Bias Correction of Ocean Bottom Temperature and Salinity Simulations From a Regional Circulation Model Using Regression Kriging

Chang, Jui-Han; Hart, Deborah R.; Munroe, Daphne M.; Curchitser, Enrique N.

doi:10.1029/2020jc017140

Cited by 6 publications

(5 citation statements)

References 41 publications

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“…We then showed that DSEM can be specified using 'arrow-andlag' notation, which is parsed to construct the sparse precision matrix for a GMRF, and this in turn is fitted within a GLMM. We first debates continue regarding the performance of these methods (Chang et al, 2021;Yang et al, 2018). Similarly, nonparametric and nonlinear interactions have been estimated using artificial neural networks to approximate nonlinear differential equations (Bhat & Munch, 2022;Bonnaffé & Coulson, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…Methods are increasingly available to estimate nonlinear causality among multiple variables in ecological systems. For example, convergent cross‐mapping has been used to predict system properties in experimental settings (Deyle et al., 2016), although debates continue regarding the performance of these methods (Chang et al., 2021; Yang et al., 2018). Similarly, nonparametric and nonlinear interactions have been estimated using artificial neural networks to approximate nonlinear differential equations (Bhat & Munch, 2022; Bonnaffé & Coulson, 2023).…”

Section: Discussionmentioning

confidence: 99%

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Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms

Thorson,

Andrews,

Essington

et al. 2024

Methods Ecol Evol

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Ecological analyses typically involve many interacting variables. Ecologists often specify lagged interactions in community dynamics (i.e. vector‐autoregressive models) or simultaneous interactions (e.g. structural equation models), but there is less familiarity with dynamic structural equation models (DSEM) that can include any simultaneous or lagged effect in multivariate time‐series analysis. We propose a novel approach to parameter estimation for DSEM, which involves constructing a Gaussian Markov random field (GMRF) representing simultaneous and lagged path coefficients, and then fitting this as a generalized linear mixed model to missing and/or non‐normal data. We provide a new R‐package dsem, which extends the ‘arrow interface’ from path analysis to represent user‐specified lags when constructing the GMRF. We also outline how the resulting nonseparable precision matrix can generalize existing separable models, for example, for time‐series and species interactions in a vector‐autoregressive model. We first demonstrate dsem by simulating a two‐species vector‐autoregressive model based on wolf–moose interactions on Isle Royale. We show that DSEM has improved precision when data are missing relative to a conventional dynamic linear model. We then demonstrate DSEM via two contrasting case studies. The first identifies a trophic cascade where decreased sunflower starfish has increased urchin and decreased kelp densities, while sea otters have a simultaneous positive effect on kelp in the California Current from 1999 to 2018. The second estimates how declining sea ice has decreased cold‐water habitats, driving a decreased density for fall copepod predation and inhibiting early‐life survival for Alaska pollock from 1963 to 2023. We conclude that DSEM can be fitted efficiently as a GLMM involving missing data, while allowing users to specify both simultaneous and lagged effects in a time‐series structural model. DSEM then allows conceptual models (developed with stakeholder input or from ecological expertise) to be fitted to incomplete time series and provides a simple interface for granular control over the number of estimated time‐series parameters. Finally, computational methods are sufficiently simple that DSEM can be embedded as component within larger (e.g. integrated population) models. We therefore recommend greater exploration and performance testing for DSEM relative to familiar time‐series forecasting methods.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms

Thorson,

Andrews,

Essington

et al. 2024

Methods Ecol Evol

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“…The use of interpolation and fitting techniques for simulating atmospheric pollutant concentrations has been a consistent focus of research. The accuracy and precision of interpolation have always been key considerations in these studies [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. In the study by Li et al [31], the results indicated that the optimal polynomial fitting (OPF) method accurately reconstructed PM 2.5 fields.…”

Section: Discussionmentioning

confidence: 99%

“…Spatial interpolation methods, widely employed in atmospheric studies and other fields, offer a solution by mitigating the impact of insufficient ground-based observation data on accurately characterizing the spatial and temporal distribution characteristics of PM 2.5. These methods include spatiotemporal statistical models based on Kriging interpolation, spatial and temporal regression models based on Kriging interpolation integrated with remote-sensing AOD data, neural network models based on RBF interpolation, and 3D RBF interpolation for hydrological structure analysis [22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Chen,

Li,

et al. 2023

Atmosphere

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Near-surface PM2.5 estimates remain a global scientific research challenge due to their effect on human fitness and atmospheric environmental quality. However, practical near-surface PM2.5 estimates are impeded by the incomplete monitoring data. In this study, we propose the trigonometric polynomial fitting (TPF) method to estimate near-surface PM2.5 concentrations in south-central China during 2015. We employ 10-fold cross-validation (CV) to assess the reliability of TPF in estimating practical PM2.5 values. When compared to alternative methods such as the orthogonal polynomial fitting (OBF) method based on Chebyshev basis functions, Kriging interpolation, and radial basis function (RBF) interpolation, our results show that utilizing TPF31, with a maximum order of 3 in the x direction and a maximum order of 1 in the y direction, leads to superior efficiency through error minimization. TPF31 reduces MAE and RMSE by 1.93%, 24%, 6.96% and 3.6%, 23.07%, 10.43%, respectively, compared to the other three methods. In addition, the TPF31 method effectively reconstructs the spatial distribution of PM2.5 concentrations in the unevenly distributed observation stations of Inner Mongolia and the marginal regions of the study area. The reconstructed spatial distribution is remarkably smooth. Despite the non-uniform distribution of observation stations and the presence of missing data, the TPF31 method demonstrates exceptional effectiveness in accurately capturing the inherent physical attributes of spatial distribution. The theoretical and experimental results emphasize that the TPF method holds significant potential for accurately reconstructing the spatial distribution of PM2.5 in China.

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“…From the perspective of the spatial estimation of ocean information, studies providing gridded data such as OISST (Optimum Interpolation SST [Sea Surface Temperature]) are ongoing 22 – 24 . In addition, research is being conducted to remove the bias of numerical models using spatial estimation techniques such as Kriging 25 . However, these techniques mainly focus on temperature and salinity.…”

Section: Background and Summarymentioning

confidence: 99%

Spatiotemporal estimation of nutrient data from the northwest pacific and east asian seas

Lee,

Cho

2023

Sci Data

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Nutrient data obtained from field observations have the potential to enhance our understanding of oceanic biogeochemical cycling and productivity changes. In particular, long-term nutrient data can provide valuable information on the links between climate change and biogeochemical changes. However, unlike other observational variables such as sea surface temperature, nutrient data are limited in terms of their broad-scale observations and automated sensor-based measurements. In this study, we analyzed nitrate and phosphate data obtained from coastal regions in Northeast Asia and the northwest Pacific from 1980 to 2019 using the spatiotemporal kriging technique and provide results in a spatiotemporal grid format. The data are available at monthly intervals and may be attractive to researchers in the fields of oceanography, marine ecology, and marine biogeochemistry at the climate change scale. Furthermore, sharing the source code of the data production process can contribute to better long-term data reproduction in the future.

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Bias Correction of Ocean Bottom Temperature and Salinity Simulations From a Regional Circulation Model Using Regression Kriging

Cited by 6 publications

References 41 publications

Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms

Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms

Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Spatiotemporal estimation of nutrient data from the northwest pacific and east asian seas

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