Paper Citation Record · LEDGER

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling

As of 14 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2506.14798.

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

pith.paper-citation-record.v1
2506.14798 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:50:47.707465Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T00:18:25.379225Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

14 of 14 outbound references displayed

verified exact2
verified fuzzy3
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation ddc77a11-1fce-4895-96de-ce7d2e4e78a4 · outbound

This paper cites Emerging AI-based weather prediction models as downscaling tools.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling Emerging AI-based weather prediction models as downscaling tools

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:46.824044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f78174e6-ab6a-42ed-9cfa-c8988d3692fe · outbound

This paper cites Satellite-assisted particulate matter (sapm) for the models, in situ, and remote sensing of aerosols (mira) working group.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling Satellite-assisted particulate matter (sapm) for the models, in situ, and remote sensing of aerosols (mira) working group

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:48.873086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0e48f985-836e-4a10-b8fa-b6ce669aaa2e · outbound

This paper cites PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:47.152072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 494439a5-dfd8-496b-94f4-73f9250ee443 · outbound

This paper cites IceDiff: High Resolution and High-Quality Sea Ice Forecasting with Generative Diffusion Prior.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling IceDiff: High Resolution and High-Quality Sea Ice Forecasting with Generative Diffusion Prior

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:47.235873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90796f9b-3e0c-4048-8bd9-a2c6fcb17866 · outbound

This paper cites downscaling of gpm satellite precipitation products based on machine learning method in complex terrain and limited observation area.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling downscaling of gpm satellite precipitation products based on machine learning method in complex terrain and limited observation area

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:48.687042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 65373347-47c9-44b3-9519-9b1175601cad · outbound

This paper cites Image Super-Resolution via Iterative Refinement.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling Image Super-Resolution via Iterative Refinement

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:47.414684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b86eba8-d6d2-42d5-a0a2-975dbdfdcad0 · outbound

This paper cites Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:47.602100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e21137b-b7a3-48f3-941d-9eeeb878c52b · outbound

This paper cites Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:47.707465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0edb8e52-6f82-43ef-9070-1d3159cb923e · outbound

This paper cites The nippon foundation—gebco seabed 2030 project: The quest to see the world’s oceans completely mapped by 2030.Geosciences, 8(2):63,.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling The nippon foundation—gebco seabed 2030 project: The quest to see the world’s oceans completely mapped by 2030.Geosciences, 8(2):63,

Reference 1996

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:48.410918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4d64c2a8-6555-42b3-974b-6e0a34244e7b · outbound

This paper cites Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:46.948058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28f10530-5795-4613-9bca-dde8adc0274f · outbound

This paper cites Can AI be enabled to dynamical downscaling? A Latent Diffusion Model to mimic km-scale COSMO5.0\_CLM9 simulations.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling Can AI be enabled to dynamical downscaling? A Latent Diffusion Model to mimic km-scale COSMO5.0\_CLM9 simulations

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:50:48.010512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b48b77e1-2d20-4186-806c-ef636ba4a7fa · outbound

This paper cites Global atmospheric data assimilation with multi-modal masked autoencoders.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling Global atmospheric data assimilation with multi-modal masked autoencoders

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:46.614406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a558fa79-37d9-46ea-af8d-e0334fb88819 · outbound

This paper cites Advancing Data-driven Weather Forecasting: Time-Sliding Data Augmentation of ERA5.

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling Advancing Data-driven Weather Forecasting: Time-Sliding Data Augmentation of ERA5

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:50:48.181287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0a80b3be-8985-474d-8e50-d9b2caa2cea7 · outbound

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

MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling The era5 global reanalysis.Quarterly journal of the royal meteorological society, 146(730):1999–2049,

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:46.377890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 18b5db4f-e174-4498-9b73-0117987f4bf5 · inbound

MotifGen: Spatiotemporal interpolation of misaligned satellite images via multi-source generative modeling, in an application to tropical cyclones cites this paper.

MotifGen: Spatiotemporal interpolation of misaligned satellite images via multi-source generative modeling, in an application to tropical cyclones MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-06-26T00:18:42.136695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.