Hurricanes have been the most expensive natural catastrophe in the United States causing significant damages and disastrous floods. Considering the increased projected destructiveness of future hurricanes due to global warming, a reliable hurricane forecasting model is a national priority. Despite significant enhancement in weather prediction models, hurricane-induced flood forecasts are not sufficiently accurate yet. This inadequacy could be attributed to inaccurate hurricane intensity and track forecasts which can be due to improper physical parameterizations of unique hurricane dynamics. Previous studies have shown that remarkable differences exist in the dynamics of hurricanes compared to regular atmospheric boundary layers (Momen et al. 2021; Li et al. 2023; Matak and Momen 2023). These differences are due to strong rotational effects in hurricanes that are not represented in current turbulence and planetary boundary layer (PBL) parameterization schemes (Romdhani et al. 2022). These discrepancies in different physical schemes can cause improper hurricane structure, trajectory, intensity, and precipitation; ultimately leading to inaccurate hurricane-induced flood forecasts. It is not well known how adjusting PBL dynamics can influence hydro-meteorological forecasts in hurricanes.
In this talk, we seek to bridge this knowledge gap by coupling Advanced Weather Research and Forecasting (WRF-ARW) with the hydrological model WRF-Hydro to simulate multiple hurricanes in the US. We will first show the impacts of various PBL, microphysics, and cumulus parameterizations on the accuracy of real hurricane intensity, track, and flood forecasts. All these runs will be conducted coupled with the WRF-Hydro model, which is extensively calibrated for three coastal regions in the US using multiple USGS gauges. The best-performing parameterizations will then be determined through a comprehensive sensitivity test.
We will next present new adjustments to the default PBL schemes of WRF that enhance hurricane intensity forecasts. Following (Matak and Momen 2023), the default vertical diffusion of WRF is reduced to enhance hurricane intensification. Then, the impacts of these adjustments and hurricane dynamics improvements on hurricane-induced flood forecasts are quantified. For example, the attached figure shows an example of our simulations. By reducing the default diffusion, the intensity of hurricanes increases, and their size decreases compared to the default model. This remarkably influences hurricane precipitation rate and aerial distribution. Intensified hurricanes were shown to generate more intense and localized precipitation. This improved representation of hurricane dynamics led to better flood forecasts for the considered hurricanes. In total, we found that by reducing the vertical diffusion, hurricane intensity forecasts were enhanced by ~40% on average compared to the default models. This led to ~16% and 34% improvements in streamflow bias and correlation forecasts, respectively. This research provides new insights into the effects of PBL dynamics on hurricane streamflow forecasts. These new adjustments play a vital role in improving the hurricane and streamflow forecasts in coupled hydro-meteorological models.
References:
Li, M., J. A. Zhang, L. Matak, and M. Momen, 2023: The Impacts of Adjusting Momentum Roughness Length on Strong and Weak Hurricane Forecasts: A Comprehensive Analysis of Weather Simulations and Observations. Mon Weather Rev, 151, 1287–1302, https://doi.org/10.1175/MWR-D-22-0191.1.
Matak, L., and M. Momen, 2023: The Role of Vertical Diffusion Parameterizations in the Dynamics and Accuracy of Simulated Intensifying Hurricanes. Boundary Layer Meteorol, https://doi.org/10.1007/s10546-023-00818-w.
Momen, M., M. B. Parlange, and M. G. Giometto, 2021: Scrambling and Reorientation of Classical Atmospheric Boundary Layer Turbulence in Hurricane Winds. Geophys Res Lett, 48, https://doi.org/10.1029/2020GL091695.
Romdhani, O., J. A. Zhang, and M. Momen, 2022: Characterizing the Impacts of Turbulence Closures on Real Hurricane Forecasts: A Comprehensive Joint Assessment of Grid Resolution, Horizontal Turbulence Models, and Horizontal Mixing Length. J Adv Model Earth Syst, 14, https://doi.org/10.1029/2021ms002796.
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Improving Hurricane Intensity and Streamflow Forecasts in Coupled Hydrometeorological Simulations by Analyzing Precipitation and Boundary Layer Schemes
Hurricanes have been the most destructive and expensive hydrometeorological event in U.S. history, causing catastrophic winds and floods. Hurricane dynamics can significantly impact the amount and spatial extent of storm precipitation. However, the complex interactions of hurricane intensity and precipitation and the impacts of improving hurricane dynamics on streamflow forecasts are not well established yet. This paper addresses these gaps by comprehensively characterizing the role of vertical diffusion in improving hurricane intensity and streamflow forecasts under different planetary boundary layer, microphysics, and cumulus parameterizations. To this end, the Weather Research and Forecasting (WRF) atmospheric model is coupled with the WRF hydrological (WRF-Hydro) model to simulate four major hurricanes landfalling in three hurricane-prone regions in the United States. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the U.S. Geological Survey (USGS) gauges. Then, 60 coupled hydrometeorological simulations were conducted to evaluate the performance of current weather parameterizations. All schemes were shown to underestimate the observed intensity of the considered major hurricanes since their diffusion is overdissipative for hurricane flow simulations. By reducing the vertical diffusion, hurricane intensity forecasts were improved by ∼39.5% on average compared to the default models. These intensified hurricanes generated more intense and localized precipitation forcing. This enhancement in intensity led to ∼16% and ∼34% improvements in hurricane streamflow bias and correlation forecasts, respectively. The research underscores the role of improved hurricane dynamics in enhancing flood predictions and provides new insights into the impacts of vertical diffusion on hurricane intensity and streamflow forecasts.
Despite significant recent improvements, numerical weather prediction models struggle to accurately forecast hurricane intensity and track due to many reasons such as inaccurate physical parameterization for hurricane flows. Furthermore, the performance of existing physics schemes is not well studied for hurricane flood forecasting. This study bridges these knowledge gaps by extensively evaluating different physical parameterizations for hurricane track, intensity, and flood forecasts using an atmospheric model coupled with a hydrological model. Then, a reduced diffusion boundary layer scheme is developed, making remarkable improvements in hurricane intensity forecasts due to the overdissipative nature of the considered schemes for major hurricane simulations. This reduced diffusion model is shown to significantly enhance hurricane flood forecasts, indicating the significance of hurricane dynamics on its induced precipitation.
- Award ID(s):
- 2228299
- PAR ID:
- 10535203
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Hydrometeorology
- Volume:
- 25
- Issue:
- 8
- ISSN:
- 1525-755X
- Format(s):
- Medium: X Size: p. 1237-1258
- Size(s):
- p. 1237-1258
- Sponsoring Org:
- National Science Foundation
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