scholarly journals Ozone variations pertaining to dry and wet monsoon seasons over Indian region

MAUSAM ◽  
2021 ◽  
Vol 57 (4) ◽  
pp. 663-668
Author(s):  
A. L. LONDHE ◽  
S. D. PATIL ◽  
B. PADMA KUMARI ◽  
D. B. JADHAV
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
T Test ◽  
Indian Region ◽  
Convective Activity ◽  
Total Column Ozone ◽  
The Difference ◽  
Column Ozone ◽  
Total Column ◽  
Ozone Variations

’kq"d vkSj vknzZ ekulwu o"kkZsa ds nkSjku Vh-lh-vks- forj.k dk v/;;u djus ds fy, Hkkjrh; {ks+= esa o"kZ 1982]1983]1987 ,oa 1988 ds dqy dkWye vkstksu ¼Vh-lh-vks-½ ds ekfld vkSlr dk mi;ksx fd;k x;k gS A bl ’kks/k&Ik= esa mDr o"kksZa ds Hkkjr ds 13 LVs’kuksa ds Vh-lh-vks- vkadM+ksa dk v/;;u fd;k x;k gSA ’kq"d vkSj vknzZ ekulwu o"kksZa ds nkSjku Vh-lh-vks- forj.k dh rqyuk ls ;g irk pyk gS fd Vh-lh-vks- ds eku vknZz o"kksZa dh rqyuk esa ’kq"d o"kksZa esa vf/kd ik, x, gSaA Vh-lh-vks- esa ifjorZu gksuk ’kq"d ,oa vknzZ o"kksZa ds nkSjku laoguh; xfrfof/k esa fHkUurk dks ekuk tk ldrk gSA ’kq"d ¼vknzZ½ o"kksZa ds nkSjku laogu esa deh ¼o`f)½ Vh-lh-vks- dh ek=k dks c<+krh ?kVkrh gSA ’kq"d ,oa vknzZ o"kksZa ds chp ds ekulwu ds eghuksa ds nkSjku Vh-lh-vks- ds egRo dh tk¡p djus ds fy, lkaf[;dh; Vh--VsLV dk iz;ksx fd;k x;k gSA ;g varj nene dks NksM+dj vU; lHkh LVs’kuksa ds fy, lkaf[;dh; n`f"V ls 5 izfr’kr rd egRoiw.kZ gSA ,slk dgk tk ldrk gS fd Hkkjr esa xzh"edkyhu ekulwu eghuksa ds nkSjku vks-,y-vkj- rFkk Vh-lh-vks- ds chp vPNs laca/k jgs gSa D;ksafd bl vof/k ds nkSjku laogu dkQh izcy jgk gS A Monthly mean total column ozone (TCO) over Indian region for the years 1982, 1983, 1987 and 1988 has been utilized to study the TCO distribution during dry and wet monsoon years. TCO data for 13 Indian stations for the above years have been considered in the study. Comparison of TCO distribution during dry and wet monsoon years suggested that TCO values are found higher during dry years than those in wet years. The changes in TCO may be attributed to difference in convective activity during dry and wet years. The suppressed (enhanced) convection during dry (wet) years may lead to increase (decrease) in TCO.   The statistical t-test is applied to test the significance of TCO difference during monsoon months between dry and wet years. The difference is statistically significant at 5% level of confidence for all stations except Dumdum. It can be said that the relation between OLR and TCO holds good during Indian summer monsoon months, as convection is stronger during this period.

scholarly journals Evaluation of the Ozone Fields in NASA’s MERRA-2 Reanalysis

2017 ◽  
Vol 30 (8) ◽  
pp. 2961-2988 ◽  
Author(s):  
Krzysztof Wargan ◽  
Gordon Labow ◽  
Stacey Frith ◽  
Steven Pawson ◽  
Nathaniel Livesey ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Spatial And Temporal Variability ◽  
Ozone Monitoring Instrument ◽  
Total Column Ozone ◽  
Time Period ◽  
Realistic Representation ◽  
The Difference ◽  
Column Ozone ◽  
Modern Era ◽  
Total Column

The assimilated ozone product from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), produced at NASA’s Global Modeling and Assimilation Office (GMAO) spanning the time period from 1980 to the present is described herein, and its quality is assessed. MERRA-2 assimilates partial column ozone retrievals from a series of Solar Backscatter Ultraviolet Radiometer (SBUV) instruments on NASA and NOAA spacecraft between January 1980 and September 2004: starting in October 2004, retrieved ozone profiles from the Microwave Limb Sounder (MLS) and total column ozone from the Ozone Monitoring Instrument on NASA’s EOS Aura satellite are assimilated. The MERRA-2 ozone is compared with independent satellite and ozonesonde data, focusing on the representation of the spatial and temporal variability of stratospheric and upper-tropospheric ozone and on implications of the change in the observing system from SBUV to EOS Aura. The comparisons show agreement within 10% (standard deviation of the difference) between MERRA-2 profiles and independent satellite data in most of the stratosphere. The agreement improves after 2004, when EOS Aura data are assimilated. The standard deviation of the differences between the lower-stratospheric and upper-tropospheric MERRA-2 ozone and ozonesondes is 11.2% and 24.5%, respectively, with correlations of 0.8 and above, indicative of a realistic representation of the near-tropopause ozone variability in MERRA-2. The agreement improves significantly in the EOS Aura period; however, MERRA-2 is biased low in the upper troposphere with respect to the ozonesondes. Caution is recommended when using MERRA-2 ozone for decadal changes and trend studies.

scholarly journals Intercomparison of total column ozone data from the Pandora spectrophotometer with Dobson, Brewer, and OMI measurements over Seoul, Korea

2016 ◽  
Author(s):  
Jiyoung Kim ◽  
Jhoon Kim ◽  
Hi-Ku Cho ◽  
Jay Herman ◽  
Sang Seo Park ◽  
...  
Keyword(s):  
Optical Absorption Spectroscopy ◽  
Ozone Monitoring Instrument ◽  
Total Column Ozone ◽  
Monitoring Instrument ◽  
Ozone Monitoring ◽  
The Difference ◽  
Column Ozone ◽  
Observation Times ◽  
Total Column ◽  
Daily Total

Abstract. Daily total column ozone (TCO) measured using the Pandora spectrophotometer (#19) was intercompared with data from the Dobson (#124) and Brewer (#148) spectrophotometers, as well as from the Ozone Monitoring Instrument (OMI), over the 2-year period between March 2012 and March 2014 at Yonsei University, Seoul, Korea. The Pandora TCO measurements are closely correlated with those from the Dobson, Brewer, and OMI instruments with regression coefficients (slopes) of 0.95, 1.00, 0.98 (OMI-TOMS), and 0.97 (OMI-DOAS), respectively, and determination coefficients (R2) of 0.95, 0.97, 0.96 (OMI-TOMS), and 0.95 (OMI-DOAS), respectively. In particular, they show a close agreement with the Brewer TCO measurements, with slope and R2 values of 1.00 and 0.97, respectively. The difference between the Pandora and Dobson data can be explained by smaller amount of Dobson data available to calculate the daily averages, observation times, solar zenith angles, SO2 effect, temperature, and humidity between the two datasets. The difference in the results obtained from the Pandora instrument and Ozone Monitoring Instrument-Differential Optical Absorption Spectroscopy (OMI-DOAS algorithm) can be explained by the dependence on seasonal variations of about ± 2 % and solar zenith angle leading to overestimation by 5 % of OMI-DOAS measurements. For the Dobson measurements in particular, the difference caused by the inconsistency in observation times when compared with the Pandora measurements was up to 12.5 % on 22 June 2013 because of diurnal variations in the TCO values. However, despite these various differences and discrepancies, the daily TCO values measured by the four instruments during the 2-year study period are accurate and closely correlated.

scholarly journals A global total column ozone climate data record

2021 ◽  
Vol 13 (8) ◽  
pp. 3885-3906
Author(s):  
Greg E. Bodeker ◽  
Jan Nitzbon ◽  
Jordis S. Tradowsky ◽  
Stefanie Kremser ◽  
Alexander Schwertheim ◽  
...  
Keyword(s):  
Global Climate ◽  
Data Sets ◽  
Climate Data ◽  
Total Column Ozone ◽  
Data Record ◽  
Difference Fields ◽  
The Difference ◽  
Column Ozone ◽  
Climate Data Record ◽  
Total Column

Abstract. Total column ozone (TCO) data from multiple satellite-based instruments have been combined to create a single near-global daily time series of ozone fields at 1.25∘ longitude by 1∘ latitude spanning the period 31 October 1978 to 31 December 2016. Comparisons against TCO measurements from the ground-based Dobson and Brewer spectrophotometer networks are used to remove offsets and drifts between the ground-based measurements and a subset of the satellite-based measurements. The corrected subset is then used as a basis for homogenizing the remaining data sets. The construction of this database improves on earlier versions of the database maintained first by the National Institute of Water and Atmospheric Research (NIWA) and now by Bodeker Scientific (BS), referred to as the NIWA-BS TCO database. The intention is for the NIWA-BS TCO database to serve as a climate data record for TCO, and to this end, the requirements for constructing climate data records, as detailed by GCOS (the Global Climate Observing System), have been followed as closely as possible. This new version includes a wider range of satellite-based instruments, uses updated sources of satellite data, extends the period covered, uses improved statistical methods to model the difference fields when homogenizing the data sets, and, perhaps most importantly, robustly tracks uncertainties from the source data sets through to the final climate data record which is now accompanied by associated uncertainty fields. Furthermore, a gap-free TCO database (referred to as the BS-filled TCO database) has been created and is documented in this paper. The utility of the NIWA-BS TCO database is demonstrated through an analysis of ozone trends from November 1978 to December 2016. Both databases are freely available for non-commercial purposes: the DOI for the NIWA-BS TCO database is https://doi.org/10.5281/zenodo.1346424 (Bodeker et al., 2018) and is available from https://zenodo.org/record/1346424. The DOI for the BS-filled TCO database is https://doi.org/10.5281/zenodo.3908787 (Bodeker et al., 2020) and is available from https://zenodo.org/record/3908787. In addition, both data sets are available from http://www.bodekerscientific.com/data/total-column-ozone (last access: June 2021).

scholarly journals Decreases in wintertime total column ozone over the Tibetan Plateau during 1979–2017

2019 ◽  
Author(s):  
Yajuan Li ◽  
Martyn P. Chipperfield ◽  
Wuhu Feng ◽  
Sandip S. Dhomse ◽  
Richard J. Pope ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Regression Models ◽  
Total Ozone ◽  
The Tibetan Plateau ◽  
Total Column Ozone ◽  
Enso Events ◽  
Column Ozone ◽  
Total Column ◽  
Ozone Variations

Abstract. We use the ozone dataset from the Copernicus Climate Change Service (C3S) during 1979–2017 to investigate the long-term variations of the total column ozone (TCO) and the relative total ozone low (TOL) over the Tibetan Plateau (TP) during different seasons. Based on various regression models, the wintertime TCO over the TP decreases overall during 1979–2017 with ongoing decreases since 1997. We perform multivariate regression analysis to quantify the influence of dynamical and chemical processes responsible for the long-term TCO variability over the TP. We use both piecewise linear trend (PWLT) and equivalent effective stratospheric chlorine loading (EESC)-based regression models that include explanatory variables such as the 11-year solar cycle, quasi-biennial oscillation (QBO) at 30 hPa and 10 hPa and the geopotential height (GH) at 150 hPa. The 150 hPa GH is found to be a major dynamical contributor to the total ozone variability (8 %) over the TP in wintertime. We also find strong correlation between TCO in DJF and the following JJA, indicating that negative/positive anomalies in the wintertime build up persist into summer. We also use the TOMCAT/SLIMCAT 3-D chemical transport model to investigate the contributions of different factors to the ozone variations over the TP. Using identical regression model on simulated TCO time series, we obtain consistent results with C3S-based data. We perform two sensitivity experiments with repeating dynamics of 2004 and 2008 to further study the role that the GH at 150 hPa plays in the ozone variations over the TP. The GH differences between the two years show an obvious, negative centre near 150 hPa over the TP in DJF. Composite analysis show that GH fluctuations associated with Inter Tropical Convergence Zone, ENSO events or Walker circulation play a key role in controlling TCO variability in the lower stratosphere.

scholarly journals Intercomparison of total column ozone data from the Pandora spectrophotometer with Dobson, Brewer, and OMI measurements over Seoul, Korea

2017 ◽  
Vol 10 (10) ◽  
pp. 3661-3676 ◽  
Author(s):  
Jiyoung Kim ◽  
Jhoon Kim ◽  
Hi-Ku Cho ◽  
Jay Herman ◽  
Sang Seo Park ◽  
...  
Keyword(s):  
Observation Time ◽  
Stray Light ◽  
Optical Absorption Spectroscopy ◽  
Linear Regression Method ◽  
Ozone Monitoring Instrument ◽  
Total Column Ozone ◽  
Total Data ◽  
The Difference ◽  
Column Ozone ◽  
Total Column

Abstract. Daily total column ozone (TCO) measured using the Pandora spectrophotometer (no. 19) was compared with data from the Dobson (no. 124) and Brewer (no. 148) spectrophotometers, as well as from the Ozone Monitoring Instrument (OMI) (with two different algorithms, Total Ozone Mapping Spectrometer (TOMS) TOMS and differential optical absorption spectroscopy (DOAS) methods), over the 2-year period between March 2012 and March 2014 at Yonsei University, Seoul, Korea. Based on the linear-regression method, the TCO from Pandora is closely correlated with those from other instruments with regression coefficients (slopes) of 0.95 (Dobson), 1.00 (Brewer), 0.98 (OMI-TOMS), and 0.97 (OMI-DOAS), and determination coefficients (R2) of 0.95 (Dobson), 0.97 (Brewer), 0.96 (OMI-TOMS), and 0.95 (OMI-DOAS). The daily averaged TCO from Pandora has within 3 % differences compared to TCO values from other instruments. For the Dobson measurements in particular, the difference caused by the inconsistency in observation times when compared with the Pandora measurements was up to 12.5 % because of diurnal variations in the TCO values. However, the comparison with Brewer after matching the observation time shows agreement with large R2 and small biases. The TCO ratio between Brewer and Pandora shows the 0.98 ± 0.03, and the distributions for relative differences between two instruments are 89.2 and 57.1 % of the total data within the error ranges of 3 and 5 %, respectively. The TCO ratio between Brewer and Pandora also is partially dependent on solar zenith angle. The error dependence by the observation geometry is essential to the further analysis focusing on the sensitivity of aerosol and the stray-light effect in the instruments.

scholarly journals Drivers of variations in the vertical profile of ozone over Summit Station, Greenland: An analysis of ozonesonde data

2018 ◽  
Author(s):  
Shima Bahramvash Shams ◽  
Von P. Walden ◽  
Samuel Oltmans ◽  
Irina Petropavlovskikh ◽  
Bryan Johnson ◽  
...  
Keyword(s):  
Greenland Ice Sheet ◽  
The Arctic ◽  
Positive Trend ◽  
Quasi Biennial Oscillation ◽  
Total Column Ozone ◽  
Eddy Heat Flux ◽  
Short Period ◽  
Column Ozone ◽  
Total Column ◽  
Ozone Variations

Abstract. Understanding the drivers of atmospheric ozone variations in the Arctic is difficult because there are few long-term records of vertical ozone profiles in this region. We present 12 years of ozone profiles over Summit Station, Greenland (72.6 N, 38.4 W; 3200 meters) that were measured from 2005 to 2016. These profiles are subjected to data screening and are extended to 60 km using a robust extrapolation method. The total column ozone and the partial column ozone in four atmospheric layers (troposphere to upper stratosphere) are analyzed. The monthly mean total column ozone reaches a maximum of about 400 DU in April, then decreases to minimum values between 275 and 300 DU in the late summer and early fall. The partial column ozone values peak at different times between late winter and early summer. There is a positive trend in the total column that is likely due to increases in springtime ozone, however, these trends are not robust given the short period of record. A stepwise multiple regression analysis is performed to determine the primary drivers of ozone variations over Summit Station. This analysis shows that the variations in total column ozone are due primarily to changes in the tropopause pressure, the quasi-biennial oscillation (QBO), and the volume of polar stratospheric clouds. The eddy heat flux is also important for variations in the partial column ozone in the different altitude regions. The importance of the QBO appears to be a unique characteristic for ozone variations over the Greenland Ice Sheet (when compared to other nearby Arctic Stations) and may be related to the fact that Greenland is particularly sensitive to the phase of the QBO.

scholarly journals A Global Total Column Ozone Climate Data Record

2020 ◽  
Author(s):  
Greg E. Bodeker ◽  
Jan Nitzbon ◽  
Jordis S. Tradowsky ◽  
Stefanie Kremser ◽  
Alexander Schwertheim ◽  
...  
Keyword(s):  
Global Climate ◽  
Data Sets ◽  
Climate Data ◽  
Total Column Ozone ◽  
Data Record ◽  
Difference Fields ◽  
The Difference ◽  
Column Ozone ◽  
Climate Data Record ◽  
Total Column

Abstract. Total column ozone (TCO) data from multiple satellite-based instruments have been combined to create a single near-global daily time series of ozone fields at 1.25° longitude by 1° latitude spanning the period 31 October 1978 to 31 December 2016. Comparisons against TCO measurements from the ground-based Dobson and Brewer spectrophotometer networks are used to remove offsets and drifts between the ground-based measurements and a subset of the satellite-based measurements. The corrected subset is then used as a basis for homogenising the remaining data sets. The construction of this database improves on earlier versions of the database maintained first by the National Institute of Water and Atmospheric Research (NIWA) and now by Bodeker Scientific (BS), referred to as the NIWA-BS TCO database. The intention is that the NIWA-BS TCO database serves as a climate data record for TCO and, to this end, the requirements for constructing climate data records, as detailed by GCOS (the Global Climate Observing System) have been followed as closely as possible. This new version includes a wider range of satellite-based instruments, uses updated sources of satellite data, extends the period covered, uses improved statistical methods to model the difference fields when homogenising the data sets, and, perhaps most importantly, robustly tracks uncertainties from the source data sets through to the final climate data record which is now accompanied by associated uncertainty fields. Furthermore, a gap-free TCO database (referred to as the BS-filled TCO database) has been created and is documented in this paper. The utility of the NIWA-BS TCO database is demon strated through an analysis of ozone trends from November 1978 to December 2016. Both databases are freely available for non-commercial purposes: the doi for the NIWA-BS TCO database is 10.5281/zenodo.1346424 (Bodeker et al., 2018) and is available from https://zenodo.org/record/1346424. The doi for the BS-filled TCO database is 10.5281/zenodo.3908787 (Bodeker et al., 2020) and is available from https://zenodo.org/record/3908787. In addition, both data sets are available from http://www.bodekerscientific.com/data/total-column-ozone.

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