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Paper Citation Record · LEDGER

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction

As of 14 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2506.08285.

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

pith.paper-citation-record.v1
2506.08285 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:21:46.813597Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T13:52:47.249619Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T04:27:37.267290Z

Reference resolution

19 of 19 outbound references displayed

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External citation measurements

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Outbound references

Observation d0ee953a-4671-41c1-91ff-d70438ccf3e9 · outbound

This paper cites Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems.Physical Review Letters, 126(9):098302, March 2021.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems.Physical Review Letters, 126(9):098302, March 2021

Reference 1

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Source-reported events for the cited work

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Observation 2e02e2c2-5874-4cc9-bf3a-9a80c2502834 · outbound

This paper cites Accurate medium-range global weather forecasting with 3D neural networks.Nature, 619(7970):533– 538, 2023.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Accurate medium-range global weather forecasting with 3D neural networks.Nature, 619(7970):533– 538, 2023

Reference 2

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Observation 271c5782-7b4f-4061-8dde-5e12a8c21ca9 · outbound

This paper cites FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead

Reference 3

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Observation 1b73643e-6afd-4452-9692-a052c9cb81b9 · outbound

This paper cites FuXi: A cascade machine learning forecasting system for 15-day global weather forecast.npj Climate and Atmospheric Science, 6(1):1–11, 2023.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction FuXi: A cascade machine learning forecasting system for 15-day global weather forecast.npj Climate and Atmospheric Science, 6(1):1–11, 2023

Reference 4

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Observation cf90791e-7682-4daa-9fc2-4a804da40ac5 · outbound

This paper cites On the lambert w function.Advances in Computational mathematics, 5:329–359, 1996.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction On the lambert w function.Advances in Computational mathematics, 5:329–359, 1996

Reference 5

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Observation e215c91e-ba86-4c66-af59-07368d15ee97 · outbound

This paper cites Espinosa, Raul Moreno, and Matthias Karlbauer.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Espinosa, Raul Moreno, and Matthias Karlbauer

Reference 6

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Observation 4ac572b9-19d6-4367-b530-4eb62884dc14 · outbound

This paper cites an unresolved cited work.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Unresolved cited work

Reference 7

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Observation f83b5427-af00-4d55-a0c6-817e5d5d269f · outbound

This paper cites The era5 global reanalysis.Quarterly journal of the royal meteorological society, 146(730):1999–2049, 2020.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction The era5 global reanalysis.Quarterly journal of the royal meteorological society, 146(730):1999–2049, 2020

Reference 8

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Observation 6e66f0ad-47c3-4b35-a11d-18efe127e782 · outbound

This paper cites Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations

Reference 9

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Observation a0f99771-bf52-4c55-9d3d-597d2585dd1e · outbound

This paper cites Durran, Raul A.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Durran, Raul A

Reference 10

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Observation 0e9ecf9b-35eb-4eb5-895a-71f24b3d6821 · outbound

This paper cites Physics-informed machine learning: Case studies for weather and climate mod- elling.Philosophical transactions.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Physics-informed machine learning: Case studies for weather and climate mod- elling.Philosophical transactions

Reference 11

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Observation 74fbb7fd-4eb5-4fbb-be78-d0a64e677be0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Adam: A Method for Stochastic Optimization

Reference 12

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Observation eaca947a-f2af-4c13-aa54-6c59ea6925a0 · outbound

This paper cites Neural General Circulation Models for Weather and Climate.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Neural General Circulation Models for Weather and Climate

Reference 13

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Observation f29a8c20-9049-436d-b00b-54d6f3f2be40 · outbound

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

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Learning skillful medium-range global weather forecasting

Reference 14

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Observation 3f6177aa-39ae-45c0-ab9a-c6ffad290b87 · outbound

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Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Unresolved cited work

Reference 15

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Observation bc6d70c3-d5ed-44b8-a048-3861e66254b5 · outbound

This paper cites NVIDIA PhysicsNeMo: An open-source framework for physics- based deep learning in science and engineering, February 2023.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction NVIDIA PhysicsNeMo: An open-source framework for physics- based deep learning in science and engineering, February 2023

Reference 16

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Observation aaac7ce4-8acc-47ff-b678-0a2054897766 · outbound

This paper cites Vallis.Atmospheric and Oceanic Fluid Dynamics.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Vallis.Atmospheric and Oceanic Fluid Dynamics

Reference 17

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Observation a3b9b8b5-4344-4773-8a3c-6b1a6476682b · outbound

This paper cites Clark, Anna Kwa, W.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Clark, Anna Kwa, W

Reference 18

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Observation 6d4f7e17-3c7b-4478-a45e-02e1ba14cdd4 · outbound

This paper cites Climsim: A large multi-scale dataset for hybrid physics-ml climate emulation.Advances in Neural Information Processing Systems, 36:22070–22084, 2023.

Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction Climsim: A large multi-scale dataset for hybrid physics-ml climate emulation.Advances in Neural Information Processing Systems, 36:22070–22084, 2023

Reference 19

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Pith citing papers

Observation 52ef2409-74cc-4f0c-a75f-1708e0e5b50e · inbound

PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models cites this paper.

PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models Imposing the Fundamental Dynamical Constraint of Hydrostatic Balance to Improve Global ML Weather Prediction

Reference 36

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