When two tropical cyclones collide in the Indian Ocean, they can intensify considerably, leading to extreme interactions between the ocean and atmosphere. The study found that effects occurred that have only been observed with much stronger cyclones, including a cooling effect of three degrees Celsius and upwelling of deep water masses.
Researchers at the University of Colorado Boulder have discovered that relatively warm and sunny days may help to trigger major dust storms on Mars. The team found that roughly two-thirds of these storms are preceded by a sharp rise in surface temperatures, which can lead to explosive weather patterns.
Researchers used CT scans to examine giant hailstones formed during a 2022 thunderstorm in Catalonia. The study revealed irregular internal shapes and heterogeneous growth patterns, overturning previous assumptions about hailstone formation.
The US Naval Research Laboratory (NRL) will present its latest advancements in Earth and space sciences at the American Geophysical Union (AGU) Conference. NRL researchers will share their work on topics such as atmospheric data assimilation, ocean sciences, and geostationary ocean color.
A new study from Tel Aviv University uses smartphone data to predict wildfire risk, overcoming individual device errors by averaging large amounts of public data. The method provides valuable insights into wildfire evaluation, especially in remote areas lacking traditional weather stations.
Researchers at University of Liverpool develop new method to measure ocean memory, revealing the North Atlantic Ocean has a nearly two-decade memory. This surpasses previous estimates and highlights the importance of ocean circulation in climate system predictability.
A new machine learning algorithm reconstructs wind fields quickly and accurately, even with limited observational data. This enhances storm forecasting and hazard preparedness by providing valuable data on a tropical cyclone's intensity and potential impact.
Researchers developed a machine-learning tool that provides accurate predictions for flood-prone areas, using historical data and weather-based predictors. The model can predict short-term river discharge with high accuracy, giving real-time data on water movement through the river.
The new system uses AI technology combined with Earth observation data to detect risks of events that might trigger forced displacement, delivering timely alerts ahead of emergencies. It will help humanitarian actors plan and respond more effectively, minimizing response times and avoiding duplication of efforts.
Researchers analyzed data from southern Israel to find significant electric field changes during heavy precipitation, suggesting early indicators for extreme weather. The study highlights the potential of incorporating electric field observations into weather monitoring systems for enhanced nowcasting capabilities.
A global convection-permitting model developed by a team from USTC accurately predicted the 2020 plum rain event in Japan, capturing its intensity and location. The model improved forecast accuracy at high resolutions, aligning well with observational data.
The Macrosystems EDDIE modules have been effective in building student and instructor quantitative literacy and data science skills in ecological forecasting, reaching over 35,000 students globally. The modules aim to introduce students to core concepts of forecasting and complement educators' work teaching ecological concepts.
A new equation developed by a UChicago scientist provides a powerful framework for understanding the processes driving atmospheric rivers. The integrated vapor kinetic energy (IVKE) model sheds light on key factors contributing to extreme weather patterns, including heavy rain and strong winds.
Researchers developed a statistical seasonal forecasting model to predict typhoons landing on Taiwan Island by mid-May, achieving an accuracy rate of 98% for the period 1979-2022. The model utilizes four pre-typhoon-season environmental predictors and will benefit disaster prevention and mitigation efforts.
Researchers at FSU and South Korea have improved hurricane intensity forecasting by accounting for the impact of sea spray on storms. By analyzing data from hurricane hunter airplanes, they found that sea spray increases heat and moisture in the atmosphere, leading to more accurate intensity forecasts.
Researchers estimate the dollar value of weather information to be over $100 billion, highlighting its significant economic impact. The study also explores lightning suppression and a unique 'storm generator' technology that can create thunderstorms in Chinese desert.
A team of OU scientists, led by Nathan Snook, will use deep learning techniques to analyze numerical simulations of tornadoes. The goal is to improve tornado forecasting by identifying key factors that influence their formation.
A new study shows that urban forests within walkable distance from residential areas are crucial in reducing heat-related health risks. Researchers found that nearby forests have a pronounced impact on reducing mortality risks, particularly those within 1 kilometre of residential areas.
Research explores changes in global precipitation patterns and how they relate to climate change. Bird nests are found to predict rainfall in rural India, while heat stress is increasing in the southeastern US, particularly at night. European 'bomb' storms may intensify due to warming climate.
A new model developed by scientists can predict intense, localized heavy rainfall events caused by rapid air rises in clouds. This breakthrough offers potential to improve public safety and preparedness through early warning systems.
A WVU statistician is developing new methods to analyze curve-type data for predicting extreme weather patterns, financial turmoil and pollution levels. The research aims to create innovative tools for understanding high-resolution data and addressing regional risks in mountainous states like West Virginia.
The American Meteorological Society recognizes outstanding contributions to the weather, water, and climate community with its 2025 Awards and Honors. This year's honorees have made significant impacts on scientific advancement and public safety, including warnings of wildfires and refinements in modeling techniques.
Researchers have discovered that changes in the ocean's mixed layer are the primary force behind Atlantic Multidecadal Variability (AMV) in the tropics. This phenomenon influences weather patterns across North America, Europe, and Africa, affecting hurricane activity and rainfall in regions like the Sahel.
Research on wind farms reveals their potential to slow tropical cyclone winds and reduce precipitation. Additionally, a study found that people experiencing homelessness are four times more likely to suffer temperature-related illnesses, such as heat stroke, than the general population.
A new study found that strong El Nino events make it easier to forecast European winters, allowing for more accurate predictions of temperature and precipitation patterns. The research team analyzed 30 years of winter forecasts from seven different prediction systems and identified common factors influencing predictability.
Researchers found that turbulent movements of air in clouds play a key role in the growth of droplets and initiation of rain. Simulations with turbulence showed rain formed around 20 minutes earlier and with seven times more rainwater than simulations without turbulence.
A new study finds that human-induced climate warming has driven increasing precipitation variability over much of the globe, with hotspots in Europe, Australia, and eastern North America. The amplified variability poses significant threats to climate resilience, infrastructure development, agriculture, and ecosystems.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
The University of Maryland has been awarded a five-year, $388 million cooperative funding agreement with NOAA to conduct collaborative research in Earth system science. The Cooperative Institute for Satellite Earth System Studies (CISESS) will continue its national leadership in this field.
Researchers have developed a new method to simulate Convectively Coupled Kelvin Waves in weather forecast models, which could enhance accuracy for predicting hurricanes and heavy rainfall. The study found that current models poorly simulate these waves, indicating a need for future improvements.
A recent study highlights the need for effective contingency plans and local community involvement in disaster response to mitigate losses. The analysis of landslides in São Sebastião, Brazil, revealed a breakdown in the early warning system, resulting in significant economic and human costs.
A new model developed by researchers at the University of São Paulo combines physical parameters and machine learning to predict storm tides. The model uses physics-informed machine learning, which harmonizes physical models with measured data to produce more precise forecasts.
Researchers used a weather model to pinpoint the source of wintertime precipitation in Arizona, finding it comes from a central Pacific moisture source rather than El Niño/La Niña events. The study's findings could improve seasonal precipitation forecasts for the region and potentially other areas globally.
Researchers analyzed a 17-minute film of the eruption to understand how the ash cloud grew upwards and outwards. They calculated the eruption rate, which determines ash cloud extent, and found that it dropped by about half during the eruption.
Texas A&M University researchers have developed a 3D visualization technology to identify potential outcomes of hurricane flooding before it occurs. This allows for improved safety and reduced damage costs. The technology can also model specific buildings, accounting for basements, back entrances, and windows.
A team of researchers found that air turbulence in the thermosphere exhibits the same physical laws as wind in the lower atmosphere, leading to a new unified principle for Earth's environmental systems. This discovery can potentially improve future forecasting of both Earth and space weather.
Researchers have discovered that the sun's magnetic field is generated about 20,000 miles below its surface, contradicting previous theories. This finding helps scientists better understand the sun's dynamic processes and could lead to more accurate forecasts of powerful solar storms.
Climate change is driving sudden onset of drying in South America and southern Africa, while Central Asia sees increased moisture due to warming temperatures
The University of Washington's STRIVE project aims to understand the troposphere and stratosphere, where the ozone layer resides, and their interface. This will help monitor changes in the ozone layer, air quality, and climate processes.
Researchers developed an impact-based forecasting system that combines various models to predict flood impacts with high resolution. The system enables early warnings for evacuation, reducing population and property damage.
A recent study by University of Nebraska-Lincoln professor Cory Armstrong found that roughly 50% of those surveyed in the mid-south region couldn't accurately define a tornado warning. Meanwhile, 35% correctly identified a warning as a tornado being sighted or indicated on weather radar.
A new model combines wildfire smoke forecasts and ground-based sensor data to help public health officials plan targeted interventions. The researchers found that urban and rural communities already burdened by pollution face higher air pollution levels during unexpected smoke events.
A new computer model uses improved artificial intelligence to predict snow and water availability more accurately, considering both time and space. This information can help farmers and water planners make better decisions about water allocation.
Researchers found that modern weather models can accurately predict satellite movements affected by weather events like tropical cyclones with tall clouds. This understanding enhances satellite tracking and control, improving efficiency and reliability of satellite operations.
Climate scientists predict a 7 in 10 chance of record-breaking temperatures and humidity in the tropics, threatening humanitarian aid and medical care. El Niño's impact on tropical heat and humidity is expected to compound the effects of global warming, leading to extreme humid heat stress.
A team of over 100 scientists from Brazil and the US ran high-resolution simulations of South America's past and future climate using a weather research and forecasting model. The goal is to better understand hydroclimatic processes and inform adaptation measures for millions affected by climate change.
According to GIST researchers, extreme cold events (WACC) have intensified until the 2020s but are expected to decline post-2030s. This drastic shift in WACC trajectory has significant implications for global climate systems and weather patterns.
A new decision support system will utilize existing technology to project scenario-based outcomes for producer operations based on possible fluctuations within factors such as continuing drought or changes in input costs. The tool aims to help farmers consider economic, environmental, and production challenges together in one place.
A study from Colorado State University explores how human activity is altering the atmospheric water cycle, leading to changes in precipitation distribution and cloud formation. The research uses science fiction narratives to imagine possible futures and spark conversations around water management policies.
The UK's 2022 drought, the worst since 1976, emphasized the importance of early mitigation measures due to its rapid onset and extensive impacts on water resources, wildlife, and people. Improved monitoring and forecasting systems can help prevent similar extreme weather events in the future.
The University of Leeds says Africa needs significant investment in scientific infrastructure and science careers to adapt to climate change. Gaps in understanding the science behind tropical weather systems hinder African weather forecasting, leaving half the population without early warnings.
A new MIT-derived algorithm corrects coarse climate model predictions by 'nudging' them toward more realistic patterns, leading to more accurate forecasts of extreme weather events. The approach uses machine learning and dynamical systems theory to improve the resolution of large-scale climate models.
A new measure called “outdoor days” describes the number of days per year that outdoor temperatures are neither too hot nor too cold for people to go about normal outdoor activities. This measure reveals significant global disparities, with the North gaining more outdoor days and the South losing them due to rising temperatures.
A Penn State technique combining satellite data with existing computer weather forecast models produces more accurate forecasts of surface gusts in severe thunderstorms. This method can be especially useful in areas lacking ground-based weather monitoring infrastructure, such as radar systems.
A new study finds that extreme tropical cyclone rainfall is increasing across the continental US, while climate warming restricts 'outdoor days' for people living in developing countries. Additionally, air pollution increases mortality risks from heat, especially when combined with other factors like wildfires and reduced aerosols.
Virginia Tech researchers have received a five-year National Science Foundation grant to develop an open-source automated forecasting system for lakes. The system will enable real-time data collection and publishing, allowing researchers to predict water quality in lakes around the globe.
Researchers found that land-atmosphere coupling has a significant impact on Asian monsoons, but its effect varies greatly from year to year. The study suggests that better modeling of land conditions may be crucial for improving seasonal forecasts in the region.
Researchers have developed a non-crossing quantile regression neural network (NCQRNN) model to enhance the statistical reliability of weather forecasts. The NCQRNN model preserves the rank order of output nodes, ensuring lower quantiles stay smaller than higher ones, boosting accuracy and improving forecast interpretability.
A new study reveals that increased meltwater in the North Atlantic can trigger hotter and drier European summers. The study suggests a predictable chain of events leading to European summer weather, due to higher levels of freshwater in the North Atlantic.
Researchers found no clear link between weather conditions and muscle and joint pain, debunking a long-standing medical myth. High temperatures, low humidity, and dehydration may increase the risk of gout flares.