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首页> 外文期刊>ASHRAE Transactions >City-Scale High-Resolution WRF- UCM Urban Weather Predictions Compared to a Dense Network of Ground-Based Weather Station Data for Assessment of Urban Building Energy Consumption
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City-Scale High-Resolution WRF- UCM Urban Weather Predictions Compared to a Dense Network of Ground-Based Weather Station Data for Assessment of Urban Building Energy Consumption

机译：城市规模的高分辨率WRF-UCM城市天气预报与地面气象站数据密集网络的比较，用于评估城市建筑能耗

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摘要

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.
机译：建筑能耗受天气条件的影响很大，因此拥有适当的天气数据对于提高建筑能耗模型的准确性很重要。通常，将最近的机场或军事基地的本地气象站数据用于气象数据输入。但是，通常认为这与城市建筑物的实际天气条件不同，特别是考虑到大多数气象站都位于远离市区的地方。结合使用天气研究和预测模型（WRF）和城市机盖模型（UCM），可以预测天气情况的局部变化。但是，与评估该模型的使用相关的主要挑战之一是缺乏与之比较结果的地面气象站数据的可用性。这通常限制了评估WRF-UCM天气预报与城市位置的实测气象数据之间的一致性水平的能力。在这项研究中，将位于德克萨斯州奥斯丁的40个地面气象站的网络与WRF / UCM预测的气象数据进行了比较，以评估模型预测的结果与实际数据之间的异同。鉴于WRF-UCM方法还考虑了许多输入参数和假设，包括可以在不同尺度下测量的城市分数，因此，本文还考虑了城市分数数据粒度对WRF-UCM预测天气的相对影响。作为案例研究，然后开发了典型住宅建筑的建筑能耗模型，并将其用于评估德克萨斯州奥斯汀的极端热浪事件期间，WRF-UCM天气与实测天气条件之间的预测建筑能耗和需求之间的差异。

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  • 来源
    《ASHRAE Transactions》 |2019年第2期|248-256|共9页
  • 作者

    Elham Jahani; Soham Vanage; Kristen Cetin; David Jahn; William Gallus;

  • 作者单位

    Department of Civil Construction and Environmental Engineering (CCEE) Iowa State University Ames Iowa;

    Mechanical Engineering Iowa State University Ames Iowa;

    Department of Civil Construction and Environmental Engineering Iowa State University Ames Iowa;

    Department of Geological and Atmospheric Sciences Iowa State University Ames Iowa;

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