{"title":"Comparative Evaluation of the Ability of the MYNN-EDMF PBL Scheme in WRF Model to Reproduce Near Surface Wind Speed Over Different Topographical Types","authors":"Yunpeng Shan, Yangang Liu, Xin Zhou","doi":"10.1029/2023JD040620","DOIUrl":null,"url":null,"abstract":"<p>This study systematically evaluates the performance of the Mellor-Yamada-Nakanishi-Niino-Eddy-Diffusion-Mass-Flux planetary boundary layer (PBL) scheme within the Weather Research and Forecasting (WRF) model in simulating near-surface wind speeds across various topographies in New York State (NYS). Simulated wind speeds are compared with in-situ measurements from 22 surface sites, grouped into six topographic categories: continental plain (CT), lakeside (LS), river valley (RV), Long Island (LI), Block Island (BI), and offshore ocean (OO). A quantitative evaluation based on Relative Euclidean Distance shows that wind speeds at the OO site are the most accurately reproduced, followed by those at LI sites, while the model performs less accurately for the remaining topographic groups. Wind speeds over CT sites tend to be overestimated by approximately 1 m/s, although their diurnal variability (DV) is well captured. In contrast, the model underestimates wind DV at LS, RV, LI, and BI sites, with the largest biases occurring at LI and BI, resulting in underestimated daytime wind speed and/or overestimated nighttime wind speed. The OO winds exhibit minimal diurnal variation, accurately captured by our WRF model. The surface wind diurnal variation is closely linked to PBL development. Among the indicators of PBL development, surface potential temperature biases most strongly correlate with wind speed biases. Our WRF model faces challenges in capturing the distinctions between winds influenced by local circulations and those over continental plains, and the significantly stronger winds at OO compared to BI. Potential causes for these biases are discussed, offering pathways for improving surface wind simulations in future.</p>","PeriodicalId":15986,"journal":{"name":"Journal of Geophysical Research: Atmospheres","volume":"130 8","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023JD040620","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysical Research: Atmospheres","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2023JD040620","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
This study systematically evaluates the performance of the Mellor-Yamada-Nakanishi-Niino-Eddy-Diffusion-Mass-Flux planetary boundary layer (PBL) scheme within the Weather Research and Forecasting (WRF) model in simulating near-surface wind speeds across various topographies in New York State (NYS). Simulated wind speeds are compared with in-situ measurements from 22 surface sites, grouped into six topographic categories: continental plain (CT), lakeside (LS), river valley (RV), Long Island (LI), Block Island (BI), and offshore ocean (OO). A quantitative evaluation based on Relative Euclidean Distance shows that wind speeds at the OO site are the most accurately reproduced, followed by those at LI sites, while the model performs less accurately for the remaining topographic groups. Wind speeds over CT sites tend to be overestimated by approximately 1 m/s, although their diurnal variability (DV) is well captured. In contrast, the model underestimates wind DV at LS, RV, LI, and BI sites, with the largest biases occurring at LI and BI, resulting in underestimated daytime wind speed and/or overestimated nighttime wind speed. The OO winds exhibit minimal diurnal variation, accurately captured by our WRF model. The surface wind diurnal variation is closely linked to PBL development. Among the indicators of PBL development, surface potential temperature biases most strongly correlate with wind speed biases. Our WRF model faces challenges in capturing the distinctions between winds influenced by local circulations and those over continental plains, and the significantly stronger winds at OO compared to BI. Potential causes for these biases are discussed, offering pathways for improving surface wind simulations in future.
期刊介绍:
JGR: Atmospheres publishes articles that advance and improve understanding of atmospheric properties and processes, including the interaction of the atmosphere with other components of the Earth system.