Correspondence between urbanization and surface wind in the Guangdong-Hong Kong-Macao Greater Bay Area: Observations and idealized large-eddy simulation

Xia, Dong; Sun, Lei; Wang, Jing; Nie, Huiwen; Chow, Kim‐Chiu; Chan, Kalok; Wang, Donghai; Jiang, Chenglin

doi:10.1016/j.uclim.2023.101475

Cited by 2 publications

(1 citation statement)

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“…Accurate wind speed forecasts have critical implications for various facets of production‐living‐ecology, including disaster prevention and mitigation, pedestrian comfort, wind power generation, etc (Xia et al., 2023). However, driven by multi‐scale interactions and regulated by local surface boundary conditions, wind speed is characterized by randomness, intermittence, and volatility (Jung & Schindler, 2019), rendering accurate wind speed forecasts a considerable challenge.…”

Section: Introductionmentioning

confidence: 99%

Deterministic Forecasting and Probabilistic Post‐Processing of Short‐Term Wind Speed Using Statistical Methods

Sun,

Lan,

Sun

et al. 2024

JGR Atmospheres

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There is a great need for an accurate short‐term wind speed forecast, and statistical forecasts have gained increased popularity for their computational efficiency and satisfactory skill. However, there has been no systematic research to fully explore the capabilities of statistical approaches and evaluate the applicability of probabilistic information from statistical ensemble. This study first compares the skills of different statistical methods, based on linear regression, machine learning (ML), and deep learning (DL), using three strategies (i.e., direct, recursive, and multi‐output) against the three operational numerical models and their bias‐corrections, for short‐term wind speed forecast over Pearl River Estuary during 2018–2021. Inter‐comparison between statistical forecasts reveals the dominant superiority of direct strategy. On this basis, Random Forest (RF) and Support Vector Machines (SVM) perform best compared to other statistical forecasts and bias correction of numerical forecasts throughout 48 hr lead time, while the performance of methods with simplified (linear) or more complex (DL) model structures degrades significantly. Moreover, the top 10 forecasts are utilized to account for forecast uncertainties but present a substantial under‐dispersed prediction. Two traditional methods and three modern methods are implemented to perform probabilistic post‐processing. Modern methods based on ML or DL present worse skills, while traditional methods, particularly for ensemble model output statistics, show added value in discriminating binary events due to limited enhancements in calibration. Overall, RF and SVM using direct strategy are highly recommended for short‐term wind speed forecasts, and efforts are ongoing to address the issues of strong wind prediction and ensemble calibration.

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Section: Introductionmentioning

confidence: 99%