Atmospheric composition modeling over the Arabian Peninsula for Solar Energy applications

Transcription

1 Atmospheric composition modeling over the Arabian Peninsula for Solar Energy applications S Naseema Beegum, Imen Gherboudj, Naira Chaouch, and Hosni Ghedira Research Center for Renewable Energy Mapping and Assessment Masdar Institute of Science and Technology, Abu Dhabi, UAE ICEM June 2015

2 Outline Introduction Methodology Results Conclusions

3 INTRODUCTION Tropospheric trace gas and aerosol pollutants have adverse effects not only on health, environment and climate but also on socio-economic sectors including solar energy industry. Dust storms are very frequent weather phenomena in arid and semi-arid regions making mineral dust as the single largest contributor to the attenuation of solar irradiance in these regions. Accurate estimation of its spatial and temporal variability is crucial for the forecasting of solar irradiance for solar energy applications (PV and CSP).

4 CHIMERE MODEL Simulation domain topography, soil and land use properties Anthropogenic Emission inventories (EMEP, HTAP) Meteorology (WRF) 3D: Pressure, humidity, wind, temperature, heat fluxes, boundary layer height Emission fluxes Mineral dust, anthropogenic, biogenic, fire emission Global model Initial boundary conditions CHIMERE Emission, Chemistry, Transport, Turbulent mixing, Deposition Validation Comparison with observations (satellite and in-situ)

5 SIMULATION DOMAIN Nested Domain Configuration Outer domain 27 km Inner domain 9 km Study period One month: August 2013

6 CHIMERE- DUST SIMULATION Highly unrealistic simulations with default soil and surface parameters Simulations with low vertical resolution of 8 levels from surface to 500 hpa Simulations with high vertical resolution: 15 levels from surface to 200 hpa MODIS deep blue AOD

7 CHIMERE MODEL Simulation domain topography, soil and land use properties Anthropogenic Emission inventories (EMEP, HTAP) Meteorology (WRF) 3D: Pressure, humidity, wind, temperature, heat fluxes, boundary layer height Emission fluxes Mineral dust, anthropogenic, biogenic, fire emission Global model Initial boundary conditions CHIMERE Emission, Chemistry, Transport, Turbulent mixing, Deposition Validation Comparison with observations (satellite and in-situ)

8 CHIMERE- DUST SIMULATION Dust Parameterization Scheme Saltation/sandblasting model by Alfaro Gomas 2001 (AG 2001) Sensitive to soil/surface data Soil erodibility Soil/Surface datasets used in the CHIMERE model Surface roughness length

9 CHIMERE- DUST SIMULATION New Soil/Surface datasets used for CHIMERE simulations Erodibility map derived from MODIS surface reflectance Surface roughness length derived from logarithmic wind profile method (ECMWRF ERA-Interim)

10 AOD - CHIMERE OBSERVATIONS VS RECALIBREATED MODEL Improved simulations with new datasets on soil/surface datasets AOD - MODIS Deep Blue Simulations on August 5 th, a moderately dusty and cloudy day

11 Simulations on August 9 th, relatively clean day AOD - CHIMERE OBSERVATIONS VS RECALIBREATED MODEL AOD - MODIS Deep Blue

12 Simulations on August 23 rd, a moderately dusty day AOD - CHIMERE OBSERVATIONS VS RECALIBREATED MODEL AOD - MODIS DEEP BLUE

13 OBSERVATIONS VS RECALIBREATED MODEL CALIPSO Vertical Profile extinction coefficient CHIMERE Vertical Profile of PM10

14 OBSERVATIONS VS RECALIBREATED MODEL Model simulated AODs are in good agreement with the AERONET observations correlation coefficient varies from 0.6 at KAUST to 0.8 at Eilat

15 DUST EVENT ON APRIL 1-3, 2015 Dust forecast (CHIMERE) Dust detection

16 CONCLUSIONS CHIMERE model results were observed to be highly sensitive to the soil and surface properties such as erodibility and aerodynamic surface roughness length. The CHIMERE has been recalibrated using new dataset on soil erodibility, derived from the MODIS reflectance, and aerodynamic surface roughness length, from the ECMWF ERA-Interim datasets and global EDGAR-HTAP anthropogenic emission inventories The calibrated model results at finer spatial resolution of 9km with nested domain configuration provided realistic simulations of AOD with correlation coefficients of 0.6 to 0.8 for different stations, against the corresponding measurements. The vertical structure of dust plume (both in its vertical extend and intensity) was consistent with the CALIPSO Lidar profiles The model is able to capture the spatial-temporal variability of AOD for dusty (mild or intense) and normal days

17 NEXT STEP The simulated AODs using the model will be used for the irradiance forecast over the Arabian Peninsula

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