Comparison between MODIS and AIRS/AMSU satellite-derived surface skin temperatures

Lee, Y.-R.; Yoo, Jae Keun; Jeong, Manhee; Won, Young-Shin; Hearty, Thomas; Shin, Dong Bin

doi:10.5194/amt-6-445-2013

Cited by 12 publications

(5 citation statements)

References 34 publications

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“…The MODIS IST product utilized the MODIS cloud mask with visible reflectance, which had lower accuracy during the night (Hall et al, 2004). Lee et al (2013) reported that the local times of observation between the MODIS and AIRS were almost the same from 60 • N-60 • S, but they were quite different in the high latitude regions. It is likely that the main cause to the observed SST differences near the sea ice boundary was in the way the surface type was classified.…”

Section: Discussionmentioning

confidence: 99%

“…The MODIS cloud mask using visible reflectance had a high accuracy during the daytime, but a lower accuracy during the nighttime due to low illumination. As another reason for the temperature difference, Lee et al (2013) suggested that there were substantial differences in observation time between MODIS and AIRS in the high latitude regions, since the different scan angles of the two instruments resulted in different footprints, which could lead to the observed difference in temperature. However, we suggested that the surface type classification method could be the primary reason for the temperature difference between the MODIS-based and AIRS-based data sets.…”

Section: H-j Kang Et Al: Uncertainties Of Satellite-derived Surfacmentioning

confidence: 99%

“…Molnar and Susskind (2005) validated the accuracy of the AIRS/AMSU cloud products using MODIS cloud analyses, which have a higher spatial resolution than that of AIRS. Knuteson et al (2006) compared the MODIS Collection 4 (C4) with the AIRS Version 3 (V3) on the land surface temperature (LST) for the eastern half of the US, showing that the monthly differences were approximately 3 K. Lee et al (2013) investigated the characteristics of the differences between the MODIS land surface skin temperature/sea surface temperature and the AIRS/AMSU surface skin temperature across the globe, and found that the MODIS C5 product was systematically lower by 1.7 K than the AIRS/AMSU V5 product over land in the 50 • N-50 • S regions, but it was higher by 0.5 K than the AIRS/AMSU product over ocean. Particularly in the sea ice regions, the MODIS annual averages were larger than the AIRS/AMSU values, due to the differential errors in ice/snow emissivity between the retrieval methods (or channels) for the two data products.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

et al. 2015

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Abstract. Uncertainties in the satellite-derived surface skin temperature (SST) data in the polar oceans during two periods (16-24 April and 15-23 September) 2003-2014 were investigated and the three data sets were intercompared as follows: MODerate Resolution Imaging Spectroradiometer Ice Surface Temperature (MODIS IST), the SST of the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. The AIRS only algorithm was developed in preparation for the degradation of the AMSU-A. MODIS IST was systematically warmer up to 1.65 K at the sea ice boundary and colder down to −2.04 K in the polar sea ice regions of both the Arctic and Antarctic than that of the AIRS/AMSU. This difference in the results could have been caused by the surface classification method. The spatial correlation coefficient of the AIRS only to the AIRS/AMSU (0.992-0.999) method was greater than that of the MODIS IST to the AIRS/AMSU (0.968-0.994). The SST of the AIRS only compared to that of the AIRS/AMSU had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere high latitudes and a bias of −0.109 K with a RMSE of 0.852 K over the Southern Hemisphere high latitudes. There was a systematic disagreement between the AIRS retrievals at the boundary of the sea ice, because the AIRS only algorithm utilized a less accurate GCM forecast over the seasonally varying frozen oceans than the microwave data. The three data sets (MODIS, AIRS/AMSU and AIRS only) showed significant warming rates (2.3 ± 1.7 ∼ 2.8 ± 1.9 K decade −1 ) in the northern high regions (70-80 • N) as expected from the icealbedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

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Section: Discussionmentioning

confidence: 99%

Section: H-j Kang Et Al: Uncertainties Of Satellite-derived Surfacmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

et al. 2015

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“…The 95 % confidence intervals for the trends were calculated using the bootstrap method (Wilks, 1995). For each air pollutant anomaly data set, 10 000 new data sets were created to produce 10 000 linear trends through random sampling (e.g., Lee et al, 2013). The random sampling was conducted by drawing data out of the respective original records of the air pollutant anomalies, allowing repetition.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal variations of air pollutants (O3, NO2, SO2, CO, PM10, and VOCs) with land-use types

et al. 2015

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Abstract. The spatiotemporal variations of surface air pollutants (O 3 , NO 2 , SO 2 , CO, and PM 10 ) with four land-use types, residence (R), commerce (C), industry (I) and greenbelt (G), have been investigated at 283 stations in South Korea during 2002-2013, using routinely observed data. The volatile organic compound (VOC) data at nine photochemical pollutant monitoring stations available since 2007 were utilized in order to examine their effect on the ozone chemistry. The land-use types, set by the Korean government, were generally consistent with the satellite-derived land covers and with the previous result showing anti-correlation between O 3 and NO 2 in diverse urban areas. The relationship between the two pollutants in the Seoul Metropolitan Area (SMA) residence land-use areas was substantially different from that outside of the SMA, probably due to the local differences in vehicle emissions. The highest concentrations of air pollutants in the diurnal, weekly, and annual cycles were found in industry for SO 2 and PM 10 , in commerce for NO 2 and CO, and in greenbelt for O 3 . The concentrations of air pollutants, except for O 3 , were generally higher in big cities during weekdays, while O 3 showed its peak in suburban areas or small cities during weekends. The weekly cycle and trends of O 3 were significantly out of phase with those of NO 2 , particularly in the residential and commercial areas, suggesting that vehicle emission was a major source in those areas. The ratios of VOCs to NO 2 for each of the land-use types were in the order of I (10.2) > C (8.7) > G (3.9) > R (3.6), suggesting that most areas in South Korea were likely to be VOC-limited for ozone chemistry. The pollutants (NO 2 , SO 2 , CO, and PM 10 ) except for O 3 have decreased, most likely due to the effective government control. The total oxidant values (OX = O 3 + NO 2 ) with the land-use types were analyzed for the local and regional (or background) contributions of O 3 , respectively, and the order of OX (ppb) was C (57.4) > R (53.6) > I (50.7) > G (45.4), indicating the greenbelt observation was close to the background.

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Section: Introductionmentioning

confidence: 99%

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

Kang¹,

Yoo²,

Jeong³

et al. 2015

Preprint

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Abstract. Uncertainties in the satellite-derived Surface Skin Temperature (SST) data in the polar oceans during two periods (16–24 April and 15–23 September) of 2003–2014 were investigated and the three datasets were intercompared as follows: MODerate Resolution Imaging Spectroradiometer Ice Surface Temperature (MODIS IST), the SST of the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. AIRS only algorithm was developed in preparation for the degradation of the AMSU-A. MODIS IST was systematically up to 1.65 K warmer at the sea ice boundary and up to 2.04 K colder in the polar sea ice regions of both the Arctic and Antarctic than that of the AIRS/AMSU. This difference in the results could have been caused by the surface classification method. The spatial correlation coefficient of the AIRS only to the AIRS/AMSU (0.992–0.999) method was greater than that of the MODIS IST to the AIRS/AMSU (0.968–0.994). The SST of the AIRS only compared to that of the AIRS/AMSU had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere high latitudes and a bias of −0.109 K with a RMSE of 0.852 K over the Southern Hemisphere high latitudes. There was a systematic disagreement between the AIRS retrievals at the boundary of the sea ice, because the AIRS only algorithm utilized a~less accurate GCM forecast over the seasonally-varying frozen oceans than the microwave data. The three datasets (MODIS, AIRS/AMSU and AIRS only) showed significant warming rates (2.3 ± 1.7 ~2.8 ± 1.9 K decade−1) in the northern high latitude regions (70–80° N) as expected from the ice-albedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

show abstract

Comparison between MODIS and AIRS/AMSU satellite-derived surface skin temperatures

Cited by 12 publications

References 34 publications

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

Spatiotemporal variations of air pollutants (O<sub>3</sub>, NO<sub>2</sub>, SO<sub>2</sub>, CO, PM<sub>10</sub>, and VOCs) with land-use types

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

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Comparison between MODIS and AIRS/AMSU satellite-derived surface skin temperatures

Cited by 12 publications

References 34 publications

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

Spatiotemporal variations of air pollutants (O&lt;sub&gt;3&lt;/sub&gt;, NO&lt;sub&gt;2&lt;/sub&gt;, SO&lt;sub&gt;2&lt;/sub&gt;, CO, PM&lt;sub&gt;10&lt;/sub&gt;, and VOCs) with land-use types

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

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Resources

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Spatiotemporal variations of air pollutants (O<sub>3</sub>, NO<sub>2</sub>, SO<sub>2</sub>, CO, PM<sub>10</sub>, and VOCs) with land-use types