Temperature interpolation based on local information: the example of France

Joly, Daniel; Brossard, Thierry; Cardot, Hervé; Cavailhès, Jean; Hilal, Mohamed; Wavresky, Pierre

doi:10.1002/joc.2220

Cited by 35 publications

(29 citation statements)

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“…Les stations ont été géo-référencées, et des cartes d'interpolation ont été produites grâce au logiciel LISDQS (logiciel d'interpolation de données quantitatives et spatiales, université de Franche-Comté). La méthode proposée -une régression locale suivie par un krigeage des résidus -s'effectue suivant différentes étapes : i) défi-nition de polygones en sélectionnant les 20 stations les plus proches ; ii) définition d'un modèle de régres-sion locale pour chaque polygone entre les paramètres O 3 et les variables explicatives ; iii) krigeage des résidus ; iv) application du modèle local à tous les pixels de 250 m de résolution (Hengl et al, 2004 ;Joly et al, 2011 42,7 ± 6,0 -0,17 ± 0,11 29,9 ± 6,9 -0,12 ± 0,21 24,9 ± 3,9 + 0,10 ± * 61 stations rurales dont 9 stations de haute altitude La définition des polygones, i.e. la zone où la régres-sion multiple est réalisée, est effectuée en sélection-nant les 20 stations les plus proches.…”

Section: Génération De Cartes Pour L'évaluation Des Risques à L'ozoneunclassified

“…Cependant, la performance de l'interpolation est fortement affectée par le nombre et la répartition spatiale des stations de surveillance disponibles, et par la prise en compte ou non du relief (Bogaert et al, 2009). Pour contrer cette difficulté, une approche hybride d'interpolation locale a été testée (Joly et al, 2011 ;Araki et al, 2015). Monks et al (2015) a souligné qu'à ce jour une bonne compréhension des tendances de l'O 3 troposphérique et une bonne quantification de ses impacts restent encore difficiles.…”

Section: Introductionunclassified

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Ozone troposphérique en France : distribution spatiale et tendances sur la période 1999-2012

Sicard¹,

Rossello²

2017

Pollution Atmosphérique

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Résumé), en particulier pendant la période froide. Cette augmentation peut être attribuée à l'augmentation des apports d'O 3 (et de ses précurseurs) par le transport transcontinental et à une plus faible destruction de l'O 3 par le NO en raison de la réduction des émissions locales de NO x . Entre 1999 et 2012, les tendances montrent que la menace pour la population et la végétation a diminué en France, ce qui démontre le succès des stratégies de contrôle des émissions en Europe au cours des 20 dernières années. Cependant, pour tous les indicateurs d'exposition, les seuils de protection sont dépassés régulièrement, et les objectifs des directives européennes de qualité de l'air ne sont pas respectés. Pour l'O 3 , la région à haut risque est le Sud-Est de la France. Cette étude apporte des informations innovantes sur i) la distribution spatiale des concentrations en O 3 ; ii) les dépassements ; iii) les tendances afin de définir des normes plus appropriées pour la protection de la santé humaine et des écosystèmes en France. Mots-clésLAOT40, ozone, test de Mann-Kendall, SOMO35, tendance(1) ACRI-HE, 260 route du Pin

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Section: Génération De Cartes Pour L'évaluation Des Risques à L'ozoneunclassified

Section: Introductionunclassified

Ozone troposphérique en France : distribution spatiale et tendances sur la période 1999-2012

Sicard¹,

Rossello²

2017

Pollution Atmosphérique

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“…Another, very frequently used variable is a distance from the sea (WHITE 1979;BJORNSSON et al 2007;BOI et al 2011;JOLY et al 2011) or distance from other major bodies of water (HOLDAWAY 1996;HIEBL et al 2009;TIETAVAINEN et al 2010). Distance from water bodies, like most of the variables capturing the impact of land use/land cover, is typical of regional or local scale.…”

Section: Introductionmentioning

confidence: 99%

“…Relatively rarely used explanatory variable of air temperature is solar irradiation (VICENTE-SERRANO et al 2003;ATTORRE et al 2007;BENAVIDES et al 2007;ESTEBAN et al 2009;JOLY et al 2011), even if this variable is complex, representing general geographic, atmospheric and astronomic energetic conditions, as well as local influence of terrain height and relief (slope, aspect, and hillshades).…”

Section: Introductionmentioning

confidence: 99%

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

Szymanowski

Kryza

2015

Pure Appl. Geophys.

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Abstract-Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various onedimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the crossvalidation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

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“…The first of those approaches consists in using local analyses (Joly et al ., , 2011. The second involves constructing the most effective explanatory variables possible, for example, the height of crests and the depth of troughs are topographical variables that are almost as important as altitude (Joly et al ., 2012).…”

Section: Introductionmentioning

confidence: 99%

Improving spatial temperature estimates by resort to time autoregressive processes

Joly

Cardot

Schaumberger

2012

Intl Journal of Climatology

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Temperature estimation methods usually involve regression followed by kriging of residuals (residual kriging). Despite the performance of such models, there is invariably a residual which is not necessarily unpredictable because it may still be correlated in time. We set out to analyse such residuals through resort to autoregressive processes. It is shown that the optimal period varies depending on whether it is identified by functions of the form res d = f (res d−1 , res d−2 , . . . , res d−p ) or by partial correlations. Autoregressive processes significantly improve estimates, which are evaluated by cross-validations. Finally, the two following points are discussed: (1) the assumptions of the autoregressive model on the residuals (the assumptions of linearity, stationarity of space and time are verified empirically) and (2) the identification of the days for which the introduction of this model is really interesting.

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Temperature interpolation based on local information: the example of France

Cited by 35 publications

References 20 publications

Ozone troposphérique en France : distribution spatiale et tendances sur la période 1999-2012

Ozone troposphérique en France : distribution spatiale et tendances sur la période 1999-2012

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

Improving spatial temperature estimates by resort to time autoregressive processes

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