A statistical approach for modeling shallow (

A statistical approach for modeling shallow (

Temperature is perhaps the most important seawater property. It is a measure of the energy content in the ocean and it affects the metabolic rates, distribution, and abundance of species that are important from the economic and ecological points of view. Satellite-derived oceanographic data have bee...

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Autor principal: Marín-Enríquez, Emigdio
Formato: Online
Lenguaje:eng
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Publicado: Iniversidad Autónoma de Baja California 2021
Acceso en línea:https://www.cienciasmarinas.com.mx/index.php/cmarinas/article/view/3027
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record_format ojs
institution Ciencias Marinas
collection OJS
language eng
spa
format Online
author Marín-Enríquez, Emigdio
spellingShingle Marín-Enríquez, Emigdio
A statistical approach for modeling shallow (
author_facet Marín-Enríquez, Emigdio
author_sort Marín-Enríquez, Emigdio
title A statistical approach for modeling shallow (
title_short A statistical approach for modeling shallow (
title_full A statistical approach for modeling shallow (
title_fullStr A statistical approach for modeling shallow (
title_full_unstemmed A statistical approach for modeling shallow (
title_sort statistical approach for modeling shallow (
description Temperature is perhaps the most important seawater property. It is a measure of the energy content in the ocean and it affects the metabolic rates, distribution, and abundance of species that are important from the economic and ecological points of view. Satellite-derived oceanographic data have been widely used to assess spatiotemporal variations of sea surface temperature on broad scales; satellites, however, are unable to reach subsurface levels, and obtaining reliable subsurface water temperature data is achieved by either numerical modeling or direct observations, the latter representing a very high-cost alternative. In this paper, a method for modeling temperature profiles is presented. A generalized additive mixed model (GAMM) with a gamma error distribution and an inverse link function was used to model shallow (200 m) temperature profiles in the Pacific Ocean off northwestern Mexico. The dataset included 656 profiles that were linearly interpolated at depth, which resulted in 127,595 observations. The database covered an area from 18.5º to 25.8ºN and from –114.5º to –105.9ºW in a time span from June 2007 to November 2016. The model included temperature as response variable; depth, surface dynamic topography, wind stress curl, latitude, longitude, and the Oceanic Niño Index as covariates; and month as random effect. The final model explained 86% of the total deviance of the dataset used to fit the GAMM. Although important deviations between the observations and the predictions of the model were observed, the results of the validation process and of predictions made on an independent dataset (correlation of observed vs. predicted temperature, 0.93; root-mean-square error, 1.5 ºC) were comparable to the results obtained with more complex modeling techniques, suggesting that this statistical approach is a valuable tool for modeling oceanographic data.
publisher Iniversidad Autónoma de Baja California
publishDate 2021
url https://www.cienciasmarinas.com.mx/index.php/cmarinas/article/view/3027
_version_ 1792095468324388864
spelling oai:cienciasmarinas.com.mx:article-30272023-11-24T22:00:21Z A statistical approach for modeling shallow ( Un enfoque estadístico para modelar perfiles someros ( Marín-Enríquez, Emigdio mixed-effects models oceanographic campaigns eastern Pacific Ocean temperature profiles modelos de efectos mixtos campañas oceanográficas océano Pacífico oriental perfiles de temperatura Temperature is perhaps the most important seawater property. It is a measure of the energy content in the ocean and it affects the metabolic rates, distribution, and abundance of species that are important from the economic and ecological points of view. Satellite-derived oceanographic data have been widely used to assess spatiotemporal variations of sea surface temperature on broad scales; satellites, however, are unable to reach subsurface levels, and obtaining reliable subsurface water temperature data is achieved by either numerical modeling or direct observations, the latter representing a very high-cost alternative. In this paper, a method for modeling temperature profiles is presented. A generalized additive mixed model (GAMM) with a gamma error distribution and an inverse link function was used to model shallow (200 m) temperature profiles in the Pacific Ocean off northwestern Mexico. The dataset included 656 profiles that were linearly interpolated at depth, which resulted in 127,595 observations. The database covered an area from 18.5º to 25.8ºN and from –114.5º to –105.9ºW in a time span from June 2007 to November 2016. The model included temperature as response variable; depth, surface dynamic topography, wind stress curl, latitude, longitude, and the Oceanic Niño Index as covariates; and month as random effect. The final model explained 86% of the total deviance of the dataset used to fit the GAMM. Although important deviations between the observations and the predictions of the model were observed, the results of the validation process and of predictions made on an independent dataset (correlation of observed vs. predicted temperature, 0.93; root-mean-square error, 1.5 ºC) were comparable to the results obtained with more complex modeling techniques, suggesting that this statistical approach is a valuable tool for modeling oceanographic data. La temperatura quizá sea la propiedad del agua marina más importante. Es una medida del contenido energético del océano y afecta las tasas metabólicas, la distribución y la abundancia de especies económicamente y ecológicamente importantes. Los datos oceanográficos derivados de satélites han sido utilizados para evaluar las variaciones espaciotemporales de la temperatura superficial del mar a escalas amplias; sin embargo, los satélites no alcanzan niveles subsuperficiales, y los datos de temperatura subsuperficial confiables se obtienen mediante modelamiento numérico u observaciones directas, estas últimas una alternativa costosa. Este artículo presenta un método para modelar perfiles de temperatura. Se utilizó un modelo mixto aditivo generalizado (GAMM, por sus siglas en inglés) con distribución de error gamma y una función de enlace inversa para modelar perfiles someros (<200 m) de temperatura en el Pacífico frente al noroeste de México. Los datos incluyeron 656 perfiles linealmente interpolados en profundidad, resultando en 127,595 observaciones que cubrieron un área de 18.5º a 25.8ºN y de –114.5º a –105.9ºW, y un periodo de junio de 2007 a noviembre de 2016. El modelo incluyó la temperatura como variable de respuesta; la profundidad, la topografía dinámica superficial, el rotacional del esfuerzo del viento, la latitud, la longitud y el Índice Oceánico de El Niño como covariables; y el mes como efecto aleatorio. El GAMM final explicó el 86% de la desviación total del conjunto de datos. Aunque se observaron importantes desviaciones entre las observaciones y las predicciones del modelo, los resultados del proceso de validación y de las predicciones hechas sobre un conjunto de datos independiente (correlación de temperatura observada vs. temperatura predicha, ~0.93; raíz del error cuadrático medio, ~1.5 ºC) fueron comparables a resultados obtenidos con técnicas de modelamiento más complejas, lo cual sugiere que este enfoque estadístico es una herramienta valiosa para modelar datos oceanográficos. Iniversidad Autónoma de Baja California 2021-09-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article Artículo Arbitrado application/pdf text/xml text/xml https://www.cienciasmarinas.com.mx/index.php/cmarinas/article/view/3027 10.7773/cm.v47i3.3027 Ciencias Marinas; Vol. 47 No. 3 (2021); 147–174 Ciencias Marinas; Vol. 47 Núm. 3 (2021); 147–174 2395-9053 0185-3880 eng spa https://www.cienciasmarinas.com.mx/index.php/cmarinas/article/view/3027/420420578 https://www.cienciasmarinas.com.mx/index.php/cmarinas/article/view/3027/420420767 https://www.cienciasmarinas.com.mx/index.php/cmarinas/article/view/3027/420420811 Copyright (c) 2021 Ciencias Marinas https://creativecommons.org/licenses/by/4.0

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