Earth & Environmental Sciences

Regional air-quality forecasting has relied on online-coupled numerical chemistry-weather models whose skill is sensitive to meteorological initial conditions, emission inventories and lateral boundary conditions. CWAI treats meteorology, particles and reactive gases as one joint atmospheric state, driven by two consecutive states, emissions and static surface descriptors through a hierarchical Swin Transformer, and rolls out 40 six-hour steps to a 10-day forecast. Against WRF-Chem over 62 January and July 2020 starts, CWAI had lower initialization-weighted RMSE for all six surface pollutants, from 15.8% for PM2.5 to 25.8% for NO2.

Regional air-quality forecasting has relied on online-coupled numerical chemistry-weather models whose skill is sensitive to meteorological initial conditions, emission inventories and lateral boundary conditions. CWAI treats meteorology, particles and reactive gases as one joint atmospheric state, driven by two consecutive states, emissions and static surface descriptors through a hierarchical Swin Transformer, and rolls out 40 six-hour steps to a 10-day forecast. Against WRF-Chem over 62 January and July 2020 starts, CWAI had lower initialization-weighted RMSE for all six surface pollutants, from 15.8% for PM2.5 to 25.8% for NO2.

Remote sensing change detection usually depends on large collections of temporally aligned, pixel-level annotations, which limits scaling across regions and platforms. SpotDiff composes DINOv3 semantic features, AnyUp pixel-resolution feature reconstruction and SAM 3 text-guided region proposals into a training-free pipeline, then compares the cosine similarity of the two dates' reconstructed features inside the proposed regions. On LEVIR-CD the ViT-7B version reaches 82.46 F1, 2.06 points above the strongest prior training-free method; on SECOND it raises building-change IoU by 10.3.

Remote sensing change detection usually depends on large collections of temporally aligned, pixel-level annotations, which limits scaling across regions and platforms. SpotDiff composes DINOv3 semantic features, AnyUp pixel-resolution feature reconstruction and SAM 3 text-guided region proposals into a training-free pipeline, then compares the cosine similarity of the two dates' reconstructed features inside the proposed regions. On LEVIR-CD the ViT-7B version reaches 82.46 F1, 2.06 points above the strongest prior training-free method; on SECOND it raises building-change IoU by 10.3.

An MIT team has developed a training-data optimization method that lets a climate emulator trained only on carefully selected synthetic scenarios predict outcomes of scenarios it never saw, while the traditional emulators evaluated failed to adapt to new situations. The method reduces the number of full earth-system model runs needed to generate training data from six to eight down to one, shortening production time for these fast surrogate models. Emulators trained this way also captured a local pollutant-control case in which one pollutant falls while other emissions keep rising.

An MIT team has developed a training-data optimization method that lets a climate emulator trained only on carefully selected synthetic scenarios predict outcomes of scenarios it never saw, while the traditional emulators evaluated failed to adapt to new situations. The method reduces the number of full earth-system model runs needed to generate training data from six to eight down to one, shortening production time for these fast surrogate models. Emulators trained this way also captured a local pollutant-control case in which one pollutant falls while other emissions keep rising.

Large-scale observations of the three-dimensional interior of low clouds have been missing because physics-based tomography repeats expensive 3D radiative-transfer calculations, a cost that has kept it from large, realistic cloud scenes. HybridCT splits the problem: a U-Net estimates cloud optical thickness projections from multi-angle passive radiances, and SART then explicitly inverts those projections into a 3D extinction field. On a synthetic low-cloud scene it reconstructs extinction with RMSE 13.3 km⁻¹ in 3.5 s, against 22.7 km⁻¹ in about 30 min for the physics baseline AT3D.

Large-scale observations of the three-dimensional interior of low clouds have been missing because physics-based tomography repeats expensive 3D radiative-transfer calculations, a cost that has kept it from large, realistic cloud scenes. HybridCT splits the problem: a U-Net estimates cloud optical thickness projections from multi-angle passive radiances, and SART then explicitly inverts those projections into a 3D extinction field. On a synthetic low-cloud scene it reconstructs extinction with RMSE 13.3 km⁻¹ in 3.5 s, against 22.7 km⁻¹ in about 30 min for the physics baseline AT3D.

Standard neural processes treat inputs as noise-free observations, so sensor measurement error degrades both predictions and uncertainty estimates. DnNP makes the clean input a latent variable inside the neural-process framework and trains a denoiser jointly with meta-learning through variational inference, predicting directly from noisy inputs. On PM2.5 spatial interpolation from 34 air-quality sensors in Brookline, Massachusetts, DnNP reduces RMSE by up to 30% and NLL by up to 73% relative to standard neural processes.

Standard neural processes treat inputs as noise-free observations, so sensor measurement error degrades both predictions and uncertainty estimates. DnNP makes the clean input a latent variable inside the neural-process framework and trains a denoiser jointly with meta-learning through variational inference, predicting directly from noisy inputs. On PM2.5 spatial interpolation from 34 air-quality sensors in Brookline, Massachusetts, DnNP reduces RMSE by up to 30% and NLL by up to 73% relative to standard neural processes.

Mass-conservation constraints had been embedded only in lumped LSTMs, which yield basin-averaged time series and cannot test whether internal states are hydrologically meaningful. PECAN recasts the constraint in a distributed convolutional LSTM, with a strict precipitation-only variant, PECANMC, and a relaxed variant, PECANMCR, trained solely on streamflow at two gauges. In the snow-dominated North Platte Headwaters in Colorado, both variants match lumped deep-learning baselines and the distributed process model VIC on outlet streamflow and expose spatially explicit internal states, yet diagnostics show the model removes winter snow water storage prematurely instead of holding it for spring release.

Mass-conservation constraints had been embedded only in lumped LSTMs, which yield basin-averaged time series and cannot test whether internal states are hydrologically meaningful. PECAN recasts the constraint in a distributed convolutional LSTM, with a strict precipitation-only variant, PECANMC, and a relaxed variant, PECANMCR, trained solely on streamflow at two gauges. In the snow-dominated North Platte Headwaters in Colorado, both variants match lumped deep-learning baselines and the distributed process model VIC on outlet streamflow and expose spatially explicit internal states, yet diagnostics show the model removes winter snow water storage prematurely instead of holding it for spring release.

Array processing has long treated inter-sensor coherence loss as a nuisance, and conventional beamforming assumes a far-field point source with incoherent noise between sensors. Topophone2 models the array covariance as a mixture of energy distributions over slowness, describing signal and environmental noise with skew-normal and angular-harmonic distributions that have analytical characteristic functions, then computes the posterior by variational Bayesian inference. On a synthetic abyssal T-phase, adding a fourth hydrophone raises the information gain on the angular-harmonic noise coefficients by 142%, while the gain on signal parameters and SNR rises by less than 8%.

Array processing has long treated inter-sensor coherence loss as a nuisance, and conventional beamforming assumes a far-field point source with incoherent noise between sensors. Topophone2 models the array covariance as a mixture of energy distributions over slowness, describing signal and environmental noise with skew-normal and angular-harmonic distributions that have analytical characteristic functions, then computes the posterior by variational Bayesian inference. On a synthetic abyssal T-phase, adding a fourth hydrophone raises the information gain on the angular-harmonic noise coefficients by 142%, while the gain on signal parameters and SNR rises by less than 8%.

As Earth-system AI models approach kilometre scales, halving grid spacing quadruples the grid-point count and the end-to-end training cost. One2N splits resolution refinement into successive increments: at each level the complete parent predictor is frozen and only a cross-resolution residual correction module is trained, so the parent's activations and optimizer states are never retained. One refinement level on the NeuralGCM-0.7° baseline yields One2N-0.07°, trained for about four weeks on a single 80 GB A800 GPU; against HRES-0.07° analyses it keeps the parent's tropospheric skill while reducing upper-stratospheric errors and resolving finer structure.

As Earth-system AI models approach kilometre scales, halving grid spacing quadruples the grid-point count and the end-to-end training cost. One2N splits resolution refinement into successive increments: at each level the complete parent predictor is frozen and only a cross-resolution residual correction module is trained, so the parent's activations and optimizer states are never retained. One refinement level on the NeuralGCM-0.7° baseline yields One2N-0.07°, trained for about four weeks on a single 80 GB A800 GPU; against HRES-0.07° analyses it keeps the parent's tropospheric skill while reducing upper-stratospheric errors and resolving finer structure.