VALIDA ALIDATION TION OF THE MODIS AEROSOL CHARACTERIZA CHARACTERIZATION TION USING THE AERONET DATA DATA BASE IN MOROCCO.
Juan Liria Fernández[1], Ángel González[1] Ralf Wiesenberg [2] [1]
Technical Meteorologist by Sun2market solutions
[2]
Managing Director. Director. Sun to Market Solutions, S.L. Avda. Gregorio Peces
Barba, 1, 28918 Leganés (Madrid), Spain. Phone: (+34)914966189.
1. Introducti Introduction on
It has been proved by multiple clear sky models that aerosols have an important role in direct dir ect bea beam m irra irradia diance nce atte attenu nuatio ation. n. Aerosols Aerosols are the therefo refore re an imp import ortant ant sou source rce of uncertainty when predicting solar beam irradiance either Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI) or Global Horizontal Irradiance (GHI), which can be critical for the construction construction of a solar Phot Photovolt ovoltaic aic plant or Conce Concentrated ntrated Solar Power plants. plants. In this case the aerosol estimation estimation formulas (for examp example: le: Yang Yang et [1] al., 2001 ) are not very precise. The main problem is that nowadays there does not exist a method reliable worldwide to characterize aerosols without having a large uncertainty. The parameters which best char ch aract acter erize ize ae aero roso sols ls are th thee Åm Åmstr stron ong’ g’ss tu turb rbid idity ity pa para rame meter ter β, th thee Åm Åmstr stron ong’ g’ss wavelength exponent α, and the Aerosol Optical Thickness (AOT), which are related by the Åmstrong’s formula: . The AErosol RObotic NETwork, from the NASA (AERONET), has a wide database of accurate measurements of AOT, provided by a web of meteorological stations spread all over the world. The problem lies not just in the lack of meteorological stations that have the necessary devices to measure the AOT --due to their elevated costs-- but also in the lack of data available from each individual station. The NAS NASA A sate satellit llitee Mod Moderat eratee Res Resolu olutio tionn Ima Imagin gingg Spe Spectr ctrorad oradiom iometer eter (MO (MODIS DIS)) provides an almost complete “view” of the world's aerosols but its uncertainty range depends on the surface coverage. It has more accuracy on surfaces with low albedo (such as oceans and land with high vegetation coverage), but its uncertainty can be over 100% for surfaces with high albedo. The purpose of this paper is to validate the MODIS results using the AERONET data.
2. Methodology
Data from the AERONET will be acquired from the stations situated in Saada (31ºN, 8ºW), 8ºW), Ras El Ain (31ºN, 7ºW), 7ºW), Oukaimeden (31ºN, 7ºW), 7ºW), and Ouarzarzate (30ºN, 6ºW). 6ºW). The period of study will be May 06, since all the stations above have data available for this period. Data are available in the AERONET site in http://aeronet.gsfc.nasa.gov/ http://aeronet.gsfc.nasa.gov/..
The aim of this study is to find a relationship between MODIS data and AERONET data which will be achieved by correlating the MODIS database with the AERONET database as it follows[2-4]: Where subindex means both AERONET and MODIS data, m would be the slope (towards one when data correlates well) and b would be the interception parameter (towards zero when data correlates well). Data from different stations will be also correlated in order to find a relationship between distance, height, and pressure [2]. The Mean Bias Error (MBE) will also be shown in order to see the deviation tendency between the data and the standard deviation of each data series, as well as temporal series of the data in order to see temporal evolution, although a month is a short period range and it is not expected to obtain conclusive results in this particular case. In the Saada station, as this station provides a wider temporal range of data, the yearlong aerosol evolution will be studied and it will be also compared to the MODIS data in order to obtain a more accurate temporal evolution. 3. References and Bibliography
[1] Viorel Badescu, Modelling Solar Radiation at the Earth Surface, Springer - Verlag Berlin Heidelberg, 2008. [2] A. Bounhir, Z. Benkhaldoun, B. Mougenot, M. Sarazin, E. Siher, L. Masmoudi, Aerosol columnar characterization in Morocco: ELT prospect, New Astronomy 13, 41– 52, 2008. [3] Khan Alam, Thomas Trautmann, Thomas Blaschke, Hussain Majid, Aerosol optical and radiative properties during summer and winter seasons over Lahore and Karachi, Atmospheric Environment 50, 234-245, 2012. [4] Ralph Kahn, Andreas Petzold, Manfred Wendisch, Eike Bierwirth,Tilman Dinter, Michael Esselborn, Marcus Fiebig, Birgit Heese, Peter Knippertz, Detlef Müller, Alexander Schladitz and Wolfgang von Hoyningen-Huene, Desert dust aerosol air mass mapping in the western Sahara, using particle properties derived from space-based multi-angle imaging, Tellus (2009), 61B, 239–251 [5] Eugenia Kalnay, Athmospheric Modelling, Data Assimilation, And Predictibility, Cambridge, 2003. [6] Daniel S. Wilks. Statistical Methods in the Atmospheric Sciences, Academic Press 2006.
