Preprints
https://doi.org/10.5194/acp-2022-152
https://doi.org/10.5194/acp-2022-152
 
16 Mar 2022
16 Mar 2022
Status: this preprint is currently under review for the journal ACP.

Sensitivity analysis of an aerosol aware microphysics scheme in WRF during case studies of fog in Namibia

Michael Weston1, Stuart Piketh2, Frédéric Burnet3, Stephen Broccardo2,a, Cyrielle Denjean3, Thierry Bourrianne3, and Paola Formenti4 Michael Weston et al.
  • 1Research and Development Division, Khalifa University, Abu Dhabi, United Arab Emirates
  • 2School of Geo- and Spatial Science, North-West University, Potchefstroom, South Africa
  • 3CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 4Université de Paris and Univ Paris Est Creteil, CNRS, LISA, F-75013 Paris, France
  • anow at: Bay Area Environmental Research Institute / NASA Ames Research Center, CA, United States

Abstract. Aerosol aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol aware microphysics scheme, within the Weather, Research and Forecasting (WRF) model, is parameterised for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the Aerosol, Radiation and Clouds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM100) provided cloud droplet size distribution, number concentration (Nt), liquid water content (LWC) and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of Nt, LWC and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius and the minimum updraft speed, important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm-3 and underestimated the cloud droplet number concentration while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of Nt, LWC, MVD and droplet size distribution. From this it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 ms-1. This scenario provided Nt, LWC, MVD and droplet size distribution in the range of the observations with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol aware scheme when parameterised with observations.

Michael Weston et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-152', Anonymous Referee #1, 08 Apr 2022
  • RC2: 'Comment on acp-2022-152', Anonymous Referee #2, 19 Apr 2022

Michael Weston et al.

Michael Weston et al.

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Short summary
An aerosol-aware microphysics scheme is evaluated in the WRF model for fog cases in Namibia. Observations from the AERCLO-sA campaign are used to access and parameterize the model. The model CCN activation is lower than the observations. The scheme is designed for clouds with updrafts, while fog typically forms in stable conditions. A pseudo updraft speed is assigned to the lowest model levels helps achieve more realistic cloud droplet number concentration and size distribution in the model.
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