A Machine Learning Technique for Spatial Interpolation of Solar Radiation Observations

Leirvik, Thomas; Yuan, Menghan

doi:10.1029/2020ea001527

Cited by 20 publications

(15 citation statements)

References 45 publications

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“…This methodology is inherited from Leirvik and Yuan (2021), who used spatial neighbourhoods to constrain a Random Forest Regression for the interpolation of a surface solar radiation dataset. We expand the methodology by considering the time dimension as well.…”

Section: Methodsmentioning

confidence: 99%

On the potential of mapping sea level anomalies from satellite altimetry with Random Forest Regression

Passaro¹,

Marie-Christin²

2023

Ocean Dynamics

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The sea level observations from satellite altimetry are characterised by a sparse spatial and temporal coverage. For this reason, along-track data are routinely interpolated into daily grids. These grids are strongly smoothed in time and space and are generated using an optimal interpolation routine requiring several pre-processing steps and covariance characterisation. In this study, we assess the potential of Random Forest Regression to estimate daily sea level anomalies. Along-track sea level data from 2004 are used to build a training dataset whose predictors are the neighbouring observations. The validation is based on the comparison against daily averages from tide gauges. The generated dataset is on average 10% more correlated to the tide gauge records than the commonly used product from Copernicus. While the latter is more optimised for the detection of spatial mesoscales, we show how the methodology of this study has the potential to improve the characterisation of sea level variability.

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Section: Methodsmentioning

confidence: 99%

On the potential of mapping sea level anomalies from satellite altimetry with Random Forest Regression

Passaro¹,

Marie-Christin²

2023

Ocean Dynamics

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“…Many GEBA stations' timeseries were omitted from this version of the data set due to insufficient coverage. However, many incomplete SSR timeseries were instead filled and temporally expanded to cover the period 1961 to 2014 by using the machine learning method random forest ; the method is described and evaluated in Leirvik and Yuan (2021). ngGEBA has 1487 full monthly datasets from between 1961 and 2014 globally, of which approximately 2/3 is generated through the random forest algorithm.…”

Section: Datamentioning

confidence: 99%

Disentangling Aerosol and Cloud Effects on Dimming and Brightening in Observations and CMIP6

et al. 2022

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 This study supports evidence that dimming and brightening trends between 1961 and 2014 in China, Japan, Europe and the United States were caused by variations in aerosol-and aerosol precursor emissions, and not by cloud cover changes.  Results confirm and reinforce previous indications that CMIP6 simulations are able to reproduce SSR trends observed in Europe, but not in China, Japan and the United States.

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“…The impact of global solar irradiation on the Earth’s surface has a significant influence on a country’s economy, including, for example, agricultural productivity, renewable energy use, food security and human health risks [ 5 ], as reported in [ 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Leirvik and Yuan [ 5 ] employed statistical methods (Random Forest (RF); Linear Regression (LR); Generalized Additive Regression (GAM); Least Squares Dummy Variable (LSDV); Ordinary Kriging (OK); and combinations, as LR + OK, GAM + OK, and LSDV + OK) to interpolate missing values in a monthly dataset spanning nearly five decades of global solar irradiation over the Earth’s surface, highlighting the benefits of using Machine Learning in environmental research.…”

Section: Introductionmentioning

confidence: 99%

Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain

Díez

Boukharta

Navas-Gracia

et al. 2022

Sensors

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In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons.

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A Machine Learning Technique for Spatial Interpolation of Solar Radiation Observations

Cited by 20 publications

References 45 publications

On the potential of mapping sea level anomalies from satellite altimetry with Random Forest Regression

On the potential of mapping sea level anomalies from satellite altimetry with Random Forest Regression

Disentangling Aerosol and Cloud Effects on Dimming and Brightening in Observations and CMIP6

Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain

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