Articles tagged with Weather Forecasting
Tiny droplets of sea spray can affect hurricane intensity, but measuring their concentration and size is challenging. Researchers at the University of Texas at Dallas are using lab experiments and simulations to develop a machine learning model that incorporates these factors.
A new framework developed by Northwestern University and UCLA scientists integrates various water-related processes with a machine-learning model to predict landslide threats. The framework identifies three main pathways leading to landslides: intense rainfall, rain on already saturated soils, and melting snow or ice.
Researchers developed a novel deep learning-based framework that improves five-day regional weather forecasting accuracy, even with limited data. The method achieved significant improvements in temperature, precipitation, and wind speed forecasts, outperforming mainstream global AI models.
A new study published in Weather and Forecasting found that Hurricane Hunter flights can increase forecast accuracy by up to 24 percent. The researchers analyzed forecasts for all tropical cyclones in the Atlantic basin from 2018 to 2022, comparing forecasts with and without NOAA G-IV data.
A study found that declining aerosols are up to 2.5 times more influential than greenhouse gases in driving changes in heatwave occurrence in populated areas. Higher levels of aerosols suppressed heatwave exposure by reflecting the sun's rays, but this trend is now reversing due to clean air policies.
Researchers found that indirect effects of tropical cyclones contribute more than twice the direct effect to rainfall during the Habagat season, with 51.5% coming from the monsoon itself. This new understanding can improve extreme weather anticipation and disaster response.
A new study reveals how variations in the stratospheric polar vortex affect where and when extreme cold hits, with a focus on the northwestern US. The research finds two distinct patterns that steer Arctic air into different areas, leading to colder winters in regions like Montana and Texas.
The CNOP-DL method extends classical CNOP for deep learning methods, breaking deterministic causality and attributing forecast errors to all input slices. This new structure identifies critical time steps and locations where additional observations can significantly improve forecasts.
A new study predicts that a 2°C temperature rise could double the frequency of short-lived summer downpours in the Alpine region, causing severe damage and posing risks to lives. The analysis of nearly 300 mountain weather stations found that warm air retains more moisture, intensifying thunderstorm activity.
Researchers estimate that temperatures several degrees above the 2022 UK heatwave are plausible in today's climate, with a simulated maximum of over 45°C (113°F). The likelihood of 40°C will continue to rise as the climate warms.
Scientists at the Naval Research Laboratory are working with NASA to better understand pyrocumulonimbus clouds, which can inject smoke into the upper atmosphere and alter weather patterns. The Injected Smoke and Pyrocumulonimbus Experiment (INSPYRE) aims to enhance predictive weather modeling through detailed analysis of wildfire-induc...
The American Meteorological Society has awarded its 1,000th CBM designation to Ryan Marando, a meteorologist at WHIO-TV in Dayton, OH. This milestone recognizes the expertise of on-air weather communicators in both meteorological science and forecasting.
The Florida Atlantic University Sensing Institute's SEA Econet network provides accurate rainfall and flood forecasting, supporting public safety during severe weather events. With over 160 atmospheric stations and more than 30 water-level stations across Florida, the network delivers real-time data to inform forecasts and warnings.
A team of researchers developed a machine learning model called Aurora that accurately forecasts various Earth systems, including air quality and tropical cyclone tracks. The model outperforms traditional systems at a fraction of the cost, enabling better preparedness for extreme weather events.
A new study projects that wind losses for homeowners in the Southeast will increase by 76% by 2060 and 102% by 2100. Texas is expected to experience the highest increase in losses, followed by Louisiana, Mississippi, and Alabama.
Researchers have discovered that urban areas experience a greater difference in wet bulb globe temperature between 0.5 m and 1.5 m above ground due to surface heat radiation, posing health risks to children and pets during extreme heat events. Total solar eclipses also alter atmospheric conditions and insect behavior, with some species...
A new study reveals that the Pacific Meridional Mode plays a major role in Hawaiian rainfall variability, particularly in spring. The research suggests that El Niño emerges as the primary driver of winter rainfall, but the PMM is crucial for spring rainfall, especially on Maui and the Island of Hawai‘i.
A new study reveals the atmosphere can store moisture over extended periods, creating a physical memory effect. This 'memory' allows monsoon systems to flip between two stable states, with severe consequences for regions relying on monsoon rainfall.
A 19-year study finds that 20,000 people died from heatstroke and 15,000 from cold exposure in India between 2001 and 2019. The study identifies working-age men as most vulnerable to heat-related deaths, highlighting the need for measures such as shaded parking areas and relief provisions.
Researchers develop mathematical modeling to predict aflatoxin outbreaks in Texas using remote sensing satellites and soil properties. The model has the potential to save farmers billions of dollars in losses by providing early risk prediction and targeted prevention strategies.
The American Meteorological Society warns that catastrophic cuts to federal science agencies like NOAA threaten the US weather enterprise, which supports public safety, private sector operations, and national security. A strong weather enterprise is essential for America's economic leadership.
Researchers used satellite data to analyze the impact of dust on snowmelt in the Colorado River Basin. The study found that dust-driven melting tends to peak earliest and be most intense in central-southern Rocky Mountains, accelerating spring melt rates by up to 1 mm water-equivalent per hour.
The US Naval Research Laboratory showcased its latest advancements in defense technology, including the OmniGlobe, a large spherical display visualizing Earth's environmental data. NRL's PROTEUS tool provides near real-time global tracking and analysis of maritime vessels.
A team of climate scientists found that six major oceanic modes influence prolonged heavy rainfall in China, which can lead to severe flooding. Winter sea temperatures in the tropical Pacific can predict summer flood potential with 75% accuracy.
Researchers have developed a new forecasting tool called iDust that offers significant benefits for solar energy production by predicting dust storms with higher accuracy. The system provides critical support for China's expanding solar energy projects in desert regions, minimizing disruptions and financial losses.
A team of scientists analyzed chemical signatures in hailstones to determine their growth histories, finding most hailstones follow simple trajectories rather than the previously assumed recycling motion. The study identified key thresholds for hail growth and suggests that strong updrafts are essential for severe hailstorms.
Recent research highlights increased fire activity in the western US, with wildfires becoming less prone to calming down at night. Meanwhile, extreme turbulence on hurricane flights has led to a new 'bumpiness' metric. Climate extremes are also evident in shifting energy demands for heating and cooling in Chinese megacities.
A new study by the UK Centre for Ecology & Hydrology reveals that soil moisture levels can increase rainfall area and amount in megastorm hotspots globally by up to 30%. This contrast results from atmospheric changes, enabling communities to better adapt to climate change.
The Albert Einstein Jewish Brazilian Hospital launches a project to evaluate the application of quantum computing in developing new drugs and improving disease diagnosis. Researchers aim to use machine learning and quantum optimization algorithms to analyze rainfall data and predict heavy rainfall events.
Researchers evaluate traditional precipitation phase partitioning methods and machine learning models, revealing near-freezing temperatures create inherent limitations in distinguishing between rain and snow. Accurate identification is critical for weather forecasting, hydrologic modeling, and climate research.
A recent study assesses the forecasting skill of subseasonal ensemble models for extreme cold events in East Asia, revealing that some ensemble members exhibit significantly high forecasting skill. These high-skill members can accurately predict rapid changes in surface air temperature and minimum temperature during an event.
The NRL-developed Narrow Field Imager is a compact coronagraph that will image the transition of the Sun's atmosphere to the solar wind, gaining insights into space plasma environments. The PUNCH mission aims to improve prediction and mitigation of space weather events like coronal mass ejections.
Aardvark Weather, a fully AI-driven system, delivers accurate forecasts tens of times faster and using thousands of times less computing power than current systems. The new approach transforms weather forecasting, providing bespoke forecasts for specific industries or locations.
Researchers developed a numerical tool to quantify sunlight intensity and its influence on plant growth, enabling accurate predictions of sunlight patterns. The model can help farmers optimize greenhouse conditions and planting schedules, leading to improved crop yields.
The University of Texas at Arlington (UTA) has been awarded a $1 million NASA grant to develop safety systems for drones and unmanned flying vehicles. The project aims to create an adaptive safety assurance architecture through extensive simulations and experimental testing, making future advanced air mobility vehicles smarter and safer.
A new study shows that climate change affects short-term precipitation events lasting hours by significantly increasing their frequency, while longer-term rainfall events lasting days are influenced by global weather phenomena like El Niéo. This distinction is crucial for assessing the risk of flooding in different regions.
The PUNCH spacecraft will study the solar corona and track space weather events in three dimensions for the first time. The constellation includes four small suitcase-sized spacecraft that will provide a clear view of the Sun's outer atmosphere, allowing scientists to discern the exact trajectory and speed of coronal mass ejections.
Climate change drives large increases in electricity demand and costs in Texas due to extreme temperatures. Meanwhile, atmospheric rivers become more frequent, larger, and moister globally. Diagnostic studies also predict malaria outbreaks with five-month lead time using sea-surface temperature anomalies.
The US weather enterprise faces significant risks due to federal science funding cuts, which could lead to reduced weather forecasting accuracy and increased vulnerability to hazardous weather. The value of weather information to the US economy exceeds $100 billion annually.
Research in Beijing reveals that differences in wind fields and thermodynamic conditions hinder or enhance thunderstorm cluster movement, with cold pools acting like topographical features to strengthen convergence. The study analyzed a specific merger process using simulation data from the Weather Research and Forecasting Model.
A new study projects that tropical cyclone numbers in the Atlantic could double compared to 1970s levels over the next decade. The total energy of these storms is also predicted to increase dramatically, with storm energy rising to twice its 1970s levels in the North Atlantic.
A new study improves solar power forecasts by applying machine learning and post-processing techniques to weather models, revealing the time of day as a crucial factor for accuracy. The research suggests that including hourly data in algorithms can significantly enhance forecasting results.
Researchers investigated the impact of AI weather models' lateral boundary conditions on convective-scale ensemble forecasts, finding comparable performance to traditional models. Reducing the vertical resolution of these conditions led to inferior forecast results.
MIT researchers have developed a new approach to assess predictions with a spatial dimension, leading to more accurate forecasts in spatial prediction tasks. The new method provides better validations than classical methods, especially in cases where assumptions about data independence break down.
A study found that excessive Tibetan Plateau spring warming was the primary factor driving catastrophic June 2024 heavy rainfall in southern China. The research used the ESM to simulate extreme warm temperatures over the TP and heavy rainfall in S. China, reproducing approximately 55% of the observed anomaly.
A recent study has employed machine learning algorithms to improve the accuracy of flood season rainfall predictions. The findings show that combining climate system numerical models with ML-based correction methods results in substantial improvements, increasing prediction scores by up to 7.87%.
A new contrastive learning model has been developed to forecast cyclone rapid intensification (RI) with high accuracy, reducing false alarms by a factor of three compared to existing techniques. The model achieved an impressive 92.3% accuracy when tested on data from the Northwest Pacific between 2020 and 2021.
Researchers analyzed tropical storm-related precipitation to understand its impact on local water resources and provide insights into climate predictions. The study aims to help communities prepare for extreme storms and manage water resources before and after the storms.
Researchers have identified a critical link between tropical ocean temperatures and rainfall patterns in the Middle East, shedding light on the complexities of forecasting seasonal weather. The study found that positive phases of the El Niño Southern Oscillation and Indian Ocean Dipole significantly increase rainfall, while negative ph...
Researchers introduced a novel approach to enhance reservoir computing, incorporating a generalized readout that offers improved accuracy and robustness compared to conventional methods. The new method uses a nonlinear combination of reservoir variables to uncover deeper patterns in input data.
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.
A team led by Penn State's College of Information Sciences and Technology will use computer vision and machine learning to accelerate weather forecast processing, improving predictions and reducing delays. The research aims to integrate satellite data into current forecasting models, enhancing accuracy and speed.
2024 saw exceptional rainfall and flooding due to El Niño in winter 2023/24. Human-induced climate change exacerbated these events, causing socioeconomic impacts. Improved forecasting, warning dissemination, and 'climate-resilient' approaches are crucial for mitigating extreme event effects.
A team led by the University of Tokyo has created a nearly 20-year-long dataset of the entire atmosphere, enabling new research on previously difficult-to-study regions. The dataset spans multiple levels of the atmosphere from ground level to the lower edge of space and could improve climate modeling and seasonal weather forecasting.
Researchers at the University of New Hampshire developed an AI-powered algorithm to categorize over 706 million aurora images from NASA's THEMIS data set. This labeled database can help scientists better understand and forecast geomagnetic storms that disrupt vital communications and security infrastructure.
Early online research reveals associations between snowmelt timing, wildfires in Alaska, and rapidly intensifying tropical cyclones. The impact of climate patterns on extreme weather events is a growing concern.
A special collection addresses solar energy's environmental and technical issues, combining atmospheric science and solar engineering expertise.
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.
A new study enhances phenology predictions using a 150-year-old dataset, revealing that plant species in the US are flowering three to four weeks earlier. The research used historical observations from a rediscovered 19th-century report to improve forecast accuracy.