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  • 1.
    Håkansson, Nina
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Adok, Claudia
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Scheirer, Ronald
    SMHI, Research Department, Atmospheric remote sensing.
    Hörnquist, Sara
    SMHI, Research Department, Atmospheric remote sensing.
    Neural network cloud top pressure and height for MODIS2018In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 11, no 5, p. 3177-3196Article in journal (Refereed)
  • 2.
    Scheirer, Ronald
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Dybbroe, Adam
    SMHI, Core Services.
    Raspaud, Martin
    SMHI, Core Services.
    A General Approach to Enhance Short Wave Satellite Imagery by Removing Background Atmospheric Effects2018In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 4, article id 560Article in journal (Refereed)
  • 3. Stengel, M.
    et al.
    Mieruch, S.
    Jerg, M.
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    Scheirer, Ronald
    SMHI, Research Department, Atmospheric remote sensing.
    Maddux, B.
    Meirink, J. F.
    Poulsen, C.
    Siddans, R.
    Walther, A.
    Hollmann, R.
    The Clouds Climate Change Initiative: Assessment of state-of-the-art cloud property retrieval schemes applied to AVHRR heritage measurements2015In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 162, p. 363-379Article in journal (Refereed)
    Abstract [en]

    Cloud property retrievals from 3 decades of the Advanced Very High Resolution Radiometer (AVHRR) measurements provide a unique opportunity for a long-term analysis of clouds. In this study, the accuracy of AVHRR-derived cloud properties cloud mask, cloud-top height, cloud phase and cloud liquid water path is assessed using three state-of-the-art retrieval schemes. In addition, the same retrieval schemes are applied to the AVHRR heritage channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) to create AVHRR-like retrievals with higher spatial resolution and based on presumably more accurate spectral calibration. The cloud property retrievals were collocated and inter-compared with observations from CloudSat, CALIPSO and AMSR-E The resulting comparison exhibited good agreement in general. The schemes provide correct cloud detection in 82 to 90% of all cloudy cases. With correct identification of clear-sky in 61 to 85% of all clear areas, the schemes are slightly biased towards cloudy conditions. The evaluation of the cloud phase classification shows correct identification of liquid clouds in 61 to 97% and a correct identification of ice clouds in 68 to 95%, demonstrating a large variability among the schemes. Cloud-top height (CTH) retrievals were of relatively similar quality with standard deviations ranging from 2.1 km to 2.7 km. Significant negative biases in these retrievals are found in particular for cirrus clouds. The biases decrease if optical depth thresholds are applied to determine the reference CTH measure. Cloud liquid water path (LWP) is also retrieved well with relative low standard deviations (20 to 28 g/m(2)), negative bias and high correlations. Cloud ice water path (IWP) retrievals of AVHRR and MODIS exhibit a relative high uncertainty with standard deviations between 800 and 1400 g/m2, which in relative terms exceed 100% when normalized with the mean IWP. However, the global histogram distributions of IWP were similar to the reference dataset MODIS retrievals are for most comparisons of slightly better quality than AVHRR-based retrievals. Additionally, the choice of different near-infrared channels, 3.7 mu M as opposed to 1.6 mu m, can have a significant impact on the retrieval quality, most pronounced for IWP, with better accuracy for the 1.6 mu m channel setup. This study presents a novel assessment of the quality of cloud properties derived from AVHRR channels, which quantifies the accuracy of the considered retrievals based on common approaches and validation data. Furthermore, it assesses the capabilities of AVHRR-like spectral information for retrieving cloud properties in the light of generating climate data records of cloud properties from three decades of AVHRR measurements. (C) 2013 Elsevier Inc. All rights reserved.

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