Ozone monitoring with the GOMOS-ENVISAT experiment version 5

Abstract. The GOMOS ozone profiles derived have been analyzed to evaluate the GOMOS ability to capture the long-term ozone evolution during its expected recovery phase. Version 5 of the GOMOS data has been compared with two of the longest ground-based instruments based on different techniques and already involved with many other previous space instrument validations. Increasing differences reported in 2006 indicate that some of the retrieved profiles are strongly biased. This bad retrieval is probably due to the increasing dark charge of the detectors combined with an inadequate method for its correction. This effect does not induce a continuous bias but is rather exhibiting a bimodal distribution including the correct profiles and the bad retrievals. For long-term analysis it is recommended to filter the data accordingly. The new method of dark charge estimate that is proposed to be implemented in the version 6 of the ESA algorithm, seems to reduce significantly the occurrence of such effects and will allow to monitor stratospheric ozone using GOMOS data with better confidence.

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Mid-latitude ozone monitoring with the GOMOS-ENVISAT experiment version 5: the noise issue

Atmospheric Chemistry and Physics ◽

10.5194/acp-10-11839-2010 ◽

2010 ◽

Vol 10 (23) ◽

pp. 11839-11849 ◽

Cited By ~ 11

Author(s):

P. Keckhut ◽

A. Hauchecorne ◽

L. Blanot ◽

K. Hocke ◽

S. Godin-Beekmann ◽

...

Keyword(s):

Stratospheric Ozone ◽

Bimodal Distribution ◽

Recovery Phase ◽

New Method ◽

Light Conditions ◽

Temperature Spectrum ◽

Background Light ◽

Spectrum Shape ◽

Ozone Monitoring

Abstract. The GOMOS ozone profiles have been analysed to evaluate the GOMOS ability to capture the long-term ozone evolution at mid-latitudes during the expected recovery phase of the ozone layer. Version 5 of the operational GOMOS ozone data has been compared with data from two of the longest ground-based instruments based on different techniques and already involved with many other previous space instrument validations. Comparisons between ground-based and GOMOS data confirm the occurrence of spurious retrievals mainly occurring since 2006. Using a selected set of data it is shown that some bad retrievals are induced by the increasing dark charge of the detectors combined with an inadequate method for its correction. This effect does not only induce a continuous bias, but is rather exhibiting a bimodal distribution including the correct profiles and the bad retrievals. For long-term analyses it is recommended filtering the data according to background light conditions and star temperature (spectrum shape). The new method of the dark charge estimate proposed to be implemented in the version 6 of the ESA algorithm seems to significantly reduce the occurrence of such effects and should allow to monitor stratospheric ozone using GOMOS data with greater confidence.

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Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset from a Chemical Transport Model

10.5194/egusphere-egu21-9613 ◽

2021 ◽

Author(s):

Sandip Dhomse ◽

Martyn Chipperfield

Keyword(s):

Machine Learning ◽

Transport Model ◽

Stratospheric Ozone ◽

Chemical Transport ◽

Recovery Phase ◽

Horizontal Resolution ◽

Chemical Transport Model ◽

High Quality ◽

Ozone Profile

High quality global ozone profile datasets are necessary to monitor changes in stratospheric ozone. Hence, various methodologies have been used to merge and homogenise different satellite datasets in order to create long-term observation-based ozone profile datasets with minimal data gaps. However, individual satellite instruments use different measurement methods and retrieval algorithms that complicate the merging of these different datasets. Furthermore, although atmospheric chemical models are able to simulate chemically consistent long-term datasets, they are prone to the deficiencies associated with the computationally expensive processes that are generally represented by simplified parameterisations or uncertain parameters.Here, we use chemically consistent output from a 3-D Chemical Transport Model (CTM, TOMCAT) and an ensemble of three machine learning (ML) algorithms (Adaboost, GradBoost, Random Forest), to create a 42-year (1979-2020) stratospheric ozone profile dataset. The ML algorithms are primarily trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) dataset by selecting the UARS-MLS (1992-1998) and AURA-MLS (2005-2019) time periods. This ML-corrected version of monthly mean zonal mean TOMCAT (hereafter ML-TOMCAT) ozone profile data is available at both pressure (1000 hPa - 1 hPa) and geometric height (surface to 50 km) levels at about 2.5 degree horizontal resolution.We will present a detailed evaluation of ML-TOMCAT profiles against range of merged satellite datasets (e.g. GOZCARDS, SAGE-CCI-OMPS, and BVertOzone) as well high quality solar occultation observations (e.g. SAGE-II v7.0 (1984-2005), HALOE v19 (1991-2005) and ACE v4.1 (2004-2020). Overall, ML-TOMCAT shows good agreement with the evaluation datasets but with poorer agreement at low latitudes. We also show that, as in different merged satellite data sets, ML-algorithms show larger spread in the tropical middle stratosphere. Finally, we will present a trend analysis from TOMCAT and ML-TOMCAT profiles for the post-1998 ozone recovery phase.

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Reevaluation of stratospheric ozone trends from SAGE II data using a simultaneous temporal and spatial analysis

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-13455-2014 ◽

2014 ◽

Vol 14 (24) ◽

pp. 13455-13470 ◽

Cited By ~ 31

Author(s):

R. P. Damadeo ◽

J. M. Zawodny ◽

L. W. Thomason

Keyword(s):

Spatial Analysis ◽

Stratospheric Ozone ◽

Recovery Period ◽

New Method ◽

Data Sets ◽

Sampled Data ◽

Data Set ◽

Multiple Data Sets ◽

Temporal And Spatial

Abstract. This paper details a new method of regression for sparsely sampled data sets for use with time-series analysis, in particular the Stratospheric Aerosol and Gas Experiment (SAGE) II ozone data set. Non-uniform spatial, temporal, and diurnal sampling present in the data set result in biased values for the long-term trend if not accounted for. This new method is performed close to the native resolution of measurements and is a simultaneous temporal and spatial analysis that accounts for potential diurnal ozone variation. Results show biases, introduced by the way data are prepared for use with traditional methods, can be as high as 10%. Derived long-term changes show declines in ozone similar to other studies but very different trends in the presumed recovery period, with differences up to 2% per decade. The regression model allows for a variable turnaround time and reveals a hemispheric asymmetry in derived trends in the middle to upper stratosphere. Similar methodology is also applied to SAGE II aerosol optical depth data to create a new volcanic proxy that covers the SAGE II mission period. Ultimately this technique may be extensible towards the inclusion of multiple data sets without the need for homogenization.

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