scholarly journals Review of the submitted article "Two decades of distributed global radiation time series across amountainous semiarid area (Sierra Nevada, Spain)" by Cristina Aguilar et al.

2020 ◽  
Author(s):  
Anonymous
Keyword(s):  
Time Series ◽  
Sierra Nevada ◽  
Global Radiation ◽  
Semiarid Area ◽  
Radiation Time

scholarly journals Two decades of distributed global radiation time series across a mountainous semiarid area (Sierra Nevada, Spain)

2021 ◽  
Vol 13 (3) ◽  
pp. 1335-1359
Author(s):  
Cristina Aguilar ◽  
Rafael Pimentel ◽  
María J. Polo
Keyword(s):  
Time Series ◽  
Sierra Nevada ◽  
Global Radiation ◽  
Mountainous Area ◽  
Mountain Range ◽  
Wet Season ◽  
Landsat Images ◽  
Extreme Statistics ◽  
Variation Characteristics ◽  
Weather Stations

Abstract. The main drawback of the reconstruction of high-resolution distributed global radiation (Rg) time series in mountainous semiarid environments is the common lack of station-based solar radiation registers. This work presents 19 years (2000–2018) of high-spatial-resolution (30 m) daily, monthly, and annual global radiation maps derived using the GIS-based model proposed by Aguilar et al. (2010) in a mountainous area in southern Europe: Sierra Nevada (SN) mountain range (Spain). The model was driven by in situ daily global radiation measurements, from 16 weather stations with historical records in the area; a 30 m digital elevation model; and 240 cloud-free Landsat images. The applicability of the modeling scheme was validated against daily global radiation records at the weather stations. Mean RMSE values of 2.63 MJ m−2 d−1 and best estimations on clear-sky days were obtained. Daily Rg at weather stations revealed greater variations in the maximum values but no clear trends with altitude in any of the statistics. However, at the monthly and annual scales, there is an increase in the high extreme statistics with the altitude of the weather station, especially above 1500 m a.s.l. Monthly Rg maps showed significant spatial differences of up to 200 MJ m−2 per month that clearly followed the terrain configuration. July and December were clearly the months with the highest and lowest values of Rg received, and the highest scatter in the monthly Rg values was found in the spring and fall months. The monthly Rg distribution was highly variable along the study period (2000–2018). Such variability, especially in the wet season (October–May), determined the interannual differences of up to 800 MJ m−2 yr−1 in the incoming global radiation in SN. The time series of the surface global radiation datasets here provided can be used to analyze interannual and seasonal variation characteristics of the global radiation received in SN with high spatial detail (30 m). They can also be used as cross-validation reference data for other global radiation distributed datasets generated in SN with different spatiotemporal interpolation techniques. Daily, monthly, and annual datasets in this study are available at https://doi.org/10.1594/PANGAEA.921012 (Aguilar et al., 2021).

scholarly journals Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies

Energy ◽  
2017 ◽  
Vol 120 ◽  
pp. 199-208 ◽  
Author(s):  
Cyril Voyant ◽  
Fabrice Motte ◽  
Alexis Fouilloy ◽  
Gilles Notton ◽  
Christophe Paoli ◽  
...  
Keyword(s):  
Time Series ◽  
Global Radiation ◽  
Training Phase ◽  
Forecasting Method ◽  
Radiation Time

scholarly journals Two decades of distributed global radiation time series across a mountainous semiarid area (Sierra Nevada, Spain)

2020 ◽  
Author(s):  
Cristina Aguilar ◽  
Rafael Pimentel ◽  
María J. Polo
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Sierra Nevada ◽  
Global Radiation ◽  
Mountainous Area ◽  
Mountain Range ◽  
Wet Season ◽  
Extreme Statistics ◽  
Variation Characteristics ◽  
Weather Stations

Abstract. The main drawback of the reconstruction of high resolution distributed global radiation (Rg) time series in mountainous semiarid environments is the common lack of station-based solar radiation registers. This work presents nineteen years (2000–2018) of high spatial resolution (30 m × 30 m) monthly and annual global radiation maps derived using the model proposed by Aguilar et al. (2010), driven by in situ daily global radiation measurements, from sixteen weather stations with historical records in the area, and a high resolution digital elevation model in a mountainous area in southern Europe: Sierra Nevada (SN) Mountain Range (Spain). The applicability of the modeling scheme was validated against daily global radiation registers at the weather stations with mean RMSE values of 2.63 MJ m−2 day−1 and best estimations on clear-sky days. Filled daily Rg at weather stations revealed quite stable minimum daily Rg values and greater variations in the maximum daily Rg, but no clear trends with altitude in any of the statistics unlike the analysis at the monthly and annual scale when there is an increase in the high extreme statistics with the altitude of the weather station, especially above 1500 m a.s.l. Monthly distributed Rg time series showed significant spatial differences of up to 200 MJ m−2 month−1 that clearly followed the terrain configuration. July and December were clearly the months with the highest and lowest values of Rg received and the highest dispersion in the monthly Rg values was found in the spring and fall months. The great heterogeneity found in the monthly distribution of Rg along the study period (2000–2018), especially at the wet season, finally determined the inter annual differences of up to 800 MJ m−2 year−1 in the incoming global radiation in SN. The time series of the surface global radiation datasets here provided can be used to analyze trends, inter-annual and seasonal variation characteristics of the global radiation received in SN with high spatial detail (30 m). Datasets are available at https://doi.pangaea.de/10.1594/PANGAEA.921012 (Aguilar et al., 2020).

scholarly journals Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case

Energy ◽  
2017 ◽  
Vol 125 ◽  
pp. 248-257 ◽  
Author(s):  
Cyril Voyant ◽  
Gilles Notton ◽  
Christophe Darras ◽  
Alexis Fouilloy ◽  
Fabrice Motte
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Multilayer Perceptron ◽  
Global Radiation ◽  
Time Series Forecasting ◽  
Radiation Time

Dynamic Modeling of Solar Radiation Disturbances Based on a Biomimetic Cloud Shading Model

2018 ◽  
Vol 140 (2) ◽  
Author(s):  
Jesús García ◽  
Iván Portnoy ◽  
Ricardo Vasquez Padilla ◽  
Marco E. Sanjuan
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Control Strategies ◽  
Ground Level ◽  
Direct Solar Radiation ◽  
Complex Phenomenon ◽  
Two Dimensional ◽  
Multi Objective Optimization ◽  
Tuning Parameters ◽  
Radiation Time

Variation in direct solar radiation is one of the main disturbances that any solar system must handle to maintain efficiency at acceptable levels. As known, solar radiation profiles change due to earth's movements. Even though this change is not manipulable, its behavior is predictable. However, at ground level, direct solar radiation mainly varies due to the effect of clouds, which is a complex phenomenon not easily predictable. In this paper, dynamic solar radiation time series in a two-dimensional (2D) spatial domain are obtained using a biomimetic cloud-shading model. The model is tuned and compared against available measurement time series. The procedure uses an objective function based on statistical indexes that allow extracting the most important characteristics of an actual set of curves. Then, a multi-objective optimization algorithm finds the tuning parameters of the model that better fit data. The results showed that it is possible to obtain responses similar to real direct solar radiation transients using the biomimetic model, which is useful for other studies such as testing control strategies in solar thermal plants.

scholarly journals A New Method for Fusion of Measured and Model-derived Solar Radiation Time-series

2014 ◽  
Vol 48 ◽  
pp. 1617-1626 ◽  
Author(s):  
Theresa Mieslinger ◽  
Felix Ament ◽  
Kaushal Chhatbar ◽  
Richard Meyer
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
New Method ◽  
Radiation Time

scholarly journals Methodology and Results of Calculating Central California Surface Temperature Trends: Evidence of Human-Induced Climate Change?

2006 ◽  
Vol 19 (4) ◽  
pp. 548-563 ◽  
Author(s):  
John R. Christy ◽  
William B. Norris ◽  
Kelly Redmond ◽  
Kevin P. Gallo
Keyword(s):  
Time Series ◽  
Sierra Nevada ◽  
Statistical Tests ◽  
Irrigated Agriculture ◽  
Daily Maximum ◽  
Central California ◽  
Temperature Trends ◽  
Significant Rate ◽  
Altered Surface ◽  
Temperature Records

Abstract A procedure is described to construct time series of regional surface temperatures and is then applied to interior central California stations to test the hypothesis that century-scale trend differences between irrigated and nonirrigated regions may be identified. The procedure requires documentation of every point in time at which a discontinuity in a station record may have occurred through (a) the examination of metadata forms (e.g., station moves) and (b) simple statistical tests. From this “homogeneous segments” of temperature records for each station are defined. Biases are determined for each segment relative to all others through a method employing mathematical graph theory. The debiased segments are then merged, forming a complete regional time series. Time series of daily maximum and minimum temperatures for stations in the irrigated San Joaquin Valley (Valley) and nearby nonirrigated Sierra Nevada (Sierra) were generated for 1910–2003. Results show that twentieth-century Valley minimum temperatures are warming at a highly significant rate in all seasons, being greatest in summer and fall (> +0.25°C decade−1). The Valley trend of annual mean temperatures is +0.07° ± 0.07°C decade−1. Sierra summer and fall minimum temperatures appear to be cooling, but at a less significant rate, while the trend of annual mean Sierra temperatures is an unremarkable −0.02° ± 0.10°C decade−1. A working hypothesis is that the relative positive trends in Valley minus Sierra minima (>0.4°C decade−1 for summer and fall) are related to the altered surface environment brought about by the growth of irrigated agriculture, essentially changing a high-albedo desert into a darker, moister, vegetated plain.

Sign in / Sign up

Export Citation Format

Share Document