scholarly journals TCCON and NDACC X<sub>CO</sub> measurements: difference, discussion and application

2019 ◽  
Vol 12 (11) ◽  
pp. 5979-5995 ◽  
Author(s):  
Minqiang Zhou ◽  
Bavo Langerock ◽  
Corinne Vigouroux ◽  
Mahesh Kumar Sha ◽  
Christian Hermans ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
A Priori ◽  
Mean Value ◽  
Scaling Factor ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Absolute Bias ◽  
Ftir Spectrometer ◽  
The Common ◽  
Using Data

Abstract. Column-averaged dry-air mole fraction of CO (XCO) measurements are obtained from two ground-based Fourier transform infrared (FTIR) spectrometer networks: the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). In this study, the differences between the TCCON and NDACC XCO measurements are investigated and discussed based on six NDACC–TCCON sites using data over the period 2007–2017. A direct comparison shows that the NDACC XCO measurements are about 5.5 % larger than the TCCON data at Ny-Ålesund, Bremen, and Izaña (Northern Hemisphere), and the absolute bias between the NDACC and TCCON data is within 2 % at Saint-Denis, Wollongong and Lauder (Southern Hemisphere). The hemispheric dependence of the bias is mainly attributed to their smoothing errors. The systematic smoothing error of the TCCON XCO data varies in the range between 0.2 % (Bremen) and 7.9 % (Lauder), and the random smoothing error varies in the range between 2.0 % and 3.6 %. The systematic smoothing error of NDACC data is between 0.1 % and 0.8 %, and the random smoothing error of NDACC data is about 0.3 %. For TCCON data, the smoothing error is significant because it is higher than the reported uncertainty, particularly at Southern Hemisphere sites. To reduce the influence from the a priori profiles and different vertical sensitivities, the scaled NDACC a priori profiles are used as the common a priori profiles for comparing TCCON and NDACC retrievals. As a result, the biases between TCCON and NDACC XCO measurements become more consistent (5.6 %–8.5 %) with a mean value of 6.8 % at these sites. To determine the sources of the remaining bias, regular AirCore measurements at Orléans and Sodankylä are compared to co-located TCCON measurements. It is found that TCCON XCO measurements are 6.1 ± 1.6 % and 8.0 ± 3.2 % smaller than the AirCore measurements at Orléans and Sodankylä, respectively, indicating that the scaling factor of TCCON XCO data should be around 1.0000 instead of 1.0672. Further investigations should be carried out in the TCCON community to determine the correct scaling factor to be applied to the TCCON XCO data. This paper also demonstrates that the smoothing error must be taken into account when comparing FTIR XCO data, and especially TCCON XCO data, with model or satellite data.

scholarly journals TCCON and NDACC X<sub><i>CO</i></sub> measurements: difference, discussion and application

2019 ◽  
Author(s):  
Minqiang Zhou ◽  
Bavo Langerock ◽  
Corinne Vigouroux ◽  
Mahesh Kumar Sha ◽  
Christian Hermans ◽  
...  
Keyword(s):  
Fourier Transform ◽  
A Priori ◽  
Mean Value ◽  
Scaling Factor ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Composition Change ◽  
Dry Air ◽  
The Common ◽  
Using Data

Abstract. Column-averaged dry-air mole fraction of CO (XCO) measurements are obtained from two ground-based Fourier transform infrared (FTIR) spectrometers networks: the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). In this study, the differences between the TCCON and NDACC XCO measurements are investigated and discussed based on six NDACC/TCCON sites using data over the period 2007–2017. The NDACC XCO measurements are about 5.5 % larger than the TCCON data at Ny-Ålesund, Bremen, and Izaña (Northern Hemisphere), and about 0.3 % larger than the TCCON data at St Denis, Wollongong and Lauder (Southern Hemisphere). The hemispheric dependence of the bias is mainly attributed to their smoothing errors. The systematic smoothing error of the TCCON XCO data varies in the range between 0.2 % (Bremen) and 7.9 % (Lauder), and the random smoothing error in the range between 2.0 % and 3.6 %. The systematic smoothing error of NDACC data is between 0.1 % and 0.8 %, and the random smoothing error of NDACC data is about 0.3 %. For TCCON data, the smoothing error can be significant in that it is much higher than the reported uncertainty for TCCON XCO. To reduce the influence from the a priori profiles and different vertical sensitivities, the scaled NDACC a priori profiles are used as the common a priori profiles for comparing TCCON and NDACC retrievals. As a result, the biases between TCCON and NDACC XCO measurements become more consistent (5.6–8.5 %) with a mean value of 6.8 % at these sites. To understand the remaining bias, regular AirCore measurements at Orleans and Sodankylä are compared to co-located TCCON measurements. It is found that TCCON XCO measurements are 6.0 ± 1.9 % and 6.9  ± 2.5 % smaller than the AirCore measurements at Orleans and Sodankylä respectively, indicating that the scaling factor of TCCON XCO data should be around 1.0000 instead of 1.0672. Further investigations should be carried out in the TCCON community to determine the correct scaling factor to be applied to the TCCON XCO data. This paper also demonstrates that the smoothing error must be taken into account when comparing FTIR XCO data, and especially TCCON XCO data, with model or satellite data.

scholarly journals Intercomparison of Arctic ground-based XH<sub>2</sub>O observations from COCCON, TCCON and NDACC, and application of COCCON XH<sub>2</sub>O for IASI and TROPOMI validation

2020 ◽  
Author(s):  
Qiansi Tu ◽  
Frank Hase ◽  
Thomas Blumenstock ◽  
Matthias Schneider ◽  
Andreas Schneider ◽  
...  
Keyword(s):  
A Priori ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Satellite Validation ◽  
Atmospheric Sounding ◽  
Ftir Spectrometer ◽  
Atmospheric State ◽  
Validation Tool ◽  
Good Agreement

Abstract. In this paper, we compare column-averaged dry-air mole fractions of water vapor (XH2O) retrievals from COCCON (COllaborative Carbon Column Observing Network) with retrievals from two co-located high-resolution FTIR (Fourier transform infrared) spectrometers as references at two boreal sites, Kiruna, Swedenand Sodankylä, Finland. In the framework of the NDACC (Network for the Detection of Atmospheric Composition Change) an FTIR spectrometer is operated in Kiruna. The H2O product derived from these observations has been generated with the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) processor. In Sodankylä, a TCCON (Total Carbon Column Observing Network) spectrometer is operated, and the official XH2O data as provided by TCCON are used for this study. The datasets are in good overall agreement, with COCCON data showing a wet bias of (49.20 ± 58.61) ppm ((3.33 ± 3.37) %, R2 = 0.9992) compared to MUSICA NDACC and (56.32 ± 45.63) ppm ((3.44 ± 1.77) %, R2 = 0.9997) compared to TCCON. Furthermore, the a priori H2O VMR (volume mixing ratio) profiles (MAP) used as a priori in the TCCON retrievals (also adopted for COCCON retrievals) are evaluated with respect to radiosonde (Vaisala RS41) profiles at Sodankylä. The MAP and radiosonde profiles show similar shapes and good correlation of integrated XH2O, indicating that MAP is a reasonable approximation for the true atmospheric state and an appropriate choice for the scaling retrieval methods as applied by COCCON and TCCON. COCCON shows a reduced dry bias (−1.66 %) in comparison to TCCON (−5.63 %) with respect to radiosonde XH2O and this small bias indicates that besides XCO2 and XCH4 COCCON is also able to serve as validation tool for space-borne XH2O measurements. Finally, we investigate the quality of satellite data at high latitudes. For this purpose, the COCCON XH2O is compared with retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) generated with the MUSICA processor (MUSICA IASI) and with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). Both paired datasets show generally good agreement and similar correlations at the two sites. COCCON measures 4.64 % less XH2O at Kiruna and 3.36 % at Sodankylä with respect to MUSICA IASI, while COCCON measures 9.71 % more XH2O at Kiruna and 7.75 % at Sodankylä compared with TROPOMI. Our study supports the assumption that COCCON also delivers a well-characterized XH2O data product. This emphasizes the approach of supplementing the TCCON network for satellite validation efforts. This is the first published study where COCCON XH2O is compared with MUSICA NDACC and TCCON retrievals, and for MUSICA IASI and TROPOMI validation.

scholarly journals Intercomparison of arctic XH<sub>2</sub>O observations from three ground-based Fourier transform infrared networks and application for satellite validation

2021 ◽  
Vol 14 (3) ◽  
pp. 1993-2011
Author(s):  
Qiansi Tu ◽  
Frank Hase ◽  
Thomas Blumenstock ◽  
Matthias Schneider ◽  
Andreas Schneider ◽  
...  
Keyword(s):  
Fourier Transform ◽  
A Priori ◽  
Fourier Transform Infrared ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Satellite Validation ◽  
Ftir Spectrometer ◽  
Priori Information ◽  
Good Agreement

Abstract. In this paper, we compare column-averaged dry-air mole fractions of water vapor (XH2O) retrievals from the COllaborative Carbon Column Observing Network (COCCON) with retrievals from two co-located high-resolution Fourier transform infrared (FTIR) spectrometers as references at two boreal sites, Kiruna, Sweden, and Sodankylä, Finland, from 6 March 2017 to 20 September 2019. In the framework of the Network for the Detection of Atmospheric Composition Change (NDACC), an FTIR spectrometer is operated at Kiruna. The H2O product derived from these observations has been generated with the MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) processor. In Sodankylä, a Total Carbon Column Observing Network (TCCON) spectrometer is operated, and the official XH2O data as provided by TCCON are used for this study. The datasets are in good overall agreement, with COCCON data showing a wet bias of (49.20±58.61) ppm ((3.33±3.37) %, R2=0.9992) compared with MUSICA NDACC and (56.32±45.63) ppm ((3.44±1.77) %, R2=0.9997) compared with TCCON. Furthermore, the a priori H2O volume mixing ratio (VMR) profiles (MAP) used as a priori information in the TCCON retrievals (also adopted for COCCON retrievals) are evaluated with respect to radiosonde (Vaisala RS41) profiles at Sodankylä. The MAP and radiosonde profiles show similar shapes and a good linear correlation of integrated XH2O, indicating that MAP is a reasonable approximation of the true atmospheric state and an appropriate choice for the scaling retrieval methods as applied by COCCON and TCCON. COCCON shows a reduced dry bias (−14.96 %) in comparison with TCCON (−19.08 %) with respect to radiosonde XH2O. Finally, we investigate the quality of satellite data at high latitudes. For this purpose, the COCCON XH2O is compared with retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) generated with the MUSICA processor (MUSICA IASI) and with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). Both paired datasets generally show good agreement and similar correlations at the two sites. COCCON measures 4.64 % less XH2O at Kiruna and 3.36 % less at Sodankylä with respect to MUSICA IASI, whereas COCCON measures 9.71 % more XH2O at Kiruna and 7.75 % more at Sodankylä compared with TROPOMI. Our study supports the assumption that COCCON also delivers a well-characterized XH2O data product. This emphasizes that this approach might complement the TCCON network with respect to satellite validation efforts. This is the first published study where COCCON XH2O has been compared with MUSICA NDACC and TCCON retrievals and has been used for MUSICA IASI and TROPOMI validation.

scholarly journals Technical note: Sensitivity of instrumental line shape monitoring for the ground-based high-resolution FTIR spectrometer with respect to different optical attenuators

2017 ◽  
Vol 10 (3) ◽  
pp. 989-997 ◽  
Author(s):  
Youwen Sun ◽  
Mathias Palm ◽  
Christine Weinzierl ◽  
Christof Petri ◽  
Justus Notholt ◽  
...  
Keyword(s):  
High Resolution ◽  
Line Shape ◽  
Radiant Energy ◽  
Technical Note ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Incident Intensity ◽  
Instrumental Line ◽  
Ftir Spectrometer ◽  
Potential Strategy

Abstract. The TCCON (Total Carbon Column Observing Network) and most NDACC (Network for Detection of Atmospheric Composition Change) sites assume an ideal ILS (instrumental line shape) for analysis of the spectra. In order to adapt the radiant energy received by the detector, an attenuator or different sizes of field stop can be inserted in the light path. These processes may alter the alignment of a high-resolution FTIR (Fourier transform infrared) spectrometer, and may result in bias due to ILS drift. In this paper, we first investigated the sensitivity of the ILS monitoring with respect to application of different kinds of attenuators for ground-based high-resolution FTIR spectrometers within the TCCON and NDACC networks. Both lamp and sun cell measurements were conducted after the insertion of five different attenuators in front of and behind the interferometer. The ILS characteristics derived from lamp and sun spectra are in good agreement. ILSs deduced from all lamp cell measurements were compared. As a result, the disturbances to the ILS of a high-resolution FTIR spectrometer with respect to the insertion of different attenuators at different positions were quantified. A potential strategy to adapt the incident intensity of a detector was finally deduced.

scholarly journals Retrieval of xCO<sub>2</sub> from ground-based mid-infrared (NDACC) solar absorption spectra and comparison to TCCON

2016 ◽  
Vol 9 (2) ◽  
pp. 577-585 ◽  
Author(s):  
Matthias Buschmann ◽  
Nicholas M. Deutscher ◽  
Vanessa Sherlock ◽  
Mathias Palm ◽  
Thorsten Warneke ◽  
...  
Keyword(s):  
Absorption Spectra ◽  
Seasonal Cycle ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Composition Change ◽  
Solar Absorption ◽  
Mid Infrared ◽  
Infrared Spectral ◽  
Ftir Spectrometer ◽  
Total Column

Abstract. High-resolution solar absorption spectra, taken within the Network for the Detection of Atmospheric Composition Change Infrared Working Group (NDACC-IRWG) in the mid-infrared spectral region, are used to infer partial or total column abundances of many gases. In this paper we present the retrieval of a column-averaged mole fraction of carbon dioxide from NDACC-IRWG spectra taken with a Fourier transform infrared (FTIR) spectrometer at the site in Ny-Ålesund, Spitsbergen. The retrieved time series is compared to colocated standard TCCON (Total Carbon Column Observing Network) measurements of column-averaged dry-air mole fractions of CO2 (denoted by xCO2). Comparing the NDACC and TCCON retrievals, we find that the sensitivity of the NDACC retrieval is lower in the troposphere (by a factor of 2) and higher in the stratosphere, compared to TCCON. Thus, the NDACC retrieval is less sensitive to tropospheric changes (e.g., the seasonal cycle) in the column average.

scholarly journals Multistation intercomparison of column-averaged methane from NDACC and TCCON: impact of dynamical variability

2014 ◽  
Vol 7 (12) ◽  
pp. 4081-4101 ◽  
Author(s):  
A. Ostler ◽  
R. Sussmann ◽  
M. Rettinger ◽  
N. M. Deutscher ◽  
S. Dohe ◽  
...  
Keyword(s):  
Near Infrared ◽  
A Priori ◽  
Polar Vortex ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Seasonal Cycles ◽  
Stratospheric Intrusion ◽  
Smoothing Effects ◽  
Key Factor ◽  
Source Sink

Abstract. Dry-air column-averaged mole fractions of methane (XCH4) retrieved from ground-based solar Fourier transform infrared (FTIR) measurements provide valuable information for satellite validation, evaluation of chemical-transport models, and source-sink-inversions. In this context, Sussmann et al. (2013) have shown that midinfrared (MIR) soundings from the Network for the Detection of Atmospheric Composition Change (NDACC) can be combined with near-infrared (NIR) soundings from the Total Carbon Column Observing Network (TCCON) without the need to apply an overall intercalibration factor. However, in spite of efforts to reduce a priori impact, some residual seasonal biases were identified, and the reasons behind remained unclear. In extension to this previous work, which was based on multiannual quasi-coincident MIR and NIR measurements from the stations Garmisch (47.48° N, 11.06° E, 743 m a.s.l.) and Wollongong (34.41° S, 150.88° E, 30 m a.s.l.), we now investigate upgraded retrievals with longer temporal coverage and include three additional stations (Ny-Ålesund, 78.92° N, 11.93° E, 20 m a.s.l.; Karlsruhe, 49.08° N, 8.43° E, 110 m a.s.l.; Izaña, 28.31° N, 16.45° W, 2.370 m a.s.l.). Our intercomparison results (except for Ny-Ålesund) confirm that there is no overall bias between MIR and NIR XCH4 retrievals, and all MIR and NIR time series reveal a quasi-periodic seasonal bias for all stations, except for Izaña. We find that dynamical variability causes MIR–NIR differences of up to ~ 30 ppb (parts per billion) for Ny-Ålesund, ~ 20 ppb for Wollongong, ~ 18 ppb for Garmisch, and ~ 12 ppb for Karlsruhe. The mechanisms behind this variability are elaborated via two case studies, one dealing with stratospheric subsidence induced by the polar vortex at Ny-Ålesund and the other with a deep stratospheric intrusion event at Garmisch. Smoothing effects caused by the dynamical variability during these events are different for MIR and NIR retrievals depending on the altitude of the perturbation area. MIR retrievals appear to be more realistic in the case of stratospheric subsidence, while NIR retrievals are more accurate in the case of stratosphere–troposphere exchange (STE) in the upper troposphere/lower stratosphere (UTLS) region. About 35% of the FTIR measurement days at Garmisch are impacted by STE, and about 23% of the measurement days at Ny-Ålesund are influenced by polar vortex subsidence. The exclusion of data affected by these dynamical situations resulted in improved agreement of MIR and NIR seasonal cycles for Ny-Ålesund and Garmisch. We found that dynamical variability is a key factor in constraining the accuracy of MIR and NIR seasonal cycles. To mitigate this impact it is necessary to use more realistic a priori profiles that take these dynamical events into account (e.g., via improved models), and/or to improve the FTIR retrievals to achieve a more uniform sensitivity at all altitudes (possibly including profile retrievals for the TCCON data).

scholarly journals First intercalibration of column-averaged methane from the Total Carbon Column Observing Network and the Network for the Detection of Atmospheric Composition Change

2013 ◽  
Vol 6 (2) ◽  
pp. 397-418 ◽  
Author(s):  
R. Sussmann ◽  
A. Ostler ◽  
F. Forster ◽  
M. Rettinger ◽  
N. M. Deutscher ◽  
...  
Keyword(s):  
Time Series ◽  
Near Infrared ◽  
A Priori ◽  
Time Dependent ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Composition Change ◽  
Data Set ◽  
The Difference ◽  
Difference Time

Abstract. We present the first intercalibration of dry-air column-averaged mole fractions of methane (XCH4) retrieved from solar Fourier transform infrared (FTIR) measurements of the Network for the Detection of Atmospheric Composition Change (NDACC) in the mid-infrared (MIR) versus near-infrared (NIR) soundings from the Total Carbon Column Observing Network (TCCON). The study uses multi-annual quasi-coincident MIR and NIR measurements from the stations Garmisch, Germany (47.48° N, 11.06° E, 743 m a.s.l.), and Wollongong, Australia (34.41° S, 150.88° E, 30 m a.s.l.). Direct comparison of the retrieved MIR and NIR XCH4 time series for Garmisch shows a quasi-periodic seasonal bias leading to a standard deviation (stdv) of the difference time series (NIR–MIR) of 7.2 ppb. After reducing time-dependent a priori impact by using realistic site- and time-dependent ACTM-simulated profiles as a common prior, the seasonal bias is reduced (stdv = 5.2 ppb). A linear fit to the MIR/NIR scatter plot of monthly means based on same-day coincidences does not show a y-intercept that is statistically different from zero, and the MIR/NIR intercalibration factor is found to be close to ideal within 2-σ uncertainty, i.e. 0.9996(8). The difference time series (NIR–MIR) do not show a significant trend. The same basic findings hold for Wollongong. In particular an overall MIR/NIR intercalibration factor close to the ideal 1 is found within 2-σ uncertainty. At Wollongong the seasonal cycle of methane is less pronounced and corresponding smoothing errors are not as significant, enabling standard MIR and NIR retrievals to be used directly, without correction to a common a priori. Our results suggest that it is possible to set up a harmonized NDACC and TCCON XCH4 data set which can be exploited for joint trend studies, satellite validation, or the inverse modeling of sources and sinks.

scholarly journals A tropopause-related climatological a priori profile for IASI-SOFRID ozone retrievals: improvements and validation

2020 ◽  
Vol 13 (10) ◽  
pp. 5237-5257
Author(s):  
Brice Barret ◽  
Emanuele Emili ◽  
Eric Le Flochmoen
Keyword(s):  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
A Priori ◽  
Correlation Coefficients ◽  
Global Scale ◽  
Atmospheric Composition ◽  
Retrieval Algorithm ◽  
Pixel Location ◽  
Tropospheric O3 ◽  
Retrieval Software

Abstract. The MetOp/Infrared Atmospheric Sounding Interferometer (IASI) instruments have provided data for operational meteorology and document atmospheric composition since 2007. IASI ozone (O3) data have been used extensively to characterize the seasonal and interannual variabilities and the evolution of tropospheric O3 at the global scale. SOftware for a Fast Retrieval of IASI Data (SOFRID) is a fast retrieval algorithm that provides IASI O3 profiles for the whole IASI period. Until now, SOFRID O3 retrievals (v1.5 and v1.6) were performed with a single a priori profile, which resulted in important biases and probably a too-low variability. For the first time, we have implemented a comprehensive dynamical a priori profile for spaceborne O3 retrievals which takes the pixel location, time and tropopause height into account for SOFRID-O3 v3.5 retrievals. In the present study, we validate SOFRID-O3 v1.6 and v3.5 with electrochemical concentration cell (ECC) ozonesonde profiles from the global World Ozone and Ultraviolet Radiation Data Centre (WOUDC) database for the 2008–2017 period. Our validation is based on a thorough statistical analysis using Taylor diagrams. Furthermore, we compare our retrievals with ozonesonde profiles both smoothed by the IASI averaging kernels and raw. This methodology is essential to evaluate the inherent usefulness of the retrievals to assess O3 variability and trends. The use of a dynamical a priori profile largely improves the retrievals concerning two main aspects: (i) it corrects high biases for low-tropospheric O3 regions such as the Southern Hemisphere, and (ii) it increases the retrieved O3 variability, leading to a better agreement with ozonesonde data. Concerning upper troposphere–lower stratosphere (UTLS) and stratospheric O3, the improvements are less important and the biases are very similar for both versions. The SOFRID tropospheric ozone columns (TOCs) display no significant drifts (<2.5 %) for the Northern Hemisphere and significant negative ones (9.5 % for v1.6 and 4.3 % for v3.5) for the Southern Hemisphere. We have compared our validation results to those of the Fast Optimal Retrievals on Layers for IASI (FORLI) retrieval software from the literature for smoothed ozonesonde data only. This comparison highlights three main differences: (i) FORLI retrievals contain more theoretical information about tropospheric O3 than SOFRID; (ii) root mean square differences (RMSDs) are smaller and correlation coefficients are higher for SOFRID than for FORLI; (iii) in the Northern Hemisphere, the 2010 jump detected in FORLI TOCs is not present in SOFRID.

scholarly journals Multi-station intercomparison of column-averaged methane from NDACC and TCCON: impact of dynamical variability

2014 ◽  
Vol 7 (7) ◽  
pp. 6743-6790
Author(s):  
A. Ostler ◽  
R. Sussmann ◽  
M. Rettinger ◽  
N. M. Deutscher ◽  
S. Dohe ◽  
...  
Keyword(s):  
Near Infrared ◽  
A Priori ◽  
Polar Vortex ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Seasonal Cycles ◽  
Stratospheric Intrusion ◽  
Smoothing Effects ◽  
Key Factor ◽  
Source Sink

Abstract. Dry-air column-averaged mole fractions of methane (XCH4) retrieved from ground-based solar Fourier transform infrared (FTIR) measurements provide valuable information for satellite validation, evaluation of chemistry-transport models, and source-sink-inversions. In this context, Sussmann et al. (2013) have shown that mid-infrared (MIR) soundings from the Network for the Detection of Atmospheric Composition Change (NDACC) can be combined with near-infrared (NIR) soundings from the Total Carbon Column Observing Network (TCCON) without the need to apply an overall intercalibration factor. However, in spite of efforts to reduce a priori impact, some residual seasonal biases were identified, and the reasons behind remained unclear. In extension to this previous work, which was based on multi-annual quasi-coincident MIR and NIR measurements from the stations Garmisch (47.48° N, 11.06° E, 743 m a.s.l.) and Wollongong (34.41° S, 150.88° E, 30 m a.s.l.), we now investigate upgraded retrievals with longer temporal coverage and include three additional stations (Ny-Ålesund, 78.92° N, 11.93° E, 20 m a.s.l.; Karlsruhe, 49.08° N, 8.43° E, 110 m a.s.l.; Izaña, 28.31° N, 16.45° W, 2.370 m a.s.l.). Our intercomparison results (except for Ny-Ålesund) confirm that there is no overall bias between MIR and NIR XCH4 retrievals, and all MIR and NIR time series reveal a quasi-periodic seasonal bias for all stations, except for Izaña. We find that dynamical variability causes MIR–NIR differences of up to ~ 30 ppb for Ny-Ålesund, ~ 20 ppb for Wollongong, ~ 18 ppb for Garmisch, and ~ 12 ppb for Karlsruhe. The mechanisms behind this variability are elaborated via two case studies, one dealing with stratospheric subsidence induced by the polar vortex at Ny-Ålesund and the other with a deep stratospheric intrusion event at Garmisch. Smoothing effects caused by the dynamical variability during these events are different for MIR and NIR retrievals depending on the altitude of the perturbation area. MIR retrievals appear to be more realistic in the case of stratospheric subsidence, while NIR retrievals are more accurate in the case of stratosphere-troposphere exchange (STE) in the upper troposphere/lower stratosphere (UTLS) region. About 35% of the FTIR measurement days at Garmisch are impacted by STE, and about 23% of the measurement days at Ny-Ålesund are influenced by polar vortex subsidence. The exclusion of data affected by these dynamical situations resulted in improved agreement of MIR and NIR seasonal cycles for Ny-Ålesund and Garmisch. We found that dynamical variability is a key factor in constraining the accuracy of MIR and NIR seasonal cycles. The only way to avoid this problem is to use more realistic a priori profiles that take these dynamical events into account (e.g. via improved models), and/or to improve the FTIR retrievals to achieve a more uniform sensitivity at all altitudes (possibly including profile retrievals for the TCCON data).

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

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