Coupling solar radiation and cloud cover data for enhanced temperature predictions over topographically complex mountain terrain

High daily temperatures in the Mediterranean and Europe have been documented in observation and modeling studies. Long-term temperature data, from 1988 to 2017, from a suburban station and an urban station in Nicosia, Cyprus have been analyzed, and the diurnal temperature range (DTR) trend was investigated. The seasonal Mann–Kendall test revealed a decreasing DTR trend of −0.24 °C/decade at the urban station and −0.36 °C/decade at the suburban station, which were attributed to an increase in the daily minimum temperature. Variations in precipitation, longwave radiation, ultraviolet-A (UVA), ultraviolet-B (UVB), cloud cover, water vapor, and urbanization were used to assess their possible relationship with regional DTR. The clustering of daytime and night-time data showed a strong relationship between the DTR and observed cloud cover, net longwave radiation, and precipitation. Clouds associated with smaller shortwave and net longwave radiation reduce the DTR by decreasing the surface solar radiation, while atmospheric absolute humidity denotes an increased daytime surface evaporative cooling and higher absorption of the short and longwave radiation. The intra-cluster variation could be reduced, and the inter-cluster variance increased by the addition of other meteorological parameters and anthropogenic sources that affect DTR in order to develop a quantitative basis for assessing DTR variations.

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Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems

10.5772/intechopen.99499 ◽

2021 ◽

Author(s):

Ines Sansa ◽

Najiba Mrabet Bellaaj

Keyword(s):

Time Series ◽

Solar Radiation ◽

Cloud Cover ◽

Moving Average ◽

Arma Model ◽

Weather Conditions ◽

Pv Systems ◽

Auto Regressive ◽

Micro Grid ◽

One Year

Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.

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Evaluation of a cloud cover based model for estimation of hourly global solar radiation in Western Canada

International Journal of Sustainable Energy ◽

10.1080/14786451.2018.1443934 ◽

2018 ◽

Vol 38 (1) ◽

pp. 64-73 ◽

Cited By ~ 2

Author(s):

Md Shamim Ahamed ◽

Huiqing Guo ◽

Karen Tanino

Keyword(s):

Solar Radiation ◽

Cloud Cover ◽

Global Solar Radiation ◽

Western Canada

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Optimizing cloud cover prediction by the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS)

10.5194/egusphere-egu21-9299 ◽

2021 ◽

Author(s):

Yen-Sen Lu ◽

Philipp Franke ◽

Dorit Jerger

Keyword(s):

Boundary Layer ◽

Solar Radiation ◽

Planetary Boundary Layer ◽

Cloud Cover ◽

Weather Forecasting ◽

Extreme Weather ◽

Atmospheric Modeling ◽

The Other ◽

Extreme Weather Events ◽

Weather Events

ESIAS is an atmospheric modeling system including the ensemble version of the Weather Forecasting and Research Model (WRF V3.7.1) and the ensemble version of the EURopean Air pollution Dispersion-Inverse Model (EURAD-IM), the latter uses the output of the WRF model to calculate, amongst others, the transportation of aerosols. To capture extreme weather events causing the uncertainty in the solar radiation and wind speed for the renewable energy industry, we employ ESIAS by using stochastic schemes, such as Stochastically Perturbed Parameterization Tendency (SPPT) and Stochastic Kinetic Energy Backscatter (SKEBS) schemes, to generate the random fields for ensembles of up to 4096 members.&#160;&#160;&#160;&#160; Our first goal is to produce 48 hourly weather predictions for the European domain with a 20 KM horizontal resolution to capture extreme weather events affecting wind, solar radiation, and cloud cover forecasts. We use the ensemble capability of ESIAS to optimize the physics configuration of WRF to have a more precise weather prediction. A total of 672 ensemble members are generated to study the effect of different microphysical schemes, cumulus schemes, and planetary boundary layer parameterization schemes. We examine our simulation outputs with 288 simulation hours in 2015 using model input from the Global Ensemble Forecast System (GEFS). Our results are validated by the cloud cover data from EUMETSAT CMSAF. Besides the precision of weather forecasting, we also determine the greatest spread by generating total 768 ensemble members: 16 stochastic members for each different configurations of physical parameterizations (48 combinations). The optimization of WRF will help for improving the air quality prediction by EURAD-IM, which will be demonstrated on a test case basis.&#160;&#160;&#160;&#160; Our results show that for the performed analysis the Community Atmosphere Model (CAM) 5.1, WRF Single-Moment 6-class scheme (WSM6), and the Goddard microphysics outstand the other 11 microphysics parameterizations, where the highest daily average matching rate is 64.2%. The Mellor&#8211;Yamada Nakanishi Niino (MYNN) 2 and MYNN3 schemes give better results compared to the other 8 planetary boundary layer schemes, and Grell 3D (Grell-3) works generally well with the above mentioned physical schemes. Overall, the combination of Goddard and MYNN3 produces the greatest spread comparing to the lowest spread (Morrison 2-moment & GFS) by 40%.

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