dataesr/scientific-paragraphs-categorization huggingface dataset:
Daily and monthly analysis/comparisons The first step in the comparison study was to fit linear regression in the TOC values measured from the SSO between July 1992 and December 2014. Figure  2  shows the scatterplot plotted for Brewer spectrophotometer data and satellite data. The comparison was performed for Dataset #1 (TOMS vs. Brewer) for the period from June 1992 to December 2005, containing 2122 data pairs (panel a), and Dataset #2 (OMI vs. Brewer) between October 2004 and December 2014, with 1507 data pairs (panel b). The scatterplots in Fig. 2  reveal a high degree of proportionality between Brewer and both the TOMS (panel a) and OMI satellite (panel b), with high R 2 values of 0. 88 and 0. 93, respectively. These results are similar to observed over the Canary Islands, Spain  (Viatte et al.
, 2011; Antón et al. , 2009 Antón et al. , , 2010a, b), b) ; Rome, Italy  (Ialongo et al. , 2008) ; and at other monitoring stations around the planet  (Hendrick et al. , 2011; Keckhut et al. , 2010). A small spread was observed in the datasets, with RMSE smaller than 3 % in both datasets (2. 67 % for Dataset #1 and 1. 87 % for Dataset #2). This is consistent with other similar studies carried out at the El Arenosillo station, where Brewer and satellite data have undergone this type of analysis (e. g. ,  Antón et al. , 2010b). The small values of the MBE index indicate a nonsignificant overestimation or underestimation of the values of TOC obtained through satellite instruments in relation to the Brewer spectrophotometer. However, it is observed that the TOMS satellite overestimates the Brewer instrument data for Dataset #1 (0.
34 ± 2. 31 %), and the OMI satellite underestimates the Brewer instrument Dataset #2 (-0. 46 ± 1. 88 %). MABE values were observed to be reduced from 2. 64 ± 2. 31 % in Dataset #1 to 1. 88 ± 1. 61 % in Dataset #2, suggesting that the daily differences between Brewer and satellite data are small and are in better agreement with the measurement from the updated Brewer equipment. This is consistent with the results obtained by  Antón et al. (2009 Antón et al. ( , 2010a) )  in their study conducted for the Iberian Peninsula. This reduction, along with the reduced standard deviation, indicates that the spread between the datasets decreased due to launching more modern satellite instruments and the repair of the algorithms of the satellite instruments, performed over time  (Bhartia and Wellemeyer, 2002) , in addition to the substitutions of the ground equipment that occurred during these periods.
To verify the differences between the datasets, daily (top) and monthly (bottom) TOC mean comparison analysis were performed between ground-based and satellite instruments (see Fig. 3 ). In Fig. 3 , the daily and monthly percent dif-Figure  3. Top: time evolution of daily percentile differences between Brewer spectrophotometer and satellite instruments (with TOMS in blue and OMI in black). Bottom: time evolution of monthly percentile differences between Brewer spectrophotometer and satellite instruments (TOMS in blue and OMI in black). Both analyses the period between 1992 and 2014 for the Southern Space Observatory. ferences between each dataset for the complete period between 1992 and 2014 are shown. The main observation here is a minor percent difference in Dataset #1 with an average of 0.
28 ± 3. 48 %, indicating a slight overestimation of the TOMS observations, and a strong decrease in performance is observed in the last operation period of the Brewer #081 between 1998 and 1999. On the other hand, there were higher percentage differences for Dataset #2, an average of -0. 50 % ± 2. 44, showing an underestimation of OMI with respect to the Brewer measurements. Similar results are also reported by  Antón et al. (2009 Antón et al. ( , 2010b) )  for the Iberian Peninsula, where Brewer and OMI total ozone datasets were compared. The analysis of the monthly percent differences between each different dataset highlights the periods of major differences observed in Fig. 3b , facilitating the identification of related reasons and these peaks in percentage differences, which are on average -0.
62 ± 1. 90 for Dataset #1 (TOMS -Brewer) and -0. 03 % ± 2. 88 for Dataset #2 (OMI -Brewer), showing an underestimation of the TOMS and OMI in the monthly dataset. The end period of the Meteor-3 operation (1994) and the beginning of the operation of Earth Probe (1996) presented the most intense overestimation peak in percent differences observed during the studied period (1992-2014); however, the adjustment of the new correction algorithms solved the technical problems and stabilized the measures of the latter instrument  (Wellemeyer et al. , 2004). Antón et al. (2010)  noted that the period between 2000 and 2001 marked the beginning of the degradation of Earth Probe satellite measurements, coinciding with an overestimation peak observed during this period, when it was compared with Brewer #056, which worked temporally in the SSO.
These results indicate that the observed modes are present in the interannual TOC variability recorded between 1992 and 2014 over the SSO by Brewer spectrophotometer and Satellite instruments (TOMS and OMI). We observed periodicities in the normalized wavelet spectrum in the 22-to 34-month bands in two different periods. The first period between 1995 and 2000 and the second period between 2004 and 2009 were more intense in the Brewer dataset (Fig. 5 ) than the satellite dataset (Fig. 6 ). These periodicities are linked to the QBO signal, which could confirm the QBO influence as the main ozone variability observed over the SSO because both are within the 95 % significance level contours (white contours), with a peak in the global wavelet spectrum inside the 95 % confidence spectrum. Periodicities in the 24-96-month band were also observed in two different periods.
The first is observed between 1993 and 2009 within the 95 % significance level contours, and the second period in 2011 is outside the 95 % significance level contours. The peak associated with this mode of TOC variability and present in the global wavelet spectrum is outside the 95 % confidence spectrum for the case of the Brewer dataset. For satellite dataset, this periodicity is weaker and also outside the 95 % of significance level contours and the cone of influence. This periodicity probably indicates the ENSO signature on the TOC over the SSO station. Furthermore, periodicity in the 11-year band, related to the influence of 11year solar cycle, was observed in the whole period outside the cone of influence and 95 % significance level contours in The wavelet power spectrum of (a) using the Morlet wavelet, normalized by 1/σ 2.
The left axis is the Fourier period (in month), the power wavelet scale is on the right axis, and the bottom axis is time (year). The white contour encloses regions of greater than the 95 % confidence level and the U-shaped curve indicates the cone of influence. (c) The global wavelet spectrum of (a), where the dashed line is the 95 % confidence spectrum. The wavelet power spectrum of (a) using the Morlet wavelet, normalized by 1/σ 2. The left axis is the Fourier period (in month), the power wavelet scale is on the right axis, and the bottom axis is time (year). The white contour encloses regions of greater than 95 % confidence level and the U-shaped curve indicates the cone of influence. (c) The global wavelet spectrum of (a), where the dashed line is the 95 % confidence spectrum. normalized wavelet spectrum and outside of the 95 % confidence level in the global wavelet spectrum in the Brewer dataset and inside the 95 % significance level contours in the normalized wavelet spectrum and global wavelet spectrum in the satellite dataset, probably caused by radiometric calibration in solar radiation incident and backscattered ultraviolet sunlight measures  (McPeters et al.
Conclusion This study shows the TOC measurement of the Southern Space Observatory (29. 26 • S, 53. 48 • and 488 m altitude), Brazil, for a quasi-continuous period of 23 years (1992-2014) by three successive Brewer spectrophotometers (#081, #056 and #167). These Brewer measurements were compared to daily and monthly observations of spaceborne satellite sensor data from TOMS and OMI and were in excellent agreement. Moreover, this study highlights the scientific significance in contributing to the understanding of the seasonal and interannual ozone variability over southern Brazil once the monthly climatology determined that the seasonal TOC variation is dominated by the annual cycle, and the wavelet analysis applied in the SSO TOC anomaly time series determined that the QBO is the main mode of interannual variability complementing studies on the global and regional scales.
Additionally, the comparison between the SSO TOC anomaly time series with the QBO index has revealed that the two are approximately in opposite phases. The excellent agreement between simultaneous Brewer and satellite measurements of TOMS (Meteor-3 and Earth Probe) and OMI was verified by high R 2 values (0. 88 and 0. 93, respectively) and a small spread in both datasets, with RMSE smaller than 3 % (2. 67 % for Dataset #1 and 1. 87 % for Dataset #2). The MBE parameter indicates that the TOMS satellite overestimates the Brewer instrument, and the OMI satellite underestimates the Brewer instrument in daily analyses; the reduction in the MABE values and their standard deviation between Brewer and satellite show a better agreement with the update in the measurement equipment. The percentage differences between the Brewer and satellite datasets show that, on average, the satellite TOMS overestimates the Brewer data (0.
28 ± 2. 43 %) and that the OMI satellite instrument underestimates the Brewer data by -0. 5 ± 2. 44 %. Periods near equipment exchange present a higher percent difference in peaks, highlighted by observed monthly analysis differences, as in the beginning of the Earth Probe operation or the end of the operation of Brewer spectrophotometer #081. These results confirm the effectiveness of the long-term Brewer spectrophotometers' measurements over the SSO since there is no major discrepancy between the datasets, maintaining the differences within the ±5 % range. The annual cycle is the dominant mode in the seasonal TOC variation, associated with the Brewer-Dobson circulation and photochemical loss variability, with a minimum ∼ 260 DU in spring (April) and a maximum ∼ 295 DU in autumn (September) and amplitude of 35 DU, consistent with the obtained results in other midlatitude sites.
The main modes of interannual TOC variability were analyzed, based on wavelet transform, and the obtained results highlighted the presence of variation within the 22-34-month band associated with QBO modulation, the variation within 24-96month band associated with ENSO modulation, and variations in the 11-year band associated with the 11-year solar cycle modulation in both ground-based and satellite datasets. However, only the QBO modulation was within the level of 95 % significance level contours, and the cone of influence (COI) in the normalized wavelet spectrum and the global wavelet spectrum in both the ground-based and satellite datasets indicate the QBO influence as the main mode of interannual variability in the Brewer and satellite TOC monthly anomalies in the SSO time series. The association of variations in the SSO Brewer and satellite TOC monthly anomaly with the equatorial zonal wind QBO index was verified and observed that the Brewer and satellite TOC are in quasi-anti-phase with the QBO modulation and have mean TOC anomaly amplitude of 6 DU.
Acknowledgements. This work is part of the  Graduate Program in Meteorology, Federal University of Santa Maria (UFSM ), together with the  Laboratory of Atmosphere and Cyclones (LACy) of the University of Reunion Island (France) , supported by  Coordination for the Improvement of Higher Education Personnel (CAPES)  Process no. 99999. 010629/2014-09and the  National Institute of Science and Technology -Antarctic Environmental Research (INCT-APA) , which receives scientific and financial support of the  National Council for Scientific and Technological Development (CNPq process : no. 574018/2008-5 ) and the  Carlos Chagas Filho Foundation for Research Support in the State of Rio de Janeiro (FAPERJ No. E-16/170 023/2008 ). The authors also acknowledge the support of the  Ministry of Science, Technology and Innovation (MCTI) , the  Ministry of Environment (MMA) , and the  Inter-ministerial Commission for Sea Resources (CIRM)  along with the  Research Support Foundation of the Rio Grande do Sul State (FAPERGS)  by scholarship, as well as the  NASA/TOMS/OMI  and  NOAA  climate index for the data used in the analysis, the ATMAN- TAR  Project, and the  MCTI/CNPq announcement of International Polar Year , Case No.
The Asian summer monsoon (ASM) creates a hemispheric-scale signature in trace-gas distributions in the upper troposphere and lower stratosphere (UTLS). Data from satellite retrievals are the best source of information for characterizing these large-scale signatures. Measurements from the Microwave Limb Sounder (MLS), a limb-viewing satellite sensor, have been the most widely used retrieval products for these types of studies. This work explores the information for the ASM influence on UTLS chemical distribution from two nadir-viewing sensors, the Infrared Atmospheric Sounding Interferometer (IASI) and the Ozone Monitoring Instrument (OMI), together with the MLS. Day-to-day changes in carbon monoxide (CO) and ozone (O 3 ) tracer distributions in response to dynamical variability are examined to assess how well the data from different sensors provide useful information for studying the impact of sub-seasonal-scale dynamics on chemical fields.
Our results, using June-August 2008 data, show that although the MLS provides relatively sparse horizontal sampling on daily timescales, interpolated daily CO distributions show a high degree of dynamical consistency with the synoptic-scale structure of and variability in the anticyclone. Our analysis also shows that the IASI CO retrieval has sufficient sensitivity to produce upper tropospheric (UT) CO with variabilities independent from the lower to middle tropospheric CO. The consistency of IASI CO field with the synoptic-scale anticyclone dynamical variability demonstrates that the IASI UT CO product is a physically meaningful dataset. Furthermore, IASI CO vertical cross sections combined with the daily maps provide the first observational evidence for a model analyses-based hypothesis on the preferred ASM vertical transport location and the subsequent horizontal redistribution via east-west eddy shedding.
, 2004; Li et al. , 2005; Randel and Park, 2006; Park et al. , 2007; Randel et al. , 2010; Vernier et al. , 2011; Garny and Randel, 2013). Although the ASM anticyclone is a strong and steady feature of seasonal-scale circulations in the UTLS, it undergoes variations on sub-seasonal timescales. These include 10-20day east-west migrations and the associated eddy shedding  (Hsu and Plumb, 2000; Popovic and Plumb, 2001; Zhang et al. , 2002; Garny and Randel, 2013). Previous studies have shown that the monsoon circulation has active-break cycles that are linked to oscillations of deep convection with timescales of 10-20 and 30-60 days (e. g. ,  Krishnamurti and Bhalme, 1976; Krishnamurti and Ardanuy, 1980; Annamalai and Slingo, 2001; Randel and Park, 2006). Zhang et al. (2002)  found that the center of the anticyclone shows bimodality in its longitude location that they classify in terms of the Tibetan mode (centered at about 90 • E) and the Iranian mode (centered at about 60 • E), although the degree of bimodality appears dependent on the meteorological dataset  (Nützel et al.
, 2016). A number of recent studies have shown that sub-seasonal-scale dynamical processes in the ASM region may play a significant role in UTLS transport of trace gases  (Yan et al. , 2011; Garny and Randel, 2013; Pan et al. , 2016; Vogel et al. , 2016). It is evident that diagnosing intraseasonal variability in chemical tracers in the UTLS and their interactions with dynamical fields is important for a more complete understanding of the ASM anticyclone's chemical impact. Satellite observations provide an essential source of information in ASM UTLS-related studies. The Aura Microwave Limb Sounder (MLS) and the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), in particular, are two widely used datasets from limb-viewing sensors for this purpose (e. g. ,  Park et al.
, 2007; Randel et al. , 2010). These limb sounders offer relatively high vertical resolution but have limited horizontal sampling on daily timescales. Nadir-viewing instruments, however, offer better horizontal sampling and daily coverage but have limited vertical resolution and are primarily used to study column abundances. This study aims to examine the representation of subseasonal chemical variability in the ASM UTLS from limband nadir-viewing sensors. Two specific nadir datasets we explore are CO from the Infrared Atmospheric Sounding Interferometer (IASI) and O 3 from the Ozone Monitoring Instrument (OMI). These two datasets will be examined together with MLS CO and O 3 data. CO is a pollution tracer and is also an effective tracer of transport in the troposphere and lower stratosphere (LS) (e.
g. ,  Bowman, 2006) , with a photochemical lifetime of ∼ 2 months in the troposphere  (Xiao et al. , 2007). O 3 is an effective transport tracer in the UTLS because of the large gradient in its mixing ratio across the tropopause and its long lifetime relative to transport timescales in the UTLS region. In the UTLS, O 3 mostly serves as a stratospheric tracer, although it also has tropospheric pollution sources. Short-term variations in O 3 in the UTLS are largely linked to synoptic-scale disturbances in the tropopause region (e. g. ,  Shapiro, 1980). These satellite datasets are examined with meteorological analyses from the Global Forecast System (GFS) to address the following questions. (1) Do these nadir-viewing instruments, designed primarily for retrieving trace-gas column abundance, have sufficient information to show the ASM dynamically driven tracegas distributions and variability at UTLS levels? (2) Are the data from nadir sensors consistent with the limb-viewing data on sub-seasonal scales with respect to dynamical variability in tracers in the ASM region? (3) What can we learn from the complementary information from limb-and nadir-viewing instruments? Although the IASI CO and OMI O 3 are compared with MLS CO and O 3 , respectively, it is not the goal of this work to evaluate the quantitative agreement between the nadir and limb data.
The difference in viewing geometries makes limbviewing and nadir-viewing datasets fundamentally different quantities since the air masses they are sensing represent very different volume and spatial extent (described in Sect. 2). The goal of these comparisons, therefore, is to evaluate whether data from the two types of sensors provide a consistent picture of the ASM dynamical impact on the UTLS tracer distributions and variability. We characterize this type of analysis as process-based retrieval evaluation. This analysis provides a perspective of whether the high-density horizontal sampling from the nadir sensors supplements information from the limb-viewing sensors, despite the relatively coarse vertical resolution, in the region of strong synoptic-scale horizontal dynamical variability.
7; De Wachter et al. , 2012) , and satellite observations from the Measurements Of Pollution In The Troposphere (MOPITT) instrument  (George et al. , 2015). This work also aims to complement previous IASI data analyses, which show the data reproduce monthly mean largescale features in the UTLS over the ASM region comparable to model results from GEOS-Chem (a chemical transport model coupled to meteorological analysis from the Goddard Earth Observing System GEOS-5;  Barret et al. , 2016). Nadir-viewing observations of O 3 are obtained from OMI, an O 3 sounder aboard the Aura satellite that provides daily global coverage in a 13 km × 24 km footprint  (Levelt et al. , 2006). OMI O 3 products include retrievals of both total O 3 columns and vertical profiles. In this study, we use the O 3 profile product by  Liu et al.
It is apparent from Fig. 1a  that IASI has a much denser horizontal coverage than the MLS. Note that both datasets have data gaps: while the MLS orbit tracks are separated by ∼ 20 • longitude, the IASI retrieval also has a significant data gap each day due to the cloud coverage (no IASI products are available if the cloud fraction in the pixel exceeds 25 %). The comparison of O 3 data sampling densities between OMI and MLS is not shown but it is conceptually similar to that shown in Fig. 1a. This disparity of sampling density and its implications for representing synoptic-scale variability motivates this work of exploring the utility of nadir-viewing data in characterizing chemical distributions in the UTLS on daily to sub-seasonal timescales. Figure  1b  shows the vertical information distribution for both IASI and MLS UT CO retrievals.
Processing daily maps Figure  1a  highlights the sampling gaps from both MLS and IASI for mapping daily CO distributions. Careful data interpolation and smoothing to fill data gaps are essential steps for producing daily maps from the available retrievals on each given day. In general, the daily representation from MLS data requires interpolation to increase the density in coverage, while the IASI (and OMI) data densities are reduced by binned averages. We have explored three interpolation algorithms, cosine smoothing, natural neighbor, and inverse distance, for mapping data. All three methods are similar, conceptually, in filling an empty cell with weighted mean of nearby observations, but the weightings are determined differently. After experimenting with various grid sizes and mapping methods, we choose to use 5 • × 5 • longitudes and latitudes for mapping MLS data and 3 • ×2 • for the IASI data.
The results shown in this paper are mapped using the natural neighbor method  (Watson, 1992)  followed by a Gaussian smoothing. We find that these steps produce daily maps with a good balance between representing the synoptic-scale variability and the information from the data in localized structures. × 5 • longitude and latitude grids. The IASI CO map is made using 3 • × 2 • grids. Both are interpolated using the natural neighbor algorithm  (Watson, 1992). Figure  2  provides an example using 1 August 2008 data, in which maps of retrieved MLS 147 hPa CO (Fig. 2a ), interpolated MLS 147 hPa CO (Fig. 2b ), and IASI CO in the UT layer (Fig. 2c ) are shown. The IASI UT layer CO mixing ratio is produced by interpolating the layer product to 150 hPa. Although the interpolation aimed to find the CO mixing ratio approximation for 150 hPa, it is clear from the averaging kernel that the retrieval represents a layer.
The retrieval information for this layer is represented by the averaging kernels for 12. 5-15. 5 km layers as highlighted in Fig. 1b. To emphasize this limitation in vertical resolution, we refer to this layer as the UT layer in the rest of the paper. Note we have used different color scales for MLS and IASI CO and the rationale for this is given in the next section. The dynamical fields of 150 hPa GPH and the horizontal wind are superimposed for identifying the location and structure of the ASM anticyclone. Comparison of the MLS data on the orbital tracks (Fig. 2a ) and the interpolated map (Fig. 2b ) provides a useful perspective that the mapping procedure we choose highlights the large-scale dynamical consistency of the CO and the flow pattern instead of the fine-scale structure. Comparison of MLS (Fig.