Articles tagged with Weather Forecasting
Scientists at Max Planck Institute for Dynamics and Self-Organization have challenged long-held assumptions about turbulent flows, finding deviations from established scaling laws in highly idealized environments. This discovery has implications for understanding turbulence in engineered flows, weather forecasts, and climate models.
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.
A new computer model forecasts yield for four key crops in the southeastern US, drawing on climate, groundwater, and agricultural data. The tool helps farmers and water resource managers identify ways to maximize crop yields while efficiently utilizing water and energy.
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.
Scientists at Rice University found a natural 150-day cycle in the north-south oscillation of atmospheric pressure patterns, influencing hemispheric-scale precipitation and ocean surface wind stress. This discovery challenges conventional wisdom about atmospheric organization and has implications for climate modeling.
A new study from Tufts University predicts a significant increase in extreme temperatures affecting wheat yields in the US and China. The research warns of potentially disastrous consequences for global food supplies if crops are impacted by heat stress, which can occur at temperatures above 27.8°C.
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.
Using satellite data from near-real-time (NRT) sources can significantly improve the accuracy of solar wind forecasts, with a potential increase of nearly 50%.
Research finds that flash droughts are becoming more frequent due to human-caused climate change, posing a major challenge for climate adaptation. The transition to flash droughts is predicted to accelerate in a warmer future, with irreversible impacts on ecosystems.
Researchers developed AI predicting heatwaves using 'deep learning' and statistical models, providing a probabilistic approach up to a month before arrival. The technology is trained on 8,000 years of weather data and combines with rare event simulation algorithms for improved forecasts
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 developed a high-speed prediction model combining physical simulations and machine learning, achieving high accuracy without compromising computation time. The technology uses correspondence between input physical conditions and abstract data space handled by machine learning algorithms.
Researchers developed a SMART approach to engage with communities in developing real-time early warning systems for floods, combining meteorological data with social factors. This approach aims to improve protection for vulnerable people and properties, particularly in mountainous regions.
Researchers at Oregon State University found a relationship between surface gravity waves and infragravity waves that fuel sneaker waves. Longer waves with more energy can run further up the beach, but not all long waves turn into sneaker waves.
A study using tree rings reveals a 400-year trend of increasing droughts and floods in the Kabul River Basin, with severe events becoming more frequent. The research suggests that climate change is intensifying hydrological cycles, leading to devastating consequences for natural resources management.
A team of scientists reviewed the effectiveness of reanalysis data products for studying the West African climate. They found that ERA5 achieved considerable progress in reducing biases and improving representation compared to its predecessor, ERA-interim.
Researchers from Leibniz Institute for Tropospheric Research and European Space Agency use Loon project data to validate Aeolus satellite wind measurements, finding almost bias-free results. They recommend increasing vertical resolution for future wind satellites to improve accuracy.
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.
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.
The strongest Arctic cyclone ever observed poleward of 70 degrees north latitude caused a 30% greater loss of sea ice than previous records, with waves reaching up to 100 kilometers towards the center of the ice pack. Researchers suggest that existing models underestimate the impact of big waves on ice floes in the Arctic Ocean.
A new study models likely future cliff retreat rates of two rock coasts in the UK, finding that rock coasts are likely to retreat at a rate not seen for 3,000-5,000 years. The researchers predict that rock coast cliffs will retreat by at least 10-22 meters inland due to accelerating sea level rise.
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 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 workshop on flood risk governance in Brazil and the UK identified research gaps, including local data, integration systems, and visualization tools. Innovative initiatives are being developed using tools such as an app that empowers citizens to input data and improve disaster prevention.
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 UiT The Arctic University of Norway have developed a new dataset measuring Arctic sea ice thickness throughout the year. This breakthrough allows for safer shipping in the Arctic and more accurate weather and climate forecasts.
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.
A case study in Laos explores the application of climate services for agriculture, demonstrating improved agricultural planning and decision-making through ICT-based platforms like LaCSA. The project successfully co-created agromet services with local stakeholders, increasing capacities and promoting resilience to climate risks.
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 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 built a two-stage warning system predicting solar flares within 48 hours via k-means clustering and neural networks. The model improved recall while increasing precision, but lost some positive sample information, affecting prediction accuracy.
A study found that winter storms in north-west India are 31% more frequent and 45% more intense during positive NAO phases, leading to increased moisture and precipitation in the western Himalayas. This could impact crop yields and water security in states like Jammu and Kashmir.
A new study revisits a deadly 2021 ultramarathon event in China, highlighting the need for ultra-high-resolution forecasts in mountainous terrain. The model accurately recreated the storm conditions and offered greater insight into what may have happened that day.
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.
A survey of Texas coaches and athletic officials revealed that many are aware of the risks of outdoor workouts during extreme heat, but some don
A recent study found that greenhouse gases are the primary reason for increased temperatures and will likely continue to be the main contributing factor. Simulations show that extreme heatwave events will increase by over 30 percentage points in coming years, primarily due to human-caused emissions.
A study by Brazilian, Chinese, Australian, and German researchers found that North Atlantic temperature can predict reduced rainfall and intense droughts in the Northeast region of Brazil. The findings suggest a more persistent influence of the North Atlantic than previously thought.
Researchers evaluated the prediction of a record-breaking heatwave in the Pacific Northwest, finding that forecasts were able to accurately predict the event. The study highlights the importance of trust, preparedness, and early action when extreme weather is forecasted.
A study predicts UK wine production will increase due to climate change, with areas in England and Wales becoming suitable for sparkling wine grape varieties. The research highlights the potential for high-quality still wine production, particularly for Pinot Noir and other disease-resistant varieties.
Researchers found a correlation between Arctic ozone depletion and extreme weather events in the Northern Hemisphere. Simulations suggest that reduced ozone levels contribute to warmer temperatures and droughts in central Europe, while wet conditions prevail in polar regions.
Researchers analyzed the Hunga Tonga-Hunga Ha'apai volcano's eruption to understand its effects on atmospheric waves. The blast provided an unprecedented view of atmospheric waves, allowing scientists to better predict the weather and climate.
Researchers created a dataset quantifying trends of evaporative water loss from 1.4 million global lakes and artificial reservoirs, revealing a 15.4% increase in long-term lake evaporation volume. This finding highlights the importance of accurate information for water management decision-makers in addressing climate change impacts.
A new study reveals that Nor'easters produce flood levels just as severe as hurricanes, but occur much more frequently in the Mid-Atlantic region. The intense storms can cause significant damage and disruption along the coastline, with some events surpassing those from tropical cyclones.
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 developed a method to predict Western wildfire severity based on winter and spring climate conditions. The study found that April snowpack has a persistent influence on land and atmosphere during the summer, making it more conducive for fires.
Scientists identified three atmospheric phenomena orientations near the equator that increase US severe weather event probabilities three to four weeks later. Using machine learning, they found correlations between MJO fluctuations and U.S. severe weather weeks after the event.
Researchers used TACC supercomputers to evaluate latest severe weather forecast tools, testing their performance in real-world settings. The goal was to improve snowfall forecasts and determine optimal model combinations to predict various winter weather aspects.
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.
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.
A new online portal developed by UKCEH enables forecasters to provide communities with more reliable warnings about large storms in the Sahel region. The portal uses state-of-the-art weather forecast models to predict storm behavior, taking into account factors such as land surface temperatures, soil moisture, and atmospheric humidity.
A new study provides a framework for predicting snowpack runoff and its impact on flood risk during rain-on-snow events. By leveraging hourly data from existing snow monitoring stations, researchers have created a decision support tool that can help water managers prepare for potential flooding.
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.
A severe rainstorm over Kauai in April 2018 resulted in $180 million in damage and 532 homes destroyed. The study found that supercell thunderstorms triggered the deluge, which set a new US 24-hour rainfall record of nearly 50 inches.
Researchers used vertical sounding data to improve forecasting of Zonda windstorms, a severe weather phenomenon affecting Argentina's agricultural communities. By analyzing atmospheric profiles and applying principal component analysis, they achieved reliable forecasts with up to 24 hours of lead time.
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.