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Key lessons from the DACCIWA project for operational meteorological services

Knippertz, Peter; Marsham, John H; Benedetti, Angela; Evans, Mat J; Fink, Andreas H; Kniffka, Anke; van der Linden, Roderick; Dearden, Christopher; Deetz, Konrad; Haslett, Sophie L; Keita, Sekou; Lohou, Fabienne; Maranan, Marlon; Mollard, James D. P.; Pante, Gregor; Reinares Martínez, Irene; Young, Matthew; Akpo, Aristide; Adler, Bianca; Amekudzi, Leonard; Babić, Karmen; Chaboureau, Jean-Pierre; Chiu, J. Christine; Coe, Hugh; Dione, Cheikh; Leal-Liousse, Catherine; Hill, Peter; Kalthoff, Norbert; Vogel, Bernhard; Yoboué, Véronique


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  <titles>
    <title>Key lessons from the DACCIWA project for operational meteorological services</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>West Africa, weather, climate, air pollution, prediction, numerical weather prediction models, satellite data, field campaign</subject>
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  <dates>
    <date dateType="Issued">2020-07-23</date>
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  <language>en</language>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="References" resourceTypeGeneral="Dataset">10.6096/dacciwa.1618</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="References" resourceTypeGeneral="Dataset">10.6096/dacciwa.1686</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="References" resourceTypeGeneral="Dataset">10.6096/ dacciwa.1702</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="References" resourceTypeGeneral="Dataset">10.6096/dacciwa.1690</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="References" resourceTypeGeneral="Dataset">10.6096/dacciwa.1659</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="References" resourceTypeGeneral="Dataset">10.6096/dacciwa.1663</relatedIdentifier>
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  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This document describes the conclusions of the&amp;nbsp;EU-funded project Dynamics-&amp;nbsp;Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA)&amp;nbsp;directly&amp;nbsp;relevant to operational meteorological services.&amp;nbsp;DACCIWA&amp;nbsp;produced&amp;nbsp;the most comprehensive observational dataset of the atmosphere over&amp;nbsp;densely populated&amp;nbsp;southern West Africa to date and used this dataset to foster&amp;nbsp;our understanding of atmospheric&amp;nbsp;processes, and to evaluate dynamical&amp;nbsp;models and satellite data. With this document DACCIWA aims to help improve atmospheric predictions across time-scales, which are important for the&amp;nbsp;development of greater&amp;nbsp;resilience of the West African population to&amp;nbsp;hazardous weather and climate change.&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
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    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/FP7/603502/">603502</awardNumber>
      <awardTitle>Dynamics-aerosol-chemistry-cloud interactions in West Africa</awardTitle>
    </fundingReference>
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