Unsupervised Segmentation Of Microwave Brightness Temperatures To Study The Changes In The Water Cycle

Due to climate change, understanding the changes in the water cycle has become a pressing issue. It is increasingly important to study prolonged periods of intense precipitation or dry spells to better manage water supply, infrastructure and agriculture. However, obtaining fine-scale precipitation data is challenging due to the intermittent nature of rain in time and space. Ground-based instruments could have mismatches between different regions due to spatial distribution, calibration, and complex topography. On the other hand, space-borne observations have uncertainties in their retrieval algorithms. This study proposes to deal directly with microwave images from space remote sensing, as this type of data makes it possible to study the evolution of the atmospheric water cycle on a global scale and with a temporal coverage of several decades by avoiding the uncertainties from retrieval methods. In recent years, convolutional neural networks have shown promising capabilities in identifying cyclones and weather fronts in large labelled climate datasets. However, these models required large labelled datasets for training and testing. The present study aims to test unsupervised segmentation approaches of microwave images, which are thus segmented into different classes. Instead of focusing only on one aspect, for example, precipitation, the obtained classes contain many physical properties. This is due to the fact that microwave brightness temperatures contain essential information relative to the atmospheric water cycle that can be used to derive many products such as rain intensity, water vapour, cloud fraction, and sea surface temperature. The unsupervised segmentation model consists of blocks of fully convolutional networks serving as feature extractors. Without labels, pseudo-targets from the feature extractors are used to train the model. The performance of the model in terms of intra-class and inter-class distances is compared with those of simpler models such as Kmeans. A major challenge in the unsupervised approach is validating and interpreting the resulting classes. Most of the obtained cluster patterns provide geographically coherent regions whose mode of variability of geophysical quantities can be highlighted. The presented study will then explore how the different classes computed by the unsupervised methods can be labelled and how the properties of the said classes change through time and space.