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Paper Citation Record · LEDGER

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation

As of 14 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2411.12640.

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

pith.paper-citation-record.v1
2411.12640 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:24:59.872317Z

measured 25 of 25 standing notices

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

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  • verified fuzzy17
  • unresolved8
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External citation measurements

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Outbound references

Observation bbf2b9d1-1a6c-4ae0-908c-d048c9f8b133 · outbound

This paper cites The era5 global reanalysis.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation The era5 global reanalysis

Reference 1

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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.

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Observation 3b072a60-82cf-46ee-9dc5-72eb045754b9 · outbound

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

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Reference 2

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Observation 4fe0b68c-4e18-4319-b4dc-962c3fd8843b · outbound

This paper cites Evaluation of ECMWF forecasts.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Evaluation of ECMWF forecasts

Reference 3

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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.

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Observation bc2f9cc9-c196-498e-bde5-5c658be46c9e · outbound

This paper cites Swinrdm: integrate swinrnn with diffusion model towards high-resolution and high-quality weather forecasting.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Swinrdm: integrate swinrnn with diffusion model towards high-resolution and high-quality weather forecasting

Reference 4

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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.

source=pdf_text observed=2026-08-12T17:24:59.642478Z digest=sha256:b3630b0476023898b61070fe1e04562ee20177e92ca7dd70d05f4844103c6697

Observation 3cb85aea-b4a0-4023-b757-5803aa2bbbf4 · outbound

This paper cites Accurate medium-range global weather forecasting with 3d neural networks.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Accurate medium-range global weather forecasting with 3d neural networks

Reference 5

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Observation 0ddd300b-1198-45a2-8564-73510e2afc0b · outbound

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

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation GraphCast: Learning skillful medium-range global weather forecasting

Reference 6

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Observation a75f1334-b59a-4560-87cc-7c3b0c01fc6e · outbound

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

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Fuxi: A cascade machine learning forecasting system for 15-day global weather forecast

Reference 7

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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.

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Observation 49d75272-0bf8-4fef-b852-a423d3cfd2fd · outbound

This paper cites Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead

Reference 8

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Observation 5427a4af-f65b-45e4-bffa-e1beafb2a280 · outbound

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

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation AIFS -- ECMWF's data-driven forecasting system

Reference 9

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Observation 06226ae7-4ed1-47f3-88d3-981588f83a92 · outbound

This paper cites W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

Reference 10

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Observation d73e60f5-7a25-487a-aa0b-f2748e9b2b49 · outbound

This paper cites An evaluation of era5 precipitation for climate monitoring.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation An evaluation of era5 precipitation for climate monitoring

Reference 11

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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.

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Observation 9dcb3b54-b16e-4249-aa08-e96a051dc605 · outbound

This paper cites A systematic study of the class imbalance problem in convolutional neural networks.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation A systematic study of the class imbalance problem in convolutional neural networks

Reference 12

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raw_fallback, observed 2026-08-12T17:25:00.735657Z

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.

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Observation 4e18fda3-73c8-456a-acd5-90ce49651529 · outbound

This paper cites Large-scale long-tailed recognition in an open world.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Large-scale long-tailed recognition in an open world

Reference 13

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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.

source=pdf_text observed=2026-08-12T17:24:59.710978Z digest=sha256:89267c018f324a2bd4afd3df7e4ca5b63f3b831cb36351dd23e71214c3f13d6b

Observation fb845320-5996-4a9c-b46e-76cced6ac770 · outbound

This paper cites Long-tailed classification by keeping the good and removing the bad momentum causal effect.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Long-tailed classification by keeping the good and removing the bad momentum causal effect

Reference 14

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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.

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Observation 7334ff10-d2ae-4f20-9fc4-2166cd2e7d55 · outbound

This paper cites Distribution alignment: A unified framework for long-tail visual recognition.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Distribution alignment: A unified framework for long-tail visual recognition

Reference 15

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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.

source=pdf_text observed=2026-08-12T17:24:59.757816Z digest=sha256:65597dcde65a9d7bffc4074747a5928e01a32237423d184a976da0b963f82699

Observation 9c42969d-5788-46b4-9851-4f785c3b4505 · outbound

This paper cites Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition

Reference 16

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raw_fallback, observed 2026-08-12T17:25:00.624269Z

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.

source=pdf_text observed=2026-08-12T17:24:59.763088Z digest=sha256:7440a99f57a02669d89d5f929a09c7bf6d303e0d0fcc2f3a4d39de12b48aedbe

Observation a1a7a43d-0fb2-4b90-bada-598b67740835 · outbound

This paper cites Customized deep learning for precipitation bias correction and downscaling.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Customized deep learning for precipitation bias correction and downscaling

Reference 17

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

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Observation 54cdd63e-bc21-4df9-927b-ab1ee0152016 · outbound

This paper cites Smaat-unet: Precipitation nowcasting using a small attention-unet architecture, 2021.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Smaat-unet: Precipitation nowcasting using a small attention-unet architecture, 2021

Reference 18

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

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Observation 9cda0b7b-3579-42e6-810f-8b8179511c10 · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation.IEEE transactions on pattern analysis and machine intelligence, 39(12):2481– 2495, 2017.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Segnet: A deep convolutional encoder-decoder architecture for image segmentation.IEEE transactions on pattern analysis and machine intelligence, 39(12):2481– 2495, 2017

Reference 19

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

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Observation 9fa79752-fc55-4a8a-8b3e-b226b97570c6 · outbound

This paper cites Moganet: Multi-order gated aggregation network.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Moganet: Multi-order gated aggregation network

Reference 20

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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.

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

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

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

Reference 21

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Observation 03c8e7fe-2065-48c1-82e5-bf8ae0e972b5 · outbound

This paper cites Long-tail learning via logit adjustment.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Long-tail learning via logit adjustment

Reference 22

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source=pdf_text observed=2026-08-12T17:24:59.836934Z digest=sha256:1f8eced481a409d2da38ed45464e5cf28e7454c6d24eaaf36fae7d830da7e8cc

Observation 5fb7f6ef-c99b-4644-ad71-6cd11fa9e8fc · outbound

This paper cites A long-term assessment of precipitation forecast skill using the fractions skill score.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation A long-term assessment of precipitation forecast skill using the fractions skill score

Reference 23

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raw_fallback, observed 2026-08-12T17:25:00.403012Z

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.

source=pdf_text observed=2026-08-12T17:24:59.851429Z digest=sha256:21900ad0315a020369b0e369540efb0406bdd7c69c54bc3f05cec66df7b0e004

Observation 75a5c962-fd98-4aee-b5c4-d6a7815e35d3 · outbound

This paper cites Cmorph: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Cmorph: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution

Reference 24

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

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Observation 02ee9794-eabc-4dc4-a1ab-65cdc41d4dd6 · outbound

This paper cites Enhancing surface wind speed and temperature prediction using surface-layer emulator and transfer learning.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation Enhancing surface wind speed and temperature prediction using surface-layer emulator and transfer learning

Reference 25

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raw_fallback, observed 2026-08-12T17:25:00.321844Z

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

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Pith citing papers

No inbound Pith citation observations are available.