Nina Håkansson

The long series of multispectral measurements from the Advanced Very High Resolution Radiometer (AVHRR), which began in 1979, is now approaching its end, with the last remaining AVHRR sensor currently operating aboard EUMETSAT's Metop-C satellite. Several climate data records (CDRs) built on AVHRR data now face the end of their observational record. However, since many modern imagers contain AVHRR-heritage spectral channels, the potential for an extension of these AVHRR-based climate data records exists. This study investigates the possibility of simulating original National Oceanic and Atmospheric Administration-19 (NOAA-19) AVHRR channels from the Suomi National Polar-orbiting Platform (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) radiances using collocated AVHRR–VIIRS datasets from 2012–2013. Spectral band adjustments (SBAs) were derived using linear regression and neural networks (NNs). The NN approach produced the best results, and separating daytime from night-time conditions when simulating AVHRR channel 3B at 3.7 µm was key. Furthermore, daytime radiance corrections in this channel must depend on actual surface and cloud reflectances to be realistic, which was achieved only through the NN approach. The cloud mask, cloud top height, and cloud phase products were produced from the simulated AVHRR radiances using the same retrieval methods for NOAA-19 data used to compile the CLARA-A3 CDR. CLARA-A3 is the third edition of the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) CDR, with cloud parameters, surface albedo, surface radiation, and top of atmosphere (TOA) radiation products from AVHRR. Products were validated using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations–Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO–CALIOP) cloud products and agreed well with the original CLARA-A3 products, with the best results provided by the NN simulation approach. The NN-based approach best reproduced the corresponding products for cloud optical thickness (COT), cloud effective radius (CRE), liquid water path (LWP), and ice water path (IWP). The CLARA-A3 CDR will be complemented and extended with VIIRS-based products to cover the period 1979–2024 (46 years). This edition will be known as CLARA-A3.5. Future extensions and editions can follow a similar approach by applying the same radiance simulation method to collocated data from the Metop-C AVHRR and the Metop Second Generation (SG) METimage sensors, with the first satellite of the latter scheduled for launch in August 2025. The successful simulation of AVHRR radiances from METimage and VIIRS data enables the CLARA CDR extension for several decades.

Radiances from the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-19 satellite were successfully simulated from Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) radiances using collocated AVHRR/VIIRS datasets from 2012–2013. Spectral Band Adjustment Factors (SBAFs) were derived using linear regression and neural networks (NNs). The NN approach produced the best results, and separating daytime from night-time conditions when simulating AVHRR channel 3B at 3.7 µm was key. Furthermore, daytime radiance corrections in this channel must depend on actual surface and cloud reflectances to be realistic, which was only achieved by the NN approach. The cloud mask, cloud top height, and cloud phase products were produced from the simulated AVHRR radiances using the same retrieval methods for NOAA-19 data used to compile the CLARA-A3 climate data record (CDR). CLARA-A3 is the third edition of the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) CDR with cloud parameters, surface albedo, surface radiation, and Top of Atmosphere (TOA) radiation products from AVHRR. Products were validated using CALIPSO cloud products and agreed well with original CLARA-A3 products, with the best results provided by the NN simulation approach. The NN-based approach best reproduced the corresponding products for cloud optical thickness (COT), cloud effective radius (CRE), liquid water path (LWP), and ice water path (IWP). The CLARA-A3 CDR will be complemented and extended with VIIRS-based products to cover the period 1979–2024 (46 years). This edition will be known as CLARA-A3.5. Future extensions and editions can follow a similar approach by applying the same radiance simulation method to collocated data from the Metop-C AVHRR and the Metop-SG METimage sensors, the first version of the latter scheduled for launch in August 2025. Successful simulation of AVHRR radiances from METimage data enables the extension of the CLARA CDR for several decades using observations from VIIRS and METimage.

Radiances from the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-19 satellite were successfully simulated from Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) radiances using collocated AVHRR/VIIRS datasets from 2012–2013. Spectral Band Adjustment Factors (SBAFs) were derived using linear regression and neural networks (NNs). The NN approach produced the best results, and separating daytime from night-time conditions when simulating AVHRR channel 3B at 3.7 µm was key. Furthermore, daytime radiance corrections in this channel must depend on actual surface and cloud reflectances to be realistic, which was only achieved by the NN approach. The cloud mask, cloud top height, and cloud phase products were produced from the simulated AVHRR radiances using the same retrieval methods for NOAA-19 data used to compile the CLARA-A3 climate data record (CDR). CLARA-A3 is the third edition of the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) CDR with cloud parameters, surface albedo, surface radiation, and Top of Atmosphere (TOA) radiation products from AVHRR. Products were validated using CALIPSO cloud products and agreed well with original CLARA-A3 products, with the best results provided by the NN simulation approach. The NN-based approach best reproduced the corresponding products for cloud optical thickness (COT), cloud effective radius (CRE), liquid water path (LWP), and ice water path (IWP). The CLARA-A3 CDR will be complemented and extended with VIIRS-based products to cover the period 1979–2024 (46 years). This edition will be known as CLARA-A3.5. Future extensions and editions can follow a similar approach by applying the same radiance simulation method to collocated data from the Metop-C AVHRR and the Metop-SG METimage sensors, the first version of the latter scheduled for launch in August 2025. Successful simulation of AVHRR radiances from METimage data enables the extension of the CLARA CDR for several decades using observations from VIIRS and METimage.

Radiances from the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-19 satellite were successfully simulated from Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) radiances using collocated AVHRR/VIIRS datasets from 2012–2013. Spectral Band Adjustment Factors (SBAFs) were derived using linear regression and neural networks (NNs). The NN approach produced the best results, and separating daytime from night-time conditions when simulating AVHRR channel 3B at 3.7 µm was key. Furthermore, daytime radiance corrections in this channel must depend on actual surface and cloud reflectances to be realistic, which was only achieved by the NN approach. The cloud mask, cloud top height, and cloud phase products were produced from the simulated AVHRR radiances using the same retrieval methods for NOAA-19 data used to compile the CLARA-A3 climate data record (CDR). CLARA-A3 is the third edition of the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) CDR with cloud parameters, surface albedo, surface radiation, and Top of Atmosphere (TOA) radiation products from AVHRR. Products were validated using CALIPSO cloud products and agreed well with original CLARA-A3 products, with the best results provided by the NN simulation approach. The NN-based approach best reproduced the corresponding products for cloud optical thickness (COT), cloud effective radius (CRE), liquid water path (LWP), and ice water path (IWP). The CLARA-A3 CDR will be complemented and extended with VIIRS-based products to cover the period 1979–2024 (46 years). This edition will be known as CLARA-A3.5. Future extensions and editions can follow a similar approach by applying the same radiance simulation method to collocated data from the Metop-C AVHRR and the Metop-SG METimage sensors, the first version of the latter scheduled for launch in August 2025. Successful simulation of AVHRR radiances from METimage data enables the extension of the CLARA CDR for several decades using observations from VIIRS and METimage.

Radiances from the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-19 satellite were successfully simulated from Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) radiances using collocated AVHRR/VIIRS datasets from 2012–2013. Spectral Band Adjustment Factors (SBAFs) were derived using linear regression and neural networks (NNs). The NN approach produced the best results, and separating daytime from night-time conditions when simulating AVHRR channel 3B at 3.7 µm was key. Furthermore, daytime radiance corrections in this channel must depend on actual surface and cloud reflectances to be realistic, which was only achieved by the NN approach. The cloud mask, cloud top height, and cloud phase products were produced from the simulated AVHRR radiances using the same retrieval methods for NOAA-19 data used to compile the CLARA-A3 climate data record (CDR). CLARA-A3 is the third edition of the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) CDR with cloud parameters, surface albedo, surface radiation, and Top of Atmosphere (TOA) radiation products from AVHRR. Products were validated using CALIPSO cloud products and agreed well with original CLARA-A3 products, with the best results provided by the NN simulation approach. The NN-based approach best reproduced the corresponding products for cloud optical thickness (COT), cloud effective radius (CRE), liquid water path (LWP), and ice water path (IWP). The CLARA-A3 CDR will be complemented and extended with VIIRS-based products to cover the period 1979–2024 (46 years). This edition will be known as CLARA-A3.5. Future extensions and editions can follow a similar approach by applying the same radiance simulation method to collocated data from the Metop-C AVHRR and the Metop-SG METimage sensors, the first version of the latter scheduled for launch in August 2025. Successful simulation of AVHRR radiances from METimage data enables the extension of the CLARA CDR for several decades using observations from VIIRS and METimage.

CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI (Spinning Enhanced Visible and InfraRed Imager), was released in December 2022. It is based on observations from SEVIRI, on board geostationary satellites Meteosat-8, 9, 10 and 11, which are operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). CLAAS-3 was produced and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which aims to provide high-quality satellite-based data records suitable for climate monitoring applications. Compared to previous CLAAS releases, CLAAS-3 is expanded in terms of both temporal extent and cloud properties included, and it is based on partly updated retrieval algorithms. The available data span the period from 2004 to present, covering Europe; Africa; the Atlantic Ocean; and parts of South America, the Middle East and the Indian Ocean. They include cloud fractional coverage, cloud-top height, phase (liquid or ice) and optical and microphysical properties (water path, optical thickness, effective radius and droplet number concentration), from instantaneous data (every 15 min) to monthly averages. In this study we present an extensive evaluation of CLAAS-3 cloud properties, based on independent reference data sets. These include satellite-based retrievals from active and passive sensors, ground-based observations and in situ measurements from flight campaigns. Overall results show very good agreement, with small biases attributable to different sensor characteristics, retrieval/sampling approaches and viewing/illumination conditions. These findings demonstrate the fitness of CLAAS-3 to support the intended applications, which include evaluation of climate models, cloud characterisation and process studies focusing especially on the diurnal cycle and cloud filtering for other applications. The CLAAS-3 data record is publicly available via the CM SAF website at https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V003 (Meirink et al., 2022).

This paper presents the third edition of The Satellite Application Facility on Climate Monitoring's (CM SAF) cloud, albedo, and surface radiation dataset from advanced very-high-resolution radiometer (AVHRR) data, CLARA-A3. The content of earlier CLARA editions, namely cloud, surface albedo, and surface radiation products, has been extended with two additional surface albedo products (blue- and white-sky albedo), three additional surface radiation products (net shortwave and longwave radiation, and surface radiation budget), and two top of atmosphere radiation budget products (reflected solar flux and outgoing longwave radiation). The record length is extended to 42 years (1979–2020) by also incorporating results from the first version of the advanced very high resolution radiometer imager (AVHRR/1). A continuous extension of the climate data record (CDR) has also been implemented by processing an interim climate data record (ICDR) based on the same set of algorithms but with slightly changed ancillary input data. All products are briefly described together with validation results and intercomparisons with currently existing similar CDRs. The extension of the product portfolio and the temporal coverage of the data record, together with product improvements, is expected to enlarge the potential of using CLARA-A3 for climate change studies and, in particular, studies of potential feedback effects between clouds, surface albedo, and radiation. The CLARA-A3 data record is hosted by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) CM SAF and is freely available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b).

CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI, was released in December 2022. It is based on observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), on board geostationary satellites Meteosat-8, 9, 10 and 11, which are operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). CLAAS-3 was produced and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which aims to provide high quality satellite-based data records suitable for climate monitoring applications. Compared to previous CLAAS releases, CLAAS-3 is expanded in terms of both temporal extent and cloud properties included, and it is based on partly updated retrieval algorithms. The available data span the period from 2004 to present, covering Europe, Africa, the Atlantic Ocean and parts of South America, the Middle East and the Indian Ocean. They include cloud fractional coverage, cloud-top height, phase (liquid or ice) and optical and microphysical properties (water path, optical thickness, effective radius and droplet number concentration), from instantaneous data (every 15 minutes) to monthly averages. In this study we present an extensive evaluation of CLAAS-3 cloud properties, based on independent reference data sets. These include satellite-based retrievals from active and passive sensors, ground-based observations and in situ measurements from flight campaigns. Overall results show very good agreement, with small biases attributable to different sensor characteristics, retrieval/sampling approaches, and viewing/illumination conditions. These findings demonstrate the fitness of CLAAS-3 to support the intended applications, which include evaluation of climate models, cloud characterization and process studies focusing especially on the diurnal cycle, and cloud filtering for other applications. The CLAAS-3 data record is publicly available via the CM SAF website (https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V003) (Meirink et al., 2022).

This paper presents the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data, CLARA-A3. The content of earlier CLARA editions, namely cloud, surface albedo, and surface radiation products, has been extended with two additional surface albedo products (blue and white sky albedo), three additional surface radiation products (net shortwave and longwave radiation, surface radiation budget) and two top of atmosphere radiation budget products (reflected solar flux and outgoing longwave radiation). The record length is extended to 42 years (1979–2020) by also incorporating results from the first version of the Advanced Very High Resolution Radiometer (AVHRR) imager (AVHRR/1). A continuous extension of the climate data record (CDR) has also been implemented by processing an interim climate data record (ICDR) based on the same set of algorithms but with slightly changed ancillary input data. All products are briefly described together with validation results and inter-comparisons with currently existing similar CDRs. The extension of the product portfolio and the temporal coverage of the data record, together with product improvements, is expected to enlarge the potential of using CLARA-A3 for climate change studies and, in particular, studies of potential feedback effects between clouds, surface albedo and radiation. The CLARA-A3 data record is hosted by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) and is freely available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003.

Cloud screening in satellite imagery is essential for enabling retrievals of atmospheric and surface properties. For climate data record (CDR) generation, cloud screening must be balanced, so both false cloud-free and false cloudy retrievals are minimized. Many methods used in recent CDRs show signs of clear-conservative cloud screening leading to overestimated cloudiness. This study presents a new cloud screening approach for Advanced Very-High-Resolution Radiometer (AVHRR) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) imagery based on the Bayesian discrimination theory. The method is trained on high-quality cloud observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The method delivers results designed for optimally balanced cloud screening expressed as cloud probabilities together with information on for which clouds (minimum cloud optical thickness) the probabilities are valid. Cloud screening characteristics over 28 different Earth surface categories were estimated. Using independent CALIOP observations (including all observed clouds) in 2010 for validation, the total global hit rates for AVHRR data and the SEVIRI full disk were 82% and 85%, respectively. High-latitude oceans had the best performance, with a hit rate of approximately 93%. The results were compared to the CM SAF cLoud, Albedo, and surface RAdiation dataset from AVHRR data–second edition (CLARA-A2) CDR and showed general improvements over most global regions. Notably, the Kuipers’ Skill Score improved, verifying a more balanced cloud screening. The new method will be used to prepare the new CLARA-A3 and CLAAS-3 (CLoud property dAtAset using SEVIRI, Edition 3) CDRs in the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project.