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Insight From a Detailed Comparison Between the Solar Irradiance Measured in the North of France,
and Its Satellite-Based and Simulation-Based Estimates
T. Elias, M. Compiegne, D. Ramon, HYGEOS, Lille, France
Motivation and measurement facilities
Evaluation of satellite products
Result from a physical model
Facilities of LOA’s measurement siteused in this study
and instrumental synergy
● Measurement of solar irradiance (every minute):✔ Pyrheliometer Kipp&Zonen CH1 + sun tracker
➢ Direct Normal Irradiance✔ Pyranometer Kipp&Zonen CMP22 + shadower
➢ Diffuse horizontal irradiance
● Characterization of the atmospheric column and sky content:
✔ Micro Lidar CIMEL (532 nm, pulse : 4.7 kHz, acq. Total acquisition time=0.84s; part of AERONET network))
➢ Provide altitude of scattering atmospheric component (vert. resolution: 15m; Δt = 1 min)t = 1 min)
✔ Pyrheliometer : ➢ provides when possible a retrieval of the
atmospheric column effective optical depth✔ Skyimager: used here as a qualitative checking tool
✔ Sun photometer: measures sun and sky radiances
(almucantar scans) at fixed wavelengths➢ Retrieves aerosol properties (optical
thickness, Angstrom coefficient) and water vapor. Use in this study as input to radiative transfer calculation.
Motivation and abstract
Contact : [email protected]
● The accurate knowledge of the solar irradiance is crucial in order to evaluate and anticipate the energetic production of PV fields and their actual efficiency. The link between solar irradiance and meteorology is particularly pertaining in the objective of solar energy forecast.
● Not only is important the amount of solar irradiance but also its angular partition (direct / diffuse fields) as well as its spectral content as efficiency cells are sensitive to those characters.
● It is thus valuable to measure the solar irradiance and its characteristics from the surface for the variety of existing atmospheric conditions (clear sky, diverse nebulosity (fractional Cumulus, Cirrus, etc.). We obtain here a climatology of solar irradiance per sequence of nebulosity and cloud type.
● The estimation of solar irradiance from algorithm whose input data are measurements from geostrophic satellites is very interesting and popular. We show here the difficulty and also the success of such satellite based products to evaluate the solar irradiance at our measurement site. Some systematic biases exist concerning the direct and diffuse solar irradiance that may compensate.
● We show also the capacity of radiative transfer simulations that assimilate retrieval from measurements or reanalysis data to simulate with higher accuracy solar irradiance in case of clear sky conditions, though with issues for cloudy conditions.
Example : 20 May 2014 : Cirrostratus et cumulus
Nicolas Ferlay, G. Chesnoiu, P. Dubuisson, F. Auriol, G. Brogniez & F. ParolLaboratoire d'Optique Atmosphérique (LOA), UMR8518, Université de Lille / CNRS
Global kWh/m2 4.89
Direct kWh/m2 2.64
Diffus kWh/m2 2.25
Energy over the day:
● Instrumental synergy✔ We used coincident measurements of the
(pyrheliometer+pyranometer) and the lidar to identify temporal sequences of cloud types (ex. : Cumulus sequence composed of cloudy, partly cloudy, sunny moments)
● EUMETSAT CMSAF, SARAH1, SARAH2 (all sky) and McClear (clear sky estimate)
● Daily mean, ½ hourly and hourly mean
Case study: 23 → 26 August 2016
Elements of (solar) climatology in Lille and its estimate
Properties of solar irradiance: exemple for Cumulus
Climatology of sky conditions and types (time scale = 1 hour)
Our (first) definition of the sky condition:
K = clearness index
Mean absolute bias – hourly scale – period 2009-2016
Mean relative bias – hourly scale – period 2009-2016
● Fast radiative transfer code SMART-G and the ARTDECO toolbox (see poster 5CV.3.18 5CV.3.18 Elias et al )
● Input data provided by AERONET (clear-sky) and by MERRA-2 (all-sky)
Satellite-based product about solar irradiance:
Additional comparison with simulation of solar irradiance (DNI, diffuse, GHI):
Daily scale and daily mean - period 2009-2016 Hourly scale and hourly mean - period 2009-2016
Satellite products tend to overestimate (resp. underestimate) the diffuse irradiance in clear (resp. cloudy) conditions to underestime (resp. overestimate) the direct irradiance in clear (resp. cloudy) conditionsBias may compensate and the score for global horizontal irradiance is often better, also for cloudy situations, but not in the case of Cirrus an Cumulus cloud fields (see on the right)
Over a year
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Better results with physics based estimate: • Very good results in clear sky (shows difference between the use of AERONET and MERRA2 inputs)● Need to pursue the evaluation for all sky conditions (in progress)
