Paper Citation Record · LEDGER

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting

As of 5 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 2 inbound Pith citation observations for arXiv:2605.00850.

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

pith.paper-citation-record.v1
2605.00850 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T03:22:44.396300Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T08:30:41.776716Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact29
verified fuzzy8
unresolved5
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f88e824b-4716-476d-ab86-922dd9c60754 · outbound

This paper cites Skillful joint probabilistic weather forecasting from marginals.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Skillful joint probabilistic weather forecasting from marginals

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:31:06.460649Z

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Observation 664683db-0cb3-4ca1-a3f1-b92e8ae4fb78 · outbound

This paper cites Deep Learning for Day Forecasts from Sparse Observations.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Deep Learning for Day Forecasts from Sparse Observations

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:31:06.402099Z

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Observation 9712a46b-8a8c-464f-a8e2-abfe494f7759 · outbound

This paper cites Baldwin and Timothy J.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Baldwin and Timothy J

Reference 3

Resolution: verified exact; doi, observed 2026-05-10T03:24:14.249057Z

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Observation 40cf1c88-1b4a-45db-8d1f-b8f2da33c5d1 · outbound

This paper cites doi: 10.1029/2020RG000708.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting doi: 10.1029/2020RG000708

Reference 4

Resolution: verified exact; doi, observed 2026-05-10T03:24:14.250810Z

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Observation 1631cd47-1082-4b80-839b-a03b0165f0e1 · outbound

This paper cites Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:31:06.392126Z

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Observation e98e46c5-c24b-4a4f-87b3-09ec025807d6 · outbound

This paper cites doi: 10.1038/s41586-023-06185-3.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting doi: 10.1038/s41586-023-06185-3

Reference 6

Resolution: verified exact; doi, observed 2026-05-10T03:24:14.247242Z

Source-reported events for the cited work

correction dated 2023-09-14. Source: crossref record 10.1038/s41586-023-06545-z->10.1038/s41586-023-06185-3:correction, observed 2026-07-11T03:00:24.47659+00:00. This notice travels one citation hop only.

Observation 4d94fdfc-dfd1-4940-a36c-5b61ca2ee2bc · outbound

This paper cites an unresolved cited work.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Unresolved cited work

Reference 7

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Observation 30edef11-2d85-41e2-91e9-d149f3471155 · outbound

This paper cites FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale

Reference 8

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Observation 7edf54eb-5a88-42dd-9606-e2f40f512d32 · outbound

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Unresolved cited work

Reference 9

Resolution: verified exact; doi, observed 2026-05-10T03:24:14.262871Z

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Observation 8b72fe47-20cd-49ee-ba74-a6cd4bc5d10d · outbound

This paper cites FuXi: A cascade machine learning forecasting system for 15-day global weather forecast.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting FuXi: A cascade machine learning forecasting system for 15-day global weather forecast

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:31:06.520347Z

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Observation 48f50f39-d243-42bc-8c44-e562ed54be65 · outbound

This paper cites Eyring, S.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Eyring, S

Reference 11

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Observation 7dccb527-c8c4-4b16-af74-a1fad8eca92c · outbound

This paper cites Eyring, S.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Eyring, S

Reference 12

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Observation 9385a4a9-6c09-41f4-b1c0-95c78c8d7436 · outbound

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Unresolved cited work

Reference 13

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Observation 116f58f6-29d2-4c36-a0f8-497a7fb585d1 · outbound

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

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:31:06.481350Z

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Observation 4a037572-a266-4a98-a793-daa8265bc87f · outbound

This paper cites Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting

Reference 15

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Observation 2d5920a4-bfd2-47d5-945c-4c3b3b854369 · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Masked Autoencoders Are Scalable Vision Learners

Reference 16

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Observation 1d972ccd-ba33-4155-b498-86f851d76016 · outbound

This paper cites Hersbach, B.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Hersbach, B

Reference 17

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Observation 5b551956-7afd-4f83-bb65-d2111b44e895 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Distilling the Knowledge in a Neural Network

Reference 18

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Observation 367245b4-e349-47d8-9942-b92da961fcc3 · outbound

This paper cites Axial Attention in Multidimensional Transformers.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Axial Attention in Multidimensional Transformers

Reference 19

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Observation 6190dd56-1261-4506-8943-b348738a8859 · outbound

This paper cites URLhttps://journals.ametsoc.org/doi/10.1175/ 2009BAMS2755.1.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting URLhttps://journals.ametsoc.org/doi/10.1175/ 2009BAMS2755.1

Reference 20

Resolution: verified exact; doi, observed 2026-05-10T03:24:14.256786Z

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Observation 47e9e029-4f8a-42f5-afcb-00feedf13f8b · outbound

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Unresolved cited work

Reference 21

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Observation 649222cb-9255-4a2f-9b88-021149cfd91c · outbound

This paper cites doi: 10.1126/science.adi2336.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting doi: 10.1126/science.adi2336

Reference 22

Resolution: verified exact; doi, observed 2026-05-10T03:24:14.241635Z

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Observation 702f1742-f841-48c1-8e9d-267181829e08 · outbound

This paper cites AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:31:06.418359Z

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Observation 03440072-0ca6-46a7-ac11-f77a3d4bb860 · outbound

This paper cites SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:31:06.386126Z

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Observation 987d63c2-b69c-4c36-8c9f-ee5bc4ccfeb0 · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 25

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This paper cites FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Reference 26

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Observation c16d7456-2818-46ee-afc1-86918a866d96 · outbound

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Unresolved cited work

Reference 27

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Observation f35c64b1-38cb-4fb6-84e5-0bdb6b2f7d01 · outbound

This paper cites doi: 10.1038/s43247-024-01812-x.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting doi: 10.1038/s43247-024-01812-x

Reference 28

Resolution: verified exact; doi, observed 2026-05-10T03:24:14.239323Z

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Observation e934ded2-703d-4f69-87f1-23348a6fbded · outbound

This paper cites WeatherBench 2: A benchmark for the next generation of data-driven global weather models.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting WeatherBench 2: A benchmark for the next generation of data-driven global weather models

Reference 29

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting doi: 10.1029/2018JD028755

Reference 30

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Observation 78913ea5-4228-431d-9bdd-0748fbfc7f9d · outbound

This paper cites Aardvark weather: end-to-end data-driven weather forecasting.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Aardvark weather: end-to-end data-driven weather forecasting

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:31:06.362955Z

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Observation 386c1829-9e10-4e22-b98a-4b151d6fc7fe · outbound

This paper cites doi: 10.1038/s41612-018-0013-0.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting doi: 10.1038/s41612-018-0013-0

Reference 32

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Observation ad23f2ef-c4c9-426a-b14c-481a46a6b166 · outbound

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting doi: 10.1029/2019MS001683

Reference 33

Resolution: verified exact; doi, observed 2026-05-10T03:24:14.237487Z

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Observation 55725cf2-3874-4fc4-836f-035d458ed6dd · outbound

This paper cites Scaling Laws of Global Weather Models.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Scaling Laws of Global Weather Models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-06-11T02:09:28.426150Z

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Observation 41d1f7fe-649c-49a2-b976-58b6a11a72bf · outbound

This paper cites 35 25%50% 25% Observation mask verticalmask variable mask spatially for each atmos.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting 35 25%50% 25% Observation mask verticalmask variable mask spatially for each atmos

Reference 35

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Observation 04b651bc-e51c-4cb6-b206-b38e2cde3a35 · outbound

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Unresolved cited work

Reference 36

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This paper cites Accordingly, we have explored variations in the perceiver module, increasing the number of Perceiver blocks, trying newer Perceiver modules, but observed a similar limitation.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Accordingly, we have explored variations in the perceiver module, increasing the number of Perceiver blocks, trying newer Perceiver modules, but observed a similar limitation

Reference 37

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Observation 645d3c4b-ad4c-4b7d-b535-e7489ed563d9 · outbound

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Unresolved cited work

Reference 38

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This paper cites We select years 2023 and 2024 as the test set.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting We select years 2023 and 2024 as the test set

Reference 39

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This paper cites In Table 16, we list the full set of variables we have used in this work.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting In Table 16, we list the full set of variables we have used in this work

Reference 40

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Observation 6f216d3a-a85a-438c-b557-526fb6f5479b · outbound

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Unresolved cited work

Reference 41

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Observation 38bc5be4-6b21-4490-921c-8533eeb17f72 · outbound

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Dataset Grid res

Reference 42

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Observation 2842c025-49f1-4f67-9991-a39b31878035 · outbound

This paper cites Consequently, the dataset only retains stations with≥90% valid hourly data.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Consequently, the dataset only retains stations with≥90% valid hourly data

Reference 43

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting •Observation filtering.We do not apply spatial or temporal interpolation; all missing values are preserved asNaN

Reference 44

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Observation 06b67c78-af77-43f2-ae59-d01627a375a8 · outbound

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Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting Unresolved cited work

Reference 45

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Pith citing papers

Observation 4a487ebf-1bd0-462a-8b86-b653414a73ba · inbound

Integrating GNSS-Derived Zenith Wet Delay into a Weather Foundation Model Improves Precipitation Forecasting cites this paper.

Integrating GNSS-Derived Zenith Wet Delay into a Weather Foundation Model Improves Precipitation Forecasting Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting

Reference 8

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Observation bdef88e6-d573-4d43-be1f-ca0bb23888ad · inbound

Physics-Informed Super-Resolution of Atmospheric Data cites this paper.

Physics-Informed Super-Resolution of Atmospheric Data Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting

Reference 267

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Source-reported events for the cited work

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