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🏛️ The physics.ao-ph Crypt
physics.ao-ph: Where physics.ao-ph papers rest without their code.
137
Total Papers
106
No Code
3
Twilight
28
Has Code
20.4%
Survival Rate
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Predicting Lake Erie Wave Heights using XGBoost
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Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions
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Generating Unseen Nonlinear Evolution in Sea Surface Temperature Using a Deep Learning-Based Latent Space Data Assimilation Framework
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Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers
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Precipitation Prediction Using an Ensemble of Lightweight Learners
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Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications
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Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds
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Contrastive Learning for Climate Model Bias Correction and Super-Resolution
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MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards
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Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data
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Meta-modeling strategy for data-driven forecasting
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Short term solar energy prediction by machine learning algorithms
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Applying High-Resolution Visible Imagery to Satellite Melt Pond Fraction Retrieval: A Neural Network Approach
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FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models
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Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
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Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations
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Scalable Climate Data Analysis: Balancing Petascale Fidelity and Computational Cost
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Data-driven Surface Solar Irradiance Estimation using Neural Operators at Global Scale
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PAUNet: Precipitation Attention-based U-Net for rain prediction from satellite radiance data
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Forecasting formation of a Tropical Cyclone Using Reanalysis Data
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Exploring Randomly Wired Neural Networks for Climate Model Emulation
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Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables
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