Intercalibration of AMSR2 NASA Team 2 Algorithm Sea Ice Concentrations With AMSR-E Slow Rotation Data
Sea ice estimates from AMSR2 are intercalibrated with AMSR-E fields through a two-step process. First, slow rotation 2 r/min AMSR-E data is used to derive regression equations from colocated pairs of AMSR2 and AMSR-E brightness temperatures (T b s). The regression equations are used to modify AMSR2...
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          | Published in | IEEE journal of selected topics in applied earth observations and remote sensing Vol. 10; no. 9; pp. 3923 - 3933 | 
|---|---|
| Main Authors | , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Goddard Space Flight Center
          IEEE
    
        01.09.2017
     The Institute of Electrical and Electronics Engineers, Inc. (IEEE)  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 1939-1404 2151-1535  | 
| DOI | 10.1109/JSTARS.2017.2719624 | 
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| Abstract | Sea ice estimates from AMSR2 are intercalibrated with AMSR-E fields through a two-step process. First, slow rotation 2 r/min AMSR-E data is used to derive regression equations from colocated pairs of AMSR2 and AMSR-E brightness temperatures (T b s). The regression equations are used to modify AMSR2 T b s into AMSR-E equivalent Tbs that are then input into the NASA Team 2 (NT2) sea ice concentration algorithm used for the AMSR-E standard products. The regressed T b s result in changes in sea ice concentration of a few percent compared to using the original un-regressed AMSR2 T b s. Next, sea ice estimates from the F17 SSMIS sensor are used as a bridge to compare AMSR-E total sea ice extent estimates in 2010 with AMSR2 total sea ice extent estimates in 2013. Based on this comparison, a further adjustment is made to a weather filter threshold used in the NT2 algorithm to minimize the total extent bias between AMSR2 and AMSR-E using a double-differencing approach. The adjustments reduced apparent bias with AMSR-E from ~200 000 km 2 for the original unmodified AMSR2 T b s to -700 and 4700 km 2 for the Arctic and Antarctic, respectively. These differences are within the range of previous passive microwave sea ice intercalibrations. The adjusted AMSR2 sea ice fields provide a nearly 15-year time series of sea ice change; depending on the lifetime of AMSR2 and possible follow-on sensors, AMSR2 has the potential to be part of a multidecadal record of sea ice change. | 
    
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| AbstractList | Sea ice estimates from AMSR2 are intercalibrated with AMSR-E fields through a two-step process. First, slow rotation 2 r/min AMSR-E data is used to derive regression equations from colocated pairs of AMSR2 and AMSR-E brightness temperatures (T b s). The regression equations are used to modify AMSR2 T b s into AMSR-E equivalent Tbs that are then input into the NASA Team 2 (NT2) sea ice concentration algorithm used for the AMSR-E standard products. The regressed T b s result in changes in sea ice concentration of a few percent compared to using the original un-regressed AMSR2 T b s. Next, sea ice estimates from the F17 SSMIS sensor are used as a bridge to compare AMSR-E total sea ice extent estimates in 2010 with AMSR2 total sea ice extent estimates in 2013. Based on this comparison, a further adjustment is made to a weather filter threshold used in the NT2 algorithm to minimize the total extent bias between AMSR2 and AMSR-E using a double-differencing approach. The adjustments reduced apparent bias with AMSR-E from ~200 000 km 2 for the original unmodified AMSR2 T b s to -700 and 4700 km 2 for the Arctic and Antarctic, respectively. These differences are within the range of previous passive microwave sea ice intercalibrations. The adjusted AMSR2 sea ice fields provide a nearly 15-year time series of sea ice change; depending on the lifetime of AMSR2 and possible follow-on sensors, AMSR2 has the potential to be part of a multidecadal record of sea ice change. Sea ice estimates from AMSR2 are intercalibrated with AMSR-E fields through a two-step process. First, slow rotation 2 r/min AMSR-E data is used to derive regression equations from colocated pairs of AMSR2 and AMSR-E brightness temperatures (Tbs). The regression equations are used to modify AMSR2 Tbs into AMSR-E equivalent Tbs that are then input into the NASA Team 2 (NT2) sea ice concentration algorithm used for the AMSR-E standard products. The regressed Tbs result in changes in sea ice concentration of a few percent compared to using the original un-regressed AMSR2 Tbs. Next, sea ice estimates from the F17 SSMIS sensor are used as a bridge to compare AMSR-E total sea ice extent estimates in 2010 with AMSR2 total sea ice extent estimates in 2013. Based on this comparison, a further adjustment is made to a weather filter threshold used in the NT2 algorithm to minimize the total extent bias between AMSR2 and AMSR-E using a double-differencing approach. The adjustments reduced apparent bias with AMSR-E from ~200 000 km2 for the original unmodified AMSR2 Tbs to -700 and 4700 km2 for the Arctic and Antarctic, respectively. These differences are within the range of previous passive microwave sea ice intercalibrations. The adjusted AMSR2 sea ice fields provide a nearly 15-year time series of sea ice change; depending on the lifetime of AMSR2 and possible follow-on sensors, AMSR2 has the potential to be part of a multidecadal record of sea ice change. Sea ice estimates from AMSR2 are intercalibrated with AMSR-E fields through a two-step process. First, slow rotation 2 r/min AMSR-E data is used to derive regression equations from colocated pairs of AMSR2 and AMSR-E brightness temperatures (Tb s). The regression equations are used to modify AMSR2 Tb s into AMSR-E equivalent Tb s that are then input into the NASA Team 2 (NT2) sea ice concentration algorithm used for the AMSR-E standard products. The regressed Tb s result in changes in sea ice concentration of a few percent compared to using the original un-regressed AMSR2 Tb s. Next, sea ice estimates from the F17 SSMIS sensor are used as a bridge to compare AMSR-E total sea ice extent estimates in 2010 with AMSR2 total sea ice extent estimates in 2013. Based on this comparison, a further adjustment is made to a weather filter threshold used in the NT2 algorithm to minimize the total extent bias between AMSR2 and AMSR-E using a double-differencing approach. The adjustments reduced apparent bias with AMSR-E from 200 000 km2 for the original unmodified AMSR2 Tb s to –700 and 4700 km2 for the Arctic and Antarctic, respectively. These differences are within the range of previous passive microwave sea ice intercalibrations. The adjusted AMSR2 sea ice fields provide a nearly 15-year time series of sea ice change; depending on the lifetime of AMSR2 and possible follow on sensors, AMSR2 has the potential to be part of a multidecadal record of sea ice change.  | 
    
| Audience | PUBLIC | 
    
| Author | Meier, Walter N. Ivanoff, Alvro  | 
    
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| SubjectTerms | Algorithms AMSR2 antarctic arctic Atmospheric modeling Bias Bridges Calibration Estimates Fields Ice Ice fields Intercalibration Mathematical analysis Mathematical models Melting Meteorology NASA Neurotrophin 2 passive microwave (PM) remote sensing Rotation Sea ice Sea surface  | 
    
| Title | Intercalibration of AMSR2 NASA Team 2 Algorithm Sea Ice Concentrations With AMSR-E Slow Rotation Data | 
    
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