Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models

Eccel, E.; Ghielmi, L.; Granitto, Pablo M.; Barbiero, R.; Grazzini, Federico; Cesari, D.

doi:10.5194/npg-14-211-2007

Cited by 46 publications

(42 citation statements)

References 40 publications

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“…Among the range of machine‐learning algorithms, Random Forests (RF) [ Breiman , ] stands out for its ability to deal with complex nonlinear relationships between variables while minimizing problems with overfitting. Due to its simplicity and capabilities, RF has been used in a wide range of hydrological‐related applications, for example, high‐resolution soil type classification over the contiguous United States [ Chaney et al ., ], seasonal streamflow forecasting [ Zhao et al ., ; He et al ., ], natural flow regime alternation [ Carlisle et al ., ], vegetation‐type distribution [ Peters et al ., ], temperature [ Eccel et al ., ], and wind [ Davy et al ., ] downscaling, and satellite rainfall estimation from cloud physical properties [ Kühnlein et al ., ]. RF also has great potential for statistical precipitation downscaling although there are few studies that have addressed this issue.…”

Section: Introductionmentioning

confidence: 99%

Spatial downscaling of precipitation using adaptable random forests

et al. 2016

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This paper introduces Prec‐DWARF (Precipitation Downscaling With Adaptable Random Forests), a novel machine‐learning based method for statistical downscaling of precipitation. Prec‐DWARF sets up a nonlinear relationship between precipitation at fine resolution and covariates at coarse/fine resolution, based on the advanced binary tree method known as Random Forests (RF). In addition to a single RF, we also consider a more advanced implementation based on two independent RFs which yield better results for extreme precipitation. Hourly gauge‐radar precipitation data at 0.125° from NLDAS‐2 are used to conduct synthetic experiments with different spatial resolutions (0.25°, 0.5°, and 1°). Quantitative evaluation of these experiments demonstrates that Prec‐DWARF consistently outperforms the baseline (i.e., bilinear interpolation in this case) and can reasonably reproduce the spatial and temporal patterns, occurrence and distribution of observed precipitation fields. However, Prec‐DWARF with a single RF significantly underestimates precipitation extremes and often cannot correctly recover the fine‐scale spatial structure, especially for the 1° experiments. Prec‐DWARF with a double RF exhibits improvement in the simulation of extreme precipitation as well as its spatial and temporal structures, but variogram analyses show that the spatial and temporal variability of the downscaled fields are still strongly underestimated. Covariate importance analysis shows that the most important predictors for the downscaling are the coarse‐scale precipitation values over adjacent grid cells as well as the distance to the closest dry grid cell (i.e., the dry drift). The encouraging results demonstrate the potential of Prec‐DWARF and machine‐learning based techniques in general for the statistical downscaling of precipitation.

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Section: Introductionmentioning

confidence: 99%

Spatial downscaling of precipitation using adaptable random forests

et al. 2016

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“…When compared with other techniques also intended to deal with nonlinear regression, in the case of similar downscaling problems the literature shows that RF and a type of NN, the multilayer perceptron, perform similarly (Eccel et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Downscaling of surface moisture flux and precipitation in the Ebro Valley (Spain) using analogues and analogues followed by random forests and multiple linear regression

Ibarra-Berastegi

Sáenz

Ezcurra

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. In this paper, reanalysis fields from the ECMWF have been statistically downscaled to predict from large-scale atmospheric fields, surface moisture flux and daily precipitation at two observatories (Zaragoza and Tortosa, Ebro Valley, Spain) during the 1961-2001 period. Three types of downscaling models have been built: (i) analogues, (ii) analogues followed by random forests and (iii) analogues followed by multiple linear regression. The inputs consist of data (predictor fields) taken from the ERA-40 reanalysis. The predicted fields are precipitation and surface moisture flux as measured at the two observatories. With the aim to reduce the dimensionality of the problem, the ERA-40 fields have been decomposed using empirical orthogonal functions. Available daily data has been divided into two parts: a training period used to find a group of about 300 analogues to build the downscaling model and a test period (1997)(1998)(1999)(2000)(2001), where models' performance has been assessed using independent data. In the case of surface moisture flux, the models based on analogues followed by random forests do not clearly outperform those built on analogues plus multiple linear regression, while simple averages calculated from the nearest analogues found in the training period, yielded only slightly worse results. In the case of precipitation, the three types of model performed equally. These results suggest that most of the models' downscaling capabilities can be attributed to the analogues-calculation stage.

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“…Based on this body of knowledge, RF should be, in theory, highly applicable to downscaling and able to rectify multivariable and nonlinear issues. Eccel et al [34] adopted RF with four linear and nonlinear models in the postprocessing of two numerical weather prediction models for the prediction of minimum temperatures in an alpine region. However, the RF just served as one of the comparative models in the study.…”

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 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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Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models

Cited by 46 publications

References 40 publications

Spatial downscaling of precipitation using adaptable random forests

Spatial downscaling of precipitation using adaptable random forests

Downscaling of surface moisture flux and precipitation in the Ebro Valley (Spain) using analogues and analogues followed by random forests and multiple linear regression

Statistical Downscaling of Temperature with the Random Forest Model

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