Abstract
Near-ground measurements by Sonic anemometer were performed during 2 years at a nearshore experimental site in Dunkirk, France for automatic detection of sea-breeze (SB) and nocturnal low-level jet (NLLJ) events. The SB detection is based on a recurrent neural network algorithm (RNN) with the accuracy of event identification equal to 95%. We found 67 and 78 SB days in 2018 and 2019 respectively. NLLJ detection algorithm uses wavelet transformation and shows better performance than the known existing methods. A total of 192 and 168 NLLJ days were found in 2018 and 2019 respectively. Our estimations show that during the NLLJ event, the peak power production can increase up to 40 times, compared to normal days. To evaluate the skill of detection algorithms, sonic and lidar wind measurements were performed simultaneously. The comparison shows a good agreement and highlights that Sonic anemometer measurements are very efficient for meteorological event detection.
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