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Frontiers of Earth Science

Front. Earth Sci.    2016, Vol. 10 Issue (2) : 222-235     DOI: 10.1007/s11707-015-0488-5
Quality control of AIRS total column ozone data within tropical cyclones
Yin LIU1,2,Xiaolei ZOU3,*()
1. Center of Data Assimilation for Research and Application, Nanjing University of Information Science & Technology, Nanjing 210044, China
2. Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China
3. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740-3823, USA
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The Atmospheric Infrared Sounder (AIRS) provides infrared radiance observations twice daily, which can be used to retrieve total column ozone with high spatial resolution. However, it was found that almost all of the ozone data within typhoons and hurricanes were flagged to be of bad quality by the AIRS original quality control (QC) scheme. This determination was based on the ratio of total precipitable water (TPW) error divided by TPW value, where TPW was an AIRS retrieval product. It was found that the difficulty in finding total column ozone data that could pass AIRS QC was related to the low TPW employed in the AIRS QC algorithm. In this paper, a new two-step QC scheme for AIRS total column ozone is developed. A new ratio is defined which replaces the AIRS TPW with the zonal mean TPW retrieved from the Advanced Microwave Sounding Unit. The first QC step is to remove outliers when the new ratio exceeds 33%. Linear regression models between total column ozone and mean potential vorticity are subsequently developed with daily updates, which are required for future applications of the proposed total ozone QC algorithm to vortex initialization and assimilation of AIRS data. In the second QC step, observations that significantly deviate from the models are further removed using a biweighting algorithm. Numerical results for two typhoon cases and two hurricane cases show that a large amount of good quality AIRS total ozone data is kept within Tropical Cyclones after implementing the proposed QC algorithm.

Keywords AIRS total column ozone      total precipitable water      mean potential vorticity      quality control     
Corresponding Authors: Xiaolei ZOU   
Issue Date: 05 April 2016
 Cite this article:   
Yin LIU,Xiaolei ZOU. Quality control of AIRS total column ozone data within tropical cyclones[J]. Front. Earth Sci., 2016, 10(2): 222-235.
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Name Data Period Domain Scale
Tembin 19 Aug. to 30 Aug. 2012 5°N?45°N, 100°E?160°E 4
Sanba 10 Sept. to 17 Sept. 2012 5°N?45°N, 100°E?160°E 5
Isaac 21 Aug. to 1 Sept. 2012 5°N?60°N, 120°W?50°W 1
Sandy 22 Oct. to 29 Oct. 2012 5°N?60°N, 90°W?20°W 2
Tab.1  Names, data periods, horizontal domains, and Saffir-Simpson scale categories of the two typhoons and two hurricanes included in this study
Fig.1  Observed tracks of (a) Tembin, (b) Sanba, (c) Isaac, and (d) Sandy.
Fig.2  Spatial distribution of total column ozone with erroneous data points removed at 0600 UTC for (a) Tembin on 24 August 2012, (b) Sanba on 15 September 2012, (c) Isaac on 29 August 2012, and (d) Sandy on 26 October 2012.
Fig.3  AIRS-retrieved TPW (left) and AMSU TPW (right) at 0600 UTC for: (a, b) Tembin on 24 August 2012, (c, d) Sanba on 15 September 2012, (e, f) Isaac on 29 August 2012, and (g, h) Sandy on 26 October 2012.
Fig.4  Zonal averaged total PW (kg·m-2) from AMSU for (a) Tembin at 0600 UTC on 24 (solid), 25 (dashed), and 26 (dotted) August 2012; for (b) Sanba on 13 (solid), 14 (dashed), and 15 (dotted) September 2012; for (c) Isaac on 27 (solid), 28 (dashed), and 29 (dotted) August 2012; and for (d) Sandy on 26 (solid), 27 (dashed), and 28 (dotted) October 2012.
Fig.5  Total precipitable water error at 0600 UTC 24 August 2012 for (a) Tembin, on 15 September 2012 for (b) Sanba, on 29 August 2012 for (c) Isaac, and on 26 October 2012 for (d) Sandy.
Fig.6  The AIRS QC ratio (left) and the modified ratio (right) at 0600 UTC for (a, b) Tembin 24 August 2012, (c, d) Sanba on 15 September 2012, (e, f) Isaac on 29 August , and (g, h) Sandy on 26 October 2012.
Fig.7  Daily variations of linear regression coefficients α (solid) and β (dashed) for (a) Tembin during 19-30 August 2012, (b) Sanba during 10-17 September 2012, (c) Isaac from 21 August to 1 September 2012, and (d) Sandy during 22-29 October 2012.
Fig.8  Scatterplot of NCEP MPV and AIRS total column ozone for (a) Tembin on 24 August 2012, (b) Sanba on 15 September 2012, (c) Isaac on 29 August 2012, and (d) Sandy on 26 October 2012. The Z-score criterion of 1.5 is indicated by a red dashed line. The red solid lines represent the linear regression models.
Fig.9  Scatterplot of differences between observed ozone and simulated ozone (DO3) against TPW error ratio (left), with 33% TPW error ratio and 1.5 Z-score (red dashed lines), and spatial distribution (shaded) of differences between AIRS-observed and NCEP-simulated total column ozone (right), as well as those points where the TPW error ratio exceeds 33% and Z-score is<1.5 (blue dots) for (a, b) Tembin at 0600 UTC 24 August 2012, (c, d) Sanba on 15 September 2012, (e, f) Isaac on 29 August 2012, and (g, h) Sandy on 26 October 2012.
Fig.10  Percentage of outliers as identified by AIRS QC, the biweight method over AIRS quality-controlled data (AIRS BW), the first TPW error step (QC 1), and the second biweight step (QC 2) for (a) Tembin on 19-30 August 2012, (b) Sanba on 10-17 September 2012, (c) Isaac from 21 August to 1 September 2012, and (d) Sandy on 22-29 October 2012.
Fig.11  Mean and standard deviation of differences between AIRS-observed and NCEP-simulated total column ozone without QC (dot) and with AIRS QC (dash), AIRS BW (short dot), and modified QC (solid). Correlations between NCEP MPV and AIRS total column ozone are also plotted. (a) Tembin 19-30 August 2012, (b) Sanba 10-17 September 2012, (c) Isaac from 21 August to 1 September 2012, and (d) Sandy 22-29 October 2012 are shown.
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