Simulation of extreme temperatures using a new method: TIN‐copula

Lazoglou, Georgia; Gräler, Benedikt; Αnagnostopoulou, Christina

doi:10.1002/joc.6124

Cited by 8 publications

(8 citation statements)

References 35 publications

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“…The results must be interpreted with caution in areas with complex terrain, since the alternation between land and sea plays an important role in the observed differences between ERA5 and E-OBS. In case there is a need to correct these temperature datasets, and especially extreme temperatures, statistics of extremes should be utilized [34][35][36].…”

mentioning

confidence: 99%

Reliability of the ERA5 in Replicating Mean and Extreme Temperatures across Europe

Velikou

Lazoglou

Τολίκα

et al. 2022

Water

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ERA5 is widely considered as a valid proxy of observation at region scales. Surface air temperature from the E-OBS database and 196 meteorological stations across Europe are being applied for evaluation of the fifth-generation ECMWF reanalysis ERA5 temperature data in the period of 1981–2010. In general, ERA5 captures the mean and extreme temperatures very well and ERA5 is reliable for climate investigation over Europe. High correlations ranging from 0.995 to 1.000 indicate that ERA5 could capture the annual cycle very well. However, the high mean biases and high Root Mean Square Error (RMSE) for some European sub-regions (e.g., the Alps, the Mediterranean) reveal that ERA5 underestimates temperatures. The biases can be mainly attributed to the altitude differences between ERA5 grid points and stations. Comparing ERA5 with the other two datasets, ERA5 temperature presents more extreme temperature and small outliers for regions southern of 40° latitude and less extreme temperatures in areas over the Black Sea. In Scandinavia, ERA5 temperatures are more frequently extreme than the observational ones.

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mentioning

confidence: 99%

Reliability of the ERA5 in Replicating Mean and Extreme Temperatures across Europe

Velikou

Lazoglou

Τολίκα

et al. 2022

Water

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“…The proposed method is a combination of the triangular irregular networks (TIN) and the copula theory. This method was first developed and presented by Lazoglou et al [32], who used TIN-copula method for the estimation of extreme temperatures in Greece using only observational data. For the application of the TIN-copula method, the appropriate database should be created.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a New Statistical Method—TIN-Copula–for the Bias Correction of Climate Models’ Extreme Parameters

et al. 2020

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During the last decades, global and regional climate models have been widely used for the estimation of future climate conditions. Unfortunately, the models’ estimated values present important biases relative to the observed values, especially when the estimations refer to extremes. Consequently, several researchers have studied several statistical methods that are able to minimize the biases between climate models and observed values. The present study evaluates a new statistical method for bias correction: The triangular irregular network (TIN)-copula method. This method is a combination of the triangular irregular networks and the copula theory. In the present research, the new method is applied to ten Mediterranean stations and its results are compared with the bias-corrected values of three other widely used methods: The delta, the scaling, and the empirical quantile mapping methods. The analysis was made for maximum mean temperature (TMX) and minimum mean temperature (TMN) as well as for extreme precipitation (R99). According to the results, the TIN-copula method is able to correct extreme temperature and precipitation values, estimated by regional climate models, with high accuracy. Additionally, it is proven that the TIN-copula method is a useful tool for bias correction as it presents several advantages compared with the other methods, and it is recommended for future works.

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“…Triangulated Irregular Network (TIN) : In the thresholdbased method, the threshold size needs to be manually set or large-scale search, so in this experiment, we introduce the method of TIN, which is usually used in geographic or meteorological information analysis [38]. It can automatically generate the graph structure according to the position relation of nodes without hyper-parameter, shown in Algorithm 2.…”

Section: K -Nearest Neighbor (Knn)mentioning

confidence: 99%

GE-STDGN: a novel spatio-temporal weather prediction model based on graph evolution

Wang

Fang

2021

Appl Intell

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Many crucial tasks in weather prediction require large-scale and long-term spatio-temporal predictions. However, these tasks usually face three challenges: high feature redundancy, dependence of long-term prediction, and complexity in spatial relations of geographical location. To overcome these challenges, the Graph Evolution-based Spatio-Temporal Dense Graph Neural (GE-STDGN) network is proposed in spatio-temporal series prediction 1 . In this paper, a graph structure learning and optimization method based on an Evolutionary Multi-objective Optimization (EMO) algorithm, called Graph Evolution (GE), is adopted to improve the model's ability in analyzing complex node correlations. Then, to avoid the over-fitting caused by Graph Neural Network (GNN) and reduce the constraint of graph structure, a GNN based on dense connection, which is named DenseGNN, is applied to message passing. Finally, by combining the multi-head attention mechanism GRU model with DenseGNN, GE-STDGN can handle complex spatio-temporal series prediction tasks. Experimental results on a public real-world weather dataset demonstrate that our model steadily outperforms many state-of-the-art models.

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Simulation of extreme temperatures using a new method: TIN‐copula

Cited by 8 publications

References 35 publications

Reliability of the ERA5 in Replicating Mean and Extreme Temperatures across Europe

Reliability of the ERA5 in Replicating Mean and Extreme Temperatures across Europe

Evaluation of a New Statistical Method—TIN-Copula–for the Bias Correction of Climate Models’ Extreme Parameters

GE-STDGN: a novel spatio-temporal weather prediction model based on graph evolution

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