Jaideep Pathak
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View article: Singularity image for ClimSim-Online
Singularity image for ClimSim-Online Open
This is a singularity image for climsim-online: https://github.com/leap-stc/climsim-online It can be used to launch E3SM-MMF climate simulations or the hybrid physics-machine-learning variants (by replacing the MMF cloud-resolving calculat…
View article: A Practical Probabilistic Benchmark for AI Weather Models
A Practical Probabilistic Benchmark for AI Weather Models Open
Since the weather is chaotic, it is necessary to forecast an ensemble of future states. Recently, multiple AI weather models have emerged claiming breakthroughs in deterministic skill. Unfortunately, it is hard to fairly compare ensembles …
View article: Diffusion Model Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
Diffusion Model Data Assimilation of Sparse Weather Station Observations at Kilometer Scales Open
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data wi…
View article: Residual corrective diffusion modeling for km-scale atmospheric downscaling
Residual corrective diffusion modeling for km-scale atmospheric downscaling Open
State of the art for weather and climate hazard prediction requires expensive km-scale numerical simulations. Here, a generative diffusion model is explored for downscaling global inputs to km-scale, as a cost-effective alternative. The mo…
View article: Idealized Baroclinic Wave Test Results
Idealized Baroclinic Wave Test Results Open
Dataset containing results of an idealized baroclinic wave test simulation. Initial condition from Bouvier et al. (2023). Model from Bonev et al. (2023). Variables: t2m (two-meter temperature), tcwv (total column water vapor), msl (mean se…
View article: Heavy-Tailed Diffusion Models
Heavy-Tailed Diffusion Models Open
Diffusion models achieve state-of-the-art generation quality across many applications, but their ability to capture rare or extreme events in heavy-tailed distributions remains unclear. In this work, we show that traditional diffusion and …
View article: Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling
Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling Open
Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme weather. By explicitly resolving convective dynamics within …
View article: Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales Open
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data wi…
View article: Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model
Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model Open
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 effect…
View article: DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations
DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations Open
This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics. The model is tra…
View article: A Practical Probabilistic Benchmark for AI Weather Models
A Practical Probabilistic Benchmark for AI Weather Models Open
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 …
View article: Residual Diffusion Modeling for Km-scale Atmospheric Downscaling
Residual Diffusion Modeling for Km-scale Atmospheric Downscaling Open
Predictions of weather hazard require expensive km-scale simulations driven by coarser global inputs. Here, a cost-effective stochastic downscaling model is trained from a high-resolution 2-km weather model over Taiwan conditioned on 25-km…
View article: Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling
Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling Open
The state of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscalin…
View article: FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators
FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators Open
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 and resolution due to high computational cost and…
View article: Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence Open
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 weath…
View article: DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting
DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting Open
Data-driven models, such as FourCastNet (FCN), have shown exemplary performance in high-resolution global weather forecasting. This performance, however, is based on supervision on mesh-gridded weather data without the utilization of raw c…
View article: FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators Open
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…
View article: Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence Open
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 weathe…
View article: FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators Open
FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.25^{\circ}$ resolution. FourCastNet accurately forecasts hig…
View article: A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics‐Based Numerical Model
A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics‐Based Numerical Model Open
This paper describes an implementation of the combined hybrid‐parallel prediction (CHyPP) approach of Wikner et al. (2020), https://doi.org/10.1063/5.0005541 on a low‐resolution atmospheric global circulation model (AGCM). The CHyPP approa…
View article: Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model Open
Earth and Space Science Open Archive This work has been accepted for publication in Journal of Advances in Modeling Earth Systems (JAMES). Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may …
View article: Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model Open
Earth and Space Science Open Archive This work has been accepted for publication in Journal of Advances in Modeling Earth Systems (JAMES). Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may …
View article: Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model Open
This paper describes an implementation of the Combined Hybrid-Parallel Prediction (CHyPP) approach of Wikner et al. (2020) on a low-resolution atmospheric global circulation model (AGCM). The CHyPP approach combines a physics-based numeric…
View article: Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations Open
Simulation of turbulent flows at high Reynolds number is a computationally challenging task relevant to a large number of engineering and scientific applications in diverse fields such as climate science, aerodynamics, and combustion. Turb…
View article: A Machine Learning‐Based Global Atmospheric Forecast Model
A Machine Learning‐Based Global Atmospheric Forecast Model Open
The paper investigates the applicability of machine learning (ML) to weather prediction by building a reservoir computing‐based, low‐resolution, global prediction model. The model is designed to take advantage of the massively parallel arc…
View article: A Machine-Learning-Based Global Atmospheric Forecast Model
A Machine-Learning-Based Global Atmospheric Forecast Model Open
The paper investigates the applicability of machine learning (ML) to weather prediction by building a reservoir-computing-based, low-resolution, global prediction model. The model is designed to take advantage of the massively parallel arc…
View article: A Machine-Learning-Based Global Atmospheric Forecast Model
A Machine-Learning-Based Global Atmospheric Forecast Model Open
The paper investigates the applicability of machine learning (ML) to weather prediction by building a low-resolution ML model for global weather prediction. The forecast performance of the ML model is assessed by comparing it to that of pe…
View article: Machine Learning Approaches for Data-Driven Analysis and Forecasting of High-Dimensional Chaotic Dynamical Systems
Machine Learning Approaches for Data-Driven Analysis and Forecasting of High-Dimensional Chaotic Dynamical Systems Open
We consider problems in the forecasting of large, complex, spatiotemporal chaotic systems and the possibility that machine learning might be a useful tool for significant improvement of such forecasts. Focusing on weather forecasting as pe…
View article: Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data Open
We use recent advances in the machine learning area known as “reservoir computing” to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic process. The technique uses a limited time series of measur…