AI Emulator Replicates Ammonia Ensembles 3300 Times Faster Than Physics Model
A new AI emulator reproduces GEM-MACH ammonia ensembles about 3300 times faster while delivering nearly identical emissions inversion results.
A new AI emulator reproduces GEM-MACH ammonia ensembles about 3300 times faster while delivering nearly identical emissions inversion results.
Researchers developed an ensemble-based Bayesian filtering method that estimates the unmeasurable heat flux between molten steel and a continuous casting ...
A hybrid framework combining floating car data, sparse traffic cameras, and physics-guided graph learning calibrates biased GPS counts into network-wide ...
A new multi-source data assimilation framework coupled with a hydrodynamic model improved monitoring accuracy at Yunjinghong Hydrological Station and extended ...
A PyTorch-based 3DVar data assimilation system for WRF-Chem achieves a 26.2-fold speedup on GPU hardware while matching the accuracy of ...
Researchers have introduced new dimension and model reduction methods that extend optimal Bayesian inference to problems with rank-deficient prior covariances.
Scientists have recovered long-lost observations from the 1970s DMSP Special Sensor H instrument and shown that assimilating them, including its ...
A four-year Southwest Atlantic modeling study found that low-cost sea-surface temperature assimilation reduced sea-surface temperature error by 70% and improved ...
A vegetation-aware strategy combining L-, C- and X-band satellite soil-moisture data improved model accuracy by selecting observations according to canopy ...
Ground-based microwave radiometers are among the most tireless instruments in atmospheric science. Bolted to the ground and staring permanently upward, ...
© 2026 Scienmag - Science Magazine
© 2026 Scienmag - Science Magazine