Articles tagged with Weather Simulations
Researchers developed an advanced terrain-smoothing technique to simulate complex weather phenomena, capturing detailed flow features and turbulence. The targeted smoothing method improves the accuracy of downslope windstorm predictions in urban areas, holding promise for more accurate weather forecasts.
The PhenoSphere enables detailed analyses of performance-related trait expression and causal biological mechanisms in plant populations exposed to weather conditions. It simulates clouds, wind speed, and direction, and applies water and fertilization automatically, allowing for systems biology analyzes and hypothesis testing.
A new model shows London's cooling demand grew by 5% per year between 1980 and 2022 as summers heat up, highlighting the need for preparedness and energy efficiency measures. The Demand.ninja model can also predict potential benefits of behavior change, such as reducing natural gas consumption and CO2 emissions.
Climate experts predict a decline in tropical storm frequency by 50% by 2050 across the Ganges and Mekong basins, but an increase in intensity, with high-resolution models showing significant increases for intense storms. This shift could impact disaster risk mitigation and climate adaptation strategies.
Researchers at the University of Melbourne have developed a new simulation model that can predict flooding during an ongoing disaster more quickly and accurately. The Low-Fidelity, Spatial Analysis and Gaussian Process Learning (LSG) model can produce predictions as accurate as advanced models but at speeds 1000 times faster.
Researchers at the University of Oklahoma are receiving US DOE funding to explore atmospheric processes that lead to extreme weather events. The project aims to better understand blocking patterns and their impact on climate change, ultimately informing local, state, and national leaders on how to prepare for these events.
A deep learning approach has unveiled a significant change in the characteristics of global daily precipitation for the first time. The research found that on more than 50% of all days, there was a clear deviation from natural variability in the daily precipitation pattern since 2015.
Researchers at the University of Surrey are developing thermo-active road solutions that could reduce carbon emissions and improve road safety. The new technology uses geothermal energy to regulate road surface temperatures, reducing damage from extreme weather.
A new study reveals that volcanic eruptions can temporarily weaken the Pacific Walker Circulation, a major driver of global precipitation. The recent strengthening of this circulation suggests that aerosols from human activity may have an opposite effect on its behavior.
A new climate modeling method called ensemble boosting can simulate a large set of extreme but plausible heat waves, providing a worst-case scenario for planning and preparation. This method helps prepare for the potential loss of tens of thousands of lives in extreme heat waves.
Researchers at UC Santa Barbara's Climate Hazards Center have made significant advancements in predicting droughts and food shortages in the Eastern Horn of Africa, enabling agencies to take effective actions and provide humanitarian assistance. These early warnings have helped prevent devastating famines and saved millions of people f...
A research group from Nagoya University simulated clear air turbulence using Japan's fastest supercomputer. They found that wind speed disturbances occur due to the collapse of Kelvin-Helmholtz instability waves, creating turbulence in the absence of visible clouds or other atmospheric disturbances.
A study of maize hybrid varieties over 81 years found that while maize's tolerance to moderate heat stress has improved, its tolerance to severe heat stress has decreased. This shift in tolerance could have significant implications for climate change's impact on agriculture.
Researchers have identified an operational strategy that can reduce shipping emissions by up to a quarter by combining modern sail technology with efficient routing systems. The study found that this approach can provide greater assurances of carbon savings by mitigating the impact of unpredictable weather patterns.
Researchers analyzed satellite data from 1967 to 2021, finding nearly twice as many grids with declining snow cover as advancing ones. Snow presence is decreasing in Arctic and southern latitudes, while some areas like Eastern Canada see an increase.
A study using state-of-the-art climate models found that two-thirds of the selected CMIP6 models showed positive correlations between Asian-Pacific Oscillation (APO) and Southeast China precipitation, consistent with observations. The best-performing model ensemble (BMME) simulated the APO-associated precipitation and atmospheric anoma...
Research from Cornell University estimates that wildfire smoke can cause between 4,000 and 9,000 premature deaths and cost between $36 to $82 billion per year in the United States. Metropolitan regions near fire sources are likely to experience a large health burden and economic loss.
Researchers at RIKEN have demonstrated a method to prevent severe weather events by inducing small perturbations in the Lorenz 96 model. This approach utilizes the inherent chaos in weather systems to limit extreme events, which become more common due to climate change.
The UK is investing £11 million to improve forecast accuracy for extreme weather events like storms, floods and droughts. Scientists are focusing on atmospheric turbulence to create more detailed models.
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.
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.
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 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.
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.
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.
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.