the El Niño Southern Oscillation and Indian Ocean Dipole significantly increase rainfall, while negative ph...
Researchers introduced a novel approach to enhance reservoir computing, incorporating a generalized readout that offers improved accuracy and robustness compared to conventional methods. The new method uses a nonlinear combination of reservoir variables to uncover deeper patterns in input data.
NRL oceanographers received a Group Achievement Award from NASA for their collaboration with S-MODE, providing real-time ocean model forecasts and glider guidance. The project successfully observed and forecasted ocean features that are too small to see from space, allowing scientists to better understand the global earth system.
A team led by Penn State's College of Information Sciences and Technology will use computer vision and machine learning to accelerate weather forecast processing, improving predictions and reducing delays. The research aims to integrate satellite data into current forecasting models, enhancing accuracy and speed.
2024 saw exceptional rainfall and flooding due to El Niño in winter 2023/24. Human-induced climate change exacerbated these events, causing socioeconomic impacts. Improved forecasting, warning dissemination, and 'climate-resilient' approaches are crucial for mitigating extreme event effects.
A team led by the University of Tokyo has created a nearly 20-year-long dataset of the entire atmosphere, enabling new research on previously difficult-to-study regions. The dataset spans multiple levels of the atmosphere from ground level to the lower edge of space and could improve climate modeling and seasonal weather forecasting.
Researchers at the University of New Hampshire developed an AI-powered algorithm to categorize over 706 million aurora images from NASA's THEMIS data set. This labeled database can help scientists better understand and forecast geomagnetic storms that disrupt vital communications and security infrastructure.
Early online research reveals associations between snowmelt timing, wildfires in Alaska, and rapidly intensifying tropical cyclones. The impact of climate patterns on extreme weather events is a growing concern.
A special collection addresses solar energy's environmental and technical issues, combining atmospheric science and solar engineering expertise.
The 105th Annual Meeting of the American Meteorological Society will address key issues in weather, water, and climate. The meeting features a Presidential Forum on physical, social, cultural, and economic impacts of climate change, with experts from the region of the Mississippi River Delta.
A team from the University of Cambridge has developed a model to predict desert locust swarms, enabling national agencies to respond quickly. The model uses weather forecast data and computational models to forecast locust swarm movements both short and long-term.
A new study enhances phenology predictions using a 150-year-old dataset, revealing that plant species in the US are flowering three to four weeks earlier. The research used historical observations from a rediscovered 19th-century report to improve forecast accuracy.
When two tropical cyclones collide in the Indian Ocean, they can intensify considerably, leading to extreme interactions between the ocean and atmosphere. The study found that effects occurred that have only been observed with much stronger cyclones, including a cooling effect of three degrees Celsius and upwelling of deep water masses.
Researchers at the University of Colorado Boulder have discovered that relatively warm and sunny days may help to trigger major dust storms on Mars. The team found that roughly two-thirds of these storms are preceded by a sharp rise in surface temperatures, which can lead to explosive weather patterns.
Researchers used CT scans to examine giant hailstones formed during a 2022 thunderstorm in Catalonia. The study revealed irregular internal shapes and heterogeneous growth patterns, overturning previous assumptions about hailstone formation.
The US Naval Research Laboratory (NRL) will present its latest advancements in Earth and space sciences at the American Geophysical Union (AGU) Conference. NRL researchers will share their work on topics such as atmospheric data assimilation, ocean sciences, and geostationary ocean color.
A new study from Tel Aviv University uses smartphone data to predict wildfire risk, overcoming individual device errors by averaging large amounts of public data. The method provides valuable insights into wildfire evaluation, especially in remote areas lacking traditional weather stations.
Researchers at University of Liverpool develop new method to measure ocean memory, revealing the North Atlantic Ocean has a nearly two-decade memory. This surpasses previous estimates and highlights the importance of ocean circulation in climate system predictability.
A new machine learning algorithm reconstructs wind fields quickly and accurately, even with limited observational data. This enhances storm forecasting and hazard preparedness by providing valuable data on a tropical cyclone's intensity and potential impact.
Researchers developed a machine-learning tool that provides accurate predictions for flood-prone areas, using historical data and weather-based predictors. The model can predict short-term river discharge with high accuracy, giving real-time data on water movement through the river.
Researchers analyzed data from southern Israel to find significant electric field changes during heavy precipitation, suggesting early indicators for extreme weather. The study highlights the potential of incorporating electric field observations into weather monitoring systems for enhanced nowcasting capabilities.
The new system uses AI technology combined with Earth observation data to detect risks of events that might trigger forced displacement, delivering timely alerts ahead of emergencies. It will help humanitarian actors plan and respond more effectively, minimizing response times and avoiding duplication of efforts.
A global convection-permitting model developed by a team from USTC accurately predicted the 2020 plum rain event in Japan, capturing its intensity and location. The model improved forecast accuracy at high resolutions, aligning well with observational data.
The Macrosystems EDDIE modules have been effective in building student and instructor quantitative literacy and data science skills in ecological forecasting, reaching over 35,000 students globally. The modules aim to introduce students to core concepts of forecasting and complement educators' work teaching ecological concepts.
A new equation developed by a UChicago scientist provides a powerful framework for understanding the processes driving atmospheric rivers. The integrated vapor kinetic energy (IVKE) model sheds light on key factors contributing to extreme weather patterns, including heavy rain and strong winds.
Researchers developed a statistical seasonal forecasting model to predict typhoons landing on Taiwan Island by mid-May, achieving an accuracy rate of 98% for the period 1979-2022. The model utilizes four pre-typhoon-season environmental predictors and will benefit disaster prevention and mitigation efforts.
Researchers at FSU and South Korea have improved hurricane intensity forecasting by accounting for the impact of sea spray on storms. By analyzing data from hurricane hunter airplanes, they found that sea spray increases heat and moisture in the atmosphere, leading to more accurate intensity forecasts.
Researchers estimate the dollar value of weather information to be over $100 billion, highlighting its significant economic impact. The study also explores lightning suppression and a unique 'storm generator' technology that can create thunderstorms in Chinese desert.
A team of OU scientists, led by Nathan Snook, will use deep learning techniques to analyze numerical simulations of tornadoes. The goal is to improve tornado forecasting by identifying key factors that influence their formation.
A new study shows that urban forests within walkable distance from residential areas are crucial in reducing heat-related health risks. Researchers found that nearby forests have a pronounced impact on reducing mortality risks, particularly those within 1 kilometre of residential areas.
Research explores changes in global precipitation patterns and how they relate to climate change. Bird nests are found to predict rainfall in rural India, while heat stress is increasing in the southeastern US, particularly at night. European 'bomb' storms may intensify due to warming climate.
A new model developed by scientists can predict intense, localized heavy rainfall events caused by rapid air rises in clouds. This breakthrough offers potential to improve public safety and preparedness through early warning systems.
A WVU statistician is developing new methods to analyze curve-type data for predicting extreme weather patterns, financial turmoil and pollution levels. The research aims to create innovative tools for understanding high-resolution data and addressing regional risks in mountainous states like West Virginia.
The American Meteorological Society recognizes outstanding contributions to the weather, water, and climate community with its 2025 Awards and Honors. This year's honorees have made significant impacts on scientific advancement and public safety, including warnings of wildfires and refinements in modeling techniques.
Researchers have discovered that changes in the ocean's mixed layer are the primary force behind Atlantic Multidecadal Variability (AMV) in the tropics. This phenomenon influences weather patterns across North America, Europe, and Africa, affecting hurricane activity and rainfall in regions like the Sahel.
Research on wind farms reveals their potential to slow tropical cyclone winds and reduce precipitation. Additionally, a study found that people experiencing homelessness are four times more likely to suffer temperature-related illnesses, such as heat stroke, than the general population.
A new study found that strong El Nino events make it easier to forecast European winters, allowing for more accurate predictions of temperature and precipitation patterns. The research team analyzed 30 years of winter forecasts from seven different prediction systems and identified common factors influencing predictability.
Researchers found that turbulent movements of air in clouds play a key role in the growth of droplets and initiation of rain. Simulations with turbulence showed rain formed around 20 minutes earlier and with seven times more rainwater than simulations without turbulence.
A new study finds that human-induced climate warming has driven increasing precipitation variability over much of the globe, with hotspots in Europe, Australia, and eastern North America. The amplified variability poses significant threats to climate resilience, infrastructure development, agriculture, and ecosystems.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
The University of Maryland has been awarded a five-year, $388 million cooperative funding agreement with NOAA to conduct collaborative research in Earth system science. The Cooperative Institute for Satellite Earth System Studies (CISESS) will continue its national leadership in this field.
Researchers have developed a new method to simulate Convectively Coupled Kelvin Waves in weather forecast models, which could enhance accuracy for predicting hurricanes and heavy rainfall. The study found that current models poorly simulate these waves, indicating a need for future improvements.
A recent study highlights the need for effective contingency plans and local community involvement in disaster response to mitigate losses. The analysis of landslides in São Sebastião, Brazil, revealed a breakdown in the early warning system, resulting in significant economic and human costs.
A new model developed by researchers at the University of São Paulo combines physical parameters and machine learning to predict storm tides. The model uses physics-informed machine learning, which harmonizes physical models with measured data to produce more precise forecasts.