English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The article investigates the impact of aerosol composition on the estimation of the downwelling surface shortwave flux (DSSF). This initiative forms part of the efforts to improve the DSSF distributed by the Land Surface Analysis Satellite Application Facility (LSA-SAF). This operational product assumes invariant aerosol properties under clear sky conditions, which can be inaccurate for some regions of the world. This is the case of West Africa, where aerosol activity is not only highly variable due to frequent dust storms but also rich because of the coexistence of different aerosol species. This study was carried out over the West African stations of Dakar and Niamey, which represent different aerosol scenarios. Several dates during the dry season of 2006 were selected and classified into four different day types according to aerosol activity: standard, clean, mixture and dusty days. The diurnal evolution of DSSF and its direct and diffuse components were estimated for the selected dates by the current LSA-SAF algorithm and two other approaches using aerosol measurements from the Aerosol Robotic Network (AERONET) as input. The first alternative approach took the diurnal evolution of the total aerosol optical depth (AOD) into account, assuming a default desert aerosol type. Experiments with this method showed a significant improvement in the estimated DSSF compared to the current LSA-SAF algorithm. For example, root mean square error (RMSE) improved from 170 W/m2 to  50 W/m2 for dusty days in Dakar and from 73 W/m2 to 21 W/m2 for mixture days in Niamey. This improvement resulted from the consideration of a time-varying AOD, which accounted for the rapidly changing aerosol load for these two day types. The second alternative approach tested included consideration of the diurnal variation of the aerosol load and composition. Again, this was done using AERONET data on the fine and coarse aerosol modes, which may be associated with different sized dust particles, sea salt, or soot from biomass burning (depending on the date). This enhanced consideration of the aerosol composition greatly improved the estimation of the diffuse component of the DSSF, further reducing the RMSE during mixture days from 50 W/m2 to less than 10 W/m2. This improvement mainly came from consideration of the right scattering properties of the aerosol particles, which may be significantly different for each aerosol type

atmosphere

On the Importance of Aerosol Composition for Estimating Incoming Solar Radiation: Focus on the Western African Stations of Dakar and Niamey during the Dry Season