Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Kurth, Thorsten"'
Weather and climate data are often available at limited temporal resolution, either due to storage limitations, or in the case of weather forecast models based on deep learning, their inherently long time steps. The coarse temporal resolution makes i
Externí odkaz:
http://arxiv.org/abs/2410.18904
Autor:
Ltaief, Hatem, Alomairy, Rabab, Cao, Qinglei, Ren, Jie, Slim, Lotfi, Kurth, Thorsten, Dorschner, Benedikt, Bougouffa, Salim, Abdelkhalak, Rached, Keyes, David E.
We exploit the widening margin in tensor-core performance between [FP64/FP32/FP16/INT8,FP64/FP32/FP16/FP8/INT8] on NVIDIA [Ampere,Hopper] GPUs to boost the performance of output accuracy-preserving mixed-precision computation of Genome-Wide Associati
Externí odkaz:
http://arxiv.org/abs/2409.01712
Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
Autor:
Mahesh, Ankur, Collins, William, Bonev, Boris, Brenowitz, Noah, Cohen, Yair, Elms, Joshua, Harrington, Peter, Kashinath, Karthik, Kurth, Thorsten, North, Joshua, OBrien, Travis, Pritchard, Michael, Pruitt, David, Risser, Mark, Subramanian, Shashank, Willard, Jared
Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampl
Externí odkaz:
http://arxiv.org/abs/2408.03100
Autor:
Mahesh, Ankur, Collins, William, Bonev, Boris, Brenowitz, Noah, Cohen, Yair, Harrington, Peter, Kashinath, Karthik, Kurth, Thorsten, North, Joshua, OBrien, Travis, Pritchard, Michael, Pruitt, David, Risser, Mark, Subramanian, Shashank, Willard, Jared
In Part I, we created an ensemble based on Spherical Fourier Neural Operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics
Externí odkaz:
http://arxiv.org/abs/2408.01581
Autor:
Wang, Chenggong, Pritchard, Michael S., Brenowitz, Noah, Cohen, Yair, Bonev, Boris, Kurth, Thorsten, Durran, Dale, Pathak, Jaideep
Seasonal climate forecasts are socioeconomically important for managing the impacts of extreme weather events and for planning in sectors like agriculture and energy. Climate predictability on seasonal timescales is tied to boundary effects of the oc
Externí odkaz:
http://arxiv.org/abs/2406.08632
Autor:
Liu-Schiaffini, Miguel, Berner, Julius, Bonev, Boris, Kurth, Thorsten, Azizzadenesheli, Kamyar, Anandkumar, Anima
Neural operators learn mappings between function spaces, which is practical for learning solution operators of PDEs and other scientific modeling applications. Among them, the Fourier neural operator (FNO) is a popular architecture that performs glob
Externí odkaz:
http://arxiv.org/abs/2402.16845
Autor:
Brenowitz, Noah D., Cohen, Yair, Pathak, Jaideep, Mahesh, Ankur, Bonev, Boris, Kurth, Thorsten, Durran, Dale R., Harrington, Peter, Pritchard, Michael S.
Since the weather is chaotic, forecasts aim to predict the distribution of future states rather than make a single prediction. Recently, multiple data driven weather models have emerged claiming breakthroughs in skill. However, these have mostly been
Externí odkaz:
http://arxiv.org/abs/2401.15305
Autor:
Karlbauer, Matthias, Cresswell-Clay, Nathaniel, Durran, Dale R., Moreno, Raul A., Kurth, Thorsten, Bonev, Boris, Brenowitz, Noah, Butz, Martin V.
Publikováno v:
Journal of Advances in Modeling Earth Systems, 16, e2023MS004021
We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3-h time resolution for up to one-year lead times on a 110-km global mesh using the Hierarchical Equal Area isoLatitude Pixelization (HEALPi
Externí odkaz:
http://arxiv.org/abs/2311.06253
Autor:
Bonev, Boris, Kurth, Thorsten, Hundt, Christian, Pathak, Jaideep, Baust, Maximilian, Kashinath, Karthik, Anandkumar, Anima
Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning. A key reason for their success is their abili
Externí odkaz:
http://arxiv.org/abs/2306.03838
Autor:
Kurth, Thorsten, Subramanian, Shashank, Harrington, Peter, Pathak, Jaideep, Mardani, Morteza, Hall, David, Miele, Andrea, Kashinath, Karthik, Anandkumar, Animashree
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy due to high computational cost and strict time-to-solution l
Externí odkaz:
http://arxiv.org/abs/2208.05419