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The aim of this paper is to broaden the scope of a recent adaptive model in order to obtain predictions of total column ozone (TCO) trends over the Amazon Inter-Tropical Confluence Zone (ITCZ). The adaptive model makes daily TCO predictions over the tropical equator-Andes-Region, relying on seasonal patterns and the solar cycle. This study uses daily observations of the sunspot number cycle, given by the World Data Center for the production, preservation and dissemination of the international sunspot number (Royal Observatory of Belgium), and satellite total-column ozone data, collected by NASA (January 1979 to April 2018), for two Colombian locations: one in and one adjacent to the ITCZ. The agreement between daily total-column predictions by the adaptive model and satellite observations is excellent. Daily averaged relative errors around of 3.7 % and 2.8 % for both locations are reported herein.

Ozonesonde launches were routinely performed in Beijing from March 2001 to February 2019 to generate a unique long-term (18 years) vertical ozone profile dataset over mainland China. This study elucidates the vertical ozone structure on various temporal scales during this 18 years period by using the entire ozonesonde data product for the first time. Moreover, the long-term variability in the integrated ozone column over the North China Plain (NCP) is also explored by comparing the retrievals from ozonesonde at the Beijing urban site and a Dobson ozone spectrometer at the Xianghe suburban site. Our results indicate that vertical ozone exhibited clear monthly variability characterized by high values of tropospheric ozone during warm seasons and high values of stratospheric ozone during cold seasons. Stratospheric intrusions frequently occurred during spring and effectively transported cold air masses with high ozone from the lower stratosphere downward into the upper troposphere. Evident interannual variability in the lower troposphere and in ozone-rich areas of the stratosphere was revealed by vertical ozone distributions. The integrated total ozone columns retrieved from ozonesonde and Dobson bear close resemblance and exhibit strong sinusoidal monthly variations. In the troposphere and boundary layer, the integrated ozone column presented a significant positive trend during 2001–2012 in Beijing; a sudden decline occurred between 2011 and 2013, which was followed by a slow and insignificant increase after the implementation of the Clean Air Action plan in 2013 on the NCP.

Spatial statistics for very large spatial data sets is challenging. The size of the data set, n, causes problems in computing optimal spatial predictors such as kriging, since its computational cost is of order [graphic removed] . In addition, a large data set is often defined on a large spatial domain, so the spatial process of interest typically exhibits non-stationary behaviour over that domain. A flexible family of non-stationary covariance functions is defined by using a set of basis functions that is fixed in number, which leads to a spatial prediction method that we call fixed rank kriging. Specifically, fixed rank kriging is kriging within this class of non-stationary covariance functions. It relies on computational simplifications when n is very large, for obtaining the spatial best linear unbiased predictor and its mean-squared prediction error for a hidden spatial process. A method based on minimizing a weighted Frobenius norm yields best estimators of the covariance function parameters, which are then substituted into the fixed rank kriging equations. The new methodology is applied to a very large data set of total column ozone data, observed over the entire globe, where n is of the order of hundreds of thousands.

Stratospheric ozone shields life on Earth from harmful ultraviolet radiation and plays a crucial role in climate formation, while tropospheric ozone is a pollutant and greenhouse gas. Satellite methods based on measurements of outgoing thermal radiation are the only methods that provide information on global ozone distribution, independent of solar illumination. Since about 90% of atmospheric ozone is concentrated in the stratosphere, ozone total column measurements can be used as stratospheric ozone measurements. We present techniques for deriving information on total ozone columns (TOCs) and tropospheric ozone columns (TrOCs) from spectra of outgoing thermal radiation measured by the IKFS-2 instrument aboard the Meteor-M No. 2 satellite. The techniques are based on principal component analysis and the artificial neural network approach, providing high accuracy in TOC (less than 3%) and TrOC (within 2–4 DU) retrieval in accordance with the WMO requirements for the quality of satellite measurements.

Global measurements of total ozone are necessary to evaluate ozone hole recovery above Antarctica. The Environmental Trace Gases Monitoring Instrument (EMI) onboard GaoFen 5, launched in May 2018, was developed to measure and monitor the global total ozone column (TOC) and distributions of other trace gases. In this study, some of the first global TOC results of the EMI using the differential optical absorption spectroscopy (DOAS) method and validation with ground-based TOC measurements and data derived from Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) observations are presented. Results show that monthly average EMI TOC data had a similar spatial distribution and a high correlation coefficient (R ≥ 0.99) with both OMI and TROPOMI TOC. Comparisons with ground-based measurements from the World Ozone and Ultraviolet Radiation Data Centre also revealed strong correlations (R > 0.9). Continuous zenith sky measurements from zenith scattered light differential optical absorption spectroscopy instruments in Antarctica were also used for validation (R = 0.9). The EMI-derived observations were able to account for the rapid change in TOC associated with the sudden stratospheric warming event in October 2019; monthly average TOC in October 2019 was 45% higher compared to October 2018. These results indicate that EMI TOC derived using the DOAS method is reliable and has the potential to be used for global TOC monitoring.

The observed ozone layer depletion is influenced by continuous anthropogenic activity. This fact enforced the regular ozone monitoring globally. Information on spatial-temporal variations in total ozone columns (TOCs) derived by various observational methods and models can differ significantly due to measurement and modelling errors, differences in ozone retrieval algorithms, etc. Therefore, TOC data derived by different means should be validated regularly. In the current study, we compare TOC variations observed by ground-based (Bruker IFS 125 HR, Dobson, and M-124) and satellite (OMI, TROPOMI, and IKFS-2) instruments and simulated by models (ERA5 and EAC4 re-analysis, EMAC and INM RAS—RSHU models) near St. Petersburg (Russia) between 2009 and 2020. We demonstrate that TOC variations near St. Petersburg measured by different methods are in good agreement (with correlation coefficients of 0.95–0.99). Mean differences (MDs) and standard deviations of differences (SDDs) with respect to Dobson measurements constitute 0.0–3.9% and 2.3–3.7%, respectively, which is close to the actual requirements of the quality of TOC measurements. The largest bias is observed for Bruker 125 HR (3.9%) and IKFS-2 (−2.4%) measurements, whereas M-124 filter ozonometer shows no bias. The largest SDDs are observed for satellite measurements (3.3–3.7%), the smallest—for ground-based data (2.3–2.8%). The differences between simulated and Dobson data vary significantly. ERA5 and EAC4 re-analysis data show slight negative bias (0.1–0.2%) with SDDs of 3.7–3.9%. EMAC model overestimates Dobson TOCs by 4.5% with 4.5% SDDs, whereas INM RAS-RSHU model underestimates Dobson by 1.4% with 8.6% SDDs. All datasets demonstrate the pronounced TOC seasonal cycle with the maximum in spring and minimum in autumn. Finally, for 2004–2021 period, we derived a significant positive TOC trend near St. Petersburg (~0.4 ± 0.1 DU per year) from all datasets considered.

Atmospheric ozone plays an important role in the biosphere’s absorbing of dangerous solar UV radiation and its contributions to the Earth’s climate. Nowadays, ozone variations are widely monitored by different local and remote sensing methods. Satellite methods can provide data on the global distribution of ozone and its anomalies. In contrast to measurement techniques based on solar radiation measurements, Fourier-transform infrared (FTIR) satellite measurements of thermal radiation provide information, regardless of solar illumination. The global distribution of total ozone columns (TOCs) measured by the IKFS-2 spectrometer aboard the “Meteor M N2” satellite for the period of 2015 to 2020 is presented. The retrieval algorithm uses the artificial neural network (ANN) based on measurements of TOCs by the Aura OMI instrument and the method of principal components for representing IKFS-2 spectral measurements. Latitudinal and seasonal dependencies on the ANN training errors are analyzed and considered as a first approximation of the TOC measurement errors. The TOCs derived by the IKFS-2 instrument are compared to independent ground-based and satellite data. The average differences between the IKFS-2 data and the independent TOC measurements are up to 2% (IKFS-2 usually slightly underestimates the other data), and the standard deviations of differences (SDDs) vary from 2 to 4%. At the same time, both the analysis of the ANN approximation errors of the OMI data and the comparison of the IKFS-2 results with independent data demonstrate an increase in discrepancies towards the poles. In the spring–winter period, SDDs reach 8% in the Southern and 6% in the Northern Hemisphere. The technique presented can be used to process the IKFS-2 spectral data, and as a result, it can provide global information on the TOCs in the period of 2015–2020, regardless of the solar illumination and the presence of clouds.

Air pollution is affected by the atmospheric dynamics. This study aims to determine that air pollution concentration values in İstanbul increased significantly and reached peak values due to atmospheric blocking between the 30th of December 2022 and the 5th of January 2023. In this study, hourly pollutant data was obtained from 16 air quality monitoring stations (AQMS), the exact reanalysis data was extracted from ERA5 database, and inversion levels and meteorological and synoptic analyses were used to determine the effects of atmospheric blocking on air pollution. Also, cloud base heights and vertical visibility measurements were taken with a ceilometer. Statistical calculations and data visualizations were performed using the R and Grads program. Omega-type blocking, which started in İstanbul on December 30, 2022, had a significant impact on the 1st and 2nd of January 2023, and PM 10 and PM 2.5 concentration values reached their peak values at 572.8 and 254.20 µg/m 3 , respectively. In addition, it was found that the average concentration values in the examined period in almost all stations were higher than the averages for January and February. As a result, air quality in İstanbul was determined as “poor” between these calendar dates. It was found that the blocking did not affect the ozone (µg/m 3 ) concentration. It was also found that the concentrations of particulate matter (PM) 10 µm or less in diameter (PM 10 ) and PM 2.5 µm or less in diameter (PM 2.5 ) were increased by the blocking effect in the İstanbul area. Finally, according to the data obtained using the ceilometer, cloud base heights decreased to 30 m and vertical visibility to 10 m.

This study aims to know the annual, monthly, and daily changes of total ozone in some selected stations in Iraq (Baghdad, Mosul, and Basra). The data presented from the Ozone Monitoring Instrument (OMI) for the period (2003-2015) were used to study the total ozone column. It was found that the total ozone column has temporal variation. As for the annual and daily changes. In the annual changes, we find that the tendency has negative value in both the station (Mosul and Baghdad) by (-0.606 and -0.459) DU with maximum annual mean value was (330.13) DU in Mosul station, while the amount of the tendency is a positive value by (0.115) and the minimum annual mean value was (292.74) DU in Basra station, and the same is the case for the daily change has a value negative (-3.88—-0.699) for each Mosul and Baghdad stations and positive (1.902) for Basra station. The monthly total ozone varied between a maximum value in spring of (353 and 344) DU - (334and321) DU in the station (Mosul and Baghdad) respectively, while the maximum value of total ozone varied between (304 and 313) DU in the summer about the station Basra.

The anthropogenic impact on the ozone layer is expressed in anomalies in the total ozone content (TOC) on a global scale, with periodic enhancements observed in high-latitude areas. In addition, there are significant variations in TOC time trends at different latitudes and seasons. The reliability of the TOC future trends projections using climate chemistry models must be constantly monitored and improved, exploiting comparisons against available measurements. In this study, the ability of the Earth’s system model SOCOLv4.0 to predict TOC is evaluated by using more than 40 years of satellite measurements and meteorological reanalysis data. In general, the model overpredicts TOC in the Northern Hemisphere (by up to 16 DU) and significantly underpredicts it in the South Pole region (by up to 28 DU). The worst agreement was found in both polar regions, while the best was in the tropics (the mean difference constitutes 4.2 DU). The correlation between monthly means is in the range of 0.75–0.92. The SOCOLv4 model significantly overestimates air temperature above 1 hPa relative to MERRA2 and ERA5 reanalysis (by 10–20 K), particularly during polar nights, which may be one of the reasons for the inaccuracies in the simulation of polar ozone anomalies by the model. It is proposed that the SOCOLv4 model can be used for future projections of TOC under the changing scenarios of human activities.