Zobrazeno 1 - 10
of 1 532
pro vyhledávání: '"Gentine, A."'
Robust integration of physical knowledge and data is key to improve computational simulations, such as Earth system models. Data assimilation is crucial for achieving this goal because it provides a systematic framework to calibrate model outputs wit
Externí odkaz:
http://arxiv.org/abs/2404.06665
Accurate representations of unknown and sub-grid physical processes through parameterizations (or closure) in numerical simulations with quantified uncertainty are critical for resolving the coarse-grained partial differential equations that govern m
Externí odkaz:
http://arxiv.org/abs/2403.02215
Earth system models (ESMs) are vital for understanding past, present, and future climate, but they suffer from legacy technical infrastructure. ESMs are primarily implemented in Fortran, a language that poses a high barrier of entry for early career
Externí odkaz:
http://arxiv.org/abs/2405.00018
Autor:
Behrens, Gunnar, Beucler, Tom, Iglesias-Suarez, Fernando, Yu, Sungduk, Gentine, Pierre, Pritchard, Michael, Schwabe, Mierk, Eyring, Veronika
Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty
Externí odkaz:
http://arxiv.org/abs/2402.03079
Autor:
Nathaniel, Juan, Qu, Yongquan, Nguyen, Tung, Yu, Sungduk, Busecke, Julius, Grover, Aditya, Gentine, Pierre
Accurate prediction of climate in the subseasonal-to-seasonal scale is crucial for disaster preparedness and robust decision making amidst climate change. Yet, forecasting beyond the weather timescale is challenging because it deals with problems oth
Externí odkaz:
http://arxiv.org/abs/2402.00712
The increasing size and severity of wildfires across the western United States have generated dangerous levels of PM$_{2.5}$ concentrations in recent years. In a changing climate, expanding the use of prescribed fires is widely considered to be the m
Externí odkaz:
http://arxiv.org/abs/2312.04291
Reconstruction of a Long-term spatially Contiguous Solar-Induced Fluorescence (LCSIF) over 1982-2022
Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for diagnosing the photosynthetic characteristics of terrestrial ecosystems. Despite the increasing spatial and temporal resolutions of these satellite retrievals, re
Externí odkaz:
http://arxiv.org/abs/2311.14987
Publikováno v:
Journal of Advances in Modeling Earth Systems, 16, e2024MS004398
Machine learning (ML)-based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid-scale processes or to accelerate computations. ML-based parameterizations within hybrid ESMs have successfully
Externí odkaz:
http://arxiv.org/abs/2311.03251
Autor:
Lin, Jerry, Yu, Sungduk, Peng, Liran, Beucler, Tom, Wong-Toi, Eliot, Hu, Zeyuan, Gentine, Pierre, Geleta, Margarita, Pritchard, Mike
Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-resolution physics without the cost of explicit simulation. However, the
Externí odkaz:
http://arxiv.org/abs/2309.16177
Autor:
Immorlano, Francesco, Eyring, Veronika, de Gouville, Thomas le Monnier, Accarino, Gabriele, Elia, Donatello, Aloisio, Giovanni, Gentine, Pierre
Accurate and precise climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties. Several approaches have been developed to reduce the spread of climate projections and feedbacks,
Externí odkaz:
http://arxiv.org/abs/2309.14780