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Extreme weather events such as hurricanes and heatwaves could cause significant damage to the economy and urban resiliency. Accurate meteorological forecasts of these extreme events could mitigate some aspects of their damage by providing precautionary alerts. The weather forecasts heavily rely on the parameterization of the planetary boundary layer (PBL), which is the lowest layer of the atmosphere that extends up to ~1 km above the surface. In hurricanes, the rotational nature of flows can suppress turbulence; however, such effects are neglected in the conventional PBL schemes, leading to over-diffusive simulations and inaccurate hurricane intensity, size, and track forecasts. In urban areas, complex surface heterogeneities and the Urban Heat Island (UHI) effects are inadequately represented by current PBL models, causing inaccurate forecasts of atmospheric stability, aerosol transport, and wind speeds. To address these issues, the dissertation characterizes the impacts of PBL parameterizations on three problems: hurricane forecasts, air quality forecasts in cities, and wind forecasts in heterogeneous urban areas. To this end, dissertation systematically explored modifications to the existing PBL schemes, urban models, and roughness parameterizations within the Weather Research and Forecasting (WRF) model. More than 500 WRF simulations encompassing major hurricane cases and multiple U.S. cities were performed by varying grid resolutions, eddy diffusivity, UHI magnitudes, and surface roughness configurations. By reducing the vertical diffusion in hurricane simulations, hurricane intensity forecasts improved by ~38% compared to the default PBL schemes in five cases, demonstrating the deficiency of existing parameterizations for rotating cyclonic flows. Our urban simulations also showed that incorporating proper UHI representations in Houston and Dallas led to ~50% and ~12% enhancements in particulate matter and ozone forecasts, respectively, as more realistic nighttime warming prevented excessive aerosol accumulation. Additionally, a novel City-wide Enhanced Directional-Adjusted Roughness (CEDAR) parameterization was introduced that improved surface wind forecasts by ~54% and enhanced the prediction of vertical profiles of winds by ~12%, demonstrating the significance of accounting for upwind surface heterogeneities. The dissertation results collectively highlight that improving PBL processes in weather/climate models can considerably reduce forecasting errors in regular and extreme weather events. Our findings guide the future development of advanced PBL schemes that account for rotation, UHI effects, and surface roughness, thereby improving weather and air quality predictions across diverse environments. The results will be helpful to enhance operational forecasting models, which ultimately could mitigate public health risks, and optimize urban design and hurricane preparedness strategies.
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Evaluating the performance of WRF in simulating winds and surface meteorology during a Southern California wildfire event
The intensity and frequency of wildfires in California (CA) have increased in recent years, causing significant damage to human health and property. In October 2007, a number of small fire events, collectively referred to as the Witch Creek Fire or Witch Fire started in Southern CA and intensified under strong Santa Ana winds. As a test of current mesoscale modeling capabilities, we use the Weather Research and Forecasting (WRF) model to simulate the 2007 wildfire event in terms of meteorological conditions. The main objectives of the present study are to investigate the impact of horizontal grid resolution and planetary boundary layer (PBL) scheme on the model simulation of meteorological conditions associated with a Mega fire. We evaluate the predictive capability of the WRF model to simulate key meteorological and fire-weather forecast parameters such as wind, moisture, and temperature. Results of this study suggest that more accurate predictions of temperature and wind speed relevant for better prediction of wildfire spread can be achieved by downscaling regional numerical weather prediction products to 1 km resolution. Furthermore, accurate prediction of near-surface conditions depends on the choice of the planetary boundary layer parameterization. The MYNN parameterization yields more accurate prediction as compared to the YSU parameterization. WRF simulations at 1 km resolution result in better predictions of temperature and wind speed than relative humidity during the 2007 Witch Fire. In summary, the MYNN PBL parameterization scheme with finer grid resolution simulations improves the prediction of near-surface meteorological conditions during a wildfire event.
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- PAR ID:
- 10519573
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Earth Science
- Volume:
- 11
- ISSN:
- 2296-6463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/feart.2023.1305124
