Articles tagged with Weather Simulations
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 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 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 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.
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.
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 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 have used computer simulations to show that small adjustments to certain variables can modify weather systems, mimicking the 'butterfly attractor' phenomenon in chaos theory. This approach could lead to weather control technology and prevent extreme windstorms.
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.
A study has shown that wind variations over the southern Red Sea are the main drivers of sea-level extremes, driving levels up and down depending on wind direction. This understanding is crucial for coastal planning and management to mitigate the impact of storm surges and coastal erosion.
A new machine learning study analyzed 10 years of weather data to identify three major categories of weather patterns and their effects on thunderstorms. The study aims to isolate the impact of aerosols, tiny particles suspended in the atmosphere, on storm severity.
A new global variable-resolution model helps meteorologists understand the hydrological cycle in the Tibetan Plateau, revealing complex topography's impact on moisture transport and precipitation. The study shows that resolving topography at a few kilometers improves precipitation simulations by 11%.
Researchers are developing new methods to predict extreme weather events, including building a 'weather dictionary' using word search technology and AI physics. By analyzing patterns and motifs in weather data, scientists aim to better understand the impact of climate change on extreme weather events.
A new study predicts that El Niño events will increase in frequency by 2040, regardless of efforts to reduce carbon emissions. The researchers used state-of-the-art climate models to examine the potential impacts of different emissions scenarios on global weather fluctuations.
A new study questions a widely-held theory that sudden stratospheric warming caused the extreme cold weather in Texas and other parts of the US. The research suggests that the polar vortex's disruption, which occurred six weeks after the initial warming event, was not significant enough to impact the weather.
A $763,930 NSF CAREER Award is supporting OU meteorologist Scott Salesky's project to improve cloud representation in weather and climate models. The research aims to better understand interactions between turbulence and clouds, with potential applications for predicting precipitation and understanding Earth's climate.
A new study reveals a significant shift in the Northern Hemisphere jet stream position, increasing by up to 330 km and speed by 8% during 1871-2011. This shift is linked to warming Arctic winters and decreased temperature gradients, with implications for climate predictions and storm activity.
A new project aims to determine the role of sea ice fragmentation in accelerating Arctic ice-cap retreat. By combining observations, theory, and process modeling, researchers hope to improve climate model accuracy.
Researchers developed a new model to predict storm damage, allowing for effective resource allocation and reduction in societal consequences. The framework can be applied to various infrastructure systems and weather events, promoting better preparedness and response.
A study by University of Zurich researchers reveals Arctic warming causes temperature anomalies and cold damage thousands of kilometers away in East Asia, leading to reduced vegetation growth and smaller harvests. The warmer Arctic winters also trigger extreme winter weather events in the region.
A new $1 million project at UCF aims to understand how raindrops interact with hypersonic shock waves. Researchers will use computer simulations and experiments to predict conditions for safe hypersonic travel. The knowledge gained could prevent damage and improve rocket launch accuracy.
Researchers developed a new avalanche forecasting method using computer simulations of snow cover, which can detect weak layers and identify hazard in a different way. The approach showed consistent results with observed frequencies over 16 years, offering potential to support forecasting in the future.
Scientists use a modified drone with a radiation shield to collect high-quality atmospheric data in polar regions. The study shows that the low-cost drone provides accurate data comparable to radiosondes, expanding the observational network and improving weather forecasts.
Persistent weather conditions are becoming more common in European summer, leading to prolonged heat waves and intense rainfall events. This trend is linked to increased hydro-climatic risks, especially for densely populated regions.
A new MIT study uses climate modeling to reconstruct global hurricane activity, finding a significant increase in North Atlantic hurricanes over the last 150 years. In contrast, global hurricane frequency remains unchanged. The study's findings suggest regional climate variations play a key role in shaping hurricane patterns.
Detailed supercomputer simulations show that the North American monsoon occurs when Mexico's Sierra Madre Occidental mountains divert the eastward-trending jet stream toward the equator, cooling moist tropical air until it condenses and falls as rain. This new understanding will impact forecasts in the region.
The North American Monsoon originates from the interaction of mountain ranges with extratropical atmospheric circulation, deflecting the jet stream and generating a stationary wave. This unique case has implications for models forecasting heavy rainfall events brought by the monsoon.
A new study has found that increasing temperatures and heavy rainfall reduce the prevalence of Varroa mite disease in UK honey bee colonies, while heavy wind reduces its severity. Climate change is predicted to impact the risk of European foulbrood disease, with a hot spot identified in the South West England.
Environmental engineers at Duke University demonstrated that sharp contrasts in small landscape features refine local weather developments and influence climate trends. The study suggests that incorporating detailed data can improve forecast accuracy.
Research finds that overlapping weather extremes increase global economic losses by an average of 20% due to supply shortages and price increases. Richer economies are hit harder, with China experiencing above-average losses of over 27%.
A Brazilian study reveals that forest fires and wildfires modify the freezing process of cloud droplets, altering natural cloud functioning and potentially impacting precipitation. The research used a large dataset to show that aerosols emitted by fires can affect cloud formation in southern Amazonia during the rainy season.
New research from the University of Arizona suggests that a slowdown in the Atlantic Meridional Overturning Circulation (AMOC) will intensify extreme cold weather in the US. The study found that without the AMOC, extremely cold winter weather would become more frequent and severe.
A study found that climate change increased the probability of heavy rainfall by 70% in Brazil's Minas Gerais state, leading to severe flooding, deaths, and massive damage. The extreme event caused over 90,000 people to be temporarily homeless and resulted in significant economic losses.
A team of researchers, led by Scott T. Salesky, will conduct field work in Antarctica to study the impact of katabatic winds on snow transport and ice sheet growth. The study aims to advance understanding of katabatic flows and their effects on regional and global atmospheric and oceanic circulations.
Researchers have assessed climate factors affecting tea growth and quality at nine locations in Kenya and Malawi, predicting substantial increases in heatwave days but declines in cold nights. The study suggests tailored interventions and plant breeding programs can help producers adapt to future climates.
A new study incorporated water vapor isotope compositions into a general circulation model to improve forecast accuracy by several percentage points. The Isotope-incorporated Global Spectral Model (IsoGSM) demonstrated improved modeling of air temperature and specific humidity.
Researchers at Stanford University have discovered the physical mechanism behind icy plumes that precede severe thunderstorms and potentially deadly tornadoes. The study reveals a hydraulic jump phenomenon triggered by fluid obstacles in the atmosphere, leading to rapid water vapor injection into the stratosphere.
Naoko Sakaeda, a University of Oklahoma weather researcher, has received a $652,000 NSF CAREER award to study tropical meteorology and improve global weather forecasting. Her research aims to better understand the dynamics of tropical clouds and precipitation at various scales.
Climate scientists have developed an improved three-month preseasonal forecast for the Indian monsoon onset using machine learning. The predictions show accuracy of +/- 4.8 days and cover climate changes since 1948, offering a promising basis for future research.
The use of additional Metop-C and Fengyun-3 C/D data has been shown to improve regional weather forecasts by filling existing data gaps. By incorporating more PMW satellite radiances, scientists can enhance the quality of forecast models, leading to better short-range predictions.