Satellite-based estimates of photosynthetically active radiation for tropical ecosystems in Ghana—West Africa

Junior, Prince Asilevi; Quansah, Emmanuel; Dogbey, Felicia

doi:10.1007/s42965-022-00234-0

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…Considering the close association of clouds and aerosols, and their attenuating effect on sunlight, the presupposition of clouds as the main determinant of sunshine amount is a rather coarse generalization, and may be expected to yield such deviations. This was the case during the DJF season for the study area, known as the dry Harmattan season, characterized by a countrywide dust-laden atmosphere owing to the influx of dust-carrying northeasterly winds [9,33,34]. Cloud amount was lowest during this season, with little to no precipitation, yet with slightly reduced daytime surface temperatures typically reaching 12 • C in parts of the northern savannah [31,32].…”

Section: Total Cloud Cover Distribution: Comparison Of Estimated and ...mentioning

confidence: 88%

Modelling Cloud Cover Climatology over Tropical Climates in Ghana

et al. 2022

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Clouds play a crucial role in Earth’s climate system by modulating radiation fluxes via reflection and scattering, and thus the slightest variation in their spatial coverage significantly alters the climate response. Until now, due to the sparse distribution of advanced observation stations, large uncertainties in cloud climatology remain for many regions. Therefore, this paper estimates total cloud cover (TCC) by using sunshine duration measured in different tropical climates in Ghana. We used regression tests for each climate zone, coupled with bias correction by cumulative distribution function (CDF) matching, to develop the estimated TCC dataset from nonlinear empirical equations. It was found that the estimated percentage TCC, 20.8–84.7 ± 3.5%, compared well with station-observed TCC, 21.9–84.4 ± 3.5%, with root mean square errors of 1.08–9.13 ± 1.8% and correlation coefficients of 0.87–0.99 ± 0.03. Overall, spatiotemporal characteristics were preserved, establishing that denser clouds tended to prevail mostly over the southern half of the forest-type climate during the June–September period. Moreover, the model and the observations show a non-normality, indicating a prevalence of above-average TCC over the study area. The results are useful for weather prediction and application in meteorology.

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Section: Total Cloud Cover Distribution: Comparison Of Estimated and ...mentioning

confidence: 88%

Modelling Cloud Cover Climatology over Tropical Climates in Ghana

et al. 2022

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“…The geopolitical region for which solar radiation repository is developed is Ghana, situated on the coastal edge of West African, bounded in latitude 4. Figure 1a shows the geopolitical map of the study area, showing locations of the twenty two (22) sunshine measurement stations distributed across the study area, and Table 1 summarizes the geographical positions of selected stations. The atmosphere is moderately transparent characterized by clearness indices ranging 0.…”

Section: Geography and Climatology Of Study Areamentioning

confidence: 99%

“…to the heavy presence of Harmattan dust aerosols(Tserenpurev et al, 2012;Asilevi et. al., 2019;Junior et al, 2022). Predominantly, the most significant deviations are overestimations in the NASA-POWER reanalysis radiation products.…”

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Site adaptation of NASA-POWER reanalysis solar radiation products for tropical climates in Ghana using bias correction for clean energy application

Quansah,

Boakye,

Quansah

et al. 2024

Preprint

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Solar radiation measurements are needed to simulate a diverse range of clean energy application systems for optimization. However worldwide, whereas radiometric measurement stations are scarce and mostly sparsely distributed, satellite and reanalysis products are available, though with resolution problems. Therefore in the absence of in-situ measurements, we developed a bias-corrected long-term solar radiation database for Ghana in West Africa, using reanalysis products retrieved from NASA’s Prediction of Worldwide Energy Resource (POWER) archives available at \(0.5^\circ \times 0.5^\circ\) spatial resolution by Cumulative Distribution Function (CDF) matching technique, with reference to a synthetic solar radiation database developed from sunshine duration measurements at 22 stations, in order to site-adapt the reanalysis products. From the results, the POWER products were improved by overall minimization in mean residual difference of -1.02–1.4 ± 0.07 kWhm− 2day− 1 to -0.85–1.2 ± 0.12 kWhm− 2day− 1 after bias correction, with a 9%, 27%, and 17% increase in smaller RD ranges of -0.5–0.5, -0.2–0.2, and − 0.1–0.1 kWhm− 2day− 1 respectively. Also, the Mean Bias Error (MBE) range minimized from − 0.51–0.6 ± 0.3 kWhm− 2day− 1 to -0.35–0.2 ± 0.1 kWhm− 2day− 1 with 17 stations showing residual differences in correlation coefficients greater than zero, indicative of higher correlation after bias correction. Overall, spatiotemporal characteristics are preserved, and the improvement is in acceptable margins for engineering application.

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A Bayesian Regression Approach for Estimating Photosynthetically Active Radiation Using Satellite Data: Implications for Soybean Yield Prediction using the CROPGRO Model

Onyekwelu,

Sharda

2024

Earth Syst Environ

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Satellite-based estimates of photosynthetically active radiation for tropical ecosystems in Ghana—West Africa

Cited by 5 publications

References 37 publications

Modelling Cloud Cover Climatology over Tropical Climates in Ghana

Modelling Cloud Cover Climatology over Tropical Climates in Ghana

Site adaptation of NASA-POWER reanalysis solar radiation products for tropical climates in Ghana using bias correction for clean energy application

A Bayesian Regression Approach for Estimating Photosynthetically Active Radiation Using Satellite Data: Implications for Soybean Yield Prediction using the CROPGRO Model

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