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Climate change and sea level rise pose an acute challenge for cities with combined sewer systems

Researchers at Drexel University model the potential extent of flooding and combined sewer overflows in Camden, New Jersey, as climate change exacerbates the problem. The team's 'all-pipes' model simulates stormwater flows through every surface and pipe in the area, providing a unique solution to mitigate environmental and health risks.

SourceDrexel University·JournalJournal of Water Management Modeling·TypeComputational simulation/modeling·DateJun 27, 2024

El Niño forecasts extended to 18 months with innovative physics-based model

A new conceptual model, XRO, significantly improves predictive skill of ENSO events at over one year in advance, offering a transparent view into the mechanisms of equatorial Pacific recharge-discharge physics. This improves conventional climate model forecasting and provides robust quantification of extratropical Pacific, tropical Ind...

SourceUniversity of Hawaii at Manoa·JournalNature·TypeMeta-analysis·DateJun 26, 2024

Model combines physical parameters and machine learning to predict storm tides

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.

SourceFundação de Amparo à Pesquisa do Estado de São Paulo·JournalProceedings of the AAAI Conference on Artificial Intelligence·DateJun 20, 2024

Computer modelling shows where Arizona’s winter precipitation originates

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.

SourceArizona State University·JournalJournal of Geophysical Research Atmospheres·TypeComputational simulation/modeling·DateJun 19, 2024

The secret sex life of coral revealed

A study published in Royal Society Open Science reveals that corals use multiple environmental inputs to synchronize their spawning timing. Water temperature is identified as the primary trigger for determining the annual window of opportunity, with rainfall and solar radiation also playing a role.

SourceUniversity of Tokyo·JournalRoyal Society Open Science·TypeObservational study·DateMay 28, 2024

Recent air quality improvements in India partially due to meteorological variation

A recent study found that approximately 30% of India's PM2.5 air quality improvements can be attributed to favorable meteorological conditions, highlighting the need for additional mitigation measures. The researchers also noted that emissions of key PM2.5 precursors have not decreased and may indicate ineffective or under-enforced pol...

SourcePrinceton School of Public and International Affairs·JournalNature Sustainability·TypeComputational simulation/modeling·DateMay 6, 2024

Climate change amplifies severity of combined wind-rain extremes over the UK and Ireland

New research shows climate change will cause more severe combined wind-rain extremes in the UK and Ireland, leading to increased flooding and damage. The study found that stronger winds and heavier rainfall are likely to occur together, posing challenges for coastal areas and emergency response resources.

SourceNewcastle University·JournalWeather and Climate Extremes·TypeComputational simulation/modeling·DateMay 3, 2024

AI weather forecasts captured Ciaran’s destructive path

A new study by the University of Reading demonstrated that AI weather forecasts can predict storm paths and intensities with similar accuracy to traditional models. However, AI systems underestimated Storm Ciaran's maximum wind speeds due to limitations in predicting certain atmospheric features.

SourceUniversity of Reading·Journalnpj Climate and Atmospheric Science·TypeObservational study·DateApr 22, 2024

Unraveling the song of ice and fire across the American landscape with machine learning

A recent machine learning study has discovered a surprising link between wildfires in the western United States and hailstorms in the central US. The research, led by Jiwen Fan, used ML algorithms to analyze vast datasets spanning two decades, predicting hail storms with remarkable accuracy.

SourceInstitute of Atmospheric Physics, Chinese Academy of Sciences·JournalAdvances in Atmospheric Sciences·DateApr 11, 2024

System uses artificial intelligence to detect wild animals on roads and avoid accidents

A team of researchers developed an AI-powered computer vision model to detect Brazilian wild animals on roads and warn drivers in real-time. The system uses roadside cameras and portable computers to identify species such as anteaters, wolves, and tapirs, with the potential to save lives and reduce roadkill.

How extratropical ocean-atmosphere interactions can contribute to the variability of jet streams in the Northern Hemisphere

Researchers from Kyushu University found that ocean-atmosphere coupling enhances teleconnection patterns, leading to more meandering jet streams and extreme weather events. The study highlights the significance of extratropical ocean-atmosphere interactions in climate variability.

SourceKyushu University·JournalCommunications Earth & Environment·TypeComputational simulation/modeling·DateMar 28, 2024

Revolutionizing field phenotyping: A novel glare correction technique using polarized light

A new method for correcting glare in plant phenotyping has been developed, using polarized light to improve accuracy and reduce complexity. The technique has been validated in field trials, showing significant improvements in image data accuracy and reduction of error and variance.

SourceNanjing Agricultural University The Academy of Science·JournalPlant Phenomics·TypeExperimental study·DateMar 19, 2024

Enhancing crop productivity analysis: a novel approach using SIF and PRI for accurate GPP estimation in rice canopies

Researchers develop a novel approach using SIF and PRI to estimate GPP in rice canopies, showing significant correlations between these indexes and GPP across various timescales. The study reveals the dynamic responses of PRI/SIF to environmental conditions and demonstrates improved correlation by distinguishing between shaded and sunl...

SourceNanjing Agricultural University The Academy of Science·JournalPlant Phenomics·TypeExperimental study·DateMar 13, 2024

Cloud clustering causes more extreme rain

A new study using a high-resolution global climate model found that cloud clustering causes more extreme rain in the tropics, leading to increased severity of precipitation events. The researchers also discovered that more extreme rain occurs at the cost of expansion of dry areas, further shifting towards extreme weather patterns.

SourceInstitute of Science and Technology Austria·JournalScience Advances·TypeComputational simulation/modeling·DateFeb 23, 2024

Pusan National University researchers decode key airflow pattern impacting global climate

A recent study published in npj Climate and Atmospheric Science reveals that changes in subtropical and midlatitude eddy activity control the variation of the Hadley cell edge latitude. The researchers analyzed 41 years of data and found associations with El Niño, La Niña, and the Arctic oscillation.

SourcePusan National University·Journalnpj Climate and Atmospheric Science·TypeComputational simulation/modeling·DateJan 31, 2024

Shape matters: How microplastic travels that far

A new study reveals that microplastic fibers settle substantially slower than spherical particles in the atmosphere, allowing them to reach remote regions such as Arctic glaciers. The research suggests that these fibers could even reach the stratosphere, with potential implications for cloud processes and ozone depletion.

SourceMax Planck Institute for Dynamics and Self-Organization·JournalEnvironmental Science & Technology·TypeExperimental study·DateJan 9, 2024

Korea Maritime & Ocean University researchers develop a new method for path-following performance of autonomous ships

Korea Maritime & Ocean University researchers have developed a new method for assessing the path-following performance of autonomous ships in adverse weather conditions. The computational fluid dynamics model can provide more accurate predictions of path-following performance and enhance safety in autonomous marine navigation.

SourceNational Korea Maritime and Ocean University·JournalOcean Engineering·TypeComputational simulation/modeling·DateJan 3, 2024

Predictive models augur that at the end of the century fields will need more water than today

Researchers from the University of Córdoba used machine learning models to predict reference evapotranspiration in Southern Spain until 2100. The projections indicate a significant increase in water needs, with air temperature being the key factor in calculating this parameter.

SourceUniversity of Córdoba·JournalComputers and Electronics in Agriculture·TypeData/statistical analysis·DateDec 11, 2023

Improving thunderstorm prediction by watching lightning flashes from space

A new study improves thunderstorm forecasting by utilizing Geostationary Operational Environmental Satellite (GOES-R) lightning flash observations. The implementation of the EnVAR assimilation capabilities successfully enhances the forecasting performance, especially over regions lacking operational weather radar observations.

SourceInstitute of Atmospheric Physics, Chinese Academy of Sciences·JournalAdvances in Atmospheric Sciences·DateDec 6, 2023

Riding the whims of the wind

Researchers develop a mathematical model that analyzes the future survival of plants in a changing climate by studying how far wind can carry seeds. The model provides fast and reliable predictions of seed movement, considering factors like seed type, plant height, and wind speed.

SourceUniversity of Missouri-Columbia·JournalEcological Modelling·DateNov 30, 2023

INU researchers develop novel deep learning-based detection system for autonomous vehicles

A new deep learning-based detection system has been developed by INU researchers to improve the detection capabilities of autonomous vehicles. The system, aided by IoT technology, generates bounding boxes and confidence scores for visible obstacles using point cloud data and RGB images as input.

SourceIncheon National University·JournalIEEE Transactions on Intelligent Transportation Systems·TypeComputational simulation/modeling·DateNov 30, 2023

Improved wind speed forecasts can help urban power generation, according to new Concordia research

A new hybrid method developed by Concordia researchers combines data from Weibull probability distribution and numerical weather prediction models to improve wind speed forecasting accuracy. This innovation has the potential to significantly enhance urban power generation, particularly in areas with high variability in wind speeds.

SourceConcordia University·JournalEnergies·TypeData/statistical analysis·DateOct 31, 2023