{"title":"Incorporating zero-plane displacement in roughness length estimation and exposure correction factor calculation","authors":"Pingzhi Fang, Hui Yu, Mingwei Zhao, Wenbo Yu","doi":"10.1002/met.70028","DOIUrl":null,"url":null,"abstract":"<p>Exposure correction is necessary for removing the distortion effects induced by nonstandard local exposure in raw near-ground wind speed datasets. The accurate calculation of the exposure correction factor (<span></span><math>\n <semantics>\n <mrow>\n <mi>ECF</mi>\n </mrow>\n <annotation>$$ \\mathrm{ECF} $$</annotation>\n </semantics></math>) for wind speeds requires reliable input of the local aerodynamic roughness length (<span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>z</mi>\n <mn>0</mn>\n </msub>\n </mrow>\n <annotation>$$ {z}_0 $$</annotation>\n </semantics></math>). In this study, we evaluate the performance of an <span></span><math>\n <semantics>\n <mrow>\n <mi>ECF</mi>\n </mrow>\n <annotation>$$ \\mathrm{ECF} $$</annotation>\n </semantics></math> formula suggested by the World Meteorological Organization and the estimation of <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>z</mi>\n <mn>0</mn>\n </msub>\n </mrow>\n <annotation>$$ {z}_0 $$</annotation>\n </semantics></math> based on gustiness model. The estimation of <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>z</mi>\n <mn>0</mn>\n </msub>\n </mrow>\n <annotation>$$ {z}_0 $$</annotation>\n </semantics></math> will be more reasonable if local zero-plane displacement (<span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>z</mi>\n <mi>d</mi>\n </msub>\n </mrow>\n <annotation>$$ {z}_d $$</annotation>\n </semantics></math>) is considered under rough terrain conditions. An empirical linear relationship <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>z</mi>\n <mi>d</mi>\n </msub>\n <mo>=</mo>\n <msub>\n <mi>C</mi>\n <mn>0</mn>\n </msub>\n <msub>\n <mi>z</mi>\n <mn>0</mn>\n </msub>\n </mrow>\n <annotation>$$ {z}_d={C}_0{z}_0 $$</annotation>\n </semantics></math> is introduced, and the ratio <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>C</mi>\n <mn>0</mn>\n </msub>\n <mo>=</mo>\n <mn>6</mn>\n </mrow>\n <annotation>$$ {C}_0=6 $$</annotation>\n </semantics></math> is recommended for meteorological stations under rough terrain conditions in China coastline. The incorporation of <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>z</mi>\n <mi>d</mi>\n </msub>\n </mrow>\n <annotation>$$ {z}_d $$</annotation>\n </semantics></math> into the <span></span><math>\n <semantics>\n <mrow>\n <mi>ECF</mi>\n </mrow>\n <annotation>$$ \\mathrm{ECF} $$</annotation>\n </semantics></math> formula suggested by the World Meteorological Organization is further performed. Sensitivity analyses indicate that the <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>z</mi>\n <mn>0</mn>\n </msub>\n </mrow>\n <annotation>$$ {z}_0 $$</annotation>\n </semantics></math> estimates and <span></span><math>\n <semantics>\n <mrow>\n <mi>ECF</mi>\n </mrow>\n <annotation>$$ \\mathrm{ECF} $$</annotation>\n </semantics></math> values are highly sensitive to factors such as mean wind duration, gust duration and anemometer height. Finally, we conducted case studies across 15 meteorological stations in China coastline, which revealed that our proposed method enhances the accuracy of both <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>z</mi>\n <mn>0</mn>\n </msub>\n </mrow>\n <annotation>$$ {z}_0 $$</annotation>\n </semantics></math> estimation and <span></span><math>\n <semantics>\n <mrow>\n <mi>ECF</mi>\n </mrow>\n <annotation>$$ \\mathrm{ECF} $$</annotation>\n </semantics></math> calculation in comparison to the existing models.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70028","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Meteorological Applications","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/met.70028","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Exposure correction is necessary for removing the distortion effects induced by nonstandard local exposure in raw near-ground wind speed datasets. The accurate calculation of the exposure correction factor () for wind speeds requires reliable input of the local aerodynamic roughness length (). In this study, we evaluate the performance of an formula suggested by the World Meteorological Organization and the estimation of based on gustiness model. The estimation of will be more reasonable if local zero-plane displacement () is considered under rough terrain conditions. An empirical linear relationship is introduced, and the ratio is recommended for meteorological stations under rough terrain conditions in China coastline. The incorporation of into the formula suggested by the World Meteorological Organization is further performed. Sensitivity analyses indicate that the estimates and values are highly sensitive to factors such as mean wind duration, gust duration and anemometer height. Finally, we conducted case studies across 15 meteorological stations in China coastline, which revealed that our proposed method enhances the accuracy of both estimation and calculation in comparison to the existing models.
期刊介绍:
The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including:
applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits;
forecasting, warning and service delivery techniques and methods;
weather hazards, their analysis and prediction;
performance, verification and value of numerical models and forecasting services;
practical applications of ocean and climate models;
education and training.