Analysis of Observational Data for Enhanced Weather Diagnosis and Forecasting
M. Lamprinidou (TU Delft - Applied Sciences)
S.R. de Roode – Mentor (TU Delft - Civil Engineering & Geosciences)
R.A. Verzijlbergh – Graduation committee member (TU Delft - Technology, Policy and Management)
M. Rohde – Graduation committee member (TU Delft - Applied Sciences)
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Abstract
Reliable forecasts of wind and solar energy are essential for the integration of renewable energy into the electricity grid. High-resolution Large-Eddy Simulation (LES) models offer improved representation of turbulence and local atmospheric processes but require accurate, site-representative observations for data assimilation.
This thesis evaluates whether in-situ and remote sensing observations collected during the REFORM 2024 field campaign at a site with co-located wind turbines and solar PVs in Warmenhuizen (NL) can be used for data assimilation in LES-based forecasting. The instrumentation included a 10 m meteorological mast, two radiometers, a sonic anemometer, a microwave radiometer, and a cloud radar, deployed over several months from March to June 2024. Comprehensive pre-processing and 10-minute resolution data aggregation enabled the analysis of surface energy balance (SEB), albedo (ranging from 0.15–0.35 with a seasonal increase), atmospheric stability, thermodynamic structure, and representativeness of the observations. A detailed case study of May 23, 2024, captured a transition from stratocumulus to shallow cumulus, demonstrating physically consistent diurnal patterns in radiation and turbulent fluxes. Observations compared well with regional reference sites and
met key criteria for physical plausibility and internal consistency. However, deviations from Monin–Obukhov Similarity Theory under stable stratification (with heat flux stability functions up to 66% below expected values) and a right-skewed distribution of roughness length estimates (median z0 = 0.048 m, skewness = 2.27) highlight the influence of local infrastructure and surface heterogeneity.
The study concludes that the Warmenhuizen dataset is suitable for high-resolution LES modeling and renewable energy forecasting, provided that limitations, such as temporal smoothing, infrastructure-induced disturbances, and lack of a nearby reference site, are explicitly accounted for. This study is among the first to test whether observations from a real-world, infrastructure-influenced site remain suitable for high-resolution weather forecasting and energy modeling; unlike most observation studies that rely on undisturbed terrain.