A new record of past Indian monsoon drought history has been developed using oxygen isotopes in stalagmites, correlating with historical accounts of severe droughts and societal changes. Protracted droughts tend to occur in clusters within decades-long intervals of weaker monsoon rainfall.
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.
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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.
Large volcanic eruptions improve monsoon predictability by synchronizing with El Niño events, making it easier to anticipate seasonal rainfall in India. This finding helps develop climate models and assess regional implications of geo-engineering experiments.
Researchers from Ruhr-University Bochum and University of Oxford studied a dripstone from India's Mawmluh Cave to reconstruct the Indian monsoon's past climate. They found the monsoon was less reliable 125,000 years ago, suggesting global warming may lead to similar changes.
A new study finds that strong Indian monsoons can push Atlantic Ocean hurricanes westward, increasing the likelihood they'll make landfall in the Americas. This newly-discovered relationship could help scientists better predict hurricane paths, especially during late summer months.
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Researchers project a significant decline in Indian monsoon low-pressure system activity, with a 45% decrease expected by the late 21st century. This change is associated with a poleward shift in LPS formation, leading to increased extreme precipitation in northern India.
Scientists predict Indian monsoon's onset two weeks earlier and its withdrawal six weeks earlier than before. The new method uses network analysis of regional weather data to improve prediction accuracy, particularly for years affected by El Nino-Southern Oscillation.
A Purdue University study found that ground moisture at landfall is a major indicator of tropical storm behavior. Storms are more likely to sustain and cause heavy rains when the ground is wet, while dry conditions tend to calm them down.
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