Search Results for author:
Found 21 papers, 12 papers with code
Deep Generative Data Assimilation in Multimodal Setting
To our knowledge, our work is the first to apply deep generative framework for multimodal data assimilation using real-world datasets; an important step for building robust computational simulators, including the next-generation Earth system models.
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming
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 many problems ranging from weather and climate prediction to turbulence simulations.
ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
Thus, we propose ChaosBench, a large-scale, multi-channel, physics-based benchmark for S2S prediction.
Simulating the Air Quality Impact of Prescribed Fires Using a Graph Neural Network-Based PM$_{2.5}$ Emissions Forecasting System
The increasing size and severity of wildfires across western North America have generated dangerous levels of PM$_{2. 5}$ pollution in recent years.
Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models
The implication is that hundreds of candidate ML models should be evaluated online to detect the effects of parameterization design choices.
Transferring climate change knowledge
Accurate and precise climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties.
Multi-fidelity climate model parameterization for better generalization and extrapolation
To extrapolate beyond the training data, the MF-RPNs are tested on high-fidelity warming scenarios, $+4K$, data.
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift.
History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96
To address these issues, we develop a new type of parameterization (closure) which is based on a Bayesian formalism for neural networks, to account for uncertainty quantification, and includes memory, to account for the non-instantaneous response of the closure.
Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: constraining an optimal stomatal model within a two-source energy balance model
For the CSIF model, the average R2 for ET estimates also improved when including the effect of soil moisture: from 0. 65 (0. 79) to 0. 71 (0. 84), with RMSE ranging between 0. 023 (0. 22) and 0. 043 (0. 54) mm depending on the site.
Deep Learning Based Cloud Cover Parameterization for ICON
A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations.
Climate-Invariant Machine Learning
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates.
On the Generalization of Agricultural Drought Classification from Climate Data
Climate change is expected to increase the likelihood of drought events, with severe implications for food security.
Global Daily CO$_2$ emissions for the year 2020
That even substantial world-wide lockdowns of activity led to a one-time decline in global CO$_2$ emissions of only 5. 4% in one year highlights the significant challenges for climate change mitigation that we face in the post-COVID era.
Atmospheric and Oceanic Physics General Economics Economics
Towards Physically-consistent, Data-driven Models of Convection
Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate simulations.
Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints.
Computational Physics Atmospheric and Oceanic Physics
Recovering the parameters underlying the Lorenz-96 chaotic dynamics
Climate projections suffer from uncertain equilibrium climate sensitivity.
Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling
Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models.
Deep learning to represent sub-grid processes in climate models
We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly.
When does vapor pressure deficit drive or reduce evapotranspiration?
Here we examine which effect dominates response to increasing VPD: atmospheric demand and increases in ET, or plant physiological response (stomata closure) and decreases in ET.
Atmospheric and Oceanic Physics
