The paper presents modular deep learning augmentations for next‑day wildfire spread prediction, including wind‑ and slope‑conditioned attention biases, physics‑feature retrieval‑augmented output correction, and fire‑conditioned dual‑stream gating. These modules are evaluated on five backbone models using the Next Day Wildfire Spread benchmark, with staged ablations, directional audits, retrieval perturbations, calibration measures, and computational comparisons. The best augmented SwinUNETR model achieves an F1 score of 0.4216 and an AUC‑PR of 0.3673, while a mixed ensemble reaches 0.4292 and 0.3790, demonstrating that predictive performance, operational trustworthiness, and computational practicality can be simultaneously improved.
By Miguel Esparza, Aydin Ayanzadeh Ahmad Mousavi, Ali Mostafavi
arXiv:2610.19191v1 Announce Type: new
Abstract: Wildfire decisions are made in hours, yet most learned spread models predict daily on targets including burned area. We propose FireGraphNet which form...
By Aiyshwariya Paulvannan Kanmani, Miguel Esparza, Ali Mostafavi
The paper presents Laxmi, a retrained version of the AIFS weather model that uses satellite-based precipitation observations instead of ERA5 reanalysis data. Laxmi achieves a 19% improvement in global probabilistic accuracy, reduces drizzle overprediction by 33%, and boosts the 95th percentile Brier skill score by 57%. In a case study of 10 Indian tropical storms, Laxmi accurately forecasted 150 mm event-total precipitation in 7 events, outperforming both the original AIFS and the leading physical model IFS.
By Julian F. Schmitt, Bertrand Delorme, Robert C. King, Yashica Patodia, Tapio Schneider, Aditi Sheshadri, Ravi Jain
arXiv:2609.36315v1 Announce Type: cross
Abstract: Wildfires are an increasing hazard to ecosystems, air quality, and human systems, creating a growing need for datasets that support systematic develo...
By Arya Kondur, Giosue Migliorini, Cameron Schmitt, Francesco Immorlano, Tairan Wang, Rebecca C. Scholten, Efi Foufoula-Georgiou, Gary Johnson, Chris Lautenberger, Valentin Waeselynck, J. Shane Romsos, Kasra Shamsaei, Alejandro Tejedor, Tianjia Liu, Yang Chen, Padhraic Smyth, James T. Randerson
arXiv:2607. 11896v1 Announce Type: cross Abstract: Forecasting particulate matter (PM10) requires both station-scale accuracy and continuous spatial fields, especially during severe dust storms.
By Shuangshuang He, Shuo Wang
The paper introduces IonoDGNN, a dynamic graph neural network that models the ionosphere over ionospheric pierce points, conditioning forecasts on future satellite trajectories (ephemeris conditioning). It predicts ionospheric irregularities, measured by the Rate of TEC Index, up to two hours ahead using GNSS data from Singapore, achieving a Brier Skill Score of 0.55 and a PR-AUC of 0.77—significant improvements over persistence and interpolation baselines. The model remains robust under simulated coverage dropouts, leveraging spatial message passing to maintain predictive skill on affected nodes.
By Mert Can Turkmen, Eng Leong Tan, Yee Hui Lee