The aim of this study is to validate the Ozone Monitoring Instrument (OMI) erythemal dose rates using ground-based measurements in Thessaloniki, Greece. In the Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki, a Yankee Environmental System UVB-1 radiometer measures the erythemal dose rates every minute, and a Norsk Institutt for Luftforskning (NILU) multi-filter radiometer provides multi-filter based irradiances that were used to derive erythemal dose rates for the period 2005–2014. Both these datasets were independently validated against collocated UV irradiance spectra from a Brewer MkIII spectrophotometer. Cloud detection was performed based on measurements of the global horizontal radiation from a Kipp & Zonen pyranometer and from NILU measurements in the visible range. The satellite versus ground observation validation was performed taking into account the effect of temporal averaging, limitations related to OMI quality control criteria, cloud conditions, the solar zenith angle and atmospheric aerosol loading. Aerosol optical depth was also retrieved using a collocated CIMEL sunphotometer in order to assess its impact on the comparisons. The effect of total ozone columns satellite versus ground-based differences on the erythemal dose comparisons was also investigated. Since most of the public awareness alerts are based on UV Index (UVI) classifications, an analysis and assessment of OMI capability for retrieving UVIs was also performed. An overestimation of the OMI erythemal product by 3–6% and 4–8% with respect to ground measurements is observed when examining overpass and noontime estimates respectively. The comparisons revealed a relatively small solar zenith angle dependence, with the OMI data showing a slight dependence on aerosol load, especially at high aerosol optical depth values. A mean underestimation of 2% in OMI total ozone columns under cloud-free conditions was found to lead to an overestimation in OMI erythemal doses of 1–5%.While OMI overestimated the erythemal dose rates over the range of cloudiness conditions examined, its UVIs were found to be reliable for the purpose of characterizing the ambient UV radiation impact.
|Number of pages||16|
|Early online date||9 Apr 2018|
|Publication status||Published - Jun 2018|
- Neural network
- UV index