Fangfang Yu 1* , Xiangqian Wu 2 , Scott Lindstrom 3 , Mat Gunshor 3 , and Mitch Goldberg 4

1. Fangfang Yu1*, Xiangqian Wu2, Scott Lindstrom3, Mat Gunshor3, and Mitch Goldberg4 • 1ERT, Inc. @ NOAA/NESDIS Center for Satellite Applications and Research (STAR), College Park, MD, 20740, U.S.A. • 2NOAA/NESDIS Center for Satellite Applications and Research (STAR), College Park, MD, 20740, U.S.A. • 3Cooperative Institute for Meteorological Satellite Studies (CIMSS), Space Science and Engineering Center (SSEC), University of Wisconsin, Madison, WI 53706, U.S.A. • 4NOAA/JPSS, Lanham, MD 20706, U.S.A. • *Fangfang Yu, Fangfang.Yu@noaa.gov, 301-683-3553, postal address: NOAA, Fangfang Yu, NCWCP, 5830 University Research Court (cube #2745), College Park, MD 20740 Introduction Time-series of Brightness Temperature (Tb) Bias with Respect to AIRS Measurements The current Geostationary (GEO) Operational Environmental Satellite (GOES) I-P series have been in-orbit observing the Western Hemisphere since 1994, providing invaluable continuous images for weather monitoring and providing important data for numerical weather prediction (NWP). Yet they have relatively limited application in climate change studies, mainly due to diurnal calibration variation and lack of long-term calibration consistency within, and between, different sensors. To generate a long-term climate data record from GOES, a common well-characterized reference is often used to harmonize radiance quality and make it traceable to a reference standard. Under the umbrella of the Global Space-based Inter-Calibration System (GSICS) project, the well-calibrated hyperspectral radiometer of the Atmospheric Infrared Sounder (AIRS) onboard the Low Earth Orbiting (LEO) satellite Aqua is selected as a reference to inter-calibrate the GOES Imager infrared (IR) radiances. NOAA/National Environmental Satellite, Data and Information Service (NESDIS) and the University of Wisconsin/Cooperative Institute for Meteorological Satellite Studies (CIMSS) recently generated the GOES-AIRS collocation database which covers all eight GOES Imager instruments since October 2002. This poster reports the GOES Imager IR radiometric calibration accuracy at various temporal scales, using AIRS observations as the reference. -1K Assessment of GOES Imager Infrared Radiometric Calibration Accuracy toward Long-term Climate Data Record G12 decontaminations GSICS GEO-LEO Inter-Calibration The spatial, temporal, and spectral collocated satellite pairs with similar viewing geometry are essential to the success of instrument inter-calibrations. The details of the GOES vs. AIRS collocation criteria are available at [1] and [2]. As shown in Figure 1, after the trial-and-error method is used to subset the GEO images and LEO(AIRS) granules, three collocation criteria are used to identify the collocation scenes: 1) spatial collocation: the distance of the two collocated GEO and LEO pixels should be within 4km; 2) temporal coincidence: GEO and LEO should view the same target within 5 minutes difference; 3) viewing alignment: the threshold for the optical path difference is <1%. The spectral matching is achieved by convolving the hyperspectral measurements with the GOES IR spectral response function (SRF). Yet the AIRS instrument does not fully cover the spectra within its spectral range. As shown in Figure 2, there are spectral gaps within the SRFs of GOES-15 Ch3(6.5µm) and Ch2(3.9µm). The Ch3 spectral gap exists at GOES-12 and beyond (G12/13/14/15) while Ch2 spectral gap exists at GOES-13 and beyond (G13/14/15/16). This grating instrument also has some dead or large noise detectors. The missing spectra, as well as the bad detector measurements should also be simulated before spectral convolution. The GSICS community applied the gap-filling method developed by the Japanese Meteorological Agency (JMA) to compensate for the missing or bad spectral radiances [3]. The valid LEO hyperspectral channel observations, and beforehand line-by-line radiative model simulated radiances from 8 typical atmospheric profiles, are used to estimate the radiances of the missing/bad hyper channels. Figure 4. Time-series of daily mean Tb bias of GOES-8 through 15 with respect to AIRS for the day-time collocations. Most GOES Imager IR channels are well-calibrated in general with mean Tb bias to AIRS less than 0.5K, especially for GOES-13/14/15 (GOES-NOP) and the long-wave window channels. The root cause to the large error of GOES-12 Ch6 (13.3µm) and its trending is mainly due to its SRF error [6] and accumulating ice-contamination [7]. GOES-9/10/11/12(GOES-I/M) show seasonal variation during their mission lives. The changes are most apparent around the time of semi-annual detector temperature at Ch3 and Ch6. Yet the response varies at different satellites. The magnitudes of the seasonal variation increase at the two long-wave IR channels of GOES-10/11. This effect needs further investigation to determine the root cause. The Tb bias at the long-wave window channel of GOES-12 is very stable over the 10 years in space, indicating a very stable long-term thermal environment during this period. G12 decontaminations Diurnal Variations Figure 1. Flowchart of GEO-LEO inter-calibration for GOES Imager IR channels Figure 2. Normalized spectral response functions of GOES-11 (left) and GOES-15 (right) Imager IR channels, as well as the simulated top-of-atmosphere (TOA) spectra of AIRS over a clear tropical atmosphere. The simulated IASI TOA spectra is also plotted over the GOES-15 SRFs. Figure 5. Average of diurnal Tb bias with respect to AIRS/IASI for GOES-8 (left, only AIRS data available), GOES-11 (middle) and GOES-12 (right). Midnight effect exists for all four IR channels. The Midnight Blackbody Calibration Correction (MBCC) was not applied to GOES-8 data. Application of MBCC slopes reduces the variation of the diurnal Tb bias at Ch2 and Ch3, Ch5, and Ch6 [8] Ch4 (10.7µm) MBCC slope doesn’t not work as effectively as at the other IR channels MBCC slope is not always activated around the midnight time. Validation of JMA’s Gap-filling Result References [1]. Wu, F.,, T. Hewison, and Y. Tahara, 2009. GSICS GEO-LEO inter-calibration: Baseline algorithm and early result. SPIE, 7456, 745604-1-745604-12. [2]. Hewison, T., X. Wu, F. Yu, Y. Tahara, X. Hu, D. Kim and M. Koenig, 2013. GSICS inter-calibration of infrared channels of geostationary Imagers using Metop/IASI, IEEE Transactions on Geoscience and Remote Sensing, 51(3), 1160-1170. [3]. Tahara, Y. and K. Kato, 2009, New spectral compensation method for inter-calibration using high spectral resolution sounder”, Meteorol. Satell. Center Tech, Note. 52(2), 1-37. [4]. Wang, L., M. Goldberg, X. Wu, C. Cao, R. Iacovazzi, F. Yu, and Y. Li, 2011. Consistency assessment of atmospheric infrared sounder and infrared atmospheric sounding interferometer radiance: Double difference versus simulaneous nadir overpass”, J. Geophys. Res. 116(D01) DOI:10.1029/2010JD014988 [5]. Tobin, D., S. Dutcher, and H. Revercomb, 2010. Evaluations of IASI and AIRS spectral radiances using simultaneous nadir overpasses, 2th International IASI Conference. [6] Yu, F. and X. Wu, 2013, Correction for GOES Imager spectral response function using GSICS. Part II: Applications, IEEE Trans. Geos. Remote Sens., 51(3), 1200-1214, doi:10.1109/TGR.2012.223659. [7]. Wang, L, X. Wu, F. Weng and M. Goldberg, 2013, Effects of ice decontamination on GOES-12 Imager calibration, IEEE Trans. Geos. Remote Sens., 51(3), 1224-1230, doi: 10.1109/TGR.2012.2225839. [8]. Yu F., X. Wu, M.K. Rama Varma Raja ,Y. Li, L. Wang and M. Goldberg, 2013. Diurnal and scan angle variations in the calibration of GOES Imager infrared channels, IEEE Transactions on Geoscience and Remote Sensing, 51(1), 671-683. Acknowledgements This work is partially funded by NOAA/NESDIS/STAR Calibration/Validation support. The contents of this document are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of NOAA or the U.S. government. Figure 3. Scatter-plots of GEO-LEO Tb difference vs. GEO scene temperature for GOES-12, 13 and 15 Ch3. The double difference technique [4] is used to estimate the systematic error with the JMA’s gap-filling method at GOES-13/15 Ch3(6.5µm). Since IASI spectra cover the whole Ch3 SRF, the simulated IASI radiance is used as reference to estimate the uncertainty of the gap-filling method, using GOES-15 measurements as the transfer. It is assumed the GOES-15 Imager calibration accuracy is very stable during the day-time. The result of the Simultaneous Nadir Overpass (SNO) method shows that the mean difference between AIRS is generally on the order of a few tenths of degrees or less with AIRS slightly warmer than IASI at this spectral range [5]. It is therefore estimated that the mean systematic error of the JMA’s gap-filling is less than 0.1K at GOES-12,13 and 15 Ch3.