Paper Citation Record · LEDGER

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers

As of 18 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2412.16763.

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pith.paper-citation-record.v1
2412.16763 v1

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measured 42 of 42 reference resolution

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42 of 42 outbound references displayed

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Outbound references

Observation bf6a44ea-900a-4c2b-a9a0-64807637ba7e · outbound

This paper cites Carbon dioxide and climate.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Carbon dioxide and climate

Reference 1

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Observation 43ba128f-0df2-4878-bb85-55b49f453ec2 · outbound

This paper cites A GCM parameterization for the shortwave radiative properties of water clouds.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers A GCM parameterization for the shortwave radiative properties of water clouds

Reference 2

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Observation 81b27cc6-d5c4-4c4a-bdf7-16cc69a93c7a · outbound

This paper cites Climate goals and computing the future of clouds.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Climate goals and computing the future of clouds

Reference 3

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Observation 062f4510-fa2f-4973-a355-9510ca22769a · outbound

This paper cites Causes of higher climate sensitivity in CMIP6 models.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Causes of higher climate sensitivity in CMIP6 models

Reference 4

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Observation d3a8aa08-89f0-4f56-9948-1c315bf32a48 · outbound

This paper cites An evaluation of proposed representations of subgrid hydrologic processes in climate models.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers An evaluation of proposed representations of subgrid hydrologic processes in climate models

Reference 5

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Observation 08efb538-b916-4d08-b8b9-9486a501de62 · outbound

This paper cites Improving a subgrid runoff parameterization scheme for climate models by the use of high resolution data derived from satellite observations.Climate Dynamics, 21:349–359, 2003.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Improving a subgrid runoff parameterization scheme for climate models by the use of high resolution data derived from satellite observations.Climate Dynamics, 21:349–359, 2003

Reference 6

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Observation b31f30c3-76b9-492d-b695-902a0078e2a0 · outbound

This paper cites Subgrid-scale physical parameterization in atmospheric modeling: How can we make it consistent? Journal of Physics A: Mathematical and Theoretical, 49(28):284001, 2016.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Subgrid-scale physical parameterization in atmospheric modeling: How can we make it consistent? Journal of Physics A: Mathematical and Theoretical, 49(28):284001, 2016

Reference 7

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Observation dd40943c-57a5-43c8-a6d8-187d7a8dd333 · outbound

This paper cites Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model

Reference 8

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Observation 6027fc84-4eab-462a-963c-ea98a59aeb27 · outbound

This paper cites A physics-incorporated deep learning framework for parameterization of atmospheric radiative transfer.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers A physics-incorporated deep learning framework for parameterization of atmospheric radiative transfer

Reference 9

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Observation 1db1b8c1-f152-46a7-8003-28b778250df6 · outbound

This paper cites Validation of a high-resolution regional climate model for the Alpine region and effects of a subgrid-scale topography and land use representation.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Validation of a high-resolution regional climate model for the Alpine region and effects of a subgrid-scale topography and land use representation

Reference 10

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Observation 17b6a18a-4c2d-41a9-8ca4-3bf2f8a2f8cb · outbound

This paper cites Ensemble data assimilation with the NCEP global forecast system.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Ensemble data assimilation with the NCEP global forecast system

Reference 11

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Observation 2ac4c7bd-9f80-49c6-81e3-0f4fcc72e736 · outbound

This paper cites A generalized approach to parameterizing convection combining ensemble and data assimilation techniques.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers A generalized approach to parameterizing convection combining ensemble and data assimilation techniques

Reference 12

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Observation bdb98ed0-1229-4660-836a-50e6da2718c1 · outbound

This paper cites A prognostic cloud water parameterization for global climate models.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers A prognostic cloud water parameterization for global climate models

Reference 13

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Observation 436cf671-41c2-4ab2-babc-190d9d1f320d · outbound

This paper cites An improved strategy for the evaluation of cloud parameterizations in GCMs.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers An improved strategy for the evaluation of cloud parameterizations in GCMs

Reference 14

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Observation a9e157a9-5651-4dbd-8f32-0828c8401921 · outbound

This paper cites Ensemble data assimilation in the whole atmosphere community climate model.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Ensemble data assimilation in the whole atmosphere community climate model

Reference 15

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Observation 91c4d758-bff0-483b-9709-6d3511f971a6 · outbound

This paper cites Bias and data assimilation.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Bias and data assimilation

Reference 16

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Observation 5ced4d47-4ae6-479b-9db8-cea9e6e168f3 · outbound

This paper cites A machine learning augmented data assimilation method for high-resolution observations.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers A machine learning augmented data assimilation method for high-resolution observations

Reference 17

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Observation b93e5e76-cf2d-4dd9-aa36-e9f6746d863f · outbound

This paper cites Deep learning to represent subgrid processes in climate models.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Deep learning to represent subgrid processes in climate models

Reference 18

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Observation 01c7c463-0a20-47fa-baff-0b1be1efab5c · outbound

This paper cites Could machine learning break the convection parameterization deadlock? Geophysical Research Letters, 45(11):5742–5751, 2018.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Could machine learning break the convection parameterization deadlock? Geophysical Research Letters, 45(11):5742–5751, 2018

Reference 19

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Observation cdf27f3f-c806-4bd3-9c23-cffa2677d7af · outbound

This paper cites Effects of stochastic parametrizations in the Lorenz’96 system.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Effects of stochastic parametrizations in the Lorenz’96 system

Reference 20

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Observation 37096ed8-f48b-4e27-9e13-5cca0c7c22d1 · outbound

This paper cites Machine learning for stochastic parameterization: Generative adversarial networks in the Lorenz’96 model.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Machine learning for stochastic parameterization: Generative adversarial networks in the Lorenz’96 model

Reference 21

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Observation c7257e2b-e313-43b8-ad6d-9f160a2a366c · outbound

This paper cites Stochastic parametrizations and model uncertainty in the lorenz’96 system.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Stochastic parametrizations and model uncertainty in the lorenz’96 system

Reference 22

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Observation 7f54109d-1036-4b3d-81a1-3602bb222e3e · outbound

This paper cites Climsim: A large multi-scale dataset for hybrid physics-ml climate emulation.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Climsim: A large multi-scale dataset for hybrid physics-ml climate emulation

Reference 23

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Observation 0389beb7-eced-4cfb-a602-7b3352ff5318 · outbound

This paper cites Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:18:19.404036Z

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Observation 9686ccb8-e9ad-4f0c-9714-1c9ef4e115c7 · outbound

This paper cites Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions

Reference 25

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Observation d86b5387-b414-4e6a-bb02-38b6de37452a · outbound

This paper cites Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations

Reference 26

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Observation 5067891f-e904-4200-8a75-f5bbe961319f · outbound

This paper cites Applications of deep learning to ocean data inference and subgrid parameterization.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Applications of deep learning to ocean data inference and subgrid parameterization

Reference 27

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Observation ce724c3a-6828-4a4f-aec9-5025af5e4e81 · outbound

This paper cites A data-driven approach to precipitation parameterizations using convolutional encoder-decoder neural networks.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers A data-driven approach to precipitation parameterizations using convolutional encoder-decoder neural networks

Reference 28

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Observation f5f5ebb7-d1c0-4066-a357-2f159d0c6db6 · outbound

This paper cites Improved weather forecasting using neural network emulation for radiation parameterization.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Improved weather forecasting using neural network emulation for radiation parameterization

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:18:19.364572Z

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Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Unresolved cited work

Reference 30

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Observation 840be35b-096c-48a8-97e7-04e0440dfdfb · outbound

This paper cites Stochastic parameterization of column physics using generative adversarial networks.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Stochastic parameterization of column physics using generative adversarial networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:18:19.339884Z

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Observation 921b135d-a79a-4027-b81a-bc6f0571a10a · outbound

This paper cites Generative data-driven ap- proaches for stochastic subgrid parameterizations in an idealized ocean model.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Generative data-driven ap- proaches for stochastic subgrid parameterizations in an idealized ocean model

Reference 32

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Observation 4355ef34-1045-46c9-8fbb-fd23414b1d70 · outbound

This paper cites Attention is all you need.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Attention is all you need

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Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Learning Deep Transformer Models for Machine Translation

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This paper cites Pre-trained language models for text generation: A survey.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Pre-trained language models for text generation: A survey

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This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 36

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Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers A transformer-based framework for multivariate time series representation learning

Reference 37

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This paper cites Transformers in Time Series: A Survey.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Transformers in Time Series: A Survey

Reference 38

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This paper cites Efficient attention: Attention with linear complexities.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Efficient attention: Attention with linear complexities

Reference 39

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Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Reformer: The Efficient Transformer

Reference 40

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This paper cites Earthformer: Exploring space-time transformers for earth system forecasting.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Earthformer: Exploring space-time transformers for earth system forecasting

Reference 41

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Observation 3de31eea-4df6-4d89-8450-ed588fa73bd6 · outbound

This paper cites Physics-informed machine learning: case studies for weather and climate modelling.

Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers Physics-informed machine learning: case studies for weather and climate modelling

Reference 42

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