Analysis of a severe weather event over Mecca, Kingdom of Saudi Arabia, using observations and high-resolution modelling
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/625878
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AbstractThe dynamic and thermodynamic characteristics of a severe weather event that caused heavy wind and rainfall over Mecca, Kingdom of Saudi Arabia, on 11 September 2015 were investigated using available observations and the Weather Research and Forecasting model configured at 1 km resolution. Analysis of surface, upper air observations and model outputs reveals that the event was initiated by synoptic scale conditions that intensified by interaction with the local topography, triggering strong winds and high convective rainfall. The model predicted the observed characteristics of both rainfall and winds well, accurately predicting the maximum wind speed of 20–25 m s−1 that was sustained for about 2 h. A time series analysis of various atmospheric variables suggests a sudden fall in pressure, temperature and outgoing long wave radiation before the development of the storm, followed by a significant increase in wind speed, latent and moisture fluxes and change in wind direction during the mature stage of the storm. The model outputs suggest that the heavy rainfall was induced by a low-level moisture supply from the Red Sea combined with orographic lifting. Latent heat release from microphysical processes increased the vertical velocities in the mid-troposphere, further increasing the low-level convergence that strengthened the event.
CitationDasari HP, Attada R, Knio O, Hoteit I (2017) Analysis of a severe weather event over Mecca, Kingdom of Saudi Arabia, using observations and high-resolution modelling. Meteorological Applications 24: 612–627. Available: http://dx.doi.org/10.1002/met.1662.
SponsorsThis research work was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia, and the Saudi ARAMCO-KAUST Marine Environmental Research Center (SAKMERC). This research made use of the Supercomputing Laboratory resources at KAUST.