Evaluation of multiple surface irradiance-based clear sky detection methods at Xianghe—A heavy polluted site on the North China Plain

Liu, Mengqi; Xia, Xiangao

doi:10.1016/j.aosl.2020.100016

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…The mean atmospheric properties of each day are summarized in Table S1, with associated AOD and AE values represented in Figure S3. Table S1 also illustrates the performance and limitation of our clear-sky filter, which appears to work well under low aerosol loads, but less so for higher AOD values, in accordance with the results of Liu et al (2021) that assessed the performance of several clear-sky detection methods under various aerosol loads.…”

Section: Focus On Several Case Studiessupporting

confidence: 81%

“…This result is consistent with the effects of lower solar zenith angles and with the observation that aerosol Ångström exponent are on average slightly lower (by 0.06) in CSWC situations compared with CSKY cases, leading to a higher DRE efficiency as illustrated on Figure 10f. Note that the study of CSWC situations allows us to analyze conditions characterized by the Strong events class (see Table 2a), which were not identified as clear-sky either because of the presence of clouds or because of their high AOD values, as the clear-sky filter used in this study tend to erroneously misidentify CSKY conditions when the AOD is high (Liu et al, 2021). The aerosol DRE for the Strong events class appears very substantial for both direct (-260 W/m 2 , i.e.…”

Section: Focus On Several Case Studiesmentioning

confidence: 99%

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Influence of cloudy/clear-sky partitions, aerosols and geometry on the recent variability of surface solar irradiance's components in northern France

Chesnoiu,

Ferlay,

Chiapello

et al. 2024

Preprint

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Abstract. The surface solar irradiance (SSI) is a fundamental parameter whose components (direct and diffuse) and variabilities are highly influenced by changes in atmospheric content and scene’s parameters. The respective importance of cloudy sky conditions and atmospheric aerosols on SSI evolutions is region dependent and only partially quantified. Here we provide a comprehensive analysis of SSI variabilities recorded in northern France, a region with extensive variabilities of sky conditions and aerosol loads. Through the application of automatic filtering methods on 1 min resolution SSI ground-based measurements over Lille, sky conditions are classified as clear-sky, 11 %, clear-sun-with-cloud, 22 %, and cloudy-sun situations, 67 %, over 2010 to 2022, for which we analyze the statistics and variabilities of the global horizontal (GHI), direct (BHI) and diffuse (DHI) solar irradiances. Coincident photometric measurements of aerosol properties and radiative transfer simulations provide the mean to conduct a multivariate analysis of the SSI observed trends and year-to-year evolutions, and to estimate aerosol and cloud forcings under clear-sun conditions. The analysis of the record value of all sky GHI in spring 2020 attributes 89 % of the changes to the exceptional sunlight conditions (57 % of clear-sun situations). It highlights also for that season the importance of solar zenithal angle’s changes whose positive effect on clear-sun conditions surpasses those due to aerosols. Our results show all-sky GHI and BHI positive trends of around +4 and +4.4 W/m2/year respectively, both in spring and summer, that are explained at more than 60 % by an increase of clear-sun occurrences of +1 % per year. Additional significant BHI’s increase under clear-sun conditions are mainly explained in spring by the negative trend in aerosol optical depth (-0.011 per year), partly by angular effects in summer. Moreover, we find that clear-sun with cloud situations are frequently marked by irradiance enhancement due to clouds, with on monthly-average 13 % more GHI and 10 % additional diffuse proportion than in clear-sky situations. Under such conditions, clouds add on average 25 W/m2 of diffuse irradiance that set the amount of solar irradiance at the remarkable level of pristine (aerosol and cloud free) conditions, or even higher by more than +10 W/m2 in summer and for low aerosol loads. Overall, our results highlight the dominant and complex influence of cloudy conditions on SSI, which precedes or combines with that of aerosols and geometrical effects, and leads to remarkable global level of SSI in clear-sun with-cloud situations.

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Section: Focus On Several Case Studiessupporting

confidence: 81%

Section: Focus On Several Case Studiesmentioning

confidence: 99%

Influence of cloudy/clear-sky partitions, aerosols and geometry on the recent variability of surface solar irradiance's components in northern France

Chesnoiu,

Ferlay,

Chiapello

et al. 2024

Preprint

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“…TSI-based sky discrimination results are further improved by manual inspection of raw TSI images to correct apparent errors, such as TSI misclassification of thin cloud into clear and misclassification of heavy haze into cloud. There are 307,995 clear samples and 441,810 cloudy samples in this study [18]. Each 1 min irradiance measurement is labeled with sky condition according to the corrected TSI measurements.…”

Section: Site and Datamentioning

confidence: 99%

Development and Validation of Machine-Learning Clear-Sky Detection Method Using 1-Min Irradiance Data and Sky Imagers at a Polluted Suburban Site, Xianghe

Liu

Xia

2021

Remote Sensing

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Clear-sky detection (CSD) is of critical importance in solar energy applications and surface radiative budget studies. Existing CSD methods are not sufficiently validated due to the lack of high-temporal resolution and long-term CSD ground observations, especially at polluted sites. Using five-year high resolution ground-based solar radiation data and visual inspected Total Sky Imager (TSI) measurements at polluted Xianghe, a suburban site, this study validated 17 existing CSD methods and developed a new CSD model based on a machine-learning algorithm (Random Forest: RF). The propagation of systematic errors from input data to the calculated global horizontal irradiance (GHI) is confirmed with Mean Absolute Error (MAE) increased by 99.7% (from 20.00 to 39.93 W·m−2). Through qualitative evaluation, the novel Bright-Sun method outperforms the other traditional CSD methods at Xianghe site, with high accuracy score 0.73 and 0.92 under clear and cloudy conditions, respectively. The RF CSD model developed by one-year irradiance and TSI data shows more robust performance, with clear/cloudy-sky accuracy score of 0.78/0.88. Overall, the Bright-Sun and RF CSD models perform satisfactorily at heavy polluted sites. Further analysis shows the RF CSD model built with only GHI-related parameters can still achieve a mean accuracy score of 0.81, which indicates RF CSD models have the potential in dealing with sites only providing GHI observations.

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“…The TSI Analyser was applied to a 1-month dataset with 5-minute interval cloud images considering over 200,000 images collected at a 480 × 320 spatial resolution. These whole-sky images have been captured using TSI440 [84][85][86] used in previous research (e.g., [82,87,88]). The TSI440 instrument consists of a reflective dome with a camera suspended above it [89,90] pointing downwards to generate a .jpeg format colour image of the whole sky.…”

Section: Sky Image Processing and Cloud Segmentationmentioning

confidence: 99%

Forecasting Photosynthetic Photon Flux Density Under Cloud Effects: Novel Predictive Model Using Convolutional Neural Network Integrated With Long Short-term Memory Network

Deo

Grant

Webb

et al. 2021

Preprint

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Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-minute) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a Total Sky Imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m-2 s-1, mean absolute error MAE = 150.24 μ mol of photons m-2 s-1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient ENS = 0.85, and Legate & McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems.

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Evaluation of multiple surface irradiance-based clear sky detection methods at Xianghe—A heavy polluted site on the North China Plain

Cited by 6 publications

References 25 publications

Influence of cloudy/clear-sky partitions, aerosols and geometry on the recent variability of surface solar irradiance's components in northern France

Influence of cloudy/clear-sky partitions, aerosols and geometry on the recent variability of surface solar irradiance's components in northern France

Development and Validation of Machine-Learning Clear-Sky Detection Method Using 1-Min Irradiance Data and Sky Imagers at a Polluted Suburban Site, Xianghe

Forecasting Photosynthetic Photon Flux Density Under Cloud Effects: Novel Predictive Model Using Convolutional Neural Network Integrated With Long Short-term Memory Network

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