FOCUSED–Short-Term Wind Speed Forecast Correction Algorithm Based on Successive NWP Forecasts for Use in Traffic Control Decision Support Systems

Kunić, Zdravko; Ženko, Bernard; Boshkoska, Biljana Mileva

doi:10.3390/s21103405

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…The machine learning algorithms like artificial neural networks were used subsequently instead of the traditional parameter-based methods. For example, Kunić et al [9] proposed a new artificial neural network called "FOCUS" that corrected wind speed results from the numerical forecasts of five years for the Krk Bridge in Croatia. The results indicated that the corrected forecasts performed better than the uncorrected results during almost half of the validation period.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

Zang,

Zou,

et al. 2024

Atmosphere

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In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases.

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Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

Zang,

Zou,

et al. 2024

Atmosphere

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“…Wind tunnel experiments are also used to examine urban flow issues, and although it can be challenging to represent the urban morphology realistically, they serve as the experimental verification for CFD simulations. NWP models are the foundation for weather forecasting and provide the wind information critical for numerous applications, including urban air quality dispersion models (Lee et al, 2009), wind power forecasting (Senatore et al, 2020), and traffic safety (Kuni c et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluating modelled winds over an urban area using ground‐based Doppler lidar observations

Filioglou

Preißler²,

Troiville³

et al. 2022

Meteorological Applications

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Wind information in urban areas is essential for many applications related to air pollution, urban climate and planning, safety of drone-related operations, and assessment of urban wind energy potential. These applications require accurate wind forecasts, and obtaining this information in an urban environment is challenging as the morphology of a city varies from street to street, altering the wind flow. Remote sensing techniques such as Doppler lidars (light detection and ranging) provide a unique opportunity for wind forecast verification as they can provide both the vertical profile of the horizontal wind and the spatial variation in the horizontal domain at high resolution. In this study, the performance of numerical weather prediction (NWP) models, analysis systems, and large-eddy simulation (LES) models have been analysed by comparing the modelled winds against Doppler lidar observations under various atmospheric conditions and from season to season, in the coastal environment of Helsinki, Finland. The long-term mean vertical profile of the modelled horizontal wind shows good agreement with observations; the NWP model and the analysis systems selected here exhibit different strengths and weaknesses depending on the atmospheric conditions but no significant diurnal variation in performance. However, both the model and analysis systems show differences in their spatially-averaged bias when investigating different wind directions. LES verification shows that these models can potentially provide winds down to Associated post doc with Vaisala through PoDoCo program.

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“…Numerical weather prediction (NWP), which can provide high-performance weather prediction for the prevention and mitigation of weather-related disasters, is one of the main objective forecast tools available for scientific research and operational forecasting. However, because NWP models cannot fully simulate the real atmosphere, we need to design effective correction methods to modify their results [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. There are two types of methods in this regard: traditional statistical modeling methods and machine learning-based methods.…”

Section: Introductionmentioning

confidence: 99%

Numerical Forecast Correction of Temperature and Wind Using a Single-Station Single-Time Spatial LightGBM Method

Tang

Ning

et al. 2021

Sensors

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Achieving high-performance numerical weather prediction (NWP) is important for people’s livelihoods and for socioeconomic development. However, NWP is obtained by solving differential equations with globally observed data without capturing enough local and spatial information at the observed station. To improve the forecasting performance, we propose a novel spatial lightGBM (Light Gradient Boosting Machine) model to correct the numerical forecast results at each observation station. By capturing the local spatial information of stations and using a single-station single-time strategy, the proposed method can incorporate the observed data and model data to achieve high-performance correction of medium-range predictions. Experimental results for temperature and wind prediction in Hainan Province show that the proposed correction method performs well compared with the ECWMF model and outperforms other competing methods.

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FOCUSED–Short-Term Wind Speed Forecast Correction Algorithm Based on Successive NWP Forecasts for Use in Traffic Control Decision Support Systems

Cited by 7 publications

References 21 publications

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

Evaluating modelled winds over an urban area using ground‐based Doppler lidar observations

Numerical Forecast Correction of Temperature and Wind Using a Single-Station Single-Time Spatial LightGBM Method

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