Estimation of monthly-mean global solar radiation using MODIS atmospheric product over China

Chen, Ji‐Long; Xiao, Bei-Bei; Chen, Chundi; Wen, Zhaofei; Jiang, Yi; Lv, Meng; Wu, Shengjun; Li, Guosheng

doi:10.1016/j.jastp.2014.01.017

Cited by 23 publications

(15 citation statements)

References 60 publications

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“…A warmer spring is intuitively thought to increase terrestrial ET (i.e., a positive γ TEM ) in such regions; however, a negative γ TEM was observed in those regions in spring. This phenomenon is perhaps related to cloud cover and solar radiation changes in the Qinghai‐Tibetan Plateau [ Chen et al ., ]. The underlying mechanism is, however, still unclear and needs further exploration.…”

Section: Discussionmentioning

confidence: 99%

Seasonal divergence in the sensitivity of evapotranspiration to climate and vegetation growth in the Yellow River Basin, China

Pei

et al. 2017

JGR Biogeosciences

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Seasonal variations in terrestrial evapotranspiration (ET) in the Yellow River Basin (YRB) have crucial impacts on the seasonal trajectories of the regional water cycle, vegetation growth, and local climate feedback. However, the possibly divergent roles of climate and vegetation growth variations in controlling seasonal ET patterns remain poorly quantified. This study therefore quantifies the interannual sensitivity and attribution of ET to climate and vegetation growth variations in different seasons and different biomes in the YRB in China between 1982 and 2011, using the satellite‐derived normalized difference vegetation index (NDVI), FLUXNET‐based upscaled ET, and concurrent climate data. The results reveal a clear seasonal divergence in the interannual sensitivity of ET to climate and vegetation growth variations in the YRB. Interannual precipitation and NDVI variations play a dominant role in controlling seasonal ET variations in the YRB, with temperature having a marginal effect. Interannual ET sensitivity to precipitation weakens with an increasing mean annual precipitation gradient in almost all seasons, especially in summer and autumn. More importantly, a seasonally varying role of vegetation growth in mediating seasonal ET was discovered, and a crucial role of late‐growing‐season vegetation growth in controlling the seasonal trajectory of regional ET was explicitly identified. These results suggest that ongoing intensive vegetation restoration has crucial impacts on seasonal water‐cycle patterns and consequent terrestrial‐atmospheric biogeochemical feedback in the YRB.

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Section: Discussionmentioning

confidence: 99%

Seasonal divergence in the sensitivity of evapotranspiration to climate and vegetation growth in the Yellow River Basin, China

Pei

et al. 2017

JGR Biogeosciences

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“…This distribution pattern is interpreted as the effect of terrain. High terrain elevation corresponds to thinner air and a lower value of aerosol optical thickness, less precipitable water vapor amount, and stronger solar radiation (Chen et al, 2014;Zhu et al, 2010), which enhance thermal convections and lead to high MMD. The results agree with the recent study of Liu et al (2015).…”

Section: Ventilation Climatologymentioning

confidence: 99%

Climatological study of the Boundary-layer air Stagnation Index for China and its relationship with air pollution

et al. 2018

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Abstract. The Air Stagnation Index (ASI) is a vital meteorological measure of the atmosphere's ability to dilute air pollutants. The original metric adopted by the US National Climatic Data Center (NCDC) is found to be not very suitable for China, because the decoupling between the upper and lower atmospheric layers results in a weak link between the near-surface air pollution and upper-air wind speed. Therefore, a new threshold for the ASI-Boundary-layer air Stagnation Index (BSI) is proposed, consisting of daily maximal ventilation in the atmospheric boundary layer, precipitation, and real latent instability. In the present study, the climatological features of the BSI are investigated. It shows that the spatial distribution of the BSI is similar to the ASI; that is, annual mean stagnations occur most often in the northwestern and southwestern basins, i.e., the Xinjiang and Sichuan basins (more than 180 days), and least over plateaus, i.e., the Qinghai-Tibet and Yunnan plateaus (less than 40 days). However, the seasonal cycle of the BSI is changed. Stagnation days under the new metric are observed to be maximal in winter and minimal in summer, which is positively correlated with the air pollution index (API) during 2000-2012. The correlations between the BSI and the concentration of fine particulate matter (PM 2.5 ) during January 2013 and November to December in 2015-2017 of Beijing are also investigated. It shows that the BSI matches the day-by-day variation of PM 2.5 concentration very well and is able to catch the haze episodes.

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“…In this paper, data from a MODIS instrument on-board Terra (EOS AM) and Aqua (EOS PM) satellites have been utilized. These satellite (MODIS) meteorological data are widely and freely available for public access [60] and, therefore, useful for solar energy exploration and modelling in a diverse range of sites.…”

Section: Modis Satellite-derived Predictor Datamentioning

confidence: 99%

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

et al. 2019

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Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration.

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Estimation of monthly-mean global solar radiation using MODIS atmospheric product over China

Cited by 23 publications

References 60 publications

Seasonal divergence in the sensitivity of evapotranspiration to climate and vegetation growth in the Yellow River Basin, China

Seasonal divergence in the sensitivity of evapotranspiration to climate and vegetation growth in the Yellow River Basin, China

Climatological study of the Boundary-layer air Stagnation Index for China and its relationship with air pollution

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

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