Validation of an hourly resolved global aerosol model in answer to solar electricity generation information needs

Abstract. Solar energy applications need global aerosol optical depth (AOD) information to derive historic surface solar irradiance databases from geostationary meteorological satellites reaching back to the 1980's. This paper validates the MATCH/DLR model originating in the climate community against AERONET ground measurements. Hourly or daily mean AOD model output is evaluated individually for all stations in Europe, Africa and the Middle East – an area highly interesting for solar energy applications being partly dominated by high aerosol loads. Overall, a bias of 0.02 and a root-mean-square error (RMSE) of 0.23 are found for daily mean AOD values, while the RMSE increases to 0.28 for hourly mean AOD values. Large differences between various regions and stations are found providing a feedback loop for the aerosol modelling community. The difference in using daily means versus hourly resolved modelling with respect to hourly resolved observations is evaluated. Nowadays state-of-the-art in solar resource assessment relies on monthly turbidity or AOD climatologies while at least hourly resolved irradiance time series are needed by the solar sector. Therefore, the contribution of higher temporally modelled AOD is evaluated.

[1]  H. Guillard,et al.  A method for the determination of the global solar radiation from meteorological satellite data , 1986 .

[2]  P. J. Rasch,et al.  Sulfur chemistry in the National Center for Atmospheric Research Community Climate Model: Description, evaluation, features, and sensitivity to aqueous chemistry , 2000 .

[3]  O. Toon,et al.  Numerical simulations of Asian dust storms using a coupled climate‐aerosol microphysical model , 2009 .

[4]  B. Holben,et al.  Single-Scattering Albedo and Radiative Forcing of Various Aerosol Species with a Global Three-Dimensional Model , 2002 .

[5]  Mian Chin,et al.  Contribution of different aerosol species to the global aerosol extinction optical thickness: Estimates from model results , 1997 .

[6]  N. Mahowald,et al.  Transport of 222radon to the remote troposphere using the Model of Atmospheric Transport and Chemistry and assimilated winds from ECMWF and the National Center for Environmental Prediction/NCAR , 1997 .

[7]  W. Collins,et al.  Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: Methodology for INDOEX , 2001 .

[8]  C. Timmreck,et al.  Monthly Averages of Aerosol Properties: A Global Comparison Among Models, Satellite Data, and AERONET Ground Data , 2003 .

[9]  L. Wald,et al.  The method Heliosat-2 for deriving shortwave solar radiation from satellite images , 2004 .

[10]  Philip J. Rasch,et al.  A Comparison of the CCM3 Model Climate Using Diagnosed and Predicted Condensate Parameterizations , 1998 .

[11]  C. N. Hewitt,et al.  A global model of natural volatile organic compound emissions , 1995 .

[12]  T. Eck,et al.  Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations , 2002 .

[13]  N. Mahowald,et al.  Estimates of atmospheric-processed soluble iron from observations and a global mineral aerosol model: Biogeochemical implications , 2004 .

[14]  M. Chin,et al.  Sources and distributions of dust aerosols simulated with the GOCART model , 2001 .

[15]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[16]  Sunling Gong,et al.  CUACE/Dust – an integrated system of observation and modeling systems for operational dust forecasting in Asia , 2007 .

[17]  David M. Winker,et al.  Mesoscale Variations of Tropospheric Aerosols , 2003 .

[18]  Thomas E. Graedel,et al.  Global gridded inventories of anthropogenic emissions of sulfur and nitrogen , 1996 .

[19]  V. Canuto,et al.  Present-Day Atmospheric Simulations Using GISS ModelE: Comparison to In Situ, Satellite, and Reanalysis Data , 2006 .

[20]  W. Collins,et al.  An AeroCom Initial Assessment - Optical Properties in Aerosol Component Modules of Global Models , 2005 .

[21]  A. H. Woodcock,et al.  THE PRODUCTION, CONCENTRATION, AND VERTICAL DISTRIBUTION OF THE SEA‐SALT AEROSOL * , 1980 .

[22]  B. Marticorena,et al.  Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme , 1995 .

[23]  Teruyuki Nakajima,et al.  Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sun photometer measurements , 2002 .

[24]  F. Giorgi,et al.  Modeling of Saharan dust outbreaks over the Mediterranean by RegCM3: case studies , 2009 .

[25]  Markus Eck,et al.  Case Studies on the Use of Solar Irradiance Forecast for Optimized Operation Strategies of Solar Thermal Power Plants , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Charles S. Zender,et al.  Interannual variability in atmospheric mineral aerosols from a 22‐year model simulation and observational data , 2003 .

[27]  H. Beyer,et al.  Modifications of the Heliosat procedure for irradiance estimates from satellite images , 1996 .

[28]  J. Kindle,et al.  Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment , 2009 .

[29]  N. Mahowald,et al.  Sensitivity study of meteorological parameters on mineral aerosol mobilization, transport, and distribution , 2003 .

[30]  A. Ansmann,et al.  Regional Saharan dust modelling during the SAMUM 2006 campaign , 2009 .

[31]  N. Mahowald,et al.  Understanding the 30‐year Barbados desert dust record , 2002 .

[32]  Giacomo R. DiTullio,et al.  A global database of sea surface dimethylsulfide (DMS) measurements and a procedure to predict sea surface DMS as a function of latitude, longitude, and month , 1999 .

[33]  S. Piketh,et al.  A Summary of First Year Activities of the United Arab Emirates Unified Aerosol Experiment: UAE(2) , 2005 .

[34]  J. Penner,et al.  A global three‐dimensional model study of carbonaceous aerosols , 1996 .

[35]  E. Patterson.  Optical properties of the crustal aerosol - Relation to chemical and physical characteristics , 1981 .

[36]  Michael Schulz,et al.  Global dust model intercomparison in AeroCom phase I , 2011 .

[37]  C. Zender,et al.  Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology , 2003 .

[38]  David G. Streets,et al.  BIOFUEL USE IN ASIA AND ACIDIFYING EMISSIONS1The above manuscript has been created by the University of Chicago as Operator of Argonne National Laboratory (“Argonne”) under Contract No. W-31-109-ENG-38 with the U.S. Department of Energy.1 , 1998 .

[39]  Joyce E. Penner,et al.  Towards the development of a global inventory for black carbon emissions , 1993 .

[40]  Johannes W. Kaiser,et al.  Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System : Forward modeling , 2009 .

[41]  Christina Mitsakou,et al.  An improved limited area model for describing the dust cycle in the atmosphere , 2010 .

[42]  Tom M. L. Wigley,et al.  Global and regional anthropogenic sulfur dioxide emissions , 2001 .

[43]  P. J. Rasch,et al.  Radiative forcing due to sulfate aerosols from simulations with the National Center for Atmospheric Research Community Climate Model, Version 3 , 2000 .

[44]  C. Schillings,et al.  Operational method for deriving high resolution direct normal irradiance from satellite data , 2004 .

[45]  Philip J. Rasch,et al.  Representations of transport, convection, and the hydrologic cycle in chemical transport models : Implications for the modeling of short-lived and soluble species , 1997 .

[46]  Axel Lauer,et al.  © Author(s) 2006. This work is licensed under a Creative Commons License. Atmospheric Chemistry and Physics Analysis and quantification of the diversities of aerosol life cycles , 2022 .

[47]  Recent Development in Transport Methods at NCAR , 1998 .

[48]  V. Ramaswamy,et al.  Global sensitivity studies of the direct radiative forcing due to anthropogenic sulfate and black carbon aerosols , 1998 .