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      <journal>
        <journal_metadata language="en" reference_distribution_opts="any">
          <full_title>Atmospheric Measurement Techniques</full_title>
          <abbrev_title>Atmos. Meas. Tech.</abbrev_title>
          <issn media_type="electronic">1867-8548</issn>
        </journal_metadata>
        <journal_issue>
          <publication_date media_type="online">
            <year>2015</year>
          </publication_date>
          <journal_volume>
            <volume>8</volume>
          </journal_volume>
          <issue>4</issue>
        </journal_issue>
        <journal_article publication_type="full_text">
          <titles>
            <title>Bayesian cloud detection for MERIS, AATSR, and their combination</title>
          </titles>
          <contributors>
            <person_name sequence="first" contributor_role="author">
              <given_name>A.</given_name>
              <surname>Hollstein</surname>
            </person_name>
            <person_name sequence="additional" contributor_role="author">
              <given_name>J.</given_name>
              <surname>Fischer</surname>
            </person_name>
            <person_name sequence="additional" contributor_role="author">
              <given_name>C.</given_name>
              <surname>Carbajal Henken</surname>
              <ORCID>https://orcid.org/0000-0002-3408-5925</ORCID>
            </person_name>
            <person_name sequence="additional" contributor_role="author">
              <given_name>R.</given_name>
              <surname>Preusker</surname>
            </person_name>
          </contributors>
          <abstract>
            <p><![CDATA[Abstract. A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud detection schemes were designed to be numerically efficient and suited for the processing of large numbers of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient numbers of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.]]></p>
          </abstract>
          <publication_date media_type="online">
            <month>04</month>
            <day>15</day>
            <year>2015</year>
          </publication_date>
          <pages>
            <first_page>1757</first_page>
            <last_page>1771</last_page>
          </pages>
          <program name="fundref">
            <assertion name="fundgroup">
              <assertion name="funder_name">
                European Space Agency
                <assertion name="funder_identifier">http://dx.doi.org/10.13039/501100000844</assertion>
              </assertion>
              <assertion name="award_number">CCI-Cloud</assertion>
            </assertion>
          </program>
          <program name="AccessIndicators">
            <free_to_read start_date="2015-04-15" />
            <license_ref applies_to="vor" start_date="2015-04-15">https://creativecommons.org/licenses/by/3.0/</license_ref>
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            <related_item>
              <intra_work_relation relationship-type="hasPreprint" identifier-type="doi">10.5194/amtd-7-11045-2014</intra_work_relation>
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          <doi_data>
            <doi>10.5194/amt-8-1757-2015</doi>
            <resource>https://amt.copernicus.org/articles/8/1757/2015/</resource>
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