Investigation of moisture field assimilation in global reanalysis |
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| Authors: | Seung-On Hwang Song-You Hong |
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| Institution: | 1. McCaig Chair in Management Haskayne School of Business, University of Calgary, Calgary, Alberta, T2N 1N4, Canada;2. Solvay Business School, University of Brussels (VUB), Belgium;3. Henley Business School, University of Reading, United Kingdom;4. Haskayne School of Business, University of Calgary, Calgary, Alberta, T2N 1N4, Canada;1. Aalto University, Espoo, Finland;3. Plum Consulting, London, United Kingdom;4. University of Bologna, Bologna, Italy;5. Beijing Jiaotong University, Beijing, China;6. University of Oulu, Oulu, Finland;7. Lund University, Lund, Sweden;8. University of Durham, Durham, United Kingdom;9. University of Twente, Enschede, Netherlands;10. Ghent University, Ghent, Belgium |
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| Abstract: | The impact of humidity data on a global reanalysis system is investigated. It is found that the inclusion of moisture in the reanalysis system causes the forecast model to produce a systematically wetter atmosphere. As such, the data assimilation tends to dry out the atmosphere by a negative analysis increment. Meanwhile, an analysis cycle without moisture beneficially affects the other first guess variables, especially the mass field. This indicates that the present reanalysis system seems to be inconsistent with the humidity field and that reanalysis without the use of humidity data is, for the most part, able to reproduce dynamic fields that are similar to reanalysis with all of the observation data. Our results also suggest that continuous refinement of the internal physics in the forecast model can be an efficient way to provide an enhanced analysis. |
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