Wednesday, 9 January 2013
Exhibit Hall 3 (Austin Convention Center)
Infrared (IR) land surface emissivity (LSE) with a high temporal and spatial resolution is very important for deriving other products using IR radiance measurements as well as assimilating IR radiances in numerical weather prediction (NWP) models over land. Retrieved from various satellite instruments, many LSE databases are available for operational and research use. Most are updated only monthly; assuming emissivity does not change within the month. However, laboratory measurements have shown that emissivity increases by 1.7% to 16% when soil moisture content becomes higher, especially in sandy soils in the 8.2 - 9.2 μm range. And a clearly defined wave-like diurnal pattern of decreasing surface soil moisture during the day and recovery (or increased soil moisture) at night was observed. Therefore, it is expected that LSE possesses a diurnal wave-pattern variation with low values during day time and high values during night time. The physically-based GOES-R ABI LSE algorithm uniquely exploits the geostationary satellites' high temporal resolution. The algorithm was developed and applied to the radiance measurements from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation (MSG) Meteosat-8/9. The results over the Sahara Desert show that 8.7 μm emissivity has a significant diurnal wave-pattern variation, with high values during night time and low values during day time. 10.8 μm emissivity also shows a similar diurnal variation, but with a smaller amplitude compared to 8.7 μm. 12 μm emissivity has an even weaker diurnal variation, and an opposite pattern as 8.7 and 10.8 μm. Evidence is provided to demonstrate that the SEVIRI LSE diurnal wave-pattern variations are real, not artifacts from the retrieval algorithm.
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