Improving Short-Range Cloud Forecasts in Harmonie-Arome Through Cloud Initialization Using Mesan Cloud Data
Sammanfattning: Previous studies, such as van der Veen (2012) and White et al. (2017), have demonstrated the potential of using measurement-based cloud data to improve Numerical Weather Prediction (NWP) based cloud forecasts. This can be done through cloud initialization; a process of injecting cloud data after the regular data assimilation in an NWP model. The purpose of this study was to use cloud data from the Mesoscale Analysis system MESAN to investigate cloud initialization in the HARMONIE-AROME model system for improving short-range cloud forecasts. The cloud initialization method that was used was similar to a method used by van der Veen (2012), where specific humidities, temperatures, and hydrometeor concentrations were altered using information on cloud fractions, cloud base heights and cloud top heights. MESAN input data analyses as well as cloud initialization investigations were carried out. MESAN input data analyses revealed significant differences in cloud fractions between MESAN and the background model field in MESAN. Overestimations of cloud fractions in MESAN over sea were caused by satellite data, particularly due to the inclusion of the fractional cloud category. Underestimations of cloud fractions over land were caused by limitations of the synoptic weather (SYNOP) stations in measuring clouds. Furthermore, larger differences between MESAN and SYNOP were found over Sweden and Finland compared to Norway, which may be tied to Norway having mostly manual SYNOP stations, and Sweden and Finland having mostly automatic stations. Shortcomings were found in the investigated cloud initialization method. Such shortcomings involved a limit check on the specific humidity change, the cloud initialization being repeated for an unnecessarily large amount of iterations, and the use of a sub-optimal profile of critical relative humidity. Using a one-dimensional vertical column version of HARMONIE-AROME, named MUSC, to integrate forward in time revealed a large sensitivity to the use of forcing profiles and forcing time scales in MUSC. Alterations made through cloud initialization were found to last over 12 h, with varying effects depending on the investigated height. A reasonably good agreement between MUSC results and results from the three-dimensional version of HARMONIE-AROME was found. Findings in this thesis point at potential to further enhance the HARMONIE-AROME cloud initialization technique. These enhancements concern a revised MESAN cloud product and taking care of some flaws in the cloud initialization method.
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