Articles tagged with Weather Simulations
Researchers from the University of Nicosia used environmental fluid dynamics to correct COVID-19 pandemic data inaccuracies. The study found that daily number of total new infections reported during the first wave was underestimated by a factor of four in cities like New York.
A study suggests that China will experience an increase in landfalling tropical cyclones by the end of the 21st century, with a projected 16% rise in annual mean frequency and a 10% increase in occurrence frequency. The study used regional climate models to simulate future changes in temperature and precipitation patterns.
A new study challenges outdated public health guidance on fan use during heatwaves, proposing increased temperature thresholds for young healthy adults. Electric fan use can be recommended most of the time in most regions, except extremely hot and arid areas.
Researchers used the hybrid OSSE method to evaluate the impact of GEO hyperspectral IR data on local severe storm forecasts. The study found improved atmospheric temperature, moisture, and precipitation forecasts, with a 5% Root Mean Square Error (RMSE) reduction.
A recent study by Helmholtz-Zentrum Hereon found that wind farms in the North Sea experience a significant slowing down of wind speeds when built close together, leading to reduced power output and economic consequences.
The OFIDIA2 project has developed an innovative wildfire monitoring and prevention platform for the regions of Puglia and Epirus. The system provides real-time fire danger forecasting, weather forecasts, and a dedicated control room for firefighting decisions.
Scientists create new approach to measure error components, finding more than half of total error variance attributed to misplacement of weather features. The study also reveals that displacement errors dominate structural inaccuracies in forecast fields.
Fengyun satellite data is available for real-time users, scientific researchers, and public users, with over 10TB of daily data volume produced daily. The data has been validated with in-situ observations and cross-referenced with other well-known satellite products.
A study analyzing high-resolution model simulations found that mountainous terrain, particularly the Nanling Mountains in Jiangxi Province, initiates back-building convective lines. This leads to torrential flooding and heavy rainfall within the mei-yu front rainband.
Researchers used local citizen photos and data to reconstruct the first documented meteotsunami in the Great Lakes, caused by an atmospheric inertia-gravity wave. Existing NOAA models may enable scientists to predict these meteotsunamis minutes to hours in advance.
The 'Destination Earth' initiative aims to create a highly accurate digital model of the Earth, incorporating observational data and new human activity data. This digital twin will enable high-resolution simulations of complex processes and inform policies on sustainable development.
Researchers predict that Alaska will experience a significant increase in thunderstorms by the end of the century, with storms becoming more frequent and intense, especially in southern regions. This shift is driven by warming temperatures and ice-free waters around Alaska, which will fuel atmospheric moisture.
The developed framework captures detailed information on agricultural groundwater use at the regional scale down to individual fields. It combines data from satellite images, weather prediction models, and land-surface hydrology models to estimate water delivery to each field.
Researchers at George Mason University are developing Python tools to facilitate the exploration of heliophysics data in 3D. The project aims to create a suite of functions that can read in measurements and simulation output, produce 3D renderings, and perform basic data reduction and manipulation.
Researchers found that air purifiers in elevators can increase saliva droplet dispersal and spread COVID-19. Installing an air purifier alters airflow significantly but does not eliminate airborne transmission.
Researchers at KAUST developed a high-frequency rainfall model using new rain gauges that measure rainfall drop by drop, providing unprecedented insight into minute-to-minute rainfall dynamics. The model captures the 'skewness' of high-frequency precipitation data and generates synthetic precipitation data for water management.
Scientists developed a new land surface model incorporating multiple processes and human activities to improve water-energy simulations and environmental protection. The CAS-LSM model can evaluate ecohydrological effects of stream water transfer and provide advice for urban planning.
A new AI model has shown promise in generating faster and more accurate weather forecasts by analyzing past weather patterns. The model uses about 7,000 times less computing power than traditional forecasting models while still simulating a year's weather around the globe.
Researchers are developing a flexible simulation-based outer-loop application tool for resilient power grid planning and optimization. The tool leverages scalable parallel processing, adaptive computing budget allocation, and scientific machine learning to predict and respond to disturbances.
The WRF4-LICOM model shows improved summer mean monsoon rainfall, circulations, and sea surface heat fluxes. Regional air-sea coupling enhances the simulated daily SST-rainfall relationship, offering a more accurate representation of the western North Pacific summer monsoon.
A team from Oklahoma State University developed techniques to model environmental hazards for autonomous aircraft, while another group at the University of Surrey investigated building wakes to enhance air quality. Researchers also found an unbalanced exchange flow in natural ventilation systems, which can slow down cooling.
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 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 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 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.