Paper Citation Record · LEDGER

Deep Learning for Day Forecasts from Sparse Observations

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2306.06079.

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

pith.paper-citation-record.v1
2306.06079 v3

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measured 23 of 23 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:24:59.831685Z

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

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External citation measurements

41
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 174a049e-c766-4551-9b83-52aed1d5603d · inbound

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation cites this paper.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Deep Learning for Day Forecasts from Sparse Observations

Reference 21

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Observation 56676170-e787-4b93-ac40-cb604980a22d · inbound

ADAF: An Artificial Intelligence Data Assimilation Framework for Weather Forecasting cites this paper.

ADAF: An Artificial Intelligence Data Assimilation Framework for Weather Forecasting Deep Learning for Day Forecasts from Sparse Observations

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T13:11:49.607334Z

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Observation e6ee1af3-920b-4d46-86ce-c4acbc784763 · inbound

Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data cites this paper.

Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data Deep Learning for Day Forecasts from Sparse Observations

Reference 47

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Observation 66d57f39-da15-4ea1-aa8e-cfa13312b01a · inbound

Data-driven Precipitation Nowcasting Using Satellite Imagery cites this paper.

Data-driven Precipitation Nowcasting Using Satellite Imagery Deep Learning for Day Forecasts from Sparse Observations

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:03.524515Z

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Observation b18f2293-7e10-42ef-afdd-b92ff723cab2 · inbound

ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting cites this paper.

ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting Deep Learning for Day Forecasts from Sparse Observations

Reference 2017

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Observation f931eb3a-9127-4c01-aa01-e9531f168e1a · inbound

GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations cites this paper.

GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations Deep Learning for Day Forecasts from Sparse Observations

Reference 24

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Observation 1495b03d-ca71-4850-89c5-7960ee741e72 · inbound

OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations cites this paper.

OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations Deep Learning for Day Forecasts from Sparse Observations

Reference 21

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Observation 4abf420e-d9b4-4dec-b08c-cd247135f39d · inbound

PEAR: Equal Area Weather Forecasting on the Sphere cites this paper.

PEAR: Equal Area Weather Forecasting on the Sphere Deep Learning for Day Forecasts from Sparse Observations

Reference 7

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Observation 91e8c3ec-9aec-4dff-a238-deb9974b8468 · inbound

Forecast error diagnostics in neural weather models cites this paper.

Forecast error diagnostics in neural weather models Deep Learning for Day Forecasts from Sparse Observations

Reference 1

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Observation fa0fd843-419a-49a9-bb99-714606b5329d · inbound

Learning from nature: insights into GraphDOP's representations of the Earth System cites this paper.

Learning from nature: insights into GraphDOP's representations of the Earth System Deep Learning for Day Forecasts from Sparse Observations

Reference 2023

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Observation 918caed5-9805-4c0b-8343-621d7df69032 · inbound

Learning from nature: insights into GraphDOP's representations of the Earth System cites this paper.

Learning from nature: insights into GraphDOP's representations of the Earth System Deep Learning for Day Forecasts from Sparse Observations

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-05T16:42:52.664119Z

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Observation 36e07814-d09c-4bdf-a9a4-a55fa4fe60dc · inbound

Observation-driven correction of numerical weather prediction for marine winds cites this paper.

Observation-driven correction of numerical weather prediction for marine winds Deep Learning for Day Forecasts from Sparse Observations

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T18:48:21.229985Z

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Observation 0fec1dde-3a60-4555-908f-941d1c5a8ab5 · inbound

FuXi-Nowcast: Environment-conditioned deep learning for severe convection nowcasting cites this paper.

FuXi-Nowcast: Environment-conditioned deep learning for severe convection nowcasting Deep Learning for Day Forecasts from Sparse Observations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T18:53:07.232361Z

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

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

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

Source-reported events for the cited work

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Observation e0116a28-77b4-414a-a713-c12212ea0845 · inbound

GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products cites this paper.

GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products Deep Learning for Day Forecasts from Sparse Observations

Reference 101

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:40:50.857536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd7eed38-002b-4d21-93b1-681f624f183c · inbound

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting cites this paper.

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting Deep Learning for Day Forecasts from Sparse Observations

Reference 98

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:21:26.096472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ae8acc32-413a-49e1-a388-9b3eaebe82d8 · inbound

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting cites this paper.

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting Deep Learning for Day Forecasts from Sparse Observations

Reference 98

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:22:59.094465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d6d9b016-d225-4429-9917-e3369f19570a · inbound

Towards a Foundation Model for the Martian Atmosphere cites this paper.

Towards a Foundation Model for the Martian Atmosphere Deep Learning for Day Forecasts from Sparse Observations

Reference 113

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:05:00.794496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 40b2f7b6-d4f5-42dd-b488-41b319bffdcf · inbound

AIFS-DOP: End-to-End Medium-Range Weather Prediction from Observations Alone with Machine Learning cites this paper.

AIFS-DOP: End-to-End Medium-Range Weather Prediction from Observations Alone with Machine Learning Deep Learning for Day Forecasts from Sparse Observations

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-26T18:59:43.815338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8776beec-7caf-47ba-bdc6-2d05cc9f27e6 · inbound

RainODE: Continuous-Time Precipitation Forecasting with Latent Neural ODEs cites this paper.

RainODE: Continuous-Time Precipitation Forecasting with Latent Neural ODEs Deep Learning for Day Forecasts from Sparse Observations

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:44:19.085090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4be74fe1-af8b-48b2-961c-8a591ad28877 · inbound

Global reanalysis from observations alone with machine learning cites this paper.

Global reanalysis from observations alone with machine learning Deep Learning for Day Forecasts from Sparse Observations

Reference 18

Resolution: malformed identifier; local_arxiv, observed 2026-07-10T16:17:21.544142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6e4c1b1b-fa08-4767-828c-0f4c7cecc437 · inbound

Geospatial Diffusion-based Evolution Synthesis (GeoDES) for Storm-Centered Weather Augmentation cites this paper.

Geospatial Diffusion-based Evolution Synthesis (GeoDES) for Storm-Centered Weather Augmentation Deep Learning for Day Forecasts from Sparse Observations

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T12:35:40.187831Z

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Observation ae5e2375-c4fa-4c65-9ae4-b6050eff5c86 · inbound

Prithvi-Precip: Integrating Satellite Observations into an Atmospheric AI Foundation Model for Precipitation Forecasting cites this paper.

Prithvi-Precip: Integrating Satellite Observations into an Atmospheric AI Foundation Model for Precipitation Forecasting Deep Learning for Day Forecasts from Sparse Observations

Reference 102

Resolution: unresolved; no resolver link, observed 2026-08-05T05:02:59.486280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.