A recent study published in npj Climate and Atmospheric Science reveals that changes in subtropical and midlatitude eddy activity control the variation of the Hadley cell edge latitude. The researchers analyzed 41 years of data and found associations with El Niño, La Niña, and the Arctic oscillation.
A study reveals that consecutive atmospheric river events lead to significantly higher economic losses in California, tripling expected damages compared to standalone events. This is crucial for emergency and water managers balancing flood risks with water shortage needs.
Researchers found that compact, faster-moving storms are more susceptible to global warming's effects, while larger, slower-moving typhoons are more resilient. This discovery could lead to improved methods for projecting typhoon strength under warming conditions.
A new compound flooding model predicts that New York City will experience historic and devastating floods every 30 years by the end of this century, a fivefold increase from the present climate. The tool helps city planners prepare and protect against future disasters by providing detailed flood forecasts.
A research team developed novel graph neural network models that improve precipitation forecasting skills by coupling physical variables, revealing a significant improvement over traditional numerical models. The models demonstrated superior consistency and forecasting skills, especially for heavy rainfall events.
A recent study from the University of Georgia found that over 70% of respondents with chronic pain would alter their daily plans based on weather-based pain forecasts. The study suggests that developing a reliable pain-based weather forecast could help individuals take preventative measures to manage their pain levels.
The Caatinga's ecosystem is projected to lose up to 87% of its mammal species and 70% of plant assemblages due to climate change. This will result in a loss of ecological functions, such as seed dispersal, and make the ecosystem less resilient.
Researchers developed a new framework to understand small-scale turbulent flows, shedding light on the chaotic butterfly effect. The framework uses chaos theory and synchronization theory to explain the critical length scale, which affects data assimilation methods.
A new study improves thunderstorm forecasting by utilizing Geostationary Operational Environmental Satellite (GOES-R) lightning flash observations. The implementation of the EnVAR assimilation capabilities successfully enhances the forecasting performance, especially over regions lacking operational weather radar observations.
Native Americans in Oklahoma have approximately five times increased risk of heavy rainfall, with two-year floods projected to be 632.6% higher than the general population. The study aims to help Native American leaders develop disaster risk reduction plans and protect vulnerable communities.
Researchers found that the coastline produces up to five times more giant sea salt aerosols than the open ocean, affecting cloud formation and rainfall around the Hawaiian Islands. The study's findings can improve numerical weather prediction of nearshore cloud formation and rainfall patterns.
A research team developed a multi-model projection system using the self-calibrating Effective Drought Index to predict drought and recommend cumulative precipitation for recovery. The required rainfall for drought recovery was estimated at 170 mm, 310 mm, and 440 mm for March, April, and May respectively.
Researchers found that green spaces alleviate extreme heat's negative impacts on human health, while densely packed buildings increase mortality risk. Urban design strategies incorporating different types of greenery are recommended to mitigate heatwave-associated mortality.
A new hybrid method developed by Concordia researchers combines data from Weibull probability distribution and numerical weather prediction models to improve wind speed forecasting accuracy. This innovation has the potential to significantly enhance urban power generation, particularly in areas with high variability in wind speeds.
New research identifies two modes of rapid intensification, one linked to favorable environmental conditions and warm surface waters, the other triggered by major bursts of thunderstorms far from the storm's center. These findings may lead to better understanding and prediction of catastrophic hurricanes.
A University of Oklahoma-led study highlights newly measured extremes in stratospheric water vapor recorded during the DCOTSS field project. High-level thunderstorms enhance water vapor in the stratosphere at levels higher than previously understood.
Researchers developed a climate network analysis method to explore teleconnections, which describe how climate events in one part affect others worldwide. The study found areas like southeastern Australia and South Africa are significantly affected by these interconnected events, with stronger connections over time.
A mathematical breakthrough provides new insights into typhoon dynamics, enabling more accurate predictions and advancements in weather forecasting. The study confirms the stability of specific vortex structures, which can be encountered in real-world fluid flows.
A research team led by Yongjie Huang is exploring the complex interactions between convective clouds and their surrounding environments. They aim to understand how convection initiates and how convective cells interact with their environment, ultimately improving computer models for forecasting.
Researchers found that fall snow levels are a good predictor of total snowpack in some western states, particularly in northern regions like Alaska, Oregon, and Washington. This prediction works due to cooler air temperatures and weather patterns that help retain snow on the ground, adding to the total snowpack.
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.
A global network of radiation sensors measured incoming sunlight at 60 locations in Oklahoma, providing important climate data for efficient use of weather satellites and photovoltaic systems. The campaign complements previous data sets and aims to improve short-term forecasts of sunlight for renewable energy.
Researchers at Stevens Institute of Technology have developed more accurate nowcasting algorithms to predict short-term weather forecasts. The study found that probabilistic models are highly accurate in predicting both long- and short-term rainfall events, while deterministic models are better suited for extremely short-term projections.
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.
The University of Cambridge team warns that Africa's lack of hydromet infrastructure will lead to a surge in climate-related disasters and deaths. The continent has just 6% of the number of radar stations as Europe and North America, making it vulnerable to floods, droughts, and heatwaves.
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.
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.
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 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
SourceCNRS·JournalPhysical Review Fluids·TypeComputational simulation/modeling·DateApr 5, 2023
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.
SourcePLOS·JournalPLOS ONE·TypeComputational simulation/modeling·DateSep 7, 2022
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.