Identifying mechanisms of low-level jets near coast of Kurzeme using Principal Component Analysis

Low-level jets are maximums in the vertical profile of the wind speed profile in the lowest levels of atmosphere. Low-level jets, when present, can make a significant impact on the wind energy. Wind conditions in low-level jets depart from traditional assumptions about wind profile and low-level jets can also influence the stability and turbulence that are important for wind energy applications.

In literature commonly an algorithm of identifying low-level jets is used to estimate frequency of low-level jets. The algorithm searches for maximum in the lowest levels of the atmosphere with a temperature inversion above the jet maximum. The algorithm is useful in identifying the presence of the low-level jets and estimating their frequency. However, low-level jets can be caused by a number of different mechanisms which leads to differences in low-level jet characteristics. Therefore, additional analysis is necessary to distinguish between different types of jets and characterize their properties. We aim to automate this process using Principal Component Analysis (PCA) to identify main patterns of wind speed and temperature. By analyzing diurnal and seasonal cycles of these patterns a better understanding about climatology of low-level jets in the region can be gained.

This study focuses on the central part of the Baltic Sea. Several recent studies have identified the presence of low-level jets near the coast of Kurzeme. Typically, maximums of low-level jets are located several hundred meters above the surface, while near the coast of Kurzeme maximums of low-level jets are usually within the lowest 100 meters of the atmosphere.

Data from UERRA reanalysis with 11 km horizontal resolution on 12 height levels in the lowest 500 meters of atmosphere was used. The algorithm that identifies low-level jets was applied to the data, to estimate frequency of low-level jets in each grid cell of the model. Jet events were grouped by the wind direction to identify main trajectories of low-level jets in the region. Several atmosphere cross-sections that low-level jets frequently flow through were chosen for further analysis.

Model data was interpolated to the chosen cross-sections and PCA was applied to the cross-section data of wind speed, geostrophic wind speed and temperature. Main patterns of these meteorological parameters, such as wind speed maximum, temperature inversion above the surface of the sea and temperature difference between sea and land were identified by the PCA. Differences of principal components between cross-sections and diurnal and seasonal patterns of principal components helped to gain better understanding of climatology, extent and mechanisms of low-level jets in the region.