WRF wind forecast over coastal complex terrain: Baja California Peninsula (Mexico) case study

Pereyra-Castro, Karla; Caetano, Ernesto; Razo, Diego Altamirano del

doi:10.1007/s12517-021-08317-3

Cited by 4 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Winds derived from simulations using mesoscale models also tend to be underestimated due to the spatial and temporal resolutions [44]. The smoothing effect conducts to the overestimation of weak ramp events in comparison to the observed ramps [45]. For this reason, it is necessary to adjust the simulated wind-ramp distribution to the observed distribution.…”

Section: Ramp Distribution Correctionmentioning

confidence: 99%

Wind-Ramp Predictability

Pereyra-Castro

Caetano

2022

Atmosphere

Self Cite

View full text Add to dashboard Cite

The intermittent nature of wind resources is challenging for their integration into the electrical system. The identification of weather systems and the accurate forecast of wind ramps can improve wind-energy management. In this study, extreme wind ramps were characterized at four different geographical sites in terms of duration, persistence, and weather system. Mid-latitude systems are the main cause of wind ramps in Mexico during winter. The associated ramps last around 3 h, but intense winds are sustained for up to 40 h. Storms cause extreme wind ramps in summer due to the downdraft contribution to the wind gust. Those events last about 1 to 3 h. Dynamic downscaling is computationally costly, and statistical techniques can improve wind forecasting. Evaluation of the North American Mesoscale Forecast System (NAM) operational model to simulate wind ramps and two bias-correction methods (simple bias and quantile mapping) was done for two selected sites. The statistical adjustment reduces the excess of no-ramps (≤|0.5| m/s) predicted by NAM compared to observed wind ramps. According to the contingency table-derived indices, the wind-ramp distribution correction with simple bias method or quantile mapping method improves the prediction of positive and negative ramps.

show abstract

Section: Ramp Distribution Correctionmentioning

confidence: 99%