Articles tagged with Weather Simulations
Cristiana Stan, a George Mason University researcher, has received $195,313 from NOAA to develop a coupled ensemble weather prediction system. The system aims to improve medium-range and subseasonal-to-seasonal forecasts by refining stochastic parameterizations and improving model coupling.
Researchers improve simulated precipitation over the Tibetan Plateau using convection-permitting models. These models better depict precipitation frequency and intensity, reducing the wet bias in traditional climate models. Higher resolution and more accurate parameterizations lead to improved simulations.
A new MIT study found that Covid-19 super-spreading events, where one person infects many others, are more common than expected. The researchers developed a mathematical model that shows limiting gatherings to 10 or fewer people could significantly reduce the number of super-spreading events and lower overall infections.
Researchers found that true forecast track error increases exponentially with lead time, implying dynamics of TC motions are linear and model-induced errors in TC position forecasts are minimal. The team's 4-parameter error model indicates potential extension of predictability limit beyond 6/8 days in 10/30 years' time.
Researchers describe daily variability of key deep currents in the South Atlantic Ocean, with variations linked to climate and weather globally. The study found that upper and deeper layers behave independently, impacting sea level rise and hurricane intensification.
Scientists have made a major breakthrough in predicting North Atlantic pressure patterns, which drive European and eastern North American winter weather. The study suggests that decadal variations in atmospheric pressure are highly predictable, enabling advanced warnings of extreme weather events.
A comprehensive blueprint for Saudi Arabia's national wind energy strategy has been developed, aiming to reach 16 gigawatts of installed capacity by 2030. The plan leverages high-resolution modeling, unique wind and weather observations, and cost analysis to guide the optimal buildout of wind turbines.
A study published in Nature Communications reveals that transient attracting profiles (TRAPs) in ocean-surface velocity data can predict the convergence of drifting objects, such as persons and debris. This information enables search teams to set a tighter search grid and make faster rescues.
Researchers at King Abdullah University of Science & Technology devised a new analytical tool to predict flood risk by adapting a classical statistical model for analyzing extreme rainfall in large datasets. The model demonstrated potential in capturing observed patterns in northeast America, promising improved prediction capabilities.
Researchers improve forecasting of strong convective weather using analogy method, which estimates occurrence environment for similar weather phenomena based on historical model forecasts. The method performs well in predicting potential for strong convection in different regions of China.
A new method using Nonlinear Forcing Singular Vector (NFSV) reduces model error in numerical weather forecasting models. The approach identifies the uncertainty of variable and area most likely to cause large errors, enabling adaptive satellite observations to improve short-term forecasts.
The new E3SM model can capture complex climate-generating behavior with high resolution, simulating regional climate and built infrastructure. It will help predict how changes in climate and water cycling respond to increasing CO2.
A new study verified multi-system forecasts for major stratospheric sudden warmings (MSSWs), showing that all four systems can be judged skilled for five-day forecasts when averaged across available MSSWs. Longer lead times present challenges, with some systems skillful but others not.
Scientists report that small altitude changes in flight paths could cut the climate impact of aircraft contrails, reducing warming effects by up to 59.3%. By targeting high-warming flights and making minor adjustments, contrail impact could be reduced by 91.8% with cleaner engines.
A WVU engineer is developing a new model to improve understanding of energy transfer between the sun's solar wind and Earth's magnetosphere. The goal is to create more accurate space weather forecasts to prevent devastating effects on power grids and technology.
Researchers at Argonne National Laboratory have developed domain-aware neural networks to replace expensive parameterizations in the Weather Research and Forecasting (WRF) model. These algorithms can predict environmental data more accurately with significantly less training data, enabling faster and higher-resolution simulations.
Researchers from Leibniz Institute for Tropospheric Research found that vertical air motions increase ice formation in mixed-phase clouds. This correlation has important implications for understanding the water cycle and predicting precipitation.
A new machine learning method, MOML, simulates weather consultation to improve forecast accuracy by 27.91% compared to ECMWF model forecasts. The approach leverages observational data and both high- and low-frequency information for more accurate results.
Scientists trained a deep learning model to recognize features of individual storms that affect hail formation and size, improving prediction accuracy by up to 10%. The study highlights the importance of considering a storm's entire structure in hail forecasting.
A new approach developed at Penn State's Center for Advanced Data Assimilation and Predictability Techniques can accurately forecast the intensity and trajectory of Hurricane Harvey. The method uses data from the GOES-16 satellite and Penn State's all-sky radiance technique, which more accurately models hurricanes.
A predictive model combining plant physiology, soil conditions, and weather forecasts helps make informed decisions about irrigation, conserving up to 40% of water used. The research also aims to identify the best method for each crop and determine the costs and benefits of switching to an automated system.
Researchers developed an AI framework that detects rotational movements in clouds from satellite images, pointing to potentially threatening storm formations. The method achieved 99% accuracy and predicted 64% of severe weather events, outperforming existing detection methods.
Dr. Wiebke Frey is using wind tunnel experiments and model simulations to investigate mixing processes at cloud edges, which affect cloud lifetime and climate sensitivity estimates. Her goal is to develop a reliable formulation for understanding entrainment, a key process in climate modeling.
The German Research Foundation is extending funding for a research project to improve weather forecasting capabilities. The project aims to understand the limits of predictability and develop better forecast models, potentially increasing accuracy beyond current seven-to-ten-day ranges.
Colorado State University's Cooperative Institute for Research in the Atmosphere (CIRA) has received a new $128 million cooperative agreement with NOAA. CIRA supports various NOAA research initiatives, including forecast model improvements and real-time satellite tools.
Researchers have developed diode-based lidar instruments that can measure atmospheric water vapor, aerosols and temperature, filling gaps in US meteorological data. The instruments are operational and have provided accurate measurements with minimal error, paving the way for improved weather forecasting and climate modeling.
Researchers confirm a long-hypothesized limit for weather prediction, first proposed in the 1960s, which is approximately two weeks on average. They used state-of-the-art models to test this limit and found that reliable forecasts are now possible nine to 10 days out in mid-latitudes.
Researchers propose a unified approach to optimize radar polarimetry data for precise weather forecasting. This combines observation-based retrievals with model-based analysis to improve quantitative precipitation estimation, warnings, and forecasts.
Researchers Inanc Senocak and Cheng-Nian Xiao uncover fluid instabilities in the Prandtl model for katabatic slope flows, a complex phenomenon crucial for reliable weather predictions. Their findings suggest that dynamic stability cannot be determined by a single dimensionless parameter alone.
Researchers found seasonal variations in raindrop size distribution in East China, with summer having larger raindrops and higher mean rain rates. These findings have implications for microwave communication, radar, and satellite remote sensing, as well as numerical weather prediction models.
A Cornell University team developed a smart control system that reduces energy usage by up to 10% by predicting accurate weather forecasts and building characteristics.
Chinese scientists have launched a rocketsonde from an unmanned semi-submersible vehicle, enabling accurate meteorological and oceanographic data collection over vast marine environments. The launch aims to improve numerical weather forecasts, typhoon tracking, and coastal zone monitoring.
Advances in weather forecasting have significantly improved accuracy, enabling reliable predictions up to 10 days in advance. This has enabled timely emergency preparations and evacuations for hazardous events like hurricanes and tornadoes, saving countless lives.
A collaborative research team examined predictive models used to forecast wind farm effects, revealing the importance of model resolution in simulating weather patterns. Higher vertical and horizontal resolutions improved simulations, but more study is needed to optimize wind energy deployment.
Scientists have developed a new approach to modeling turbulence, which allows for the simplification of complex systems. By representing both growing and decaying motions, researchers can greatly improve existing models and tackle previously intractable problems, such as fusion experiments and weather forecasting.
Researchers used big data from human medical studies and physics approaches to develop novel anti-aging therapeutics and biomarkers of aging. The strategy involves analyzing data from large biobanks and applying concepts from complex dynamic systems to predict biological age, aging rate, and potential targets for therapies.
Researchers found that US maize has seen a yield increase of around 28% since 1981 due to beneficial changes in weather and farming practices. The study attributes this growth to warmer temperatures allowing for earlier planting and more favorable conditions.
Researchers at UCSB and Cambridge developed models to optimize wind farm productivity by improving airflow mixing and using vertical-axis turbines, potentially leading to orders-of-magnitude improvements in energy production. This could enable more efficient use of land and reduce costs.
Researchers at Penn State have developed a new weather model that incorporates data from next-generation satellites to improve tornado forecasting. The model was able to forecast supercell thunderstorms with high accuracy and suggests that lead times for tornado warnings can be extended beyond 30 minutes.
The USDA Forest Service introduces the Hot-Dry-Windy Index (HDW) to predict wildland fire danger, leveraging temperature, moisture, and wind variables. Researchers tested HDW against existing indexes and found promising results, with potential applications worldwide.
Researchers from the University of California, Irvine, and other institutions have developed a new method using deep machine learning to simulate cloud physics. The approach achieved stable and accurate multiyear simulations that included realistic precipitation extremes and tropical waves.
Researchers from CIRES and NOAA are utilizing drones and tethered balloons to collect weather data in the Arctic, filling critical gaps in atmospheric measurements. These unmanned technologies provide a comprehensive understanding of the region's lower atmosphere, enabling more accurate weather and climate forecasts.
Researchers at the University of Oklahoma are investigating tropopause polar vortices, which limit predictability in the Arctic. The team aims to develop new models and strategies to better understand TPVs and their impact on weather events.
Japanese researcher develops simple method for generating large data required for ensemble modeling, providing high computational stability and versatility in simulating various weather events. The new approach demonstrates effectiveness in initial conditions for typhoon and global warming simulations.
Researchers found statistically significant associations between yearly changes in PM2.5 and ozone concentrations with yearly change in hospital admission rates for ARDS among seniors. Hospital admissions increased even when seniors were exposed to levels below current air quality standards.
Researchers found that the Unified Model's microphysics scheme produces unrealistic raindrop size distributions, affecting storm structures. The study emphasizes the importance of selecting the right microphysics scheme for accurate weather prediction.
A new analysis of 22 recent hurricanes suggests they would become stronger and wetter in a warmer climate, with increased rainfall rates. The study found that average hourly maximum wind speeds would rise by 6% and rainfall rates would increase by 24%, leading to more devastating flooding.
The study analyzed 22 recent hurricanes and found that they would become a little stronger, a little slower-moving, and a lot wetter if they formed under predicted late 21st-century conditions. The rainfall rate of simulated future storms would increase by an average of 24 percent.
Scientists successfully simulated FY-2D infrared cloud images using a high-resolution WRF regional numerical weather prediction model product and forward radiation transfer model RTTOV. The results show improved accuracy, with correlation coefficients greater than 0.5 in the first 24 hours.
Researchers at the University of Bristol used high-resolution meteorological modeling to study volcanic ash transport patterns. They identified how wind flow around the volcano topography controls ash deposit patterns, influencing ash deposition over hundreds of km from St Vincent, including on nearby islands and ships.
Researchers found that urban-induced warming in Beijing increased with urban land expansion, but the rate of increase slowed down after 2000. The study also revealed an interrelationship between urban-induced warming and heat waves, suggesting that urban expansion may heighten heat waves in urban areas.
Researchers analyzed 732 Formula One races to find that drivers of similar status, age, or competing in safe conditions are more likely to experience collisions. The study suggests that targeting these factors could aid in preventing conflicts among drivers.
Researchers Raphaël Huser and Jennifer Wadsworth developed an integrated statistical approach to modeling weather extremes, eliminating the need for guessing dependence strength. The new model can handle different spatial dependence scenarios, from strong correlations to complete independence.
Researchers at Colorado State University have developed an empirical model that accurately predicts atmospheric river activity in the western US three weeks in advance. This breakthrough could enhance long-term weather forecasting capabilities, allowing for earlier warnings of extreme precipitation, floods, and droughts.
Researchers at the University of New Hampshire found that climate change effects could worsen by mid-century, leading to fewer snow days, more extreme heat, and reduced cold water fish habitats. The study used models benchmarked to field measurements to evaluate the Merrimack River watershed in New Hampshire.
A special issue of Advances in Atmospheric Sciences highlights recent research on Arctic-midlatitude linkages. The study aims to improve understanding and prediction of Arctic change, which influences extreme weather events in the mid-latitudes.
A new IASI channel selection method has been developed to improve weather forecasting accuracy by reducing bias in humidity forecasts. The method used a one-dimensional variational analysis approach to select 200 new channels from the available 314 channels.
A new analysis has calculated the likelihood of a massive rogue wave during Hurricane Joaquin in 2015, which may help improve the prediction of rogue waves to understand risks posed by these unusual wave patterns. The study suggests a one-in-130 chance of a 46ft high rogue wave occurring during the hurricane.
A new weather forecasting model, Subseasonal Experiment (SubX), combines multiple global models to produce more accurate forecasts for the US. The multi-model approach improves temperature and precipitation outlooks for weeks 3-4, providing critical insights for decision makers.
Texas A&M University researchers have developed a predictive model that can identify potential vulnerabilities in electric grids and predict weather hazards. The model uses historical and close-to-real-time data to analyze the impact of these factors on power system outages, enabling utility companies to take proactive measures.