Initialization of Cloud Water Content in a Data Assimilation System

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  • 1 Danish Meteorological Institute, Copenhagen, Denmark
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Abstract

Cloud water content (CWC) is not treated in most operational objective analyses and initialization schemes. When CWC is used as a prognostic variable in a forecast model, it is necessary to define this variable at the initial time. A commonly used method is to set the initial CWC to zero or use a forecast CWC field from the previous data-assimilation cycle (the first-guess field for the objective analysis) without any modification. The inconsistent treatment of CWC and other fields leads to an imbalance between the first-guess cloud water field and other analyzed fields (winds, temperature humidity, and surface pressure). In this study. the diabatic digital-filtering initialization scheme is used to alleviate this imbalance. It is shown that an intermittent data assimilation system with this initialization scheme can produce a better cloud evolution, a shorter spinup time, and a removal of the initial shock in precipitation.

Abstract

Cloud water content (CWC) is not treated in most operational objective analyses and initialization schemes. When CWC is used as a prognostic variable in a forecast model, it is necessary to define this variable at the initial time. A commonly used method is to set the initial CWC to zero or use a forecast CWC field from the previous data-assimilation cycle (the first-guess field for the objective analysis) without any modification. The inconsistent treatment of CWC and other fields leads to an imbalance between the first-guess cloud water field and other analyzed fields (winds, temperature humidity, and surface pressure). In this study. the diabatic digital-filtering initialization scheme is used to alleviate this imbalance. It is shown that an intermittent data assimilation system with this initialization scheme can produce a better cloud evolution, a shorter spinup time, and a removal of the initial shock in precipitation.

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