A correlation between visual observations and instrumental records of cloudiness in Mexico

Tejeda, Adalberto; Vargas, Alfonso

doi:10.22201/igeof.00167169p.1996.35.4.535

Cited by 5 publications

(1 citation statement)

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“…For example, Rangarajan et al (1984) calculated the SD values from 10-year mean cloud cover data using an empirical relationship with an accuracy of about 4-7 percent. Mean monthly SD for different latitudes was estimated with a RMSE% error in between 7% and 18% by Tejeda and Vargas (1996). Essa and Etman (2004) computed SD using cloud cover data for stations in Cairo, Bahtim and Sedi-Barrani in Egypt with the standard error of estimate (SEE) changing from 0.198 h to 0.844 h. El-Metwally (2005) proposed a nonlinear model which was based on cloud cover fraction and maximum and minimum temperatures to predict the relative SD for Egypt and it was shown that MBE% and RMSE% values changed from − 0.2% to -13.3% and from 2.3-14.5%, respectively.…”

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confidence: 99%

Estimation of Monthly Sunshine Duration Using Satellite Derived Cloud Data

Kaba

Erdi²,

Avcı³

et al. 2022

Preprint

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Sunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Turkey using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.

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mentioning

confidence: 99%

Estimation of Monthly Sunshine Duration Using Satellite Derived Cloud Data

Kaba

Erdi²,

Avcı³

et al. 2022

Preprint

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Estimation of monthly sunshine duration using satellite derived cloud data

Kaba,

Erdi,

Avcı

et al. 2024

Theor Appl Climatol

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Sunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Türkiye using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.

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