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Karlsson, Karl-GöranORCID iD iconorcid.org/0000-0001-8256-0228
Publications (10 of 47) Show all publications
Benas, N., Meirink, J. F., Karlsson, K.-G., Stengel, M. & Stammes, P. (2020). Satellite observations of aerosols and clouds over southern China from 2006 to 2015: analysis of changes and possible interaction mechanisms. Atmospheric Chemistry And Physics, 20(1), 457-474
Open this publication in new window or tab >>Satellite observations of aerosols and clouds over southern China from 2006 to 2015: analysis of changes and possible interaction mechanisms
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2020 (English)In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 20, no 1, p. 457-474Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-5621 (URN)10.5194/acp-20-457-2020 (DOI)000507314100001 ()
Available from: 2020-01-29 Created: 2020-01-29 Last updated: 2020-01-29Bibliographically approved
Eliasson, S., Karlsson, K.-G., van Meijgaard, E., Meirink, J. F., Stengel, M. & Willén, U. (2019). The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model. Geoscientific Model Development, 12(2), 829-847
Open this publication in new window or tab >>The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model
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2019 (English)In: Geoscientific Model Development, ISSN 1991-959X, E-ISSN 1991-9603, Vol. 12, no 2, p. 829-847Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-5301 (URN)10.5194/gmd-12-829-2019 (DOI)000459423200001 ()
Available from: 2019-07-31 Created: 2019-07-31 Last updated: 2019-07-31Bibliographically approved
Karlsson, K.-G. & Håkansson, N. (2018). Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: demonstration of results based on the CM SAF CLARA-A2 climate data record. Atmospheric Measurement Techniques, 11(1), 633-649
Open this publication in new window or tab >>Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: demonstration of results based on the CM SAF CLARA-A2 climate data record
2018 (English)In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 11, no 1, p. 633-649Article in journal (Refereed) Published
Abstract [en]

The sensitivity in detecting thin clouds of the cloud screening method being used in the CM SAF cloud, albedo and surface radiation data set from AVHRR data (CLARA-A2) cloud climate data record (CDR) has been evaluated using cloud information from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite. The sensitivity, including its global variation, has been studied based on collocations of Advanced Very High Resolution Radiometer (AVHRR) and CALIOP measurements over a 10-year period (2006-2015). The cloud detection sensitivity has been defined as the minimum cloud optical thickness for which 50% of clouds could be detected, with the global average sensitivity estimated to be 0.225. After using this value to reduce the CALIOP cloud mask (i.e. clouds with optical thickness below this threshold were interpreted as cloud-free cases), cloudiness results were found to be basically unbiased over most of the globe except over the polar regions where a considerable underestimation of cloudiness could be seen during the polar winter. The overall probability of detecting clouds in the polar winter could be as low as 50% over the highest and coldest parts of Greenland and Antarctica, showing that a large fraction of optically thick clouds also remains undetected here. The study included an in-depth analysis of the probability of detecting a cloud as a function of the vertically integrated cloud optical thickness as well as of the cloud's geographical position. Best results were achieved over oceanic surfaces at mid-to high latitudes where at least 50% of all clouds with an optical thickness down to a value of 0.075 were detected. Corresponding cloud detection sensitivities over land surfaces outside of the polar regions were generally larger than 0.2 with maximum values of approximately 0.5 over the Sahara and the Arabian Peninsula. For polar land surfaces the values were close to 1 or higher with maximum values of 4.5 for the parts with the highest altitudes over Greenland and Antarctica. It is suggested to quantify the detection performance of other CDRs in terms of a sensitivity threshold of cloud optical thickness, which can be estimated using active lidar observations. Validation results are proposed to be used in Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulation Package (COSP) simulators for cloud detection characterization of various cloud CDRs from passive imagery.

National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4502 (URN)10.5194/amt-11-633-2018 (DOI)000423980700002 ()
Available from: 2018-02-20 Created: 2018-02-20 Last updated: 2018-02-20Bibliographically approved
Karlsson, K.-G. & Devasthale, A. (2018). Inter-Comparison and Evaluation of the Four Longest Satellite-Derived Cloud Climate Data Records: CLARA-A2, ESA Cloud CCI V3, ISCCP-HGM, and PATMOS-x. Remote Sensing, 10(10), Article ID 1567.
Open this publication in new window or tab >>Inter-Comparison and Evaluation of the Four Longest Satellite-Derived Cloud Climate Data Records: CLARA-A2, ESA Cloud CCI V3, ISCCP-HGM, and PATMOS-x
2018 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 10, article id 1567Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-5008 (URN)10.3390/rs10101567 (DOI)000448555800067 ()
Available from: 2018-11-23 Created: 2018-11-23 Last updated: 2018-11-23Bibliographically approved
Riihela, A., Key, J. R., Meirink, J. F., Munneke, P. K., Palo, T. & Karlsson, K.-G. (2017). An intercomparison and validation of satellite-based surface radiative energy flux estimates over the Arctic. Journal of Geophysical Research - Atmospheres, 122(9), 4829-4848
Open this publication in new window or tab >>An intercomparison and validation of satellite-based surface radiative energy flux estimates over the Arctic
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2017 (English)In: Journal of Geophysical Research - Atmospheres, ISSN 2169-897X, E-ISSN 2169-8996, Vol. 122, no 9, p. 4829-4848Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4119 (URN)10.1002/2016JD026443 (DOI)000402039000005 ()
Available from: 2017-06-21 Created: 2017-06-21 Last updated: 2017-06-21Bibliographically approved
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Meirink, J. F., Devasthale, A., . . . Hollmann, R. (2017). CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data. Atmospheric Chemistry And Physics, 17(9), 5809-5828
Open this publication in new window or tab >>CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data
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2017 (English)In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 17, no 9, p. 5809-5828Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4108 (URN)10.5194/acp-17-5809-2017 (DOI)000401103400002 ()
Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-06-07Bibliographically approved
Stengel, M., Stapelberg, S., Sus, O., Schlundt, C., Poulsen, C., Thomas, G., . . . Hollmann, R. (2017). Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project. Earth System Science Data, 9(2), 881-904
Open this publication in new window or tab >>Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project
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2017 (English)In: Earth System Science Data, ISSN 1866-3508, E-ISSN 1866-3516, Vol. 9, no 2, p. 881-904Article in journal (Refereed) Published
National Category
Remote Sensing
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4451 (URN)10.5194/essd-9-881-2017 (DOI)000416002500001 ()
Available from: 2017-12-12 Created: 2017-12-12 Last updated: 2017-12-12Bibliographically approved
Karlsson, K.-G., Håkansson, N., Mittaz, J. P. D., Hanschmann, T. & Devasthale, A. (2017). Impact of AVHRR Channel 3b Noise on Climate Data Records: Filtering Method Applied to the CM SAF CLARA-A2 Data Record. Remote Sensing, 9(6), Article ID 568.
Open this publication in new window or tab >>Impact of AVHRR Channel 3b Noise on Climate Data Records: Filtering Method Applied to the CM SAF CLARA-A2 Data Record
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2017 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 9, no 6, article id 568Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4141 (URN)10.3390/rs9060568 (DOI)000404623900059 ()
Available from: 2017-08-07 Created: 2017-08-07 Last updated: 2017-11-29Bibliographically approved
Wu, D. L., Baum, B. A., Choi, Y.-S., Foster, M. J., Karlsson, K.-G., Heidinger, A., . . . Watts, P. (2017). TOWARD GLOBAL HARMONIZATION OF DERIVED CLOUD PRODUCTS. Bulletin of The American Meteorological Society - (BAMS), 98(2), ES49-ES52
Open this publication in new window or tab >>TOWARD GLOBAL HARMONIZATION OF DERIVED CLOUD PRODUCTS
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2017 (English)In: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 98, no 2, p. ES49-ES52Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4047 (URN)10.1175/BAMS-D-16-0234.1 (DOI)000395826700001 ()
Available from: 2017-04-12 Created: 2017-04-12 Last updated: 2017-11-29Bibliographically approved
Karlsson, K.-G., Johansson, E. & Devasthale, A. (2015). Advancing the uncertainty characterisation of cloud masking in passive satellite imagery: Probabilistic formulations for NOAA AVHRR data. Remote Sensing of Environment, 158, 126-139
Open this publication in new window or tab >>Advancing the uncertainty characterisation of cloud masking in passive satellite imagery: Probabilistic formulations for NOAA AVHRR data
2015 (English)In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 158, p. 126-139Article in journal (Refereed) Published
Abstract [en]

Two alternative methods for probabilistic cloud masking of images from the Advanced Very High Resolution Radiometer (AVHRR) sensor have been examined. Both methods are based on Bayesian theory and were trained using data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. Results were evaluated by comparing to independent CALIPSO-CALIOP observations and to a one-year ground-based cloud dataset composed from five different remote sensing systems over the observation site in Cabauw in the Netherlands. In addition, results were compared to two different cloud masks; one derived from the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor and one from the Climate Monitoring Satellite Application Facility Clouds (CMSAF), Albedo and Radiation dataset from AVHRR data (CLARA-A1). It was demonstrated that the probabilistic methods compare well with the referenced satellite datasets and for daytime conditions they provide even better performance than the reference methods. Among the two probabilistic approaches, it was found that the formulation based on a Naive Bayesian formulation (denoted PPS-Prob Naive) performed clearly superior to the formulation based on a linear summation of conditional cloud probabilities (denoted PPS-Prob SPARC) for daytime conditions. For the study based on the observations over the Cabauw site, the overall daytime Kuipers Skill Score for PPS-Prob Naive was 0.84, for PPS-Prob SPARC 0.79, for CLARA-A1 0.74 and for SEVIRI 0.66. Corresponding results for night-time conditions were less favourable for the probabilistic formulations (Kuipers Skill Score 0.74 for PPS-Prob Naive, 0.68 for PPS-Prob SPARC, 0.80 for CLARA-A1 and 0.79 for SEVIRI) but still relatively close to the reference dataset The Cabauw distribution of cloudiness occurrences in different octa categories was reproduced very closely by all methods, including the probabilistic formulations. Results based on Cabauw observations were also largely in good agreement with results deduced from comparisons with the CALIPSO-CALIOP cloud mask. The PPS-Prob Naive approach will be implemented in an upcoming version of the Polar Platform System (PPS) cloud software issued by the EUMETSAT Nowcasting Satellite Application Facility (NWC SAF). It will also be used in the second release of the CMSAF CLARA cloud climate data record based on historic AVHRR GAC data (to be denoted CIARA-A2). (C) 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-SA license

National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-2012 (URN)10.1016/j.rse.2014.10.028 (DOI)000348879100010 ()
Available from: 2016-04-06 Created: 2016-03-03 Last updated: 2017-11-30Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-8256-0228

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