Articles tagged with Weather Simulations
Researchers developed a new data assimilation algorithm using the LETKF technique to enhance global precipitation estimates. The model improved accuracy and computational efficiency, making it suitable for large-scale applications, particularly in mountainous regions.
NRL oceanographers received a Group Achievement Award from NASA for their collaboration with S-MODE, providing real-time ocean model forecasts and glider guidance. The project successfully observed and forecasted ocean features that are too small to see from space, allowing scientists to better understand the global earth system.
Researchers identified alkene ozonolysis as the dominant driver of O₃ formation during cold January days in Lanzhou, China. The study proposes actionable mitigation strategies to reduce O₃ levels by targeting alkene and nitrogen oxide emissions.
The one-core-neuron system (OCNS) minimizes model size while maintaining high performance in deep learning. OCNS employs a single neuron to encode high-dimensional data into a one-dimensional time-series representation, reducing parameters by an average of 0.035%.
Computer models predict that climate change will lead to an increase in spongy moth outbreaks, causing widespread damage to forests. The fungus that normally curbs the moths' spread is expected to decline, allowing the moths to destroy more trees as temperatures rise.
The 105th Annual Meeting of the American Meteorological Society will address key issues in weather, water, and climate. The meeting features a Presidential Forum on physical, social, cultural, and economic impacts of climate change, with experts from the region of the Mississippi River Delta.
A team from the University of Cambridge has developed a model to predict desert locust swarms, enabling national agencies to respond quickly. The model uses weather forecast data and computational models to forecast locust swarm movements both short and long-term.
Researchers developed a unique numerical decision-making framework for solar panel protection against extreme weather conditions. The framework treats individual panels as independent decision-makers, identifying creative solutions to reduce stress and minimize damage during high-wind events.
A new study shows that coordinating the siting of solar farms, wind farms, and storage systems can improve efficiency and reduce costs by aligning renewable power generation with demand. The approach uses fine-grained weather data and energy system modeling to maximize utilization of renewable resources.
New research suggests that regional climate impacts are poorly constrained by climate sensitivity, contradicting expert suggestions to exclude 'hotter' models. The study found no clear correlation between global warming rates and regional drivers of climate change.
The Amazon rainforest is a significant source of condensation nuclei for clouds, according to two studies. The rainforest's plant transpiration and thunderstorms produce aerosol particles that can be transported thousands of kilometers, influencing marine cloud formation.
A new machine learning algorithm reconstructs wind fields quickly and accurately, even with limited observational data. This enhances storm forecasting and hazard preparedness by providing valuable data on a tropical cyclone's intensity and potential impact.
A new study will use advanced integrated hydrologic models to predict New England's water budget changes, tracking shifts in the water table, rainstorm intensity, and drought timing. The research aims to help prepare for an uncertain future by understanding where climate change is altering weather patterns.
Researchers at the Alfred Wegener Institute have developed a new approach called 'storylines' that uses probabilistic attribution studies and 'nudging' techniques to identify the fingerprints of climate change in extreme weather events. By comparing hypothetical scenarios with reality, they can determine how much more probable an event...
The Macrosystems EDDIE modules have been effective in building student and instructor quantitative literacy and data science skills in ecological forecasting, reaching over 35,000 students globally. The modules aim to introduce students to core concepts of forecasting and complement educators' work teaching ecological concepts.
A new study uses deep learning to infer the frequency of atmospheric blocking events over the past 1,000 years, shedding light on their potential impact under climate change. The model was trained using historical data and large ensembles of climate model simulations.
Researchers at Rice University develop OpenSafe.AI, a system that leverages responsible AI and hazard models to provide timely insights for emergency response organizations and communities before, during, and after tropical cyclones and coastal storm events. The goal is to enable better preparation and navigation for severe weather.
Researchers analyzed atmospheric mercury concentrations and found a 10% decline between 2005 and 2020, contrary to global inventories that indicate an increase. The study suggests that human activity-driven emissions are driving the trend, but limitations in data and scientific understanding remain.
A new study led by University of Illinois Chicago scientist Akintomide Akinsanola found that most Americans can expect wetter winters in the future due to global warming. Winter precipitation and extreme weather events are expected to increase across most of the country, with six regions experiencing more frequent very wet winters.
A team of OU scientists, led by Nathan Snook, will use deep learning techniques to analyze numerical simulations of tornadoes. The goal is to improve tornado forecasting by identifying key factors that influence their formation.
A new study published in Nature suggests that Earth's last ice age may provide crucial insights into future El Niño weather events. Researchers used ancient shells of marine organisms and advanced climate modeling to shed light on how El Niño patterns might change in a warming world.
A new study shows that urban forests within walkable distance from residential areas are crucial in reducing heat-related health risks. Researchers found that nearby forests have a pronounced impact on reducing mortality risks, particularly those within 1 kilometre of residential areas.
Researchers from Leipzig University studied cloud movement in the Arctic using a large-scale international research campaign called HALO (AC)³. They observed air mass transformations over open ocean, sea ice, and central Arctic sea ice.
A new model developed by scientists can predict intense, localized heavy rainfall events caused by rapid air rises in clouds. This breakthrough offers potential to improve public safety and preparedness through early warning systems.
A WVU statistician is developing new methods to analyze curve-type data for predicting extreme weather patterns, financial turmoil and pollution levels. The research aims to create innovative tools for understanding high-resolution data and addressing regional risks in mountainous states like West Virginia.
A new study by NSF NCAR reveals that detecting Kelvin waves can predict active hurricane periods weeks in advance, allowing forecasters to prepare for potential storms. The research used innovative computer modeling to analyze the connection between Kelvin waves and hurricanes.
A new AI approach accurately links heat waves to global warming, estimating that record-setting heat waves could occur multiple times per decade under higher warming levels. The method uses actual historical weather data and machine learning to predict the magnitude of extreme events.
Researchers have created a global library of hailstones to better understand and predict damaging storms. The study found that non-spherical, naturally shaped hailstones significantly affect storm modeling outcomes, leading to different pathways, growth, and landing locations.
A new approach incorporating machine learning and a novel radar technique has solved the 'cloud cover' problem in remote sensing, improving land surface temperature tracking accuracy. This is achieved by combining better elevation models with multiple radar echoes from SAR images to reconstruct optical data.
A mathematical model developed by Chris Zobel and colleagues optimizes resource allocation in urban infrastructure while considering social vulnerabilities and projected sea level rise impacts. The approach prioritizes investments that enhance resilience and mitigate socio-economic disparities, enabling informed decision-making that ba...
A new study from the University of Illinois Chicago proposes an alternative theory for the formation of Earth's continents, challenging the long-held leading theory. The researchers used computer models to investigate the origin of Archaean zircons, which date back to 2.5-4 billion years ago.
Researchers from MARUM and the National Oceanography Centre analyzed sensor data over four years to determine seafloor currents in deep waters. The study found that currents sped up, slowed down, reversed direction, and were steered by seafloor relief, contradicting previous steady models.
A study by Stanford University researchers reveals a previously unknown relationship between Sahara dust plumes and hurricane rainfall. Thicker dust plumes can lead to heavier rainfall, while thinner ones may suppress hurricane formation over the ocean.
Researchers have found that the Southern Ocean absorbs 25% more carbon dioxide than previously estimated. The new study used direct measurements to assess existing flux products in the Southern Ocean.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
The University of Maryland has been awarded a five-year, $388 million cooperative funding agreement with NOAA to conduct collaborative research in Earth system science. The Cooperative Institute for Satellite Earth System Studies (CISESS) will continue its national leadership in this field.
Researchers have developed a new method to simulate Convectively Coupled Kelvin Waves in weather forecast models, which could enhance accuracy for predicting hurricanes and heavy rainfall. The study found that current models poorly simulate these waves, indicating a need for future improvements.
Researchers from Kyushu University found that early-onset El Niño leads to warm winters in East Asia due to anomalous warming of the tropical Indian Ocean. Late-onset El Niño, on the other hand, results in colder winters. This study aimed to improve climate modeling and prediction during El Niño events.
Researchers found that higher atmospheric moisture produced weaker and slower-moving African easterly waves, making it harder for hurricanes to form. The study used innovative modeling capabilities to simulate the effects of increased regional moisture on hurricane formation.
Researchers at Drexel University model the potential extent of flooding and combined sewer overflows in Camden, New Jersey, as climate change exacerbates the problem. The team's 'all-pipes' model simulates stormwater flows through every surface and pipe in the area, providing a unique solution to mitigate environmental and health risks.
A study by Dan Chavas and his team found that the rough texture of the land surface in South America may prevent tornadoes from forming, while a smoother ocean surface in North America enhances their frequency. This challenges established wisdom about the role of the Gulf of Mexico in tornado formation.
A new conceptual model, XRO, significantly improves predictive skill of ENSO events at over one year in advance, offering a transparent view into the mechanisms of equatorial Pacific recharge-discharge physics. This improves conventional climate model forecasting and provides robust quantification of extratropical Pacific, tropical Ind...
A new study harnesses GNSS-R technology to accurately measure sea ice thickness, offering a vital tool for climate scientists and polar explorers. The three-layer model significantly enhances the precision of thickness estimation, making it applicable to ice thicknesses up to 1.1 meters.
A new model developed by researchers at the University of São Paulo combines physical parameters and machine learning to predict storm tides. The model uses physics-informed machine learning, which harmonizes physical models with measured data to produce more precise forecasts.
Researchers used a weather model to pinpoint the source of wintertime precipitation in Arizona, finding it comes from a central Pacific moisture source rather than El Niño/La Niña events. The study's findings could improve seasonal precipitation forecasts for the region and potentially other areas globally.
Researchers developed a quantum annealing approach to accelerate data assimilation, significantly reducing computational cost. The method achieves comparable accuracy to conventional approaches but in a fraction of the time.
Texas A&M University researchers have developed a 3D visualization technology to identify potential outcomes of hurricane flooding before it occurs. This allows for improved safety and reduced damage costs. The technology can also model specific buildings, accounting for basements, back entrances, and windows.
A study published in Royal Society Open Science reveals that corals use multiple environmental inputs to synchronize their spawning timing. Water temperature is identified as the primary trigger for determining the annual window of opportunity, with rainfall and solar radiation also playing a role.
A new study suggests that the sun's magnetic field could arise from instabilities in the outermost layers of the sun, rather than deep within. This finding may enable scientists to better forecast solar activity and space weather.
Professor Cristiana Stan of George Mason University will conduct an evaluation of subseasonal prediction skill using foundational models, focusing on Madden-Julien Oscillation and its teleconnections. The funding will contribute to making weather and climate applications faster and more accurate.
Researchers developed an impact-based forecasting system that combines various models to predict flood impacts with high resolution. The system enables early warnings for evacuation, reducing population and property damage.
A new model combines wildfire smoke forecasts and ground-based sensor data to help public health officials plan targeted interventions. The researchers found that urban and rural communities already burdened by pollution face higher air pollution levels during unexpected smoke events.
A study found that heavy snowfall and rain contribute to earthquake swarms in northern Japan by altering underground pressure. Climate conditions are linked to seismic activity, with the timing of intense precipitation events correlating with the start of quakes.
A recent study found that approximately 30% of India's PM2.5 air quality improvements can be attributed to favorable meteorological conditions, highlighting the need for additional mitigation measures. The researchers also noted that emissions of key PM2.5 precursors have not decreased and may indicate ineffective or under-enforced pol...
New research shows climate change will cause more severe combined wind-rain extremes in the UK and Ireland, leading to increased flooding and damage. The study found that stronger winds and heavier rainfall are likely to occur together, posing challenges for coastal areas and emergency response resources.
A recent analysis of cloud measurements and satellite data suggests that even tiny aerosol particles may play a significant role in cloud formation. This finding challenges current models and could have significant implications for predicting future climate change.
A new study by the University of Reading demonstrated that AI weather forecasts can predict storm paths and intensities with similar accuracy to traditional models. However, AI systems underestimated Storm Ciaran's maximum wind speeds due to limitations in predicting certain atmospheric features.
Researchers found that modern weather models can accurately predict satellite movements affected by weather events like tropical cyclones with tall clouds. This understanding enhances satellite tracking and control, improving efficiency and reliability of satellite operations.
A team of over 100 scientists from Brazil and the US ran high-resolution simulations of South America's past and future climate using a weather research and forecasting model. The goal is to better understand hydroclimatic processes and inform adaptation measures for millions affected by climate change.
A recent machine learning study has discovered a surprising link between wildfires in the western United States and hailstorms in the central US. The research, led by Jiwen Fan, used ML algorithms to analyze vast datasets spanning two decades, predicting hail storms with remarkable accuracy.