Statistical Downscaling of Monthly Precipitation Using NCEP/NCAR Reanalysis Data for Tahtali River Basin in Turkey

Fıstıkoğlu, Okan; Okkan, Umut

doi:10.1061/(asce)he.1943-5584.0000300

Cited by 68 publications

(59 citation statements)

References 56 publications

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“…Informative predictors can be identified using statistical measures, such as the Pearson, Spearmen, and Kendall correlation analysis [9], CCA [14], maximum covariance analysis (MCA) [15], partial correlation (PAR) [16][17][18], and principal component analysis (PCA) [19,20]. Interactive model fitting approaches are also used in predictor selection [21]. However, some limitations are found during the application, such as the limited ability of traditional correlation analysis for interpreting nonstationary and nonlinear relationships [22].…”

Section: Introductionmentioning

confidence: 99%

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Statistical Downscaling of Temperature with the Random Forest Model

Pang

Yue

Zhao

et al. 2017

Advances in Meteorology

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The issues with downscaling the outputs of a global climate model (GCM) to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF) model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction-National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA) and partial correlation analysis (PAR) were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.

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Section: Introductionmentioning

confidence: 99%

“…[35,36]. The Pearl River basin ( Figure 1) is located in tropical and subtropical climate zones, the annual temperature is [14][15][16][17][18][19][20][21][22] ∘ C, and the annual precipitation is 1200-2200 mm. The distribution of precipitation is gradually reduced from east to west.…”

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of Temperature with the Random Forest Model

Pang

Yue

Zhao

et al. 2017

Advances in Meteorology

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“…The performance of the model shows that there is a significant relationship between the modeled and observed Tmax (r-0.87, P < 0.05); Tmin (r-0.84, P < 0.05) and mean temperature (r-0.76, P < 0.05). This method of validation has been used by [28], but it however varies from the one adopted by [29].…”

Section: Methodsmentioning

confidence: 99%

The Minimum and Maximum Temperature Forecast Using Statistical Downscaling Techniques for Port-Harcourt Metropolis, Nigeria

Weli¹,

Nwagbara²,

Ozabor³

2017

ACS

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This study centers on applying the statistical downscaling technique to the daily minimum and maximum temperatures of Port Harcourt from the period 1985-2014. To select the period of calibration, the wilby and wigley assumption of 2014 was adopted. However, the Bruckner circle assumption was adopted in selecting the normal under review. Secondary data of minimum and maximum temperatures for Port Harcourt were collected from the archive of Nigerian meteorological agency (NIMET). The grid cell of the HadCM3 corresponding to the Port Harcourt meteorological station was selected from the HadCM3 website to generate the largescale predictors. Data for temperature was there after normalized for the period of calibration. To analyze data, ANOVA and Paired t tests were used. Result showed that, the model was significant at p < 0.05 implying that the largescale predictors of the HadCM3 have performed significantly and that temperature pattern in the area is significantly dependent on them. The Duncan statistics showed that in the A2 scenario Maximum temperature will rise with a mean difference of 3.1˚C from 1960-2080, while for B2 the increase will be 0.18˚C for same period. For minimum temperature, the ANOVA also showed a difference of 0.21˚C and 0.11˚C for A2 and B2 respectively. The paired t test statistics showed that these variations in temperatures for both maximum and minimum at A2 and B2 scenarios are significant at p < 0.05. Reforestation, afforestation, carbon sequestration are strongly advocated.

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“…Among different statistical approaches, regression-based methods are famous for simplicity in implementation. Different tools have been developed for statistical downscaling using artificial neural networks (ANNs) (Pasini, 2009;Tomassetti et al, 2009;Mendes and Marengo, 2010;Fistikoglu and Okkan, 2011), knearest neighbors (Yates, et al, 2003;Gangopadhyay et al, 2005;Raje and Mujumdar, 2011), support vector 2562 H. TAVAKOL-DAVANI ET AL. machines (SVMs) (Tripathi et al, 2006;Chen et al, 2010) and linear regression (Wilby et al, 2002;Hessami et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Improved statistical downscaling of daily precipitation using SDSM platform and data‐mining methods

Tavakol-Davani

Nasseri

Zahraie

2012

Intl Journal of Climatology

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ABSTRACT:In this paper, an extension of the statistical downscaling model (SDSM), namely data-mining downscaling model (DMDM), has been developed. DMDM has the same platform as the most cited statistical downscaling models, namely SDSM and ASD. Multiple linear regression (MLR), ridge regression (RR), multivariate adaptive regression splines (MARS) and model tree (MT) constitute the mathematical core of DMDM. DMDM uses linear basis functions in MARS and linear regression rules in MT to keep the linear structure of SDSM; therefore, all of the SDSM assumptions are also valid in DMDM. These methods highlight the effect of data partitioning for meteorological predictors in the downscaling procedure. Inputs and output of the presented approaches are the same as SDSM and ASD. In the case study of this research, NCEP/NCAR databases have been used for calibration and validation. According to the inherent linearity of the methods, suitable predictor selection has been done with stepwise regression as a preprocessing stage. The results of DMDM have been compared with observed precipitation in 12 rain gauge stations that are scattered in different basins in Iran and represent different climate regimes. Comparison between the results of SDSM and DMDM has indicated that the presented approach can highly improve downscaling efficiency in terms of reproducing monthly standard deviation and skewness for both calibration and validation datasets. Among the proposed methods in DMDM, the results of the case study have shown that MT has provided better performances both in modelling occurrence and amount of precipitation. Also, MT is potentially recognized as a powerful diagnostic tool that could extract information in key atmospheric drivers affecting local weather. It also has fewer parameters during dry seasons, in which the number of historical precipitation events might not be enough for calibrating SDSM model in many arid and semi-arid regions.

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Statistical Downscaling of Monthly Precipitation Using NCEP/NCAR Reanalysis Data for Tahtali River Basin in Turkey

Cited by 68 publications

References 56 publications

Statistical Downscaling of Temperature with the Random Forest Model

Statistical Downscaling of Temperature with the Random Forest Model

The Minimum and Maximum Temperature Forecast Using Statistical Downscaling Techniques for Port-Harcourt Metropolis, Nigeria

Improved statistical downscaling of daily precipitation using SDSM platform and data‐mining methods

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