Publication: A Postprocessing methodology for direct normal irradiance forecasting using cloud information and aerosol load forecasts
Loading...
Official URL
Full text at PDC
Publication Date
2017-06
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Amer meteorological soc
Citations
Exportar
Abstract
A method for direct normal irradiance (DNI) forecasting for specific sites is proposed. It is based on the combination of a numerical weather prediction (NWP) model, which provides cloud information, with radiative transfer simulations fed with external aerosol forecasts. The NWP model used is the ECMWF Integrated Forecast System, and the radiative transfer information has been obtained from the Library of Radiative Transfer (libRadtran). Two types of aerosol forecasts have been tested: the global Monitoring Atmospheric Composition and Climate (MACC) model, which predicts five major components of aerosols, and the Dust Regional Atmospheric Model (BSC-DREAM8b) added to a fixed background calculated as the 20th percentile of the monthly mean of AERONET 2.0 observations from a different year. The methodology employed is valid for all meteorological situations, providing a stable and continuous DNI curve. The performance of the combined method has been evaluated against DNI observations and compared with the pure ECMWF forecasts at eight locations in the southern half of mainland Spain and the Canary Islands, which received high loadings of African dust for 2013 and 2014. Results for 1-day forecasts are presented. Although clouds play a major role, aerosols have a significant effect, but at shorter time scales. The combination of ECMWF and MACC forecasts gives the best global results, improving the DNI forecasts in events with high aerosol content. The regional BSC-DREAM8b yields good results for some extremely high dust conditions, although more reliable predictions, valid for any aerosol conditions, are provided by the MACC model.
Description
©2017 American Meteorological Society
The authors acknowledge the libRadtran developers for their radiative transfer tools used in this work and ECMWF for their forecasts. We thank the MACC project, funded by the European Commission under the EU-Horizon 2020 Programme and coordinated by the ECMWF, for their AOD data, freely available on its website (http://www.gmes-atmosphere.eu/). Dust forecasts from the BSC-DREAM8b, operated by the Barcelona Supercomputing Center (http://www.bsc.es/projects/earthscience/BSC-DREAM/) have been used. AERONET provided observational data. We thank all the PIs and their staff for establishing and maintaining the sites used in this investigation. We also thank the Group of Atmospheric Optics, Valladolid University, for the provision of theCAELIS tool (http://www.caelis.uva.es) used in this publication, and the Forecast and Load Scheduling Department from Red Electrica de España for their useful comments.