A distributional regression network acts as a backward operator to produce uncertainty-quantified, multivariate Gaussian retrievals of cloud properties from six solar channels for data assimilation.
Quarterly Journal of the Royal Meteorological Society , title =
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ENUFFT computes local Fourier coefficients from irregular orography samples on unstructured grids via NUFFT and elastic mode selection, producing more compact spectra and closer Parseval compliance than prior methods in monochromatic, Alpine, and mountain-wave tests.
Machine learning models recover most warm-rain and ice microphysical process rates from standard ICON model outputs for accumulation intervals of 10 minutes or less using a two-step classification-regression approach with calibrated uncertainty.
Neural networks predict orographic gravity wave momentum fluxes from coarse state variables with offline R² of 0.56-0.72, learn physically meaningful relationships via SHAP, and are compared to the Lott-Miller parameterization.
Dynamic traffic emissions from an online-calibrated SUMO model improve hyperlocal NO2 hotspot predictions and peak representation over static baselines when coupled to the CAIRDIO dispersion model.
A multimodal GNN ablation for Nordic precipitation nowcasting shows sparse point observations improve station and onset scores while NWP and CRPS losses improve radar-grid performance, indicating local and field skills are distinct targets.
GPU port of entropy-stable DG Euler solver with non-conservative buoyancy terms reaches nearly 70% of 64-bit peak on A100 volume kernels, delivers 10x speedup and 13x better energy efficiency versus CPU, and preserves symmetry-based flux savings.
WP-MIP creates a centralized forecast database and evaluation framework to compare physically based, machine-learning, and hybrid weather prediction models across global centers.
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Hyperlocal urban NO2 hotspot modeling driven by microscopic traffic data
Dynamic traffic emissions from an online-calibrated SUMO model improve hyperlocal NO2 hotspot predictions and peak representation over static baselines when coupled to the CAIRDIO dispersion model.