A global total column ozone climate data record

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).

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A Global Total Column Ozone Climate Data Record

10.5194/essd-2020-218 ◽

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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A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range

10.5194/essd-2021-319 ◽

2021 ◽

Author(s):

Christian Borger ◽

Steffen Beirle ◽

Thomas Wagner

Keyword(s):

Water Vapour ◽

Reference Data ◽

Retrieval Algorithm ◽

Time Range ◽

Data Sets ◽

Climate Data ◽

Data Set ◽

Data Record ◽

Climate Data Record ◽

Total Column

Abstract. We present a long-term data set of 1° × 1° monthly mean total column water vapour (TCWV) based on global measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. In comparison to the retrieval algorithm of Borger et al. (2020) several modifications and filters have been applied accounting for instrumental issues (such as OMI's "row-anomaly") or the inferior quality of solar reference spectra. For instance, to overcome the problems of low quality reference spectra, the daily solar irradiance spectrum is replaced by an annually varying mean Earthshine radiance obtained in December over Antarctica. For the TCWV data set only measurements are taken into account for which the effective cloud fraction < 20 %, the AMF > 0.1, the ground pixel is snow- and ice-free, and the OMI row is not affected by the "row-anomaly" over the complete time range of the data set. The individual TCWV measurements are then gridded to a regular 1° × 1° lattice, from which the monthly means are calculated. In a comprehensive validation study we demonstrate that the OMI TCWV data set is in good agreement to reference data sets of ERA5, RSS SSM/I, and ESA CCI Water Vapour CDR-2: over ocean ordinary least squares (OLS) as well as orthogonal distance regressions (ODR) indicate slopes close to unity with very small offsets and high correlation coefficients of around 0.98. However, over land, distinctive positive deviations are obtained especially within the tropics with relative deviations of approximately +10 % likely caused by uncertainties in the retrieval input data (surface albedo, cloud information) due to frequent cloud contamination in these regions. Nevertheless, a temporal stability analysis proves that the OMI TCWV data set is consistent with the temporal changes of the reference data sets and shows no significant deviation trends. Since the TCWV retrieval can be easily applied to further satellite missions, additional TCWV data sets can be created from past missions such as GOME-1 or SCIAMACHY, which under consideration of systematic differences (e.g. due to different observation times) can be combined with the OMI TCWV data set in order to create a data record that would cover a time span from 1995 to the present. Moreover, the TCWV retrieval will also work for all missions dedicated to NO2 in future such as Sentinel-5 on MetOp-SG. The MPIC OMI total column water vapour (TCWV) climate data record is available at https://doi.org/10.5281/zenodo.5776718 (Borger et al., 2021b).

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Global and Regional Satellite-based Surface Solar Radiation data sets provided by the CM SAF

10.5194/egusphere-egu21-8167 ◽

2021 ◽

Author(s):

Jörg Trentmann ◽

Uwe Pfeifroth ◽

Jaqueline Drücke ◽

Roswitha Cremer

Keyword(s):

Solar Radiation ◽

Global Climate ◽

Surface Radiation ◽

Data Sets ◽

Climate Data ◽

Surface Solar Radiation ◽

Climate Monitoring ◽

Data Record ◽

Irradiance Data ◽

Climate Data Record

The incoming surface solar radiation has been defined as an essential climate variable by GCOS. Long term monitoring of this part of the earth&#8217;s energy budget is required to gain insights on the state and variability of the climate system. In addition, climate data sets of surface solar radiation have received increased attention over the recent years as an important source of information for solar energy assessments, for crop modeling, and for the validation of climate and weather models.The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) is deriving climate data records (CDRs) from geostationary and polar-orbiting satellite instruments. Within the CM SAF these CDRs are accompanied by operational data at a short time latency to be used for climate monitoring. All data from the CM SAF are freely available via www.cmsaf.eu.Here we present the regional and global climate data records of surface solar radiation from the CM SAF. The regional SARAH-2.1 climate data record (Surface Solar Radiation Dataset &#8211; Heliosat, doi: 10.5676/EUM_SAF_CM/SARAH/V002_01) is based on observations from the series of Meteosat satellites. SARAH-2.1 provides high resolution data (temporal and spatial) of the surface solar radiation (global and direct) and the sunshine duration from 1983 to 2017 for the full view of the Meteosat satellite (i.e, Europe, Africa, parts of South America, and the Atlantic ocean). The global climate data record CLARA (CM SAF Clouds, Albedo and Radiation dataset from AVHRR data, doi: 10.5676/EUM_SAF_CM/CLARA_AVHRR/V002_01) is based on observations from the series of AVHRR instruments onboard polar-orbiting satellites. CLARA provides daily- and monthly-averaged global data of the solar irradiance (SIS) from January 1982 to June 2019 with a spatial resolution of 0.25&#176;. In addition to the solar surface radiation, also the longwave surface radiation as well as surface albedo and numerous cloud properties are provided in CLARA. The high accuracy and stability of these data record allows the assessment of the spatial and temporal variability and trends as well as a number of other applications that require high-resolution surface irradiance data.Both Thematic Climate Data Records (TCDR) are accompanied and temporally-extended by consistent data records, so-called Interim Climate Data Records (ICDR), which are provided with a latency of 5 days to support applications that require more recent surface irradiance data, e.g., operational climate monitoring.In late 2021 / early 2022 new versions of both data records, SARAH and CLARA, will be provided by the CM SAF. The quality of these data records will be improved, e.g, by a better treatment of snow-covered surfaces, and temporally extended to cover the WMO climate reference period 1991 to 2020. Here, first results of the updated data records and their improvements will be presented.

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Quality assessment and improvement of the EUMETSAT Meteosat Surface Albedo Climate Data Record

Atmospheric Measurement Techniques ◽

10.5194/amt-8-4561-2015 ◽

2015 ◽

Vol 8 (10) ◽

pp. 4561-4571 ◽

Cited By ~ 7

Author(s):

A. Lattanzio ◽

F. Fell ◽

R. Bennartz ◽

I. F. Trigo ◽

J. Schulz

Keyword(s):

Seasonal Variation ◽

Surface Albedo ◽

Global Climate ◽

Radiation Budget ◽

Limited Information ◽

Cloud Detection ◽

Climate Data ◽

Data Record ◽

Cloud Mask ◽

Climate Data Record

Abstract. Surface albedo has been identified as an important parameter for understanding and quantifying the Earth's radiation budget. EUMETSAT generated the Meteosat Surface Albedo (MSA) Climate Data Record (CDR) currently comprising up to 24 years (1982–2006) of continuous surface albedo coverage for large areas of the Earth. This CDR has been created within the Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) framework. The long-term consistency of the MSA CDR is high and meets the Global Climate Observing System (GCOS) stability requirements for desert reference sites. The limitation in quality due to non-removed clouds by the embedded cloud screening procedure is the most relevant weakness in the retrieval process. A twofold strategy is applied to efficiently improve the cloud detection and removal. The first step consists of the application of a robust and reliable cloud mask, taking advantage of the information contained in the measurements of the infrared and visible bands. Due to the limited information available from old radiometers, some clouds can still remain undetected. A second step relies on a post-processing analysis of the albedo seasonal variation together with the usage of a background albedo map in order to detect and screen out such outliers. The usage of a reliable cloud mask has a double effect. It enhances the number of high-quality retrievals for tropical forest areas sensed under low view angles and removes the most frequently unrealistic retrievals on similar surfaces sensed under high view angles. As expected, the usage of a cloud mask has a negligible impact on desert areas where clear conditions dominate. The exploitation of the albedo seasonal variation for cloud removal has good potentialities but it needs to be carefully addressed. Nevertheless it is shown that the inclusion of cloud masking and removal strategy is a key point for the generation of the next MSA CDR release.

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Extending the CM SAF global satellite-based climate data record of cloud properties

10.5194/egusphere-egu2020-20229 ◽

2020 ◽

Author(s):

Irina Solodovnik ◽

Diana Stein ◽

Jan Fokke Meirink ◽

Karl-Göran Karlsson ◽

Martin Stengel

Keyword(s):

Data Sets ◽

Climate Data ◽

Cloud Parameters ◽

Cloud Properties ◽

Spectral Bands ◽

Fractional Cloud ◽

Data Record ◽

Level 3 ◽

Earth’S Climate ◽

Climate Data Record

Global data records of cloud properties are an important part for the analysis of the Earth's climate system and its variability. One of the few sources facilitating such records are the measurements of the satellite-based Advanced Very High Resolution Radiometer (AVHRR) sensor that provides spatially homogeneous and high resolved information in multiple spectral bands. This information can be used to retrieve global cloud properties covering multiple decades, as, for example, composed as part of the CM SAF Cloud, Albedo, Radiation data record based on AVHRR (CLARA) series.In this presentation we introduce the edition 2.1 (CLARA-A2.1) of this record series, which is the temporally extended version of CLARA-A2. This extension includes three and a half more years at the end of the data record, which now covers the time period January 1982 to June 2019 (37.5 years). CLARA-A2.1 includes a comprehensive set of cloud parameters: fractional cloud cover, cloud top products, cloud thermodynamic phase and cloud physical properties, such as cloud optical thickness, particle effective radius and cloud water path. Cloud products are available as daily and monthly averages and histograms (Level 3) on a regular 0.25&#176;&#215;0.25&#176; global grid and as daily, global composite products (Level 2b) with a spatial resolution of 0.05&#176;&#215;0.05&#176;. Time series analyses of the CLARA-A2.1 cloud products show the homogeneity and stability of the extension.In addition to the general characteristics of the CLARA-A2.1 record, we will summarize the results of the thorough evaluation efforts that were conducted by validation against reference observations (e.g. SYNOP, DARDAR, CALIOP) and by comparisons to similar well established data records (e.g. Patmos-X, ISCCP-H and MODIS C6.1). CLARA-A2.1 cloud products show generally a very good agreement with all the compared data sets and fulfil CM SAF's accuracy, precision and decadal stability requirements. As an additional aspect, we will touch upon the CLARA Interim Climate Data Record (ICDR) concept that will soon be used for extending CLARA-A2.1 in near-real-time mode.

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An evaluation of the simulation of the edge of the Antarctic vortex by chemistry-climate models

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-8-20155-2008 ◽

2008 ◽

Vol 8 (6) ◽

pp. 20155-20192 ◽

Cited By ~ 1

Author(s):

H. Struthers ◽

G. E. Bodeker ◽

J. Austin ◽

S. Bekki ◽

I. Cionni ◽

...

Keyword(s):

Climate Models ◽

Measurement Data ◽

Chemical Processes ◽

Data Sets ◽

Dynamical Structure ◽

Total Column Ozone ◽

Ozone Loss ◽

Column Ozone ◽

The Antarctic ◽

Total Column

Abstract. The dynamical barrier to meridional mixing at the edge of the Antarctic spring stratospheric vortex is examined. Diagnostics are presented which demonstrate the link between the shape of the meridional mixing barrier at the edge of the vortex and the meridional gradients in total column ozone across the vortex edge. Results derived from reanalysis and measurement data sets are compared with equivalent diagnostics from five coupled chemistry-climate models to test how well the models capture the interaction between the dynamical structure of the stratospheric vortex and the chemical processes occurring within the vortex. Results show that the accuracy of the simulation of the dynamical vortex edge varies widely amongst the models studied here. This affects the ability of the models to simulate the large observed meridional gradients in total column ozone. Three of the models in this study simulated the inner edge of the vortex to be more than 7° closer to the pole than observed. This is expected to have important implications for how well these models simulate the extent of severe springtime ozone loss that occurs within the Antarctic vortex.

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The Sea State CCI dataset v1: towards a Sea State Climate Data Record based on satellite observations

10.5194/essd-2019-253 ◽

2020 ◽

Cited By ~ 3

Author(s):

Guillaume Dodet ◽

Jean-François Piolle ◽

Yves Quilfen ◽

Saleh Abdalla ◽

Mickaël Accensi ◽

...

Keyword(s):

Global Climate ◽

European Space Agency ◽

Full Potential ◽

Climate Data ◽

Sea State ◽

Denoising Method ◽

State Data ◽

Data Record ◽

Along Track ◽

Climate Data Record

Abstract. Sea state data are of major importance for climate studies, marine engineering, safety at sea, and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state Climate Data Record, the Global Climate Observing System has listed Sea State as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a program of the European Space Agency, whose objective is to realize the full potential of global Earth Observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in-situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/. Three products are available: a multi-mission along-track L2P product (https://doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (https://doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (https://doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c).

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Investigations into the development of a satellite-based aerosol climate data record using ATSR-2, AATSR and AVHRR data over north-eastern China from 1987 to 2012

Atmospheric Measurement Techniques ◽

10.5194/amt-12-4091-2019 ◽

2019 ◽

Vol 12 (7) ◽

pp. 4091-4112 ◽

Cited By ~ 1

Author(s):

Yahui Che ◽

Jie Guang ◽

Gerrit de Leeuw ◽

Yong Xue ◽

Ling Sun ◽

...

Keyword(s):

Eastern China ◽

Data Sets ◽

Climate Data ◽

Data Set ◽

North Eastern ◽

Data Record ◽

Single Sensor ◽

Sun Photometer ◽

Climate Data Record ◽

Better Than

Abstract. Satellites provide information on the temporal and spatial distributions of aerosols on regional and global scales. With the same method applied to a single sensor all over the world, a consistent data set is to be expected. However, the application of different retrieval algorithms to the same sensor and the use of a series of different sensors may lead to substantial differences, and no single sensor or algorithm is better than any other everywhere and at all times. For the production of long-term climate data records, the use of multiple sensors cannot be avoided. The Along Track Scanning Radiometer (ATSR-2) and the Advanced ATSR (AATSR) aerosol optical depth (AOD) data sets have been used to provide a global AOD data record over land and ocean of 17 years (1995–2012), which is planned to be extended with AOD retrieved from a similar sensor. To investigate the possibility of extending the ATSR data record to earlier years, the use of an AOD data set from the Advanced Very High Resolution Radiometer (AVHRR) is investigated. AOD data sets used in this study were retrieved from the ATSR sensors using the ATSR Dual View algorithm ADV version 2.31, developed by Finnish Meteorological Institute (FMI), and from the AVHRR sensors using the aerosol optical depth over land (ADL) algorithm developed by RADI/CAS. Together, these data sets cover a multi-decadal period (1987–2012). The study area includes two contrasting areas, both in regards to aerosol content and composition and surface properties, i.e. a region over north-eastern China, encompassing a highly populated urban/industrialized area (Beijing–Tianjin–Hebei) and a sparsely populated mountainous area. Ground-based AOD observations available from ground-based sun photometer AOD data in AERONET and CARSNET are used as a reference, together with broadband extinction method (BEM) data at Beijing to cover the time before sun photometer observations became available in the early 2000s. In addition, MODIS-Terra C6.1 AOD data are used as a reference data set over the wide area where no ground-based data are available. All satellite data over the study area were validated against the reference data, showing the qualification of MODIS for comparison with ATSR and AVHRR. The comparison with MODIS shows that AVHRR performs better than ATSR in the north of the study area (40∘ N), whereas further south ATSR provides better results. The validation against sun photometer AOD shows that both AVHRR and ATSR underestimate the AOD, with ATSR failing to provide reliable results in the wintertime. This is likely due to the highly reflecting surface in the dry season, when AVHRR-retrieved AOD traces both MODIS and reference AOD data well. However, AVHRR does not provide AOD larger than about 0.6 and hence is not reliable when high AOD values have been observed over the last decade. In these cases, ATSR performs much better for AOD up to about 1.3. AVHRR-retrieved AOD compares favourably with BEM AOD, except for AOD higher than about 0.6. These comparisons lead to the conclusion that AVHRR and ATSR AOD data records each have their strengths and weaknesses that need to be accounted for when combining them in a single multi-decadal climate data record.

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OMI total column ozone: extending the long-term data record

Atmospheric Measurement Techniques ◽

10.5194/amt-8-4845-2015 ◽

2015 ◽

Vol 8 (11) ◽

pp. 4845-4850 ◽

Cited By ~ 37

Author(s):

R. D. McPeters ◽

S. Frith ◽

G. J. Labow

Keyword(s):

Cross Sections ◽

Total Ozone ◽

Retrieval Algorithm ◽

Data Set ◽

Total Column Ozone ◽

Ozone Data ◽

Combining Data ◽

Data Record ◽

Column Ozone ◽

Total Column

Abstract. The ozone data record from the Ozone Monitoring Instrument (OMI) onboard the NASA Earth Observing System (EOS) Aura satellite has proven to be very stable over the 10-plus years of operation. The OMI total column ozone processed through the Total Ozone Mapping Spectrometer (TOMS) ozone retrieval algorithm (version 8.5) has been compared with ground-based measurements and with ozone from a series of SBUV/2 (Solar Backscatter Ultraviolet) instruments. Comparison with an ensemble of Brewer–Dobson sites shows an absolute offset of about 1.5 % and almost no relative trend. Comparison with a merged ozone data set (MOD) created by combining data from a series of SBUV/2 instruments again shows an offset, of about 1 %, and a relative trend of less than 0.5 % over 10 years. The offset is mostly due to the use of the old Bass–Paur ozone cross sections in the OMI retrievals rather than the Brion–Daumont–Malicet cross sections that are now recommended. The bias in the Southern Hemisphere is smaller than that in the Northern Hemisphere, 0.9 % vs. 1.5 %, for reasons that are not completely understood. When OMI was compared with the European realization of a multi-instrument ozone time series, the GTO (GOME type Total Ozone) data set, there was a small trend of about −0.85 % decade−1. Since all the comparisons of OMI relative to other ozone measuring systems show relative trends that are less than 1 % decade−1, we conclude that the OMI total column ozone data are sufficiently stable that they can be used in studies of ozone trends.

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Impact of using different ozone cross sections on ozone profile retrievals from Global Ozone Monitoring Experiment (GOME) ultraviolet measurements

Atmospheric Chemistry and Physics ◽

10.5194/acp-7-3571-2007 ◽

2007 ◽

Vol 7 (13) ◽

pp. 3571-3578 ◽

Cited By ~ 49

Author(s):

X. Liu ◽

K. Chance ◽

C. E. Sioris ◽

T. P. Kurosu

Keyword(s):

Cross Section ◽

Cross Sections ◽

Data Sets ◽

Total Column Ozone ◽

Section Data ◽

Ozone Profile ◽

Ozone Monitoring ◽

Column Ozone ◽

Flight Model ◽

Total Column

Abstract. We investigate the effect of using three different cross section data sets on ozone profile retrievals from Global Ozone Monitoring Experiment (GOME) ultraviolet measurements (289–307 nm, 326–337 nm). These include Bass-Paur, Brion, and GOME flight model cross sections (references below). Using different cross sections can significantly affect the retrievals, by up to 12 Dobson Units (DU, 1 DU=2.69×1016 molecules cm−2) in total column ozone, up to 10 DU in tropospheric column ozone, and up to 100% in retrieved ozone values for individual atmospheric layers. Compared to using the Bass-Paur and GOME flight model cross sections, using the Brion cross sections not only reduces fitting residuals by 15–60% in the Huggins bands, but also improves retrievals, especially in the troposphere, as seen from validation against ozonesonde measurements. Therefore, we recommend using the Brion cross section for ozone profile retrievals from ultraviolet measurements. The total column ozone retrieved using the GOME flight model cross sections is systematically lower, by 7–10 DU, than that retrieved using the Brion and Bass-Paur cross sections and is also systematically lower than Total Ozone Mapping Spectrometer (TOMS) observations. This study demonstrates the need for improved ozone cross section measurements in the ultraviolet to improve profile retrievals of this key atmospheric constituent.

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