A high-resolution climate modeling study reveals that rising evaporative demand will significantly escalate seasonal and annual meteorological drought severity across ...
A new machine learning framework using the Artificial Bee Colony algorithm enhances the spatial resolution of GRACE satellite data, revealing ...
A nine-year merged radar–gauge precipitation dataset for South Korea delivers 10-minute, 500-meter rainfall estimates with near-zero bias across seasons and ...
A systematic comparison of twelve bias correction methods for CMIP6 models in Jiangsu Province, China, shows that machine learning approaches ...
A study combining bias-corrected CMIP6 projections and machine learning projected rising temperatures and declining Kulfo River discharge under three future-emissions ...
Researchers have repackaged NOAA's operational HRRR weather model into an energy-ready toolkit that outperforms existing wind resource datasets and extends ...
A new multi-model climate study projects that Peninsular Malaysia will see heat stress days nearly triple and moderate drought frequency ...
A satellite and machine learning study tracking the Indian Himalayan Region from the 2020 lockdown to 2024 finds air quality ...
Researchers have developed a physically based method that corrects cold-biased temperatures in regional climate model output using atmospheric lapse rates, ...
A new study tested five satellite and reanalysis rainfall products against 23 Egyptian rain gauges and used deep neural networks ...
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