Articles tagged with Weather Simulations
The CloudSat spacecraft will produce detailed three-dimensional images of vertical cloud structures, missing from current weather forecasting models. Colorado State technology also contributes to a second NASA mission studying aerosols and thin clouds, enhancing climate change understanding.
A portfolio optimization model (POM) developed in 1989 helps Chicago's Peoples Energy utilities balance costs by reserving natural gas supply, transportation, and storage. The model generates significant annual cost savings of $50 million since 1995, improving efficiency and decision-making.
A recent study suggests that El Nino events can be predicted several months in advance, with seasonal averages in the tropics being the most predictable. The research identifies sea surface temperature, soil wetness, vegetation, and snow cover as key factors influencing weather patterns.
A new study by Armin Bunde and colleagues found that temperature correlations persist worldwide, with a universal exponent gamma of -0.65, holding for at least a decade and potentially centuries.
Researchers created a mathematical model that builds on current FAA ground-holding policies, aiming to reduce cost and improve efficiency in air traffic management. The model has been partially implemented in Europe and shows a potential improvement of up to 30 percent in reducing congestion.
A Colorado State University study found that irrigated agricultural crops and landscaping can produce local cooling, potentially exceeding global warming predictions. This leads to cooler temperatures, increased moisture, and altered tree distribution patterns in Rocky Mountain National Park.
Scientists have discovered giant convection cells on the sun, which could play a major role in its rotation and solar activity. These cells are large enough to swallow Jupiter and last for long periods, influencing space weather.
Researchers have developed computer crop models to predict crop yields and prices, helping farmers make informed decisions before an El Niño event. The models combine weather and soil information with virtual farming decisions, allowing for tailored growing strategies that reduce risks and uncertainties.
A computer model developed by Penn State researchers can predict the probability of land use change and microclimate formation in urban areas. The model uses satellite image data to calculate land use types, vegetation, and surface climate parameters, providing insights into urban growth and its impact on local liveability.
Researchers at Penn State used global and regional climate models to investigate climate change differences. They found that regional models produced warmer temperatures, leading to more rain and less snowfall in the Northeastern United States.
A new coupled hydrologic model system simulates storm events and river basin response by linking atmospheric and hydrological models. The model was tested on the West Branch of the Susquehanna River basin, showing improved results with historic meteorological data compared to atmospheric predictions.
Researchers successfully controlled El Nino's behavior in a complex prediction model by altering values for deep ocean waves. This achievement may help improve El Nino event predictions, leading to measures that reduce global damage.
Researchers have developed a new model that accurately predicts Dungeness crab population fluctuations, combining internal and external factors. The study provides a rare instance where a modeled understanding agrees with field data, offering insights into the elusiveness of population predictability.
The International Research Institute (IRI) will distribute climate predictions several months in advance to a global network of agencies and policymakers. The institute combines the scientists and resources of two leading climate research centers, aiming to improve longer-range predictions and mitigate extreme weather.
Scientists argue that computer models and satellites are useful tools, but only when their limitations are well understood. They found that most of the difference between MSU and surface trends could be explained by two significant drops in MSU data for 1981 and 1991.
A new weather-forecasting system using a powerful Sun Microsystems computer is providing the Pacific Northwest region with highly accurate temperature and wind forecasts. The system helps predict weather patterns such as snow, rain, and precipitation, enabling agencies to prepare for extreme weather events and issue air-quality alerts.
The review paper reviews powerful computational tools in studying ecology, genetics and evolution, infectious diseases and immunology. Computational biologists use models to understand the behavior of entire systems, mediated by smaller and larger scales.
Research suggests that mountains are key to determining the location of severe winter storms. The Rocky Mountains and Tibetan Plateau influence storm tracks over North America and Asia. Understanding these dynamics can lead to more accurate weather predictions and insights into the Earth's atmosphere.
Researchers at Johns Hopkins University created a scale model of Jupiter that simulates the banding pattern, lending support to the idea that the atmospheric bands have deep roots. The model's findings are backed by data from the Galileo spacecraft, which shows that wind speeds persist deep into the atmosphere.
Scientists at Yale University found that seasonal temperature trends have been arriving earlier each year since the turn of the century, contradicting predictions by two leading computer models. Regional patterns and variations in temperature changes were also discovered, highlighting limitations in current climate modeling.