Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Chattopadhyay, Ashesh"'
Predicting the long-term behavior of chaotic systems remains a formidable challenge due to their extreme sensitivity to initial conditions and the inherent limitations of traditional data-driven modeling approaches. This paper introduces a novel fram
Externí odkaz:
http://arxiv.org/abs/2410.05572
We present a lightweight, easy-to-train, low-resolution, fully data-driven climate emulator, LUCIE, that can be trained on as low as $2$ years of $6$-hourly ERA5 data. Unlike most state-of-the-art AI weather models, LUCIE remains stable and physicall
Externí odkaz:
http://arxiv.org/abs/2405.16297
While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land, vertical structure, and flow non-linearity. This study introduces Ocea
Externí odkaz:
http://arxiv.org/abs/2310.00813
Earth system models suffer from various structural and parametric errors in their representation of nonlinear, multi-scale processes, leading to uncertainties in their long-term projections. The effects of many of these errors (particularly those due
Externí odkaz:
http://arxiv.org/abs/2309.13211
There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we apply a common equation-discovery technique with expansive libraries to l
Externí odkaz:
http://arxiv.org/abs/2306.05014
Long-term stability is a critical property for deep learning-based data-driven digital twins of the Earth system. Such data-driven digital twins enable sub-seasonal and seasonal predictions of extreme environmental events, probabilistic forecasts, th
Externí odkaz:
http://arxiv.org/abs/2304.07029
Autor:
Chattopadhyay, Ashesh1 (AUTHOR) aschatto@ucsc.edu, Gray, Michael2 (AUTHOR), Wu, Tianning2 (AUTHOR), Lowe, Anna B.2 (AUTHOR), He, Ruoying2 (AUTHOR) rhe@ncsu.edu
Publikováno v:
Scientific Reports. 9/11/2024, Vol. 14 Issue 1, p1-10. 10p.
Data assimilation (DA) is a key component of many forecasting models in science and engineering. DA allows one to estimate better initial conditions using an imperfect dynamical model of the system and noisy/sparse observations available from the sys
Externí odkaz:
http://arxiv.org/abs/2206.04811
Transfer learning (TL) is becoming a powerful tool in scientific applications of neural networks (NNs), such as weather/climate prediction and turbulence modeling. TL enables out-of-distribution generalization (e.g., extrapolation in parameters) and
Externí odkaz:
http://arxiv.org/abs/2206.03198
Autor:
Chattopadhyay, Ashesh, Pathak, Jaideep, Nabizadeh, Ebrahim, Bhimji, Wahid, Hassanzadeh, Pedram
Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models if trained on observations can mitigate certain biases in current state-of-the-art weather models, s
Externí odkaz:
http://arxiv.org/abs/2205.04601