Comparative Performance of WRF and MPAS in Simulating Extreme Rainfall During Hurricane Ida in the New York Metropolitan Area

dynamics preserved Ida's convective potential, leading to substantial rainfall in the Northeastern United States, particularly impacting the New York Metropolitan Area on September 1, 2021.

In this comparative analysis, we employ two advanced mesoscale models, the Weather Research and Forecasting (WRF) model and Model for Prediction Across Scales (MPAS), both configured at a 3 km spatial resolution with hourly outputs. The primary goal of this study is to simulate rainfall in urban environments and assess the performance of the WRF and MPAS models. The comparison utilizes site-based measurements from Mesonet stations and grid-based rainfall data from the Multi-Radar Multi-Sensor (MRMS) system, to evaluate each model's accuracy in capturing Hurricane Ida's precipitation patterns and magnitude.

For this analysis, WRF model simulations incorporate urban-tailored schemes, including the Bulk Urban Canopy Model for urban environments and the Purdue Lin scheme for microphysics. Our forthcoming presentation will detail each model's ability to estimate rainfall rates and timing, using statistical evaluation metrics such as Root Mean Square Error, Kling–Gupta efficiency, Precent Bias, and Correlation Coefficient. Initial results suggest that the MPAS model outperforms the WRF in estimating peak rainfall timing.
Further, we will discuss the spatial variability of model outputs and their correspondence with MRMS data over urban settings like the New York Metropolitan Area. The presentation will also explore how different parameterizations, particularly urban-centric schemes, influence the models' ability to simulate rainfall. This includes an analysis of how these parameterizations affect the accuracy and reliability of rainfall simulations in urban regions.
Our findings aim to contribute valuable insights into the adaptation and optimization of mesoscale models for urban hydro-meteorological forecasting, ultimately aiding in the enhancement of emergency preparedness and urban planning strategies.