scholarly journals Variability of Total Column Ozone with Solar Activity Features at Northern and Eastern Regions of India

2020 ◽  
Vol 8 (6) ◽  
pp. 441-450
Author(s):  
Y Chandra ◽  
S Pande ◽  
B Pande ◽  
M C Mathpal
Keyword(s):  
Solar Activity ◽  
Total Column Ozone ◽  
Column Ozone ◽  
Total Column

scholarly journals Application of Soft Computing (ANN) Techniques to study the relationship between Solar Activity Features and Total Column Ozone

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Seema Pande ◽  
Mahesh Chandra Mathpal ◽  
Bimal Pande
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Activity ◽  
Solar Flares ◽  
Total Column Ozone ◽  
Active Prominence ◽  
Artificial Neural Network Ann ◽  
Column Ozone ◽  
The Relationship ◽  
Total Column

Using 30 years data (1986-2015) we have made an attempt to study the dependency of total column ozone (TCO) on solar activity features: solar flares (SF), solar active prominence (SAP) and sunspot numbers (SN) for two hill stations of Uttarakhand viz. Nainital (29.40 N.79.470E) and Mussorie (30.270 N 78.060 E) by Artificial neural network (ANN) technique. Our study supports the fact that solar activity features contribute to the production of ozone.

scholarly journals Statistical Study of Solar Forcing of Total Column Ozone Variation Over Three Cities in Kenya

2016 ◽  
Vol 8 (3) ◽  
pp. 77
Author(s):  
Carolyne M. M. Songa ◽  
Jared H. O. Ndeda ◽  
Gilbert Ouma
Keyword(s):  
Solar Activity ◽  
Sunspot Number ◽  
Correlation Coefficients ◽  
Total Column Ozone ◽  
Solar Activity Indices ◽  
Multiple Correlation Coefficients ◽  
Highly Correlated ◽  
Column Ozone ◽  
Total Column ◽  
Activity Indices

In this study, a statistical analysis between three solar activity indices (SAI) namely; sunspot number (ssn), F10.7 index (sf) and Mg II index (mg) and total column ozone (TCO) time series over three cities in Kenya namely; Nairobi (1.17º S; 36.46º E), Kisumu (0.03º S; 34.45º E) and Mombasa (4.02º S; 39.43º E) for the period 1985 - 2011 are considered. Pearson and cross correlations, linear and multiple regression analyses are performed. All the statistical analyses are based on 95% confidence level. SAI show decreasing trend at significant levels with highest decrease in international sunspot number and least in Mg II index. TCO are highly correlated with each other at (0.936< r < 0.955, p < 0.001). SAI are also highly correlated with each other at (0.941< r < 0.976, p < 0.001) and are significantly positively correlated with TCO over the study period except Mg II index at Kisumu. TCO and SAI have correlations at both long and short lags. At all the cities, F10.7 index has an immediate impact and Mg II index has a delayed impact on TCO. A linear relationship exists between the two variables in all the cities. An increase in TCO of about 2 – 3 % (Nairobi), 1 – 2% (Kisumu) and 3 – 4 % (Mombasa) is attributed to solar activity indices. The multiple correlation coefficients and significant levels obtained show that 3 – 5% of the TCO at Nairobi, Kisumu and Mombasa can be predicted by the SAI.

Solar activity-total column ozone relationships: Observations and model studies with heterogeneous chemistry

1997 ◽  
Vol 102 (D1) ◽  
pp. 1561-1569 ◽  
Author(s):  
C. S. Zerefos ◽  
K. Tourpali ◽  
B. R. Bojkov ◽  
D. S. Balis ◽  
B. Rognerund ◽  
...  
Keyword(s):  
Solar Activity ◽  
Heterogeneous Chemistry ◽  
Total Column Ozone ◽  
Model Studies ◽  
Column Ozone ◽  
Total Column

scholarly journals Ozone layer adaptive model from direct relationship between solar activity and total column ozone for the tropical equator-Andes-Colombian region

Atmósfera ◽  
2018 ◽  
Vol 31 (2) ◽  
pp. 155-164 ◽  
Author(s):  
Julio César González-Navarrete ◽  
◽  
Julián Salamanca ◽  
Ingrid Mónica Pinzón-Verano ◽  
◽  
...  
Keyword(s):  
Solar Activity ◽  
Direct Relationship ◽  
Ozone Layer ◽  
Adaptive Model ◽  
Total Column Ozone ◽  
Column Ozone ◽  
Total Column

scholarly journals Application of Soft Computing (ANN) Techniques to study the relationship between Solar Activity Features and Total Column Ozone

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Seema Pande ◽  
Mahesh Chandra Mathpal ◽  
Bimal Pande
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Activity ◽  
Solar Flares ◽  
Total Column Ozone ◽  
Active Prominence ◽  
Artificial Neural Network Ann ◽  
Column Ozone ◽  
The Relationship ◽  
Total Column

Using 30 years data (1986-2015) we have made an attempt to study the dependency of total column ozone (TCO) on solar activity features: solar flares (SF), solar active prominence (SAP) and sunspot numbers (SN) for two hill stations of Uttarakhand viz. Nainital (29.40 N.79.470E) and Mussorie (30.270 N 78.060 E) by Artificial neural network (ANN) technique. Our study supports the fact that solar activity features contribute to the production of ozone

scholarly journals Spatial mapping of ground-based observations of total ozone

2015 ◽  
Vol 8 (10) ◽  
pp. 4487-4505 ◽  
Author(s):  
K.-L. Chang ◽  
S. Guillas ◽  
V. E. Fioletov
Keyword(s):  
Spatial Interpolation ◽  
Stochastic Partial Differential Equation ◽  
Positive Impact ◽  
The Novel ◽  
Total Column Ozone ◽  
Column Ozone ◽  
Source Of Information ◽  
Total Column ◽  
Ozone Levels

Abstract. Total column ozone variations estimated using ground-based stations provide important independent source of information in addition to satellite-based estimates. This estimation has been vigorously challenged by data inhomogeneity in time and by the irregularity of the spatial distribution of stations, as well as by interruptions in observation records. Furthermore, some stations have calibration issues and thus observations may drift. In this paper we compare the spatial interpolation of ozone levels using the novel stochastic partial differential equation (SPDE) approach with the covariance-based kriging. We show how these new spatial predictions are more accurate, less uncertain and more robust. We construct long-term zonal means to investigate the robustness against the absence of measurements at some stations as well as instruments drifts. We conclude that time series analyzes can benefit from the SPDE approach compared to the covariance-based kriging when stations are missing, but the positive impact of the technique is less pronounced in the case of drifts.

scholarly journals Characterizing ozone throughout the atmospheric column over the tropical Andes from in situ and remote sensing observations

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
María Cazorla ◽  
René Parra ◽  
Edgar Herrera ◽  
Francisco Raimundo da Silva
Keyword(s):  
Boundary Layer ◽  
Atmospheric Ozone ◽  
Tropical Andes ◽  
Total Column Ozone ◽  
Study Region ◽  
Boundary Layer Height ◽  
Mixing Ratios ◽  
The Andes ◽  
Column Ozone ◽  
Total Column

In this study, we characterize atmospheric ozone over the tropical Andes in the boundary layer, the free troposphere, and the stratosphere; we quantify each contribution to total column ozone, and we evaluate the performance of the multi-sensor reanalysis (MSR2) in the region. Thus, we present data taken in Ecuador and Peru (2014–2019). The contribution from the surface was determined by integrating ozone concentrations measured in Quito and Cuenca (Ecuador) up to boundary layer height. In addition, tropospheric and stratospheric column ozone were quantified from ozone soundings (38) launched from Quito during the study time period. Profiles were compared against soundings at Natal (SHADOZ network) for being the closest observational reference with sufficient data in 2014–2019. Data were also compared against stratospheric mixing ratios from the Aura Microwave Limb Sounder (Aura MLS). Findings demonstrate that the stratospheric component of total column ozone over the Andes (225.2 ± 8.9 Dobson Units [DU]) is at similar levels as those observed at Natal (223.3 ± 8.6 DU), and observations are comparable to Aura MLS data. In contrast, the tropospheric contribution is lower over the Andes (20.2 ± 4.3 DU) when compared to Natal (35.4 ± 6.4 DU) due to a less deep and cleaner troposphere. From sounding extrapolation of Quito profiles down to sea level, we determined that altitude deducts about 5–7 DU from the total column, which coincides with a 3%–4% overestimation of the MSR2 over Quito and Marcapomacocha (Peru). In addition, when MSR2 data are compared along a transect that crosses from the Amazon over Quito, the Ecuadorian coast side, and into the Pacific, observations are not significantly different among the three first locations. Results point to coarse reanalysis resolution not being suitable to resolve the formidable altitude transition imposed by the Andes mountain chain. This work advances our knowledge of atmospheric ozone over the study region and provides a robust time series of upper air measurements for future evaluations of satellite and reanalysis products.

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