A Case Study: Evaluation of PAFOG One‐D Model With Advection in Simulations of Fog/Stratus From C‐FOG Experiment
Abstract
Fog simulation is a challenge due to the complex microphysical, radiative, and turbulent processes. C‐FOG (Coastal Fog) was a comprehensive experiment which aimed to improve our understanding and forecasting skill of fog in coastal areas of Atlantic Canada. An intermittent fog event, containing two fog periods occurring over the Grand Banks of Newfoundland beginning at 00:00 UTC on September 13, 2018, is investigated in this study. Our approach is to apply the Weather Research and Forecasting (WRF) model, in conjunction with a 1‐D model for the atmospheric boundary layer, PAFOG (Parameterized Fog model). Model results are evaluated against observations collected onboard the research vessel Hugh R. Sharp. We introduced an advection term to PAFOG to investigate the ability of the model to simulate the two‐level fog/stratus system and we developed a set of sensitivity experiments. Results suggest that PAFOG with the advection terms calculated from ERA5 shows a good ability to simulate intermittent fog caused by fog lifting as stratus, and by comparison, outperform results with advection terms derived from WRF simulations. Regarding the latter, in an experiment with high vertical resolution, WRF gives a delayed fog event, whereas with low vertical resolution, WRF gives descending stratus. Our results provide a possible methodology to study the detailed structure of fog/stratus using PAFOG, a sounding profile as initial conditions, and advection profiles derived from ERA5, with low computing cost.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 26, 2021
- Source ID
- 10.1029/2021jd034812
Entities
People
- Changshuo Chen
- Harindra J. S. Fernando
- Ismail Gultepe
- Minghong Zhang
- Rachel Chang
- William Perrie
- Xianyao Chen
Organizations
- Bedford Institute of Oceanography
- Dalhousie University
- Environment and Climate Change Canada
- Marine Environmental Observation Prediction and Response Network
- Office of Naval Research
- Ontario Tech University
- Polar Knowledge Canada
- University of Notre Dame