Paper Citation Record · LEDGER

Using Generative Models to Produce Realistic Populations of UK Windstorms

As of 11 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2501.16110.

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

pith.paper-citation-record.v1
2501.16110 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:47:50.121004Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

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External citation measurements

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Outbound references

Observation cb8a65c8-7dd1-4084-afb0-cdfd361b445b · outbound

This paper cites an unresolved cited work.

Using Generative Models to Produce Realistic Populations of UK Windstorms Unresolved cited work

Reference 1

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Observation 0a2c22da-ecb7-4b22-a7af-51b96c367104 · outbound

This paper cites ERA5 provides hourly estimates of atmospheric variables, which covers the period from 1940 to the present with a spatial resolution of 0.25°×0.25° (Hersbach et al., 2020).

Using Generative Models to Produce Realistic Populations of UK Windstorms ERA5 provides hourly estimates of atmospheric variables, which covers the period from 1940 to the present with a spatial resolution of 0.25°×0.25° (Hersbach et al., 2020)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:47:50.387939Z

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Observation 7699e32c-7b00-4cad-ae4f-b0bd5e43279d · outbound

This paper cites Copyright in this work may be transferred without further notice.

Using Generative Models to Produce Realistic Populations of UK Windstorms Copyright in this work may be transferred without further notice

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:47:50.360730Z

Source-reported events for the cited work

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Observation d5f3de7c-84e3-4d18-96fb-ab55173b2ede · outbound

This paper cites Prior to model training, the ERA5 data were normalized to a range of [0,1] using global minimum and maximum values across the entire domain and period.

Using Generative Models to Produce Realistic Populations of UK Windstorms Prior to model training, the ERA5 data were normalized to a range of [0,1] using global minimum and maximum values across the entire domain and period

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:47:50.373449Z

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This paper cites These connections ensure that the model retains important spatial features and recovers fine details in the outputs (Drozdzal et al., 2016).

Using Generative Models to Produce Realistic Populations of UK Windstorms These connections ensure that the model retains important spatial features and recovers fine details in the outputs (Drozdzal et al., 2016)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:47:50.307945Z

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Observation 0de313ee-d59a-4b10-8299-a590fad9ffe7 · outbound

This paper cites LeakyReLU activations are used after convolutional layers in both networks, which return small values for negative inputs instead of zeros in the ReLU activation functions.

Using Generative Models to Produce Realistic Populations of UK Windstorms LeakyReLU activations are used after convolutional layers in both networks, which return small values for negative inputs instead of zeros in the ReLU activation functions

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:47:50.347137Z

Source-reported events for the cited work

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Observation be24a7ff-4ef6-4fbc-a774-087889faabb9 · outbound

This paper cites an unresolved cited work.

Using Generative Models to Produce Realistic Populations of UK Windstorms Unresolved cited work

Reference 7

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This paper cites an unresolved cited work.

Using Generative Models to Produce Realistic Populations of UK Windstorms Unresolved cited work

Reference 8

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Source-reported events for the cited work

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Observation d25b6587-cd33-4daa-b1bd-7c2819f084c8 · outbound

This paper cites It consists of an encoder (down-sample) and a decoder (up-sample).

Using Generative Models to Produce Realistic Populations of UK Windstorms It consists of an encoder (down-sample) and a decoder (up-sample)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:47:50.295310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 11b29ee9-b1a8-4317-a06a-f8e1424a8ad8 · outbound

This paper cites an unresolved cited work.

Using Generative Models to Produce Realistic Populations of UK Windstorms Unresolved cited work

Reference 11

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This paper cites This work has been submitted to Artificial Intelligence for the Earth Systems.

Using Generative Models to Produce Realistic Populations of UK Windstorms This work has been submitted to Artificial Intelligence for the Earth Systems

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:47:50.263658Z

Source-reported events for the cited work

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Observation e9750baf-381e-40a6-b0eb-b86473754ab8 · outbound

This paper cites REFERENCES Adam, E.F., Brown, S., Nicholls, R.J.

Using Generative Models to Produce Realistic Populations of UK Windstorms REFERENCES Adam, E.F., Brown, S., Nicholls, R.J

Reference 13

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Observation 55c074bd-acff-4543-a6aa-d32bc4fb590b · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Using Generative Models to Produce Realistic Populations of UK Windstorms Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 17

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This paper cites and Courville, A.C., 2017: Improved training of wasserstein gans.

Using Generative Models to Produce Realistic Populations of UK Windstorms and Courville, A.C., 2017: Improved training of wasserstein gans

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:47:50.233339Z

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Observation 54000cc5-a2b4-4735-9a9b-e7a9c2186e4a · outbound

This paper cites Natural Hazards and Earth System Sciences, 11(10), pp.2847-2857.

Using Generative Models to Produce Realistic Populations of UK Windstorms Natural Hazards and Earth System Sciences, 11(10), pp.2847-2857

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:47:50.208329Z

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Observation 5d14080f-376a-4cd6-9127-10693d22b813 · outbound

This paper cites Diffusion-GAN: Training GANs with Diffusion.

Using Generative Models to Produce Realistic Populations of UK Windstorms Diffusion-GAN: Training GANs with Diffusion

Reference 31

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Observation 8261f44c-49b6-4a50-83e5-315ae7b8249a · outbound

This paper cites an unresolved cited work.

Using Generative Models to Produce Realistic Populations of UK Windstorms Unresolved cited work

Reference 1990

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:47:50.401644Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 32a80ec3-6d64-4403-864e-f70a8ce547a2 · outbound

This paper cites Precipitation nowcasting with generative diffusion models.

Using Generative Models to Produce Realistic Populations of UK Windstorms Precipitation nowcasting with generative diffusion models

Reference 2014

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Pith citing papers

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