Enhancing understanding of mesoscale convective systems in Eastern Canada using the convection-permitting climate model

Abstract

We evaluated the performance of the convection-permitting climate model (CPCM) simulation in representing precipitation systems in Eastern Canada using the Weather Research and Forecasting (WRF) model. The accuracy of this simulation was assessed by comparing it with observed Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) data over a 10-year period (2012 to 2021). Our analysis focused on high-resolution spatial and temporal distributions of precipitation events, characteristics of Mesoscale Convective Systems (MCSs), and hourly rain rates, with the CPCM simulation at a 4 km resolution and observations at a 10 km resolution. The CPCM simulation demonstrated a two-dimensional correlation coefficient of 0.53, though it overestimated precipitation. For daytime MCSs, the model exhibited good performance in event counts, areal coverage, propagation speeds, and longevity. In contrast, nighttime MCSs were underestimated those characteristics in the simulation. Our study explored the relationships between pre-convection parameters and MCS longevity, revealing critical insights into the structure of convection and MCSs. Key meteorological factors such as moisture, wind shear, and helicity were found to play significant roles. Precipitable Water (PW) emerged as a crucial determinant of MCS duration, showing strong statistical significance across both daytime and nighttime MCSs. Additionally, wind shear and helicity influenced MCS characteristics, with variations in wind shear impacting both the propagation and longevity of MCSs, and helicity affecting their development. While Most Unstable Convection Inhibition (MUCIN) did not demonstrate similar significance, the findings underscore the essential roles of moisture, wind, and helicity in shaping MCS structure and longevity. This study provides valuable insights into these meteorological factors, enhancing predictive capabilities and laying a foundation for future research to refine the model simulation and improve weather forecasting.