Analysis of cloud effects on long-term global and diffuse photosynthetically active radiation at a Mediterranean site

Lozano, Ismael L.; Sánchez-Hernández, Guadalupe; Guerrero-Rascado, Juan Luís; Alados, I.; Foyo-Moreno, I.

doi:10.1016/j.atmosres.2021.106010

Cited by 16 publications

(15 citation statements)

References 94 publications

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“…The monthly distribution of PAR 7+/,*+ shows a clear cycle, with maximum mean and median values around May and July for all locations, and the lowest values during winter. This seasonality trend is similarly observed in other studies for the northern hemisphere, such as the study byLozano et al (2022) in Granada, Spain (37.16° N, 3.61° W). However, the magnitude of PAR 7+/,*+ differs.…”

supporting

confidence: 89%

“…For the investigated 374 sites, the trend is similar for all the years with a slight alteration in 2018 due to decreased amount of clouds, 375 which brought overall lower values of PAR .01123-. The annual mean PAR .01123-value for the locations 376 studied was 46.65 W/m 2 , marginally lower (59 W/m 2 ) than the one reported by Lozano et al (2022) in 377 Granada (Spain) 2008-2018 and higher (35 W/m 2 ) than the one reported by Trisolino et al (2018) The analysis demonstrates that the seasonality variation of PAR components and the relationship with cloudiness in high latitudes is similar to mid-latitudes in the northern hemisphere. However, the magnitude of the PAR components decreases as the location moves further north.…”

Section: 335contrasting

confidence: 59%

“…= GHI/E "#$ ) increases. In contrary, Lozano et al (2022) found no significant dependence of the ratio on 𝑘 ! .…”

Section: Introductionmentioning

confidence: 72%

“…The authors concluded that the atmospheric water content (expressed by the degree of cloudiness, actual water vapour, optical thickness, or dew point temperature) and the clearness index were the most influential factors in the ratio. Air temperature and related meteorological variables (relative humidity, vapour pressure deficit and saturation vapour pressure) were found to have no significant effect on the ratio.Regarding PAR diffuse estimations, the latest work byLozano et al (2022) found a clear dependence of the 𝑘 %&' on the clearness index and total cloud cover (TCC) at a Mediterranean site. The authors proposed a model to estimate 𝑘 %&' obtained through the first adaptation of the Boland-Ridley-Lauret (BRL) model(Ridley et al, 2010) based on the clearness index, solar elevation angle, apparent solar time (AST), daily clearness index and persistence index.…”

mentioning

confidence: 99%

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Photosynthetically Active Radiation Separation Model for High-Latitude Regions in Agrivoltaic Systems Modelling

Lu¹,

Yang²,

Anderson³

et al. 2024

Preprint

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Photosynthetically active radiation (PAR) is a key parameter for modelling the photosynthetic behaviour of plants in response to sunlight and, subsequently, for determining crop yield. Separating PAR into direct and diffuse components is of significance to agrivoltaic systems, which combine solar energy conversion and agricultural farming on the same portion of land. Placing photovoltaic on agricultural land results in varying shading conditions throughout the day and seasons, producing a higher contribution of incident diffuse PAR to the crops beneath the system in these shaded regions. Additionally, photosynthesis is more efficient under conditions of diffuse PAR than direct PAR per unit of total PAR. This work introduces a new separation model for PAR, which is able to accurately estimate diffuse PAR from the global one. The model modifies the YANG2 model, by adding four new predictors: the optical thickness of PAR, vapour pressure deficit, aerosol optical depth, and albedo of PAR. The proposed model has been calibrated, tested, and validated at three sites in Sweden with latitudes above 58° N, obtaining R2 exceeding 0.91 and nRMSE less than 17%. Compared to YANG2, which was previously found to be a high-performance model, the new model is superior by up to 1% both in R2 and nRMSE. Additionally, an analysis of the seasonal trends and variation of the different PAR components is provided to alleviate the dearth of PAR studies in high-latitude regions.

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supporting

confidence: 89%

Section: 335contrasting

confidence: 59%

“…= GHI/E "#$ ) increases. In contrary, Lozano et al (2022) found no significant dependence of the ratio on 𝑘 ! .…”

Section: Introductionmentioning

confidence: 72%

mentioning

confidence: 99%

See 2 more Smart Citations

Photosynthetically Active Radiation Separation Model for High-Latitude Regions in Agrivoltaic Systems Modelling

Lu¹,

Yang²,

Anderson³

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…These two last variables in the PAR range are defined as:

{K}_{t,PAR}^{\prime }=\frac{\sum\limits _{i=1}^{24}{PAR}_{Global}}{\sum\limits _{i=1}^{24}{I}_{TOA,PAR}}

{{\Psi }}_{PAR}=\frac{{k}_{t,PAR}^{time-1}+{k}_{t,PAR}^{time+1}}{2}

The third model (M3) is the adaptation of the Ridley's model suggested by Lozano et al. (2022) who analyzed the relationship between k PAR and k t concluding in their preliminary analysis that k PAR can be predicted directly from TSI. Therefore, the resulting expression is the following:

{k}_{PAR}=\frac{1}{1+{e}^{\left({c}_{1}+{c}_{2}{k}_{t}+{c}_{3}AST+{c}_{4}\alpha +{c}_{5}{K}_{t}^{\prime }+{c}_{6}{\Psi }\right)}}

where c i are the fitting coefficients, k ' t is the daily clearness index and Ψ is the persistence index, and are defined as:

K_{t}^{'} = \frac{{falsefalse}_{\sum}^{i = 1} T S I_{G l o b a l}}{{falsefalse}_{\sum}^{i = 1} ...}

…”

Section: Methodsmentioning

confidence: 99%

Improving the Estimation of the Diffuse Component of Photosynthetically Active Radiation (PAR)

Lozano,

Alados,

Sánchez‐Hernández

et al. 2023

JGR Atmospheres

Self Cite

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Most weather forecasting models are not able to accurately reproduce the great variability existing in the measurements of the diffuse component of photosynthetically active radiation (PAR; 400–700 nm) under all sky conditions. Based on the well‐known relationship between the diffuse fraction (k) and the clearness index (kt), this study addresses improvements in estimations by proposing adaptations of previous models, which were previously applied only to the total solar irradiance (TSI; 280–3,000 nm). In order to reproduce this variability, additional parameters were introduced. The models were tested employing a multisite database gathered at the Mediterranean basin. Since Artificial Neural Network (ANN) models are not limited to fixed coefficients to predict the diffuse fraction of PAR (kPAR), these types of models are more accurate than empirical ones, reaching determination coefficients (r2) up to 0.998. However, the simpler linear model proposed by Foyo‐Moreno et al. (2018), https://doi.org/10.1016/j.atmosres.2017.12.012 shows a similar performance to the ANN models, directly predicting the diffuse component of PAR (PARDiffuse) from TSIDiffuse, with a r2 up to 0.997. Results obtained here also determine that the most important variables for estimating PARDiffuse are kt or kt,PAR, and the apparent solar time (AST). Therefore, PARDiffuse can be modeled using TSI measured in most radiometric stations, reaching r2 up to 0.858 for empirical models and 0.970 for ANN models. This modified approach will allow for the very accurate construction of long‐term data series of PARDiffuse in regions where continuous measurements of PAR are not available.

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Integrated use of field sensors, PhenoCam, and satellite data for pheno-phase monitoring in a tropical deciduous forest of Dalma Wildlife Sanctuary, Jharkhand, India: initial results from the Indian Phenology Network

Jeganathan,

Singh,

Singh

et al. 2024

Biodivers Conserv

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Analysis of cloud effects on long-term global and diffuse photosynthetically active radiation at a Mediterranean site

Cited by 16 publications

References 94 publications

Photosynthetically Active Radiation Separation Model for High-Latitude Regions in Agrivoltaic Systems Modelling

Photosynthetically Active Radiation Separation Model for High-Latitude Regions in Agrivoltaic Systems Modelling

Improving the Estimation of the Diffuse Component of Photosynthetically Active Radiation (PAR)

Integrated use of field sensors, PhenoCam, and satellite data for pheno-phase monitoring in a tropical deciduous forest of Dalma Wildlife Sanctuary, Jharkhand, India: initial results from the Indian Phenology Network

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