Articles tagged with Weather Forecasting
The world's ocean is losing its year-to-year memory due to global warming, making it harder to predict ocean conditions. This decline in ocean memory will have significant impacts on fisheries management, population estimates, and land-based weather forecasts.
A new study provides a framework for predicting snowpack runoff and its impact on flood risk during rain-on-snow events. By leveraging hourly data from existing snow monitoring stations, researchers have created a decision support tool that can help water managers prepare for potential flooding.
A new online portal developed by UKCEH enables forecasters to provide communities with more reliable warnings about large storms in the Sahel region. The portal uses state-of-the-art weather forecast models to predict storm behavior, taking into account factors such as land surface temperatures, soil moisture, and atmospheric humidity.
Scientists developed a method to estimate summer rainfall in the Southwest months in advance, performing well in Arizona. By analyzing lower atmosphere moisture, they created monthly forecasts to aid reservoir storage and water allocation decisions.
A team of scientists has developed a solution to accurately simulate how the atmosphere works by linking large- and small-scale simulations. This helps model winds, transport of pollutants, climate projections, and weather forecasts with greater accuracy.
A severe rainstorm over Kauai in April 2018 resulted in $180 million in damage and 532 homes destroyed. The study found that supercell thunderstorms triggered the deluge, which set a new US 24-hour rainfall record of nearly 50 inches.
Researchers used vertical sounding data to improve forecasting of Zonda windstorms, a severe weather phenomenon affecting Argentina's agricultural communities. By analyzing atmospheric profiles and applying principal component analysis, they achieved reliable forecasts with up to 24 hours of lead time.
Tel Aviv University researchers develop innovative model that explains wave formation, tested in complex experiments. The model takes into account all unstable harmonics and limitations of previous models, providing high reliability for describing physical situation.
A $150,000 grant from the NV Energy Foundation will support DRI's development of a Weather and Research Forecast model to simulate weather, fire, and smoke for firefighting operations. The tool will provide critical air quality forecasts and risk assessment for specific locations.
Researchers at Mainz University created artificial hailstones using a 3D printer to study their behavior in a vertical wind tunnel. The experiments revealed that the form of hailstones determines their velocity before impact, which can affect the severity of precipitation events.
A new machine learning study analyzed 10 years of weather data to identify three major categories of weather patterns and their effects on thunderstorms. The study aims to isolate the impact of aerosols, tiny particles suspended in the atmosphere, on storm severity.
The Texas cold snap highlighted the critical role of electricity in society and the need for improved power system planning to withstand extreme weather events. The event showed that traditional planning based on historical weather patterns is not sufficient, and new information from climate projection models is needed.
The Bureau of Reclamation released a new report evaluating current and emerging snow measurement technologies to enhance water supply forecasting. The report identifies under-utilized emerging technologies with potential to improve forecast accuracy in the near term.
A new study questions a widely-held theory that sudden stratospheric warming caused the extreme cold weather in Texas and other parts of the US. The research suggests that the polar vortex's disruption, which occurred six weeks after the initial warming event, was not significant enough to impact the weather.
Researchers assess predictive skills of ocean heat content in two state-of-the-art seasonal forecasting systems, finding potential to predict sub-surface warming up to two seasons in advance. This could aid mitigation of extreme events and provide early warnings for industries such as aquaculture and fishing.
A $763,930 NSF CAREER Award is supporting OU meteorologist Scott Salesky's project to improve cloud representation in weather and climate models. The research aims to better understand interactions between turbulence and clouds, with potential applications for predicting precipitation and understanding Earth's climate.
A new study reveals a significant shift in the Northern Hemisphere jet stream position, increasing by up to 330 km and speed by 8% during 1871-2011. This shift is linked to warming Arctic winters and decreased temperature gradients, with implications for climate predictions and storm activity.
A new project aims to determine the role of sea ice fragmentation in accelerating Arctic ice-cap retreat. By combining observations, theory, and process modeling, researchers hope to improve climate model accuracy.
Researchers developed a new model to predict storm damage, allowing for effective resource allocation and reduction in societal consequences. The framework can be applied to various infrastructure systems and weather events, promoting better preparedness and response.
The NUIST-CFS1.0 model has positive skill in seasonal precipitation forecasts across Ethiopia, Kenya, Uganda, and Tanzania, with relatively accurate products throughout the rest of East Africa.
Researchers found that machine learning models can help predict village food insecurity outcomes in Sub-Saharan African countries. The models incorporate publicly available data on weather, geography, and food prices to capture a wide variety of factors that can influence food insecurity. Key takeaways include the need for interpretabl...
A new technique combining microwave data with existing computer weather forecast models reduced forecast errors for Hurricane Harvey, improving track, intensity, and rainfall forecasts. This could lead to better warnings and preparedness for tropical cyclone-associated hazards.
Researchers developed a new avalanche forecasting method using computer simulations of snow cover, which can detect weak layers and identify hazard in a different way. The approach showed consistent results with observed frequencies over 16 years, offering potential to support forecasting in the future.
A recent study found that large wildfires and severe heat events worsen air pollution across the western United States, affecting 68% of the region's population in one day. The study revealed an increasing trend in days with high levels of both particulate matter and ozone, tied to rising temperatures and wildfires.
Scientists use a modified drone with a radiation shield to collect high-quality atmospheric data in polar regions. The study shows that the low-cost drone provides accurate data comparable to radiosondes, expanding the observational network and improving weather forecasts.
Detailed supercomputer simulations show that the North American monsoon occurs when Mexico's Sierra Madre Occidental mountains divert the eastward-trending jet stream toward the equator, cooling moist tropical air until it condenses and falls as rain. This new understanding will impact forecasts in the region.
The North American Monsoon originates from the interaction of mountain ranges with extratropical atmospheric circulation, deflecting the jet stream and generating a stationary wave. This unique case has implications for models forecasting heavy rainfall events brought by the monsoon.
A new prediction framework can forecast extreme climate events like floods and heatwaves up to two days in advance, allowing for crucial preparation time. This network-based approach analyzes large-scale connectivity patterns in observational data to improve forecasting accuracy.
The Mountain Rain or Snow project collects observations of rain, snow, and mixed precipitation from across mountainous regions. Volunteers can submit their own reports via smartphone or computer to improve satellite monitoring and model predictions, leading to better weather forecasts, skiing conditions, and avalanche risk assessments.
A University of Arizona-led study reconstructs Earth's climate since the last ice age, highlighting unprecedented global temperatures and the speed of human-caused warming. The research combines two independent datasets to create a more complete picture of past temperatures.
A recent survey reveals a new method using cloud-cleared radiances improves forecasting of high-impact weather events like hurricanes and typhoons. The technique is now being applied to numerical models for enhanced daily forecasts.
Environmental engineers at Duke University demonstrated that sharp contrasts in small landscape features refine local weather developments and influence climate trends. The study suggests that incorporating detailed data can improve forecast accuracy.
A Brazilian study reveals that forest fires and wildfires modify the freezing process of cloud droplets, altering natural cloud functioning and potentially impacting precipitation. The research used a large dataset to show that aerosols emitted by fires can affect cloud formation in southern Amazonia during the rainy season.
A study in Nature Communications finds that strong El Niño events can drive nearly 6 million children into undernutrition, affecting 70% or more of children globally. Proactive climate action could prevent this tragedy by providing targeted interventions to vulnerable areas.
A new machine learning approach has been developed by researchers at the Department of Energy's Pacific Northwest National Laboratory to improve hurricane intensity predictions. The model uses artificial intelligence techniques and can run on a commercial laptop, offering more accurate forecasts than existing models. It also enables th...
The new Fengyun-3E satellite provides 100% global data coverage, with 11 instruments on board to observe cloud, radiation, and more. China joins Europe and the US in sharing a global responsibility for early morning orbit observations.
A new study incorporated water vapor isotope compositions into a general circulation model to improve forecast accuracy by several percentage points. The Isotope-incorporated Global Spectral Model (IsoGSM) demonstrated improved modeling of air temperature and specific humidity.
A recent survey of people affected by hurricanes found that the public is willing to pay more than $500 million a year for improved hurricane forecasts. The study also found that wind speed forecast is the most valuable improvement, with an average willingness to pay of $28.89 per household per year.
Researchers at Stanford University have discovered the physical mechanism behind icy plumes that precede severe thunderstorms and potentially deadly tornadoes. The study reveals a hydraulic jump phenomenon triggered by fluid obstacles in the atmosphere, leading to rapid water vapor injection into the stratosphere.
Researchers found that late-season weeds had a significant impact on corn yields, with minimal control resulting in an average loss of 50% and exacerbating crop losses under hot or dry conditions. The study suggests that climate change is not the only factor affecting corn yield, but rather its interaction with weeds.
Researchers are working to improve space weather forecasting to prevent power grid damage and satellite communications disruptions. The University of Michigan's Space Weather Modeling Framework uses a global representation of Earth's Geospace environment to predict magnetic disturbances on the ground.
Naoko Sakaeda, a University of Oklahoma weather researcher, has received a $652,000 NSF CAREER award to study tropical meteorology and improve global weather forecasting. Her research aims to better understand the dynamics of tropical clouds and precipitation at various scales.
Researchers found that high-impact weather events can be extreme, especially in context of human activities. Detailed hypothermia risk forecasts were not available for the race site, contributing to tragedy. The study highlights the need for improved warning systems and collaborations among scientists, governments, social media, and ci...
Researchers developed an AI method called CU-net to improve weather forecast accuracy by learning from uncertainty. The model corrected four weather variables and showed improved performance using 10-year data, promising innovative diagnostics for climate variability.
Climate scientists have developed an improved three-month preseasonal forecast for the Indian monsoon onset using machine learning. The predictions show accuracy of +/- 4.8 days and cover climate changes since 1948, offering a promising basis for future research.
Researchers attribute the record-breaking meiyu rainfall in the Yangtze River basin to an anomalous western North Pacific subtropical high pressure system. This pressure system's change and associated air-sea interactions were found to be independent of ENSO, providing accurate forecasts despite neutral ENSO conditions.
The University of Houston's Air Quality Forecasting and Modeling Lab has developed an artificial intelligence system that can accurately predict ozone levels up to two weeks in advance. This breakthrough could lead to improved ways to control high ozone problems and contribute to solutions for climate change issues.
The use of additional Metop-C and Fengyun-3 C/D data has been shown to improve regional weather forecasts by filling existing data gaps. By incorporating more PMW satellite radiances, scientists can enhance the quality of forecast models, leading to better short-range predictions.
Researchers from The University of Tokyo Institute of Industrial Science have developed a new flood forecasting system that can predict extreme flooding events with a 32-hour lead time. This system is based on models of land surface and river routes, combined with meteorological data and statistical analysis.
A new mathematical model developed by KAUST and KCST improves pandemic prediction accuracy by incorporating biological uncertainty and latest case data. The model, which uses an ensemble approach and data assimilation process, provides reliable forecasts for up to 14 days in advance.
Researchers used simulations to explore the fate of landfalling hurricanes, discovering that cold cores grow from the bottom of the hurricane as it decays. This unexpected finding could help forecasters predict extreme weather events and distinguish between decaying and re-intensifying storms.
A KIT research team developed a new statistical method to improve global forecasts, enabling more accurate seasonal meteorological forecasts on the regional level. This can help mitigate local consequences of climate change, such as droughts and floods, by providing early warning of wet or dry periods.
A new forecasting method has been developed to improve TC intensity forecast skill, providing more precise 5-7 day forecasts for coastal regions of East Asia. The logistic growth equation-based approach outperforms conventional methods, offering potential for forecasting rapid intensification and weakening.
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 computer simulation tool called PoPS predicts when and where pests and diseases will attack crops or forests, helping manage strategies and pesticide applications. The tool improves with data feedback from natural resource managers, enabling more accurate predictions of future spread.
Researchers developed new software for improved space weather prediction, leveraging supercomputers and advanced computing techniques to analyze magnetized solar wind plasma. This effort aims to enhance the accuracy of predictions for coronal mass ejections and their impact on Earth's magnetosphere.
A space physicist from UTA is collaborating with an international team to study the impact of coronal mass ejections on earth's magnetic field and technological systems. The research aims to improve forecasting and mitigation capabilities for solar flares, which can cause widespread blackouts.
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.
Researchers will use accurate weather forecasts to anticipate food shortages, poaching, and other threats to elephants, allowing conservation groups to take action weeks or months in advance. The project aims to reduce human-elephant conflict and help at-risk species withstand climate change.
Researchers at the University of North Carolina developed a data assimilation method to improve multi-day forecast accuracy of coastal water levels. The method yielded substantially smaller errors in water level estimates and is now used by NOAA's Extratropical Surge and Tide Operational Forecast System.