Study of Antarctic blowing snow storms using MODIS and CALIOP observations with a machine learning model

As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget and planetary boundary processes. This paper presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used for training. Model performance results show that machine-learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the CALIPSO coverage limit from 820S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 1300E and 1600E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes.