Articles tagged with Weather Simulations
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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.
A new study found that the 2022 snow season had the highest snowpack dust concentrations of any year since observations began in 2009, accelerating snowmelt by 17 days. The dry lakebed of the Great Salt Lake contributed the highest dust emissions per surface area, threatening Utah's water supply.
Researchers highlight the importance of human observation of storm events to understand wet weather's impact on the natural world. Direct observation can enhance understanding, incite curiosity, and strengthen bonds with nature, enriching environmental education and inspiring research.
Researchers found that controlled burns in key areas of northern California, western Oregon, and eastern Washington could drastically reduce wildfire smoke exposure throughout the entire western U.S. This is due to prevailing winds carrying smoke across the continent and abundant dense vegetation fueling smokey fires.
A Brazilian study combined urban expansion and land-use changes with hydrodynamic models to identify flood-prone areas of cities. The methodology, validated using actual data for São Caetano do Sul, can be used by other cities to devise public policies and make decisions to address flooding impacts.
A research team studied the source of hazardous March and April 2023 dust storms in China, finding that Mongolia is the main dust source affecting northern China's weather. The team used advanced models and machine learning techniques to forecast dust events, providing a reference for addressing global dust storm hazards.
A new study suggests that modular production techniques can minimize construction delays caused by weather, resulting in cost savings for builders. The research estimates that using modular construction can reduce costs by approximately AUD$40,000 on a $6.4 million project.
Research finds extreme precipitation days will become more frequent, with up to 20-30% increase in moisture release. The LOCA2 climate projections cover the lower 48 US states, southern Canada, and northern Mexico, providing granular-level information for local planners.
A study by GIST researchers found that Arctic warming is correlated with severe winters in East Asia and North America. The 'Warm Arctic-Cold Continent' phenomenon will persist but become more difficult to predict under warmer climates.
Researchers at NIST have developed a new method of digitally simulating hurricanes using AI techniques, accurately representing the trajectory and wind speeds of real storms. The simulations can help develop improved guidelines for building design in hurricane-prone regions.
A machine learning model developed by Colorado State University researchers has been tested with forecasters at the Storm Prediction Center to improve medium-range severe weather forecasts. The tool provides a probabilistic measure of hazardous weather events, such as tornadoes and hail, four to eight days in advance.
A new high-resolution simulation shows different pollutants accumulate along highways and over Lake Michigan, affecting communities in distinct ways. The study provides a detailed, neighborhood-by-neighborhood estimate of air pollution, highlighting areas that need targeted solutions to address poor air quality.
Researchers at Lancaster University have discovered how energy disappears in quantum turbulence, a crucial step towards mastering this phenomenon and its applications. The study reveals the role of Kelvin waves in transferring energy from macroscopic to microscopic length scales.
A new technique combining underused satellite and radar data in weather models may improve thunderstorm predictions by capturing a more accurate picture of initial conditions in the boundary layer. This technique showed promise in improving forecasts of convection initiation, several hours before the thunderstorms occurred.
A new model investigates long-term temporal trends in synoptic-scale weather conditions favoring significant tornado occurrence over the central United States. The study suggests that specific weather patterns have contributed to increased tornado activity in this region.
A study published in Nature Ecology & Evolution found over 74,000 active fires in the Brazilian Amazon during the period, mainly due to recent deforestation. The research also revealed a sharp increase in forest fires on public lands and areas of Indigenous reservations.
Tiantian Yang aims to develop an integrated solution addressing precipitation variability and uncertainty with a novel AI and data mining tool. His research will be tested in collaboration with U.S. agencies, aiming to better inform water management decisions.
Researchers found that global wave models can vary in their estimates of extreme wave heights by up to 20 feet, which can lead to underprotected areas during extreme events. The study emphasizes the need for considering multiple models and modern observational data to better assess offshore and coastal risks.
A team of researchers from Xi'an Jiaotong-Liverpool University and other institutions has identified a flexible and user-friendly model for predicting flood frequency in a changing environment. The fractional polynomial-based regression method is more effective than existing models, which often fail to account for factors like climate ...
A new research project led by Associate Professor Marta Victoria aims to describe periods of extreme weather and design strategies for reliable renewable energy systems. The project, funded with DKK 6.2 million, will run for four years and focus on predicting and adapting to multiple adverse events.
A University of Houston researcher has developed a method to describe complex systems using the least number of variables possible, reducing complexity from millions to just one. This advancement speeds up science with efficiency and ability to understand and predict natural system behavior.
Researchers found that the Southern Hemisphere is stormier than the Northern, with a 24% difference attributed to ocean circulation and mountain ranges in the Northern Hemisphere. The study also shows that storminess has increased since the 1980s, consistent with climate change forecasts.
The Southern Hemisphere is stormier than the Northern due to ocean circulation and mountain ranges in the Northern Hemisphere. Since the 1980s, storminess has increased in the Southern Hemisphere, while the Northern Hemisphere has seen negligible changes.
A study by the University of Reading found that up to 20-25% of UK land may be suitable for growing high-quality Chardonnay still wines by 2050. The regions with the best conditions are expected to be South East England, East of England, and Central England.
Researchers found a strong association between hotter weather, more sunshine, and higher volumes of polytrauma CT scans. The study used machine learning algorithms to forecast daily polytrauma CT occurrence, predicting 73% of high-demand days and 83% of low-demand days.
A new model reveals that warmer Arctic Ocean evaporation transports moisture south, leading to increased snowfall in northern Eurasia. The study sheds light on the mechanism of this phenomenon and its impact on severe weather events.
A research team at UNIST has identified subtropical low cloud feedback as a key mechanism driving teleconnections between the Southern Ocean and tropical precipitation. Their findings suggest that this impact is stronger than previously thought, with implications for mid-latitude climate predictions.
New research finds climate change could lead to more frequent lightning strikes over mountains and Northern Europe, triggering increased wildfire risks. However, relatively fewer lightning hazards are expected over populated areas of Central Europe.
Researchers developed an AI-based model that combines artificial intelligence and weather forecast models to predict extreme wildfire danger with high accuracy. The new method can produce forecasts of extreme fire danger out to one week at finer scales (4km x 4km resolution), increasing its utility for fire suppression and management.
A new study led by PSU researchers finds that climate models can accurately simulate large-scale weather patterns over the Pacific Northwest. The study uses data from the state-of-the-art CMIP6 model and machine learning techniques to compare model outputs with historical observed data, finding a strong match between the two.
A new study by Prof. Zhe-Min Tan and colleagues improves typhoon intensity forecasts using a regional ensemble Kalman filter, producing more accurate results than global models for short forecast lead times. The regional forecasts show better performance in predicting typhoon intensity, especially with higher spatial resolution.
A team of researchers has developed a novel technique using high-energy muon particles to track and visualize tropical cyclones. This technique, called muography, creates X-ray-like images of large objects, including atmospheric weather systems. The resulting images reveal density variations essential to understanding how cyclones work.
Researchers used machine learning algorithms to optimize climate models, increasing their accuracy and detail. By applying Generative Adversarial Networks (GANs) to climate simulations, the team was able to improve the models' ability to represent extreme precipitation events.
A team of scientists from the University of Exeter has made a key breakthrough in predicting fluctuations in the rotation of the Earth and the length of the day. They used mathematical modeling to show that changes in the atmosphere can be predicted more than a year in advance, linking geodesy with climate prediction.
Researchers at Ohio State University have developed a new machine learning method called next-generation reservoir computing that can learn spatiotemporal chaotic systems in a fraction of the time. The algorithm is more accurate and uses less training data, making it easier to predict complex physical processes like Earth's weather.
Researchers used counterfactual history to analyze the 2011 cloudburst, demonstrating a clear correlation between heat in the atmosphere leading up to the event and its intensity. The study shows that a century of human-caused temperature increases doubled the risk of similar or stronger cloudbursts in the future.
Researchers found climate models are less accurate in projecting how tropical cyclones will affect individual coral reefs, highlighting the importance of targeted conservation efforts. The study urges caution when using climate models to identify vulnerable reef communities to storm damage.
A new study finds that climate data, particularly UV radiation levels, can help accurately model the spread of COVID-19. The research analyzed data from 196 countries and found that high UV radiation levels are strongly associated with reduced COVID-19 transmission rates.
Researchers create new 'roadmap' for turbulence by analyzing weak turbulent flow between two independently rotating cylinders. They discover that turbulence follows a predictable pattern of recurrent solutions, which explain the emergence of coherent structures in turbulent flows.
Researchers found that non-growing season nitrous oxide emissions in the Midwest can account for up to 60% of annual emissions. Environmental factors such as precipitation and temperature drive these emissions, with more intensive freezing causing increased emissions in the southeastern Midwest.
Physicists at Ural Federal University developed a mathematical model to predict El Niño's behavior, accounting for wind, humidity, temperature, and ocean currents. They found that stronger winds can cause unpredictable weather phenomena.
Researchers in Japan have demonstrated that incorporating radar data from the Antarctic Syowa Station enhances atmospheric parameter reproducibility, improving mid-latitude cyclone forecasts. This sustainable approach reduces environmental waste and error uncertainty.
Researchers at Hebrew University of Jerusalem discovered that coarse sea spray significantly reduces the amount of lightning in storm clouds. The study found that aerosols larger than 1 micron, or coarse sea spray, inhibit lightning by up to 90%, while smaller aerosols actually increase lightning and affect rainfall.
Researchers at Princeton University used artificial intelligence to simulate ice formation by individual atoms and molecules with quantum accuracy. This breakthrough enables tracking of hundreds of thousands of atoms over longer timespans than previous simulations.
Researchers from Aarhus University are developing a new approach to turbulence modelling using physics-constrained machine learning to accurately simulate complex turbulent systems. The goal is to reduce computational costs while maintaining accuracy, enabling more efficient designs and predictions in various fields.
Aerosol particles in the atmosphere have a bigger impact on cloud cover than previously thought, increasing it by approximately 10%. Clouds hold more water before rainfall occurs due to smaller and more numerous droplets, leading to reduced precipitation. The study uses satellite data and machine learning to improve climate models.
Climate change is intensifying Western US wildfire activity, resulting in taller smoke plumes and more aerosols injected into the atmosphere. The study models 4.6 million smoke plumes and finds that maximum plume height increased by an average of 320 ft per year in four regions, with some areas experiencing a 750 ft per year increase.
Researchers identified peak emergence times for 15 problematic weed species in the Northeast, revealing that waiting till after June 1 can avoid common ragweed. Flexible crop rotations and weather-based predictions can help farmers plan ahead and control weeds more effectively.
Researchers found that wetter pre-growing seasons reduced soil nitrogen through leaching, but applying more fertilizer can mitigate this effect. The model also showed that cold pre-growing season temperatures limited early growth in ways that affected yield potential, making extra fertilizer less effective.
A study published in Nature Geoscience found that clouds likely prevented oceans from being completely covered by ice, allowing life to survive. The research used global climate models and an idealized energy balance model to investigate Cryogenian climatic conditions, revealing the importance of clouds in predicting climate changes.
A recent study by Colorado State University economists and NOAA found that improved wind forecasts result in significant energy savings for consumers. The research shows a net gain of over $150 million in annual savings through better predictions of wind patterns.
Researchers found that improved wind forecasts resulted in at least $384 million in energy savings for US consumers over the last decade. The study used NOAA's HRRR model to compare forecast accuracy and quantify cost savings.
Researchers from The University of Tokyo developed a novel machine-learning approach to predict local precipitation with high accuracy. By recognizing complex relationships in meteorological data, they created a bias correction method that produced accurate hourly estimates of precipitation.
The world's ocean is losing its year-to-year memory due to global warming, making it harder to predict ocean conditions. This decline in ocean memory will have significant impacts on fisheries management, population estimates, and land-based weather forecasts.
Scientists developed a method to estimate summer rainfall in the Southwest months in advance, performing well in Arizona. By analyzing lower atmosphere moisture, they created monthly forecasts to aid reservoir storage and water allocation decisions.
A team of scientists has developed a solution to accurately simulate how the atmosphere works by linking large- and small-scale simulations. This helps model winds, transport of pollutants, climate projections, and weather forecasts with greater accuracy.
Researchers predict widespread increases in flash flooding events across the US, with a predicted 10% increase in the Southwest and 8.6% in the central US. Climate change may lead to more frequent and intense flash floods, requiring improved infrastructure and early warning systems.
Researchers used satellite spectral albedo data to develop a scheme that optimizes albedo parameters at the local level, significantly reducing model albedo overestimation and improving snowfall simulations. This improves the accuracy of water cycle modeling on the Tibetan Plateau.
Tel Aviv University researchers develop innovative model that explains wave formation, tested in complex experiments. The model takes into account all unstable harmonics and limitations of previous models, providing high reliability for describing physical situation.
A $150,000 grant from the NV Energy Foundation will support DRI's development of a Weather and Research Forecast model to simulate weather, fire, and smoke for firefighting operations. The tool will provide critical air quality forecasts and risk assessment for specific locations.
Researchers at RIKEN Center for Computational Science used computer simulations to show that extreme weather phenomena can be controlled by making small adjustments to variables in the weather system. The study's findings promise multiple applications, including preventing and mitigating extreme windstorms.