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1. Addition of Multilayer Urban Canopy Models to a Nonlocal Planetary Boundary Layer Parameterization and Evaluation Using Ideal and Real Cases

   https://doi.org/10.1175/JAMC-D-19-0142.1  

   Hendricks, Eric A.; Knievel, Jason C.; Wang, Yi (August 2020, Journal of Applied Meteorology and Climatology)

   null (Ed.)

   Abstract The multilayer urban canopy models (UCMs) building effect parameterization (BEP) and BEP + building energy model (BEM; a building energy model integrated in BEP) are added to the Yonsei University (YSU) planetary boundary layer (PBL) parameterization in the Weather Research and Forecasting (WRF) Model. The additions allow for the first analysis of the detailed effects of buildings on the urban boundary layer in a nonlocal closure scheme. The modified YSU PBL parameterization is compared with the other 1.5-order local PBL parameterizations that predict turbulent kinetic energy (TKE), Mellor–Yamada–Janjić and Bougeault–Lacarerre, using both ideal and real cases. The ideal-case evaluation confirms that BEP and BEP+BEM produce the expected results in the YSU PBL parameterization because the simulations are qualitatively similar to the TKE-based PBL parameterizations in which the multilayer UCMs have long existed. The modified YSU PBL parameterization is further evaluated for a real case. Similar to the ideal case, there are larger differences among the different UCMs (simple bulk scheme, BEP, and BEP+BEM) than across the PBL parameterizations when the UCM is held fixed. Based on evaluation against urban near-surface wind and temperature observations for this case, the BEP and BEP+BEM simulations are superior to the simple bulk scheme for each PBL parameterization. 

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2. Evaluation of Urban Canopy Models against Near-Surface Measurements in Houston during a Strong Frontal Passage

   https://doi.org/10.3390/atmos13101548  

   Hendricks, Eric A.; Knievel, Jason C. (October 2022, Atmosphere)

   Urban canopy models (UCMs) in mesoscale numerical weather prediction models need evaluation to understand biases in urban environments under a range of conditions. The authors evaluate a new drag formula in the Weather Research and Forecasting (WRF) model’s multilayer UCM, the Building Effect Parameterization combined with the Building Energy Model (BEP+BEM), against both in-situ measurements in the urban environment as well as simulations with a simple bulk scheme and BEP+BEM using the old drag formula. The new drag formula varies with building packing density, while the old drag formula is constant. The case study is a strong cold frontal passage that occurred in Houston during the winter of 2017, causing high winds. It is found that both BEP+BEM simulations have lower peak wind speeds, consistent with near-surface measurements, while the bulk simulation has winds that are too strong. The constant-drag BEP+BEM simulation has a near-zero wind speed bias, while the new-drag simulation has a negative bias. Although the focus is on the impact of drag on the urban wind speeds, both BEP+BEM simulations have larger negative biases in the near-surface temperature than the bulk-scheme simulation. Reasons for the different performances are discussed. 

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3. Evaluation of boundary-layer and urban-canopy parameterizations for simulating wind in Miami during Hurricane Irma (2017)

   https://doi.org/10.1175/MWR-D-20-0278.1  

   Hendricks, Eric A.; Knievel, Jason C.; Nolan, David S. (April 2021, Monthly Weather Review)

   null (Ed.)

   Abstract The simulated winds within the urban canopy of landfalling tropical cyclones are sensitive to the representation of the planetary-boundary and urban-canopy layers in numerical weather prediction models. In order to assess the sub-grid-scale parameterizations of these layers, mesoscale model simulations were executed and evaluated against near-surface observations as the outer wind field of Hurricane Irma (2017) interacted with the built-up region from downtown Miami northward to West Palm Beach. Four model simulations were examined, comprised of two different planetary boundary layer (PBL) parameterizations (a local closure scheme with turbulent kinetic energy prediction and a nonlocal closure scheme) and two different urban canopy models (UCMs) [a zeroth order bulk scheme and a multilayer Building Effect Parameterization (BEP) that mimics the three-dimensionality of buildings]. Overall, the simulated urban canopy winds were weakly sensitive to the PBL scheme and strongly sensitive to the UCM. The bulk simulations compared most favorably to an analyzed wind swath in the urban environment, while the BEP simulations had larger negative biases in the same region. There is uncertainty in magnitude of the urban environment biases due to the lack of many urban sheltered measurements in the wind swath analysis. Biases in the rural environment were similar among the bulk and BEP simulations. An improved comparison with the analyzed wind swath in the urban region was obtained by reducing the drag coefficient in BEP in one of the PBL schemes. The usefulness of BEP was demonstrated in its ability to predict realistic heterogeneous near-surface velocity patterns in urban regions. 

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4. Urban Weather Predictions Compared to a Dense Network of Ground-Based Weather Station Data for Assessment of Urban Building Energy Consumption

   Jahani, E; Vanage, S.; Jahn, D.; Gallus, W.; Cetin, K.S. (June 2019, ASHRAE Annual Conference papers)

   Building energy consumption is highly influenced by weather conditions, thus having appropriate weather data is important for improving the accuracy of building energy models. Typically local weather station data from the nearest airport or military base is used for weather data input. However this is generally known to differ from the actual weather conditions experienced by an urban building, particularly considering most weather stations are located far from urban areas. The use of the Weather Research and Forecasting Model (WRF) coupled with an Urban Canopy Model (UCM) provides a means to be able to predict more localized variations in weather conditions. However, one of the main challenges associated with the assessment of the use of this model is the lack of availability of ground based weather station data with which to compare its results. This has generally limited the ability to assess the level of agreement between WRF-UCM weather predictions and measured weather data in urban locations. In this study, a network of 40 ground based weather stations located in Austin, TX are compared to WRF/UCM-predicted weather data, to assess similarities and differences between model-predicted results and actual data. Given that the WRF-UCM method also takes into account many input parameters and assumptions, including the urban fraction which can be measured at different scales, this work also considers the relative impact of the granularity of the urban fraction data on WRF-UCM predicted weather. As a case study, a building energy model of a typical residential building is then developed and used to assess the differences in predicted building energy use and demands between the WRF-UCM weather and measured weather conditions during an extreme heatwave event in Austin, TX 

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5. Improving Hurricane Intensity and Streamflow Forecasts in Coupled Hydrometeorological Simulations by Analyzing Precipitation and Boundary Layer Schemes

   https://doi.org/10.1175/JHM-D-23-0153.1  

   Khondaker, Md_Murad_Hossain; Momen, Mostafa (August 2024, Journal of Hydrometeorology)

   Abstract 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. Significance StatementDespite 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. 

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