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

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

<p>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.</p><p>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°×0.25° global grid and as daily, global composite products (Level 2b) with a spatial resolution of 0.05°×0.05°. Time series analyses of the CLARA-A2.1 cloud products show the homogeneity and stability of the extension.</p><p>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.</p>

scholarly journals The Cloud_cci simulator for the ESA Cloud_cci climate data record and its application to a global and a regional climate model

2018 ◽  
Author(s):  
Salomon Eliasson ◽  
Karl Göran Karlsson ◽  
Erik van Meijgaard ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Climate Data ◽  
European Continent ◽  
Cloud Properties ◽  
Data Record ◽  
The Impact ◽  
Climate Data Record

Abstract. The Cloud_cci satellite simulator has been developed to enable comparisons between the Cloud_cci Climate Data Record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth Global Climate Model as well as the RACMO Regional Climate Model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in Liquid Water Path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces Total Cloud Fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian sub-continent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean, but lower than retrieved over the European continent, where in addition the Cloud Top Pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modelled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

scholarly journals 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

2019 ◽  
Vol 12 (7) ◽  
pp. 4091-4112 ◽  
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.

Development of a microwave-based precipitation climate data record for the Copernicus Climate Change Service

2020 ◽  
Author(s):  
Giulia Panegrossi ◽  
Paolo Sanò ◽  
Leonardo Bagaglini ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Satellite Observations ◽  
Passive Microwave ◽  
Monthly Precipitation ◽  
Climate Data ◽  
Data Record ◽  
Level 3 ◽  
Climate Data Record ◽  
Level 2

<p>Within the Copernicus Climate Change Service (C3S), the Climate Data Store (CDS) built by ECMWF will provide open and free access to global and regional products of Essential Climate Variables (ECV) based on satellite observations spanning several decades, amongst other things. Given its significance in the Earth system and particularly for human life, the ECV precipitation will be of major interest for users of the CDS.</p><p>C3S strives to include as many established, high-quality data sets as possible in the CDS. However, it also intends to offer new products dedicated for first-hand publication in the CDS. One of these products is a climate data record based on merging satellite observations of daily and monthly precipitation by both passive microwave (MW) sounders (AMSU-B/MHS) and imagers (SSMI/SSMIS) on a 1°x1° spatial grid in order to improve spatiotemporal satellite coverage of the globe.</p><p>The MW sounder observations will be obtained using, as input data, the FIDUCEO Fundamental Climate data Record (FCDR) for AMSU-B/MHS in a new global algorithm developed specifically for the project based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR; Sanò et al., 2015), adapted for climate applications (PNPR-CLIM). The algorithm consists of two Artificial Neural Network-based modules, one for precipitation detection, and one for precipitation rate estimate, trained on a global observational database built from Global Precipitation Measurement-Core Observatory (GPM-CO) measurements. The MW imager observations by SSM/I and SSMIS will be adopted from the Hamburg Ocean Atmosphere Fluxes and Parameters from Satellite data (HOAPS; Andersson et al., 2017), based on the CM SAF SSM/I and SSMIS FCDR (Fennig et al., 2017). The Level 2 precipitation rate estimates from MW sounders and imagers are combined through a newly developed merging module to obtain Level 3 daily and monthly precipitation and generate the 18-year precipitation CDR (2000-2017).</p><p>Here, we present the status of the Level 2 product’s development. We carry out a Level-2 comparison and present first results of the merged Level-3 precipitation fields. Based on this, we assess the product’s expected plausibility, coverage, and the added value of merging the MW sounder and imager observations.</p><p><strong>References</strong></p><p>Anderssonet al., 2017, DOI:10.5676/EUM_SAF_CM/HOAPS/V002</p><p>Fennig, et al., 2017, DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V003</p><p>Sanò, P., et al., 2015, DOI: 10.5194/amt-8-837-2015</p>

scholarly journals The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model

2019 ◽  
Vol 12 (2) ◽  
pp. 829-847 ◽  
Author(s):  
Salomon Eliasson ◽  
Karl Göran Karlsson ◽  
Erik van Meijgaard ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Climate Data ◽  
European Continent ◽  
Cloud Properties ◽  
Data Record ◽  
The Impact ◽  
Climate Data Record

Abstract. The Cloud Climate Change Initiative (Cloud_cci) satellite simulator has been developed to enable comparisons between the Cloud_cci climate data record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth global climate model as well as the Regional Atmospheric Climate Model (RACMO) regional climate model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in liquid water path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces total cloud fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian subcontinent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean but lower than retrieved over the European continent, where in addition the cloud top pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modeled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

scholarly journals Uncertainties in CERES Top-of-Atmosphere Fluxes Caused by Changes in Accompanying Imager

2020 ◽  
Vol 12 (12) ◽  
pp. 2040
Author(s):  
Wenying Su ◽  
Lusheng Liang ◽  
Hailan Wang ◽  
Zachary A. Eitzen
Keyword(s):  
Optical Depth ◽  
Zenith Angle ◽  
Radiation Budget ◽  
Climate Data ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Data Record ◽  
Climate Data Record ◽  
Earth’S Radiation Budget

The Clouds and the Earth’s Radiant Energy System (CERES) project provides observations of Earth’s radiation budget using measurements from CERES instruments on board the Terra, Aqua, Suomi National Polar-orbiting Partnership (S-NPP), and NOAA-20 satellites. The CERES top-of-atmosphere (TOA) fluxes are produced by converting radiance measurements using empirical angular distribution models, which are functions of cloud properties that are retrieved from imagers flying with the CERES instruments. As the objective is to create a long-term climate data record, not only calibration consistency of the six CERES instruments needs to be maintained for the entire time period, it is also important to maintain the consistency of other input data sets used to produce this climate data record. In this paper, we address aspects that could potentially affect the CERES TOA flux data quality. Discontinuities in imager calibration can affect cloud retrieval which can lead to erroneous flux trends. When imposing an artificial 0.6 per decade decreasing trend to cloud optical depth, which is similar to the trend difference between CERES Edition 2 and Edition 4 cloud retrievals, the decadal SW flux trend changed from − 0.3 5 ± 0.18 Wm − 2 to 0.61 ± 0.18 Wm − 2 . This indicates that a 13% change in cloud optical depth results in about 1% change in the SW flux. Furthermore, different CERES instruments provide valid fluxes at different viewing zenith angle ranges, and including fluxes derived at the most oblique angels unique to S-NPP (>66 ∘ ) can lead to differences of 0.8 Wm − 2 and 0.3 Wm − 2 in global monthly mean instantaneous SW flux and LW flux. To ensure continuity, the viewing zenith angle ranges common to all CERES instruments (<66 ∘ ) are used to produce the long-term Earth’s radiation budget climate data record. The consistency of cloud properties retrieved from different imagers also needs to be maintained to ensure the TOA flux consistency.

scholarly journals The International Satellite Cloud Climatology Project H-Series climate data record product

2018 ◽  
Vol 10 (1) ◽  
pp. 583-593 ◽  
Author(s):  
Alisa H. Young ◽  
Kenneth R. Knapp ◽  
Anand Inamdar ◽  
William Hankins ◽  
William B. Rossow
Keyword(s):  
Control Measures ◽  
Radiation Budget ◽  
Series Data ◽  
Climate Data ◽  
Cloud Properties ◽  
Data Record ◽  
Polar Orbiting Satellite ◽  
Basic Characteristics ◽  
Climate Data Record ◽  
Series Product

Abstract. This paper describes the new global long-term International Satellite Cloud Climatology Project (ISCCP) H-series climate data record (CDR). The H-series data contain a suite of level 2 and 3 products for monitoring the distribution and variation of cloud and surface properties to better understand the effects of clouds on climate, the radiation budget, and the global hydrologic cycle. This product is currently available for public use and is derived from both geostationary and polar-orbiting satellite imaging radiometers with common visible and infrared (IR) channels. The H-series data currently span July 1983 to December 2009 with plans for continued production to extend the record to the present with regular updates. The H-series data are the longest combined geostationary and polar orbiter satellite-based CDR of cloud properties. Access to the data is provided in network common data form (netCDF) and archived by NOAA's National Centers for Environmental Information (NCEI) under the satellite Climate Data Record Program (https://doi.org/10.7289/V5QZ281S). The basic characteristics, history, and evolution of the dataset are presented herein with particular emphasis on and discussion of product changes between the H-series and the widely used predecessor D-series product which also spans from July 1983 through December 2009. Key refinements included in the ISCCP H-series CDR are based on improved quality control measures, modified ancillary inputs, higher spatial resolution input and output products, calibration refinements, and updated documentation and metadata to bring the H-series product into compliance with existing standards for climate data records.

scholarly journals A global total column ozone climate data record

2021 ◽  
Vol 13 (8) ◽  
pp. 3885-3906
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 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).

scholarly journals Investigations into the Development of a Satellite-Based Aerosol Climate Data Record using ATSR-2, AATSR and AVHRR data

2019 ◽  
Author(s):  
Yahui Che ◽  
Jie Guang ◽  
Gerrit de Leeuw ◽  
Yong Xue ◽  
Ling Sun ◽  
...  
Keyword(s):  
Land Surface ◽  
Reference Data ◽  
Mountainous Area ◽  
Data Sets ◽  
Climate Data ◽  
Data Set ◽  
North East ◽  
Data Record ◽  
Single Sensor ◽  
Climate Data Record

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 others everywhere and at any time. 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, i.e. the Sea and Land Surface Temperature Radiometer (SLSTR) which flies on Sentinel-3A launched in early 2016. However, this leaves a gap of about 4 years between the end of the AATSR and the start of the SLSTR data records. To fill this gap, and to investigate the possibility to extend 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 v2.31 developed by Finnish Meteorological Institute (FMI), and from the AVHRR sensors using the ADL algorithm developed by RADI/CAR. Together these data sets cover a multi-decadal period (1983–2014). The study area includes two contrasting areas, both as regards aerosol content and composition and surface properties, i.e. a region over North-East (NE) 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 sunphotometer AOD data in AERONET and CARSNET are used as reference, together with radiation-derived AOD data at Beijing to cover the time before sunphotometer observations became available in the early 2000s. In addition, MODIS-Terra C6.1 AOD data are used as reference data set over the wide area where no ground-based data are available. All satellite data over the study area were validated versus the reference data, showing the qualification of MODIS for comparison with ATSR and AVHRR. The comparison with MODIS shows that AVHRR performs better that ATSR in the north of the study area (40° N), whereas further south ATSR provides better results. The validation versus sunphotometer AOD shows that both AVHRR and ATSR underestimate the AOD, with ATSR failing to provide reliable results in the winter time. 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 in the summer season 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 radiance-derived 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 which need to be accounted for when combining them in a single multi-decadal climate data record.

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

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).

scholarly journals A Global Total Column Ozone Climate Data Record

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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