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Wind profiler radars are capable of measuring three-dimensional wind profiles at various altitudes of the atmosphere, at very high temporal and spatial resolution. The Advanced Centre for Atmospheric Radar Research (ACARR), located at Cochin University of Science and Technology (CUSAT), operates the World's first 205 MHz stratospheretroposphere wind profiler radar which provides three-dimensional wind profiles for an altitude range of 315 m to 20 km. During non-rainy condition, the radar Doppler power spectrum bears the signature of ambient air motion whereas during rainy conditions, it contains signatures of both ambient air motion and fall velocity of rain droplets. The classification of Doppler power spectra for rainy (Precipitation) and non-rainy (Clear) conditions is necessary as wind profile retrieval from the former needs careful separation of ambient air motion from fall velocity of droplets. A manual classification of the power spectrum is cumbersome, time-consuming, and therefore not practical due to the vast database. This work intends to automate Doppler power Article Title spectra classification using the deep learning Convolutional Neural Network (CNN). The proposed Convolutional Neural Network model gives a k-fold validation accuracy of 99.77 % and testing accuracy of 99.60% for power spectra classification. The performance of CNN is compared against other popular machine learning classifiers such as Support Vector Machine, Decision Tree, K Nearest Neighbour and Naive Bayes. The performance comparison results show that the proposed CNN outperforms other models in radar Doppler power spectra classification.

Wind profiler radars are capable of measuring three-dimensional wind profiles at various altitudes of the atmosphere, at very high temporal and spatial resolution. The Advanced Centre for Atmospheric Radar Research (ACARR), located at Cochin University of Science and Technology (CUSAT), operates the World’s first 205 MHz stratospheretroposphere wind profiler radar which provides three-dimensional wind profiles for an altitude range of 315 m to 20 km. During non-rainy condition, the radar Doppler power spectrum bears the signature of ambient air motion whereas during rainy conditions, it contains signatures of both ambient air motion and fall velocity of rain droplets. The classification of Doppler power spectra for rainy (Precipitation) and non-rainy (Clear) conditions is necessary as wind profile retrieval from the former needs careful separation of ambient air motion from fall velocity of droplets. A manual classification of the power spectrum is cumbersome, time-consuming, and therefore not practical due to the vast database. This work intends to automate Doppler power spectra classification using the deep learning Convolutional Neural Network (CNN). The proposed Convolutional Neural Network model gives a k-fold validation accuracy of 99.77 % and testing accuracy of 99.60% for power spectra classification. The performance of CNN is compared against other popular machine learning classifiers such as Support Vector Machine, Decision Tree, K Nearest Neighbour and Naive Bayes. The performance comparison results show that the proposed CNN outperforms other models in radar Doppler power spectra classification.