1.1k citations · 3k across the 33 of their papers we have counts for
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Pre-averaging fractional processes contaminated by noise, with an application to turbulence
David Chen, Yu Cheng, Carsten Chong +4
In this article, we consider the problem of estimating fractional processes based on noisy high-frequency data. Generalizing the idea of pre-averaging to a fractional setting, we e…
History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96
Mohamed Aziz Bhouri, Pierre Gentine
Physical parameterizations are used as representations of unresolved subgrid processes within weather and global climate models or coarse-scale turbulent models, whose resolutions…
Carbon Monitor-Power: near-real-time monitoring of global power generation on hourly to daily scales
Biqing Zhu, Xuanren Song, Zhu Deng +13
We constructed a frequently updated, near-real-time global power generation dataset: Carbon Monitor-Power since January, 2016 at national levels with near-global coverage and hourl…
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning
Griffin Mooers, Mike Pritchard, Tom Beucler +5
Global Storm-Resolving Models (GSRMs) have gained widespread interest because of the unprecedented detail with which they resolve the global climate. However, it remains difficult…
Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: constraining an optimal stomatal model within a two-source energy balance model
Jingyi Bu, Guojing Gan, Jiahao Chen +8
Evapotranspiration (ET) represents the largest water loss flux in drylands, but ET and its partition into plant transpiration (T) and soil evaporation (E) are poorly quantified, es…
Machine-learned cloud classes from satellite data for process-oriented climate model evaluation
A. Kaps, A. Lauer, G. Camps-Valls +3
Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks…
Non-Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models
Gunnar Behrens, Tom Beucler, Pierre Gentine +3
Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack…