The field of wildfire forecasting and climate resilience is rapidly advancing, with a focus on improving predictive accuracy and uncertainty quantification. Recent research has highlighted the importance of incorporating spatial uncertainty and multimodal Earth observation inputs into wildfire spread forecasting models. Additionally, advancements in self-supervised deep learning and geostationary remote sensing are enhancing wildfire and air quality monitoring capabilities. Noteworthy papers in this area include: Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning, which demonstrates the use of a novel distance metric to identify high-uncertainty regions in wildfire forecasting. Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring, which showcases the efficacy of deep learning for mapping wildfire fronts and smoke plumes using GOES and TEMPO data.
Wildfire Forecasting and Climate Resilience
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Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Attention-Enhanced LSTM Modeling for Improved Temperature and Rainfall Forecasting in Bangladesh
Rethinking deep learning: linear regression remains a key benchmark in predicting terrestrial water storage
The Fire We Share
Knowledge-Guided Machine Learning Models to Upscale Evapotranspiration in the U.S. Midwest
Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response
Leveraging Teleconnections with Physics-Informed Graph Attention Networks for Long-Range Extreme Rainfall Forecasting in Thailand
An Operational Deep Learning System for Satellite-Based High-Resolution Global Nowcasting
RainDiff: End-to-end Precipitation Nowcasting Via Token-wise Attention Diffusion