The DNICast team A.Kazantzidis Laboratory of Atmospheric Physics, University of Patras, Greece Solar resource and forecasting needs

Presentation outlook DNICast at glance Needs for solar resource and forecasting: Aerosol optical depth, Cloud properties, Enhancements due to cloudiness A tip for future use of ultraviolet radiation products (in liaison with health scientific community)

DNICast: Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies DNICast at a glance

DNICast at a glance: the testbed

The basic steps for solar resource and forecasting Water vapor?

Aerosol optical properties: why needed? Concentrated Solar Technologies are dependent from DNI. Aerosols variability can be the major source of DNI variability. Differences (%) between the mean DNI for each year ( ) and the average for the 13-year period. Only data for May-September are considered Nikitidou et al., Renewable Energy, 2014

Difference (%) between the daily DNI and the corresponding monthly mean, for 5 areas in Europe Aerosol optical properties: why needed?

What aerosol properties can we get?

Goodness of Fit Statistics (AERONET vs MACC) AOD values from AERONET and MACC are compared in terms of MBE, RMSE and CC ( 550nm) |MBE|<20%, RMSE<30%

Trend Analysis Highest Trend Sig. Trend for AERONET Non Sig. Trend for MACC

Estimation of impact on clear-sky DNI Relative MBE (%)

Estimation of impact on clear-sky DNI Relative MBE (%) Including corrections on AODs

23/7/201424/7/2014 AOD(500) = 0.09 AOD(500) = 0.43 The DNICast approach to estimate AOD

440nm500nm675nm Mean difs Median difs Std The DNICast approach to estimate AOD

Cloud properties for solar forecasting Previous study has determined homogenous spatial clusters of similar CCI variability using cluster analysis and cluster validity assessment methodologies (Zagouras et al., Solar Energy, 2013, 2014).

CCI forecast: Seasonal Analysis (1) Spatial distribution of the seasonal average mean MSE error (per pixel) of CMF between the predicted and the measured CCI values during winter Measures “fit- quality” Squaring emphasizes larger differences Mean Squared Error

CCI forecast: Seasonal Analysis (2) Spatial distribution of the seasonal average mean MSE error (per pixel) of CCI between the predicted and the measured CMF values during summer Smaller error than in winter Error distinguished between land-sites and sea

Meteotest method using GFS based Weather Research and Forecasting model for wind fields DNICast: Cloud properties for solar resource and forecasting

DLR-PA method using Meteosat Rapid-Scan-Modus HRV channel DLR-DFD method using a sectoral method based on Meteosat Second Generation imagery DNICast: Cloud properties for solar resource and forecasting

Enhancements due to cloudiness A typical sky image (left panel) and the three selected areas (upper, middle, low) that correspond to different parts of the sky and solar zenith angles (right panel).

The CRE as a function of the cosine of the solar zenith angle and the ratio of upper cloud cover to the total one. The upper clouds correspond to zenith angles 0 to 45 o (cos45 o =0.707) and this area is highlighted. Tzoumanikas et al., Renewable Energy, 2016 Enhancements due to cloudiness

Calculate end summer 25(OH)D (A) required for 95% to remain ≥ 25 nmol/L by end winter Calculate monthly spend of 25(OH)D (B) Calculate UV dose (C) required to increase 25(OH)D from winter low to A, account for spend B Determine safe midday exposure time (no sunburn), D Calculate dose in time D for every day March - Sept. Integrate to summer total E. Is E ≥ C across UK? A tip for future use of ultraviolet radiation products (in liaison with health scientific community) UV risks and benefits are highly correlated to ambient UV exposure UV effects are dependent of human behavior, skin type and age

Acknowledgements All Colleagues from the DNICast project ( The Hellenic Network of Solar Energy Lab. of Atmospheric Physics University of Patras, Greece Thank you!