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Johansson, Erik
Publications (6 of 6) Show all publications
Johansson, E., Devasthale, A., Ekman, N. M. L., Tjernstrom, M. & L'Ecuye, R. (2019). How Does Cloud Overlap Affect the Radiative Heating in the Tropical Upper Troposphere/Lower Stratosphere?. Geophysical Research Letters, 46(10), 5623-5631
Open this publication in new window or tab >>How Does Cloud Overlap Affect the Radiative Heating in the Tropical Upper Troposphere/Lower Stratosphere?
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2019 (English)In: Geophysical Research Letters, ISSN 0094-8276, E-ISSN 1944-8007, Vol. 46, no 10, p. 5623-5631Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-5279 (URN)10.1029/2019GL082602 (DOI)000471237500068 ()
Available from: 2019-07-31 Created: 2019-07-31 Last updated: 2019-07-31Bibliographically approved
Johansson, E., Devasthale, A., Tjernstrom, M., Ekman, A. M. L. & L'Ecuyer, T. (2017). Response of the lower troposphere to moisture intrusions into the Arctic. Geophysical Research Letters, 44(5), 2527-2536
Open this publication in new window or tab >>Response of the lower troposphere to moisture intrusions into the Arctic
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2017 (English)In: Geophysical Research Letters, ISSN 0094-8276, E-ISSN 1944-8007, Vol. 44, no 5, p. 2527-2536Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4049 (URN)10.1002/2017GL072687 (DOI)000398183700054 ()
Available from: 2017-04-19 Created: 2017-04-19 Last updated: 2017-04-19Bibliographically 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
Johansson, E., Devasthale, A., L'Ecuyer, T., Ekman, A. M. & Tjernstrom, M. (2015). The vertical structure of cloud radiative heating over the Indian subcontinent during summer monsoon. Atmospheric Chemistry And Physics, 15(20), 11557-11570
Open this publication in new window or tab >>The vertical structure of cloud radiative heating over the Indian subcontinent during summer monsoon
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2015 (English)In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 15, no 20, p. 11557-11570Article in journal (Refereed) Published
Abstract [en]

Clouds forming during the summer monsoon over the Indian subcontinent affect its evolution through their radiative impact as well as the release of latent heat. While the latter is previously studied to some extent, comparatively little is known about the radiative impact of different cloud types and the vertical structure of their radiative heating/cooling effects. Therefore, the main aim of this study is to partly fill this knowledge gap by investigating and documenting the vertical distributions of the different cloud types associated with the Indian monsoon and their radiative heating/cooling using the active radar and lidar sensors on-board CloudSat and CALIPSO. The intraseasonal evolution of clouds from May to October is also investigated to understand pre-to-post monsoon transitioning of their radiative heating/cooling effects. The vertical structure of cloud radiative heating (CRH) follows the northward migration and retreat of the monsoon from May to October. Throughout this time period, stratiform clouds radiatively warm the middle troposphere and cool the upper troposphere by more than +/- 0.2 K day(-1) (after weighing by cloud fraction), with the largest impacts observed in June, July and August. During these months, the fraction of high thin cloud remains high in the tropical tropopause layer (TTL). Deep convective towers cause considerable radiative warming in the middle and upper troposphere, but strongly cool the base and inside of the TTL. This cooling is stronger during active (-1.23 K day(-1)) monsoon periods compared to break periods (-0.36 K day(-1)). The contrasting radiative warming effect of high clouds in the TTL is twice as largeduring active periods than in break periods. These results highlight the increasing importance of CRH with altitude, especially in the TTL. Stratiform (made up of alto- and nimbostratus clouds) and deep convection clouds radiatively cool the surface by approximately -100 and -400Wm(-2) respectively while warming the atmosphere radiatively by about 40 to 150Wm(-2). While the cooling at the surface induced by deep convection and stratiform clouds is largest during active periods of monsoon, the importance of stratiform clouds further increases during break periods. The contrasting CREs (cloud radiative effects) in the atmosphere and at surface, and during active and break periods, should have direct implications for the monsoonal circulation.

National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-1942 (URN)10.5194/acp-15-11557-2015 (DOI)000364316800008 ()
Available from: 2016-04-29 Created: 2016-03-03 Last updated: 2017-11-30Bibliographically approved
Karlsson, K.-G. & Johansson, E. (2014). Multi-Sensor Calibration Studies of AVHRR-Heritage Channel Radiances Using the Simultaneous Nadir Observation Approach. Remote Sensing, 6(3), 1845-1862
Open this publication in new window or tab >>Multi-Sensor Calibration Studies of AVHRR-Heritage Channel Radiances Using the Simultaneous Nadir Observation Approach
2014 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 6, no 3, p. 1845-1862Article in journal (Refereed) Published
Abstract [en]

The European Space Agency project for studies of cloud properties in the Climate Change Initiative programme (ESA-CLOUD-CCI) aims at compiling the longest possible time series of cloud products from one single multispectral sensor-The five-channel Advanced Very High Resolution Radiometer (AVHRR) instrument. A particular aspect here is to include corresponding products based on other existing (Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Along-Track Scanning Radiometer (AATSR), MEdium Resolution Imaging Spectrometer (MERIS), Visible and Infrared Radiometer Suite (VIIRS)) and future Sea and Land Surface Temperature Radiometer (SLSTR) sensors measuring in similar (AVHRR-heritage) spectral channels. Initial inter-comparisons of the involved AVHRR-heritage channel radiances over a short demonstration period (2007-2009) were performed. Using Aqua-MODIS as reference, AVHRR (NOAA-18), AATSR, and MERIS channel radiances were evaluated using the simultaneous nadir (SNO) approach. Results show generally agreeing radiances within approximately 3% for channels at 0.6 mu m and 0.8 mu m. Larger deviations (+5%) were found for the corresponding AATSR channel at 0.6 mu m. Excessive deviations but with opposite sign were also indicated for AATSR 1.6 mu m and MERIS 0.8 mu m radiances. Observed differences may largely be attributed to residual temporal and spatial matching differences while excessive AATSR and MERIS deviations are likely partly attributed to incomplete compensation for spectrally varying surface and atmospheric conditions. However, very good agreement was found for all infrared channels among all the studied sensors. Here, deviations were generally less than 0.2% for the measured brightness temperatures with the exception of some remaining non-linear deviations at extreme low and high temperatures.

Keywords
MERIS, AVHRR, calibration, MODIS, ESA-CLOUD-CCI, simultaneous nadir observations, AATSR
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-123 (URN)10.3390/rs6031845 (DOI)000334797000005 ()
Available from: 2015-04-10 Created: 2015-03-26 Last updated: 2017-12-04Bibliographically approved
Karlsson, K.-G. & Johansson, E. (2013). On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset. ATMOSPHERIC MEASUREMENT TECHNIQUES, 6(5), 1271-1286
Open this publication in new window or tab >>On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset
2013 (English)In: ATMOSPHERIC MEASUREMENT TECHNIQUES, ISSN 1867-1381, Vol. 6, no 5, p. 1271-1286Article in journal (Refereed) Published
Abstract [en]

A method for detailed evaluation of a new satellite-derived global 28 yr cloud and radiation climatology (Climate Monitoring SAF Clouds, Albedo and Radiation from AVHRR data, named CLARA-A1) from polar-orbiting NOAA and Metop satellites is presented. The method combines 1 km and 5 km resolution cloud datasets from the CALIPSO-CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite - Observation Cloud-Aerosol Lidar with Orthogonal Polarization) cloud lidar for estimating cloud detection limitations and the accuracy of cloud top height estimations. Cloud detection is shown to work efficiently for clouds with optical thicknesses above 0.30 except for at twilight conditions when this value increases to 0.45. Some misclassifications of cloud-free surfaces during daytime were revealed for semi-arid land areas in the sub-tropical and tropical regions leading to up to 20 % overestimated cloud amounts. In addition, a substantial fraction (at least 20-30 %) of all clouds remains undetected in the polar regions during the polar winter season due to the lack of or an inverted temperature contrast between Earth surfaces and clouds. Subsequent cloud top height evaluation took into account the derived information about the cloud detection limits. It was shown that this has fundamental importance for the achieved results. An overall bias of -274m was achieved compared to a bias of -2762m when no measures were taken to compensate for cloud detection limitations. Despite this improvement it was concluded that high-level clouds still suffer from substantial height underestimations, while the opposite is true for low-level (boundary layer) clouds. The validation method and the specifically collected satellite dataset with optimal matching in time and space are suggested for a wider use in the future for evaluation of other cloud retrieval methods based on passive satellite imagery.

National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-406 (URN)10.5194/amt-6-1271-2013 (DOI)000321679200012 ()
Available from: 2015-04-01 Created: 2015-03-31 Last updated: 2016-04-07Bibliographically approved
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