Paper Citation Record · LEDGER

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere

As of 23 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 5 inbound Pith citation observations for arXiv:2411.10191.

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

pith.paper-citation-record.v1
2411.10191 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:56:32.339450Z

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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, observed 2026-08-06T11:00:55.351282Z

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

Source: pith, observed 2026-07-08T07:14:45.212738Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 94d08ed2-6fa1-42ae-b87c-7453c0b363bb · outbound

This paper cites an unresolved cited work.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 1

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This paper cites an unresolved cited work.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 2

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This paper cites an unresolved cited work.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 3

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Observation 83063e69-3339-4dd1-a47e-aa41043ea7aa · outbound

This paper cites N., Doblas -Reyes, F.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere N., Doblas -Reyes, F

Reference 4

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This paper cites an unresolved cited work.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 5

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This paper cites an unresolved cited work.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 6

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This paper cites an unresolved cited work.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 7

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 8

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This paper cites an unresolved cited work.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 9

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Observation 40f586a3-db2b-4d2b-a70d-28e8f1c57f7c · outbound

This paper cites AIFS -- ECMWF's data-driven forecasting system.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere AIFS -- ECMWF's data-driven forecasting system

Reference 10

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Observation 03d5a9f2-5c94-4574-87d4-3b996f86eb31 · outbound

This paper cites & Craig, G.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere & Craig, G

Reference 11

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Observation bac4b615-9f06-4935-98dc-4f3047137aea · outbound

This paper cites Ben et al.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Ben et al

Reference 12

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This paper cites an unresolved cited work.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 13

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Observation 28b4b270-3eea-4834-b080-540ad8ca0bf3 · outbound

This paper cites Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models

Reference 14

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Observation e13e18af-88cf-4f1c-a39b-76d22000c380 · outbound

This paper cites Robustness of AI-based weather forecasts in a changing climate.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Robustness of AI-based weather forecasts in a changing climate

Reference 15

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Observation dcfa10c3-021d-4b03-842e-7f65172e9b7c · outbound

This paper cites Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model

Reference 16

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Observation 7cc79cec-0931-4b87-9a59-564609249b15 · outbound

This paper cites Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction

Reference 17

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Observation 70719238-6b3f-4e48-9d09-22c8dc473cc4 · outbound

This paper cites an unresolved cited work.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 18

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Observation 51c59356-719b-43cd-81c6-6cb144832d6e · outbound

This paper cites CAS-Canglong: A skillful 3D Transformer model for sub-seasonal to seasonal global sea surface temperature prediction.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere CAS-Canglong: A skillful 3D Transformer model for sub-seasonal to seasonal global sea surface temperature prediction

Reference 19

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 20

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Seamless prediction of the Earth System: from minutes to months

Reference 21

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 22

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Observation 93f8bb80-2975-4f35-a61a-50f291402cb4 · outbound

This paper cites G., Yin, Y ., Alves, O.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere G., Yin, Y ., Alves, O

Reference 23

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere & Raftery, A

Reference 24

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Observation 6588fd8f-a8e3-4dc2-bd20-07c6d3f16050 · outbound

This paper cites W., Kumar, A., Peña, M.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere W., Kumar, A., Peña, M

Reference 25

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere A., Durran, D

Reference 26

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 29

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere & Derome, J

Reference 30

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 31

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Observation 91abc1f5-6afe-4429-9726-fbdd3361a3c8 · outbound

This paper cites Intraseasonal interaction between the Madden–Julian Oscillation and the North Atlantic Oscillation.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Intraseasonal interaction between the Madden–Julian Oscillation and the North Atlantic Oscillation

Reference 32

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This paper cites To assess NAO prediction skill at subseasonal timescales, we focused on daily NAO index forecasts initialized from each day of December-February36.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere To assess NAO prediction skill at subseasonal timescales, we focused on daily NAO index forecasts initialized from each day of December-February36

Reference 33

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

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This paper cites Teleconnections in the geopotential height field during the Northern Hemisphere winter.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Teleconnections in the geopotential height field during the Northern Hemisphere winter

Reference 36

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 37

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 38

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere & Kalnay, E

Reference 42

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 43

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FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere Unresolved cited work

Reference 44

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This paper cites A scoring system for probability forecasts of ranked categories.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere A scoring system for probability forecasts of ranked categories

Reference 45

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This paper cites S1A, c urrent deep learning models involve multi -level encoding and decoding, leading to the extraction of features from low -level to high-level spaces.

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere S1A, c urrent deep learning models involve multi -level encoding and decoding, leading to the extraction of features from low -level to high-level spaces

Reference 46

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Pith citing papers

Observation 05e02605-2c41-407c-b657-9750ffa5637d · inbound

Data-driven global ocean model resolving ocean-atmosphere coupling dynamics cites this paper.

Data-driven global ocean model resolving ocean-atmosphere coupling dynamics FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere

Reference 6

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LUCIE-3D: A three-dimensional climate emulator for forced responses cites this paper.

LUCIE-3D: A three-dimensional climate emulator for forced responses FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere

Reference 10

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Earth-o1: A Grid-free Observation-native Atmospheric World Model cites this paper.

Earth-o1: A Grid-free Observation-native Atmospheric World Model FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere

Reference 16

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Observation 06921832-654a-4bb5-bfc5-ead487e9eb4f · inbound

AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales cites this paper.

AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-11T08:50:01.837338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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A Definition and Roadmap for World Models cites this paper.

A Definition and Roadmap for World Models FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere

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