Improving supercooled liquid water representation in the microphysical scheme ICE3

Abstract

Most numerical weather prediction (NWP) models have a significant bias in predicting supercooled liquid water (SLW). For this reason, icing risk diagnostic tools do not use supercooled liquid water forecast by the models as an input parameter, but rather temperature and humidity, which are forecast better than SLW. The main objective of this study is to improve the SLW representation in the microphysical scheme ICE3 (Three Ice categories) used in the operational Applications de la Recherche à l'Opérationnel à Méso‐Echelle (AROME) model. For this purpose, several parametrizations of the microphysical processes were evaluated to find a better representation of SLW in the Mesoscale Non‐Hydrostatic model (MESO‐NH), which also uses ICE3. Elements of the microphysical scheme of the HARMONIE‐AROME model and work carried out by the Centre National de Recherches Météorologiques (CNRM) have been tested and compared with the current scheme. After a preliminary study, three parametrizations of the microphysical scheme were selected, in which the processes of ice initiation, snow and graupel collection of cloud droplets, condensation, Bergeron–Findeisen, and saturation adjustment were modified. Then, MESO‐NH simulations were performed and compared with observations from the In‐Cloud ICing and Large‐drop Experiment (ICICLE) airborne campaign. Three case studies were used with different icing weather conditions such as freezing rain, freezing drizzle, lake effect, etc. The results show a better representation of SLW with a greater presence of cloud droplets for colder temperatures up to 30 C. However, the liquid water content remains underestimated and the ice mass is overestimated. The ice initiation and cloud droplet collection by snow and graupel play a major role in the SLW representation. Parametrizations with restrictive ice initiation criteria reduce cloud droplet consumption and provide better agreement with observations. The results are promising and need to be investigated further with more cases and in the operational model AROME.