Examination of WRF-ARW Experiments Using Different Planetary Boundary Layer Parameterizations to Study the Rapid Intensification and Trajectory of Hurricane Otto (2016)

Abstract

Hurricane Otto (2016) was characterised by remarkable meteorological features of relevance for the scientific community and society. Scientifically, among the most important attributes of Otto is that it underwent a rapid intensification (RI) process. For society, this cyclone severely impacted Costa Rica and Nicaragua, leaving enormous economic losses and many fatalities. In this study, a set of three numerical simulations are performed to examine the skill of model estimations in reproducing RI and trajectory of Hurricane Otto by comparing the results of a global model to a regional model using three different planetary boundary layer parameterizations (PBL). The objective is to set the basis for future studies that analyse the physical reasons why a particular simulation (associated with a certain model setup) performs better than others in terms of reproducing RI and trajectory. We use the regional model Weather Research and Forecasting—Advanced Research WRF (WRF-ARW) with boundary and initial conditions provided by the Global Forecast System (GFS) analysis (horizontal resolution of 0.5 degrees). The PBL used are the Medium Range Forecast, the Mellor-Yamada-Janjic (MYJ), and the Yonsei University (YSU) parameterizations. The regional model is run in three static domains with horizontal grid spacing of 27, 9 and 3 km, the latter covering the spacial extent of Otto during the simulation period. WRF-ARW results improve the GFS forecast, in almost every aspect evaluated in this study, particularly, the simulated trajectories in WRF-ARW show a better representation of the cyclone path and movement compared to GFS. Even though the MYJ experiment was the only one that exhibited an abrupt 24-h change in the storm’s surface wind, close to the 25-knot threshold, the YSU scheme presented the fastest intensification, closest to reality.

Keywords

• WRF
• tropical cyclones
• natural hazards
• regional numerical modelling
• Costa Rica
• Central America