Abstract. Ten years of ERA5 reanalysis data are combined with met-mast and lidar observations from 10 offshore platforms to investigate low-level jet characteristics over the Dutch North Sea. The objective of this study is to combine the best of two worlds: (1) ERA5 data with a large spatiotemporal extent but inherent accuracy limitations due to a relatively coarse grid and an incomplete representation of physical processes and (2) observations that provide more reliable estimates of the measured quantity but are limited in both space and time. We demonstrate the effect of time and range limitations on the reconstructed wind climate, with special attention paid to the impact on low-level jets. For both measurement and model data, the representation of wind speed is biased. The limited temporal extent of observations leads to a wind speed bias on the order of ±1 m s−1 as compared to the long-term mean. In part due to data-assimilation strategies that cause abrupt discontinuities in the diurnal cycle, ERA5 also exhibits a wind speed bias of approximately 0.5 m s−1. The representation of low-level jets in ERA5 is poor in terms of a one-to-one correspondence, and the jets appear vertically displaced (“smeared out”). However, climatological characteristics such as the shape of the seasonal cycle and the affinity with certain circulation patterns are represented quite well, albeit with different magnitudes. We therefore experiment with various methods to adjust the modelled low-level jet rate to the observations or, vice versa, to correct for the erratic nature of the short observation periods using long-term ERA5 information. While quantitative uncertainty is still quite large, the presented results provide valuable insight into North Sea low-level jet characteristics. These jets occur predominantly for circulation types with an easterly component, with a clear peak in spring, and are concentrated along the coasts at heights between 50 and 200 m. Further, it is demonstrated that these characteristics can be used as predictors to infer the observed low-level jet rate from ERA5 data with reasonable accuracy.
In the winter of 2012/13, the Katabatic Winds and Stability over Cadarache for the Dispersion of Effluents (KASCADE) observational campaign was carried out in southeastern France to characterize the wind and thermodynamic structure of the (stable) planetary boundary layer (PBL). Data were collected with two micrometeorological towers, a sodar, a tethered balloon, and radiosoundings. Here, this dataset is used to evaluate the representation of the boundary layer in the Weather Research and Forecasting (WRF) Model. In general, it is found that diurnal temperature range (DTR) is largely underestimated, there is a strong negative bias in both longwave radiation components, and evapotranspiration is overestimated. An illustrative case is subjected to a thorough model-physics evaluation. First, five PBL parameterization schemes and two land surface schemes are employed. A marginal sensitivity to PBL parameterization is found, and the sophisticated Noah land surface model represents the extremes in skin temperature better than does a more simple thermal diffusion scheme. In a second stage, sensitivity tests for land surface–atmosphere coupling (through parameterization of z0h/z0m), initial soil moisture content, and radiation parameterization were performed. Relatively strong surface coupling and low soil moisture content result in a larger sensible heat flux, deeper PBL, and larger DTR. The larger sensible heat flux is not supported by the observations, however. It turns out that, for the selected case, a combination of subsidence and warm-air advection is not accurately simulated, but this inaccuracy cannot fully explain the discrepancies found in the WRF simulations. The results of the sensitivity analysis reiterate the important role of initial soil moisture values.
Numerical weather prediction models play an important role in the field of wind energy, for example, in power forecasting, resource assessment, wind farm (wake) simulations, and load assessment. Continuous evaluation of their performance is crucial for successful operations and further understanding of meteorology for wind energy purposes. However, extensive offshore observations are rarely available. In this paper, we use unique met mast and Lidar observations up to 315 m from met mast “IJmuiden,” located in the North Sea 85 km off the Dutch coast, to evaluate the representation of wind and other relevant variables in three mainstream meteorological models: ECMWF‐IFS, HARMONIE‐AROME, and WRF‐ARW, for a wide range of weather conditions. Overall performance for hub‐height wind speed is found to be comparable between the models, with a systematic wind speed bias <0.5 m/s and random wind speed errors (centered RMSE) <2 m/s. However, the model performance differs considerably between cases, with better performance for strong wind regimes and well‐mixed wind and potential temperature profiles. Conditions characterized by moderate wind speeds combined with stable stratification, which typically produce substantial wind shear and power fluctuations, lead to the largest misrepresentations in all models.
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