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.
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 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.
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.
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.
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 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.
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.
The Bureau of Reclamation is providing $2.5 million to support 12 projects advancing snow measurement technology and improving water supply forecasting. The initiatives will enhance snow monitoring programs and leverage satellite data to better predict snowmelt and reservoir operations.
Research Scientist Bok Haeng Baek is collaborating with other institutions on a project addressing anthropogenic emission updates and wildfires. The team will use the National Air Quality Forecast Capability Community Emission Testbed to solve these problems, incorporating NOAA satellite remote sensing and data sources.
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.
Researchers used weather radar to create a forecasting system for nocturnal bird migration in the US, finding that just 10 nights of action can reduce risk by 50% for avian migrants passing over an area. This data enables more 'mindful' approaches to protect migrating birds from threats like light pollution and collisions with structures.
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.
A new study by the CMCC Foundation explores the potential of ERA5 reanalysis data to improve landslide early warning systems. By incorporating antecedent slope wetness conditions, these systems can minimize false alarms and improve disaster prevention.
Researchers found that the Kuroshio Current's large meander is responsible for increased humidity and temperature in Tokyo, leading to 160% more discomfort days. The study uses satellite data to examine the impact of ocean currents on regional climate.
A recent study by researchers at the University of Johannesburg shows how AI can forecast municipal solid waste in a large African city. By using machine learning algorithms and combining data from various sources, including census data and landfill site records, the team was able to predict the city's waste management needs until 2050...
A new study provides definitive evidence that shattering drizzle droplets drive explosive 'ice multiplication' events in Arctic clouds. The findings have significant implications for weather forecasts, climate modeling, water supplies, energy, and transportation infrastructure. The research used six years of data from a millimeter-wave...
A new method to rate tornado warning skill improves understanding of forecast challenges, particularly nighttime and summer twisters. The study finds that these events are harder to predict due to unfavorable conditions and fewer eyewitness reports.
Researchers at Cornell University are developing a hyperlocal weather forecasting system to improve winter-storm emergency response in rural New York communities. The system, led by professor Max Zhang, will integrate computer vision, numerical weather forecasting, and Internet of Things-based sensing packages.
Researchers explore incorporating Earth observation data into flood forecast-based early action to reduce disaster risk and losses. The study identifies opportunities for using EO data in FbA, focusing on predefined triggers and impact-based forecasting.
A new theoretical model of wave formation in straits and channels accounts for nonlinear effects and improves wave prediction. The research can protect coastline infrastructure and make maritime travel safer.
A new study predicts that the Southeastern United States is at high risk of extreme drought, which could expose its water-supply infrastructure to stress beyond its design limit. The researchers used short-term climate modeling forecasts to highlight the urgency of drought risk and inform policymakers' actions.
Researchers developed a framework to better predict extreme rainfall events in Mediterranean countries by analyzing weather data from 1979 to today. The study found strong relations between nine distinct atmospheric patterns and the location of extreme weather events.
Researchers analyzed temperatures from Dec. 1, 2020, to mid-January 2021, finding that warming in the Arctic and cooling in the tropical Pacific contributed to record-breaking low temperatures across 58 cities in China. The synergistic effect intensified cold air intrusions into mid-high latitudes, influencing extreme cold conditions.
Researchers have found that ECMWF's SEAS5 system accurately predicts the timing of the monsoon in India's major agricultural regions, a month in advance. This information can be crucial for farmers to prepare for unexpected heavy rainfall or extended dry periods, which regularly destroy crops in India.
Researchers have completed a multi-level hydrological tracking of the Yangtze River to investigate cloud formation during the intense mei-yu rainy season. The effort will enable more accurate forecasts of this key meteorological phenomenon in East Asia.
Researchers found that cloudy weather leads to lower investments in risky equity crowdfunding campaigns, with novice investors reacting more strongly. To mitigate this effect, entrepreneurs can target experienced investors or increase marketing efforts on cloudy days.
A group of scientists recommends four major improvements to enhance environmental observations in the Indian Ocean, a region warming faster than others. The enhancements aim to improve weather and climate forecasting for countries bordering its rim, vulnerable to climate change.
Researchers studied severe thunderstorms and lightning activity in Beijing, finding that squall lines and multi-cell thunderstorms are the main types of lightning disaster weather systems. They also discovered a strong relationship between lightning frequency and model parameters, which can be used to improve forecast accuracy.
An international research team proposes a 'glocal' approach to better predict and prevent flooding disasters by combining global forecasting with local observations. The proposed system, GHS-F, aims to provide highly detailed and consistent rain-flood information, which could help avoid some flooding damage.
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.
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 developed statistical methods to track and forecast wildlife populations across borders, revealing detailed dynamics of entire populations across large spatial scales. The study's findings provide new insights into wildlife management and conservation efforts for species like brown bears, grey wolves, and wolverines.
A team of researchers found that cloud-induced infrared radiative feedback creates a localized greenhouse effect, trapping heat and accelerating the formation of tropical cyclones. This process is believed to be instrumental in the origin of many tropical storm events.
New research from Cold Spring Harbor Laboratory highlights the importance of model evaluation in neuroscience. By building and comparing several models of neural signaling, researchers found that good predictive power does not necessarily indicate a model's representation of real neural networks.
The University of Miami is collaborating with Brazilian researchers to share large-scale datasets in real time, improving weather and climate models. This collaboration will test the high-speed FABRIC infrastructure's ability to support fast data exchange.
Researchers at the University of Leeds and the Met Office have developed a predictive tool to identify regions at increased risk of tornadoes on UK cold fronts. The tool uses wind fields ahead and behind the cold front to compute a percentage probability that tornadoes will occur.
Researchers argue that current approaches to attributing extreme weather events to global warming focus too much on raising the threshold for false alarms, neglecting the importance of accurate warnings. By prioritizing probability of detection, climate scientists can strike a better balance between caution and timely warning.
Norbert regained tropical storm strength on Oct. 13, with estimated rainfall rates of up to 30mm/hour near its center. NASA's IMERG satellite product estimated rainfalls ranging from 5-15mm/hour throughout the storm.
Hurricane Delta is expected to produce significant flash, urban, and small stream flooding in Louisiana, with isolated maximum totals of 15 inches. NASA's satellite rainfall product estimated rainfall rates around the center of circulation as high as 50 mm per hour.
A new tool developed by Utah State University scientists can predict drought and water flow in the Colorado River several years in advance. The model uses long-term ocean memory and associated atmospheric effects, as well as filtering effects of land systems to temper short-term precipitation events.
A new study found that including Arctic radiosonde data reduces uncertainty in forecasting hurricanes' tracks, particularly when upper-level troughs affect their movements. This improvement can lead to better preparation and mitigation of human casualties and socioeconomic losses caused by these storms.
NASA's Aqua satellite gathered water vapor data on Hurricane Delta, revealing highest concentrations of water vapor and coldest cloud top temperatures around the center of circulation. The storm is expected to produce heavy rainfall and flash flooding in the northern Yucatan Peninsula.
Typhoon Chan-hom is generating heavy rainfall with rates of up to 30 mm/h in its ragged eyewall. The storm is expected to bring significant rain to Japan, with warnings issued for the southernmost big island.
Tropical Depression 19E formed into Tropical Storm Norbert, while Marie weakened and declined. Norbert is moving northwestward with maximum sustained winds near 45 mph, while Marie will become a remnant low-pressure area within 48 hours.
Tropical Storm Chan-hom is consolidating, indicating a strengthening trend, according to NASA's infrared data. The AIRS instrument captured cloud top temperatures as cold as minus 63 degrees Fahrenheit, indicating strong storms with heavy rain potential.
Tropical cyclones exhibit non-uniform strength, with stronger sides extending higher into the troposphere. NASA's AIRS instrument captured cloud top temperatures of -63°F, indicating a strong storm capable of heavy rain. The storm's structure has improved since Sunday, with deep convection and a more circular shape.
Infrared light analysis from NASA's Aqua satellite shows wind shear pushing Marie's strongest storms east of the center, with cloud top temperatures as cold as minus 50 degrees Fahrenheit. The storm is expected to weaken and degenerate into a remnant low by Tuesday night.
Researchers from Nanjing University of Information Science & Technology evaluate subseasonal prediction skill for heatwaves in Yangtze River basin using long-term hindcast data from three operational models. They found that superior model fidelity is crucial to predicting heatwave occurrence, intensity and duration at longer lead times.