Long-term global earth surface ultraviolet radiation exposure derived from ISCCP and TOMS satellite measurements

A long-term (1983–2000) global dataset of Earth’s surface daily-integrated UV exposure was developed from a combination of ISCCP-D1 3 h reflectance measurements (in order to resolve the diurnal variation of cloud conditions) and TOMS total ozone amount. The inversion algorithm developed in our previ...

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Veröffentlicht in:Agricultural and forest meteorology 2003-12, Vol.120 (1), p.51-68
Hauptverfasser: Ciren, Pubu, Li, Zhanqing
Format: Artikel
Sprache:eng
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Zusammenfassung:A long-term (1983–2000) global dataset of Earth’s surface daily-integrated UV exposure was developed from a combination of ISCCP-D1 3 h reflectance measurements (in order to resolve the diurnal variation of cloud conditions) and TOMS total ozone amount. The inversion algorithm developed in our previous study was employed with modifications addressing the conversion of visible reflectance to UV albedo and narrowband UV albedo to broadband albedo over the range of 280–400 nm. Validation of the product was carried out using ground-based measurements at six stations. In general, good agreements between the estimated and measured daily UV exposure are found at most stations; the relative mean and root mean square (RMS) difference varies from 3 to 14% and from 20 to 45%, respectively. Among the stations, San Diego has a fairly low mean difference (9%) and the lowest RMS difference (33%), owing to the prevailing clear sky or uniform cloud cover condition. The RMS difference increases with cloud amount, which is largely caused by mismatch between satellite and ground-based measurements. The effect of diurnal variation in atmospheric opacity associated primarily with cloud on the estimation of daily erythemal UV doses is investigated with both ground-based measurements and ISCCP-D1 data. It is found that daily erythemal UV doses estimated from only a noontime satellite “snapshot” may incur errors larger than 20%, which may be reduced for long-term averaging.
ISSN:0168-1923
1873-2240
DOI:10.1016/j.agrformet.2003.08.033