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Accurate prediction of electric load is crucial in power grid planning and management. In this paper, we solve the electric load forecasting problem under extreme events such as scorching heats. One challenge for accurate forecasting is the…

Machine Learning · Computer Science 2023-06-16 Hengbo Liu , Ziqing Ma , Linxiao Yang , Tian Zhou , Rui Xia , Yi Wang , Qingsong Wen , Liang Sun

Wildfire prediction is important for early warning and resource allocation, yet existing Earth foundation models (Earth FMs) are pretrained for general atmospheric and geophysical objectives rather than wildfire forecasting. To address this…

Machine Learning · Computer Science 2026-05-25 Yangshuang Xu , Yuyang Dai , Liling Chang , Qi Wang , Yushun Dong

Climate sensitivity is defined as the change in global mean equilibrium temperature after a doubling of atmospheric CO2 concentration and provides a simple measure of global warming. An early estimate of climate sensitivity, 1.5-4.5{\deg}C,…

Atmospheric and Oceanic Physics · Physics 2012-04-24 Tamsin L. Edwards , Michel Crucifix , Sandy P. Harrison

Risk management in many environmental settings requires an understanding of the mechanisms that drive extreme events. Useful metrics for quantifying such risk are extreme quantiles of response variables conditioned on predictor variables…

Machine Learning · Statistics 2024-03-08 Jordan Richards , Raphaël Huser

We consider the high-resolution seismic imaging method called full-waveform inversion (FWI). FWI is a data fitting method aimed at inverting for subsurface mechanical parameters. Despite the large adoption of FWI by the academic and…

Full waveform inversion (FWI) is a process in which seismic numerical simulations are fit to observed data by changing the wave velocity model of the medium under investigation. The problem is non-linear, and therefore optimization…

Computational Engineering, Finance, and Science · Computer Science 2017-06-06 Eran Treister , Eldad Haber

Forecasting the wide variety of high-impact weather events experienced globally is a challenge for both Artificial Intelligence (AI) and Numerical Weather Prediction (NWP) models and it is critical that such models be properly verified…

Flood quantile estimation is of great importance for many engineering studies and policy decisions. However, practitioners must often deal with small data available. Thus, the information must be used optimally. In the last decades, to…

Applications · Statistics 2009-11-13 Mathieu Ribatet , Taha B. M. J. Ouarda , Eric Sauquet , Jean-Michel Grésillon

Subseasonal forecasting of the weather two to six weeks in advance is critical for resource allocation and advance disaster notice but poses many challenges for the forecasting community. At this forecast horizon, physics-based dynamical…

In recent years, the climate change research community has become highly interested in describing the anthropogenic influence on extreme weather events, commonly termed "event attribution." Limitations in the observational record and in…

Accurate weather forecasting is critical for science and society. Yet, existing methods have not managed to simultaneously have the properties of high accuracy, low uncertainty, and high computational efficiency. On one hand, to quantify…

Machine Learning · Computer Science 2025-05-06 Jimeng Shi , Bowen Jin , Jiawei Han , Sundararaman Gopalakrishnan , Giri Narasimhan

Full-Waveform Inversion (FWI) is a nonlinear iterative seismic imaging technique that, by reducing the misfit between recorded and predicted seismic waveforms, can produce detailed estimates of subsurface geophysical properties.…

Geophysics · Physics 2024-11-22 Vahid Negahdari , Seyed Reza Moghadasi , Mohammad Reza Razvan

Full waveform inversion (FWI) is a high-resolution seismic inversion technique popularly used in oil and gas exploration. Traditional FWI employs the $l_2$ norm measurement to minimize the misfit between observed and predicted seismic data.…

Geophysics · Physics 2025-04-03 Liangsheng He , Chao Song , Cai Liu

Atmospheric pollution regulations have emerged as a dominant obstacle to prescribed burns. Thus, forecasting the pollution caused by wildland fires has acquired high importance. WRF and SFIRE model wildland fire spread in a two-way…

Atmospheric and Oceanic Physics · Physics 2014-11-05 Adam K. Kochanski , Jonathan D. Beezley , Jan Mandel , Craig B. Clements

The increasing frequency and intensity of wildfires poses severe threats to the secure and stable operation of power grids, particularly one that is interspersed with renewable generation. Unlike conventional contingencies, wildfires affect…

Systems and Control · Electrical Eng. & Systems 2026-04-06 Satyaprajna Sahoo , Anamitra Pal

We present a methodology to change the state of the Weather Research Forecasting (WRF) model coupled with the fire spread code SFIRE, based on Rothermel's formula and the level set method, and with a fuel moisture model. The fire perimeter…

Atmospheric and Oceanic Physics · Physics 2012-08-07 Jan Mandel , Jonathan D. Beezley , Adam K. Kochanski , Volodymyr Y. Kondratenko , Minjeong Kim

Space heating in buildings is becoming a key element of sector-coupled energy system research. Data availability limits efforts to model the buildings sector, because heat consumption is not directly metered in most countries. Space heating…

Physics and Society · Physics 2019-04-04 Smail Kozarcanin , Gorm Bruun Andresen , Iain Staffell

Power transformers are subjected to electrical currents and temperature fluctuations that, if not properly controlled, can lead to major deterioration of their insulation system. Therefore, monitoring the temperature of a power transformer…

Machine Learning · Computer Science 2025-01-29 Francis Tembo , Federica Bragone , Tor Laneryd , Matthieu Barreau , Kateryna Morozovska

In this study we consider the problem of detecting and quantifying changes in the distribution of the annual maximum daily maximum temperature (TXx) in a large gridded data set of European daily temperature during the years 1950-2018.…

Applications · Statistics 2022-01-03 Graeme Auld , Gabriele Hegerl , Ioannis Papastathopoulos

In recent years, the increasing threat of devastating wildfires has underscored the need for effective prescribed fire management. Process-based computer simulations have traditionally been employed to plan prescribed fires for wildfire…

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