Paper Citation Record · LEDGER

AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

As of 23 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2412.15832.

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

pith.paper-citation-record.v1
2412.15832 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-23T06:31:01.910684+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T15:21:24.194078Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f357812c-feb2-4c14-8e59-1f36a0bbee70 · inbound

EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules cites this paper.

EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T12:01:21.507444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation c0cdfb00-b07e-438b-ba16-da41961c506e · inbound

Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators cites this paper.

Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:16:37.781272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 6f7d770d-51ff-4da4-b502-042f2d222e51 · inbound

Generative 3D Gaussian Splatting for Arbitrary-ResolutionAtmospheric Downscaling and Forecasting cites this paper.

Generative 3D Gaussian Splatting for Arbitrary-ResolutionAtmospheric Downscaling and Forecasting AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:41:04.398580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 2895771e-c647-4fbe-b9b0-1ea8b7f7e348 · inbound

U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster cites this paper.

U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-10T20:45:46.866168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 134dc690-2d80-45d0-a543-a9c839e9d3bd · inbound

Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction cites this paper.

Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:27:51.472007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 631498b4-0173-4c19-930a-bfec8d620626 · inbound

Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations cites this paper.

Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:34:57.924413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 18336236-2a2f-45f0-9a8e-6e86de7d429a · inbound

The physics of AI weather models cites this paper.

The physics of AI weather models AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-25T02:25:14.233589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation cf62f97f-939e-49d3-9010-ff93accc8b31 · inbound

RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges cites this paper.

RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T11:54:38.123225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation c86dc44c-bb0b-4b22-852d-636c1c7dc51c · inbound

Probabilistic storyline attribution using machine learning cites this paper.

Probabilistic storyline attribution using machine learning AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-28T11:42:04.347893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 710cb84d-8f76-4a84-b5e2-7ab96dbb0316 · inbound

AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret cites this paper.

AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:56:16.253224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation ce0daed2-fb78-4481-a1b0-9e9618ed7161 · inbound

Performance Evaluation of GraphCast for Medium-Range Weather Forecasting over Brazil cites this paper.

Performance Evaluation of GraphCast for Medium-Range Weather Forecasting over Brazil AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-06-28T03:11:30.299834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation c65314cf-d718-4217-8361-03eb19fb2ab6 · inbound

Disentangling the effects of sea surface temperature and CO$_2$ in global machine learned weather-climate emulators cites this paper.

Disentangling the effects of sea surface temperature and CO$_2$ in global machine learned weather-climate emulators AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-06-27T19:31:10.549210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 4406b492-0448-4bea-9aa9-70ddb8b87ca7 · inbound

Reliability of Probabilistic Emulation of Physical Systems cites this paper.

Reliability of Probabilistic Emulation of Physical Systems AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:38:19.233559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation abfaac7b-4caf-47cd-8e88-25d2c0ac6c16 · inbound

Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks cites this paper.

Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 103

Resolution: verified exact; arxiv_id, observed 2026-06-26T22:20:09.524497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 93ae15e5-fbd3-4ec7-b186-889ef293fa70 · inbound

Decision-Aware Training for Sample-Based Generative Models cites this paper.

Decision-Aware Training for Sample-Based Generative Models AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T15:27:04.738429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.