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Smart irrigation model predicts rainfall to conserve water

A predictive model combining plant physiology, soil conditions, and weather forecasts helps make informed decisions about irrigation, conserving up to 40% of water used. The research also aims to identify the best method for each crop and determine the costs and benefits of switching to an automated system.

SourceCornell University·JournalIEEE Transactions on Control Systems Technology·DateJul 19, 2019

Using artificial intelligence to better predict severe weather

Researchers developed an AI framework that detects rotational movements in clouds from satellite images, pointing to potentially threatening storm formations. The method achieved 99% accuracy and predicted 64% of severe weather events, outperforming existing detection methods.

SourcePenn State·JournalIEEE Transactions on Geoscience and Remote Sensing·DateJul 2, 2019

Using prevalent technologies and 'Internet of Things' data for atmospheric science

Researchers review existing works on using IoT and crowdsourced data for atmospheric research, highlighting its potential to improve weather forecasting and monitoring environmental processes. The innovative approach can provide valuable insights into atmospheric conditions, such as temperature, pressure, and air pollution.

SourceInstitute of Atmospheric Physics, Chinese Academy of Sciences·JournalAdvances in Atmospheric Sciences·DateJun 13, 2019

Lessons learned from the drift analysis of MH370 debris

A team of scientists at GEOMAR Helmholtz Centre of Ocean Research Kiel used simulation techniques to analyze the drift of MH370 debris and propose new strategies for tracking marine objects. The study found that Stokes drift, caused by surface waves, plays a crucial role in determining the final position of drifting objects.

SourceHelmholtz Centre for Ocean Research Kiel (GEOMAR)·JournalJournal of Operational Oceanography·DateApr 17, 2019

Predictability limit for tropical cyclones over the western North Pacific

Researchers used NLLE approach to estimate predictability limit of TCs over WNP basin, finding a baseline uncertainty of 102 hours comparable to TC intensity. The spatial distribution characteristics reveal varying predictability limits across regions, with highest in eastern WNP and lowest in South China Sea.

SourceInstitute of Atmospheric Physics, Chinese Academy of Sciences·JournalAdvances in Atmospheric Sciences·DateOct 18, 2018