P1.9
Applying split window technique for land surface temperature measurement from GOES-R advanced baseline imager
Yunyue Yu, NOAA/NESDIS, Camp Springs, MD; and D. Tarpley, M. K. Rama Varma Raja, H. Xu, and K. Y. Vinnikov
On board the GOES-R satellite which has a planned launch-ready date of December 2014, the Advanced Baseline Imager (ABI) will provide a best-ever opportunity for measuring land surface temperature (LST) from geostationary orbit. The ABI sensor will have three major advantages for LST measurement. First, horizontal resolution of ABI is 2 km at nadir, fairly close to the polar-orbiting meteorology satellite sensors such as AVHRR. It allows for near simultaneous and spatially coincident LST retrievals from GOES and POES (polar-orbiting operational environmental satellites). Second, the refresh rate of ABI observation is 5 minutes, which is significantly higher than current GOES Imagers. The high refresh rate will provide more cloud-free measurements and therefore a better observation of the diurnal LST cycle, which is the greatest advantage of the geostationary satellite data. Finally, the ABI sensor noise level will be significantly lower than current GOES Imagers. At the thermal infrared band, the sensor noise equivalent temperature will be less than 0.1 K, which will allow a more accurate LST product.
This paper describes a split window (SW) algorithm being developed for LST measurement from the ABI sensor. We simulated ABI sensor data using a moderate resolution radiative transfer model (MODTRAN) and NOAA88 atmospheric profiles and ran regression analyses for the LST algorithm development. The algorithm was developed by optimizing existing SW LST algorithms published in the literature and adding a path length correction term to minimize retrieval errors due to difference in atmospheric path absorption from nadir view to the edge-of-scan. Primary evaluation of the algorithm has been performed using coincident LST values estimated from surface radiant budget dataset (SURFRAD) ground measurements. The algorithm has been applied for LST seasonal, diurnal and weather related cycles' studies using GOES-8 data (Vinnikov et al., 2007). It has also been used to produce LST data from MSG/SEVIRI data.
Poster Session 1, Fifth GOES Users' Confererence Poster Session
Wednesday, 23 January 2008, 2:30 PM-4:00 PM, Exhibit Hall B
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