@Article{BencherifPDMPBBMSSMA:2024:OzTrAn,
               author = "Bencherif, Hassan and Pinheiro, Damaris Kirsch and Delage, Olivier 
                         and Millet, Tristan and Peres, Lucas Vaz and B{\`e}gue, Nelson 
                         and Bittencourt, Gabriela and Martins, Maria Paulete Pereira and 
                         Silva, Francisco Raimundo da and Steffenel, Luiz Angelo and 
                         Mbatha, Nkanyiso and Anabor, Vagner",
          affiliation = "Universit{\'e} de la R{\'e}union, M{\'e}t{\'e}o-France and 
                         {Universidade Federal de Santa Maria (UFSM)} and Universit{\'e} 
                         de la R{\'e}union, M{\'e}t{\'e}o-France and Universit{\'e} de 
                         la R{\'e}union, M{\'e}t{\'e}o-France and {Universidade Federal 
                         do Oeste do Par{\'a} (UFOPA)} and Universit{\'e} de la 
                         R{\'e}union, M{\'e}t{\'e}o-France and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Universit{\'e} de Reims Champagne Ardenne} and 
                         {University of Zululand} and {Universidade Federal de Santa Maria 
                         (UFSM)}",
                title = "Ozone Trend Analysis in Natal (5.4°S, 35.4°W, Brazil) Using 
                         Multi-Linear Regression and Empirical Decomposition Methods over 
                         22 Years of Observations",
              journal = "Remote Sensing",
                 year = "2024",
               volume = "16",
               number = "1",
                pages = "e208",
                month = "Jan.",
             keywords = "Brazil, EAWD, EMD, empirical decompositions, multi-linear 
                         regression, ozone variability and trends, southern tropics, 
                         stratospheric ozone, tropospheric ozone.",
             abstract = "Ozone plays an important role in the Earths atmosphere. It is 
                         mainly formed in the tropical stratosphere and is transported by 
                         the BrewerDobson Circulation to higher latitudes. In the 
                         stratosphere, ozone can filter the incoming solar ultraviolet 
                         radiation, thus protecting life at the surface. Although 
                         tropospheric ozone accounts for only ~10%, it is a powerful GHG 
                         and pollutant, harmful to the health of the environment and living 
                         beings. Several studies have highlighted biomass burning as a 
                         major contributor to the tropospheric ozone budget. Our study 
                         focuses on the Natal site (5.40°S, 35.40°W, Brazil), one of the 
                         oldest ozone-observing stations in Brazil, which is expected to be 
                         influenced by fire plumes in Africa and Brazil. Many studies that 
                         examined ozone trends used the total atmospheric columns of ozone, 
                         but it is important to assess ozone separately in the troposphere 
                         and the stratosphere. In this study, we have used radiosonde ozone 
                         profiles and daily TCO measurements to evaluate the variability 
                         and changes of both tropospheric and stratospheric ozone 
                         separately. The dataset in this study comprises daily total 
                         columns of colocalized ozone and weekly ozone profiles collected 
                         between 1998 and 2019. The tropospheric columns were estimated by 
                         integrating ozone profiles measured by ozone sondes up to the 
                         tropopause height. The amount of ozone in the stratosphere was 
                         then deduced by subtracting the tropospheric ozone amount from the 
                         total amount of ozone measured by the Dobson spectrometer. It was 
                         assumed that the amount of ozone in the mesosphere is negligible. 
                         This produced three distinct time series of ozone: tropospheric 
                         and stratospheric columns as well as total columns. The present 
                         study aims to apply a new decomposition method named Empirical 
                         Adaptive Wavelet Decomposition (EAWD) that is used to identify the 
                         different modes of variability present in the analyzed signal. 
                         This is achieved by summing up the most significant Intrinsic Mode 
                         Functions (IMF). The Fourier spectrum of the original signal is 
                         broken down into spectral bands that frame each IMF obtained by 
                         the Empirical Modal Decomposition (EMD). Then, the Empirical 
                         Wavelet Transform (EWT) is applied to each interval. Unlike other 
                         methods like EMD and multi-linear regression (MLR), the EAWD 
                         technique has an advantage in providing better frequency 
                         resolution and thus overcoming the phenomenon of mode-mixing, as 
                         well as detecting possible breakpoints in the trend mode. The 
                         obtained ozone datasets were analyzed using three methods: MLR, 
                         EMD, and EAWD. The EAWD algorithm exhibited the advantage of 
                         retrieving ~90% to 95% of ozone variability and detecting possible 
                         breakpoints in its trend component. Overall, the MRL and EAWD 
                         methods showed almost similar trends, a decrease in the 
                         stratosphere ozone (\−1.3 ± 0.8%) and an increase in the 
                         tropospheric ozone (+4.9 ± 1.3%). This study shows the relevance 
                         of combining data to separately analyze tropospheric and 
                         stratospheric ozone variability and trends. It highlights the 
                         advantage of the EAWD algorithm in detecting modes of variability 
                         in a geophysical signal without prior knowledge of the underlying 
                         forcings.",
                  doi = "10.3390/rs16010208",
                  url = "http://dx.doi.org/10.3390/rs16010208",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-16-00208.pdf",
        urlaccessdate = "18 maio 2024"
}