Paper Citation Record · LEDGER

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting

As of 8 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.14555.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.14555 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

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A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

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External citation measurements

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Outbound references

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Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Unresolved cited work

Reference 1

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Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Unresolved cited work

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Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Unresolved cited work

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Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Unresolved cited work

Reference 4

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This paper cites Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

Reference 5

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Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Unresolved cited work

Reference 7

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Reference 8

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Reference 11

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Reference 12

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Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Unresolved cited work

Reference 13

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This paper cites GraphCast: Learning skillful medium-range global weather forecasting.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting GraphCast: Learning skillful medium-range global weather forecasting

Reference 14

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Reference 15

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This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Fourier Neural Operator for Parametric Partial Differential Equations

Reference 16

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Reference 17

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This paper cites Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Reference 18

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Reference 19

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Reference 20

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This paper cites ClimaX: A foundation model for weather and climate.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting ClimaX: A foundation model for weather and climate

Reference 24

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Reference 27

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This paper cites ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling

Reference 28

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This paper cites GenCast: Diffusion-based ensemble forecasting for medium-range weather.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting GenCast: Diffusion-based ensemble forecasting for medium-range weather

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Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

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Reference 40

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This paper cites Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling

Reference 41

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Reference 46

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This paper cites In the forecasting experiments, we divide each dataset into train, validation, and test sets using an 8:1:1 ratio in chronological order.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting In the forecasting experiments, we divide each dataset into train, validation, and test sets using an 8:1:1 ratio in chronological order

Reference 500

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This paper cites In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Reference 2022

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This paper cites In Proceedings of the AAAI Conference on Artificial Intelligence , Vol.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting In Proceedings of the AAAI Conference on Artificial Intelligence , Vol

Reference 2023

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Pith citing papers

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