Exploring the sensitivity of vertical and spatial interpolations for varying heights and terrain for bias correction of NEWA wind data using observations

High-resolution numerical weather prediction datasets, such as the New European Wind Atlas (NEWA), are widely used in meteorological applications including forecasting, wind resource assessment and observation modelling. Despite their high spatial and temporal resolution, these datasets often exhibit systematic biases when compared to site-specific observations, particularly in complex terrain and at hub heights relevant for modern wind energy applications. Understanding and addressing these biases is important for improving the consistency between modelled and observed wind conditions.

This study focuses on a sensitivity analysis of different interpolation approaches used in the representation of wind speed profiles. The aim is to investigate how methodological choices influence wind speed estimates, with particular attention to biases arising from varying heights and terrain characteristics.

The analysis is based on mesoscale wind data in the height range of 150 m to 300 m above ground level. In a first step, wind speeds at relevant hub heights are derived at observation locations using a range of vertical interpolation approaches, including polynomial, logarithmic, power-law and piecewise cubic Hermite interpolation (PCHIP). The sensitivity of the resulting wind profiles to interpolation method, height and terrain characteristics is evaluated. In a second step, the study explores how these differences can be represented spatially by applying observationally informed adjustments to mesoscale data. The focus is on assessing general spatial interpolation strategies that incorporate terrain-related information, with the aim of examining how local variations may influence the transferability of corrections across locations and heights.

Overall, the work is intended to provide a structured assessment of how vertical and spatial interpolation choices affect wind speed profile representation in the context of observational data. The findings are expected to contribute to a better understanding of bias characteristics in mesoscale datasets and to inform the selection of appropriate methods for applications involving wind profile estimation.