The technology used to predict sand and dust storm (SDS) severity has for decades systematically over-estimated when and where sediment is transported across the Earth’s surface, a new study shows.

Nations around the globe are grappling with a massive dual challenge: maintaining economic momentum while drastically slashing carbon outputs. Many policymakers have placed their bets on the digital economy as a modern solution for climate change. However, the exact mechanics of how data and connectivity actually clean up our air have remained somewhat murky. Now, a comprehensive evaluation of 259 Chinese cities cuts through the noise, mapping exactly how digital transformation drives environmental progress.

Recently, the team led by Professor Xin Xu from the School of Atmospheric Sciences, Nanjing University published a short communication in Science Bulletin entitled “Complex Terrain Causes Global Model Prediction Biases of 21.7 Zhengzhou Extreme Precipitation”. The study reveals that the orographic gravity wave drag triggered by complex terrain can cause significant location and intensity biases of the “21.7” Zhengzhou extreme precipitation in global numerical weather prediction (NWP) models.

Previous measurements of lightning on Jupiter were from dark-side optical observations and yielded conflicting conclusions about the power they release. A UC Berkeley scientist used new data from Juno’s microwave detector to calculate the power in 613 pulses, concluding they range from Earth size to hundreds of times Earth’s bolts, and perhaps even greater. The strength helps to understand convection on the planet, which creates long-lasting storm clouds 10 times higher than those on Earth.

Just before World Water Day, the Institute of Science and Technology Austria (ISTA) announces it will lead the new MountAInWater project, an ambitious endeavor funded by Schmidt Sciences with a grant of USD 9.5 million. Scientists will carry out the first-ever global reanalysis of mountain water resources using high-resolution models, assessing the effects of climate change on these critical water supplies, and identifying potential tipping points in mountain environments. To achieve this, the team from six countries will make use of a unique combination of field work, physically-based modeling and AI—and also engage with affected regions and communities. Their results will be a crucial resource in managing future water security challenges.