Spatio‐temporal downscaling emulator for regional climate models

Barboza, Luis A.; Chen, Shu Wei Chou; Alfaro-Córdoba, Marcela; Alfaro, Eric J.; Hidalgo, Hugo G.

doi:10.1002/env.2815

Cited by 4 publications

(3 citation statements)

References 49 publications

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“…Temporal downscaling serves as a crucial technique in various scientific applications [29][30][31][32], particularly in environmental modeling where high-frequency fluctuations often matter. The traditional ways to tackle this issue have primarily been statistical.…”

Section: Temporal Downscalingmentioning

confidence: 99%

A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net

Wang,

Li,

Peng

et al. 2024

Remote Sensing

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Temporal downscaling of gridded geophysical data is essential for improving climate models, weather forecasting, and environmental assessments. However, existing methods often cannot accurately capture multi-scale temporal features, affecting their accuracy and reliability. To address this issue, we introduce an Enhanced Residual U-Net architecture for temporal downscaling. The architecture, which incorporates residual blocks, allows for deeper network structures without the risk of overfitting or vanishing gradients, thus capturing more complex temporal dependencies. The U-Net design inherently can capture multi-scale features, making it ideal for simulating various temporal dynamics. Moreover, we implement a flow regularization technique with advection loss to ensure that the model adheres to physical laws governing geophysical fields. Our experimental results across various variables within the ERA5 dataset demonstrate an improvement in downscaling accuracy, outperforming other methods.

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Section: Temporal Downscalingmentioning

confidence: 99%

A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net

Wang,

Li,

Peng

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Temporal downscaling serves as a crucial technique in various scientific applications [17][18][19][20], particularly in environmental modeling where high-frequency fluctuations often matter. The traditional ways to tackle this issue have primarily been statistical.…”

Section: Temporal Downscalingmentioning

confidence: 99%

A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net

Wang,

Li,

Peng

et al. 2023

Preprint

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Temporal downscaling of gridded geophysical data is essential for improving climate models, weather forecasting, and environmental assessments. However, existing methods often could not accurately capture multi-scale temporal features, affecting their accuracy and reliability. To address this issue, we introduce an Enhanced Residual U-Net architecture for temporal downscaling. The architecture, which incorporates residual blocks, allows for deeper network structures without the risk of overfitting or vanishing gradients, thus capturing more complex temporal dependencies. The U-Net design inherently could capture multi-scale features, making it ideal for simulating various temporal dynamics. Moreover, we implement a flow regularization technique with advection loss to ensure that the model adheres to physical laws governing geophysical fields. Our experimental results across various variables within the ERA5 dataset demonstrate an improvement in downscaling accuracy, outperforming other methods.

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“…Recent papers published in the literature have highlighted the growing interest in the INLA method and its diverse applications. Barboza et al (2023), for instance, validated the superiority of the INLA method for estimating parameters for a spatio‐temporal statistical model used as a downscaling emulator for regional climate models. Castro‐Camilo et al (2022) implemented a novel blended generalized extreme value (bGEV) distribution in the R‐INLA package and demonstrated their effective performance through simulations and a case study on

{\mathrm{NO}}_2

pollution levels in California.…”

Section: Introductionmentioning

confidence: 99%

Bayesian spatio‐temporal survival analysis for all types of censoring with application to a wildlife disease study

Yao,

Zhu,

O'Brien

et al. 2023

Environmetrics

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In this article, we consider modeling arbitrarily censored survival data with spatio‐temporal covariates. We demonstrate that under the piecewise constant hazard function, the likelihood for uncensored or right‐censored subjects is proportional to the likelihood of multiple conditionally independent Poisson random variables. To address left‐ or interval‐censored subjects, we propose to impute the exact event times and convert them into uncensored subjects, enabling the application of the integrated nested Laplace approximation to update model parameters using the imputed data. We introduce an iterative algorithm that alternates between imputing event times for left‐ and interval‐censored subjects and re‐estimating model parameters. The proposed method is assessed through a simulation study and applied to analyze a spatio‐temporal survival dataset in a wildlife disease study investigating bovine tuberculosis in white‐tailed deer in Michigan.

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Spatio‐temporal downscaling emulator for regional climate models

Cited by 4 publications

References 49 publications

A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net

A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net

A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net

Bayesian spatio‐temporal survival analysis for all types of censoring with application to a wildlife disease study

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