This study applied representation learning algorithms to satellite images and evaluated the learned latent spaces with classifications of various weather events. The algorithms investigated include the classical linear transformation, i.e., principal component analysis (PCA), state-of-the-art deep learning method, i.e., convolutional autoencoder (CAE), and a residual network pre-trained with large image datasets (PT). The experiment results indicated that the latent space learned by CAE consistently showed higher threat scores for all classification tasks. The classifications with PCA yielded high hit rates but also high false-alarm rates. In addition, the PT performed exceptionally well at recognizing tropical cyclones but was inferior in other tasks. Further experiments suggested that representations learned from higher-resolution datasets are superior in all classification tasks for deep-learning algorithms, i.e., CAE and PT. We also found that smaller latent space sizes had little impact on the classification task’s hit rate. Still, a latent space dimension smaller than 128 caused a significantly higher false-alarm rate. Though the CAE can learn latent spaces effectively and efficiently, the interpretation of the learned representation lacks direct connections to physical attributions. Therefore, developing a physics-informed version of CAE can be a promising outlook for the current work.

The present study objectively classify the convective cloud objects detected by the space-borne CloudSat radar over the Asian-Australian monsoon region using the hierarchical agglomerative clustering algorithm. Based on key properties representing the morphological features and convective intensity of the systems, five distinct convective cloud regimes are derived. The unique Coastal-Intense regime exhibits the most expansive horizontal scales (> 1000 km), high convective strength, the strongest cloud radiative effects, the highest probability of extreme rainfall, and a significant coupling with the sharp onset of the Asian summer monsoon circulation. Secondly, the Coastal regime illustrates smaller but also highly organized coastal convections, with the strongest convective strength. Less than 10% of the systems in the CI and Coastal regimes overlap with the tropical cyclones. The rest three regimes mark the less organized convection at various life cycle stages mainly over the land areas, with small seasonal variation in their occurrence.

In this study, we proposed a volume-to-point framework for quantitative precipitation estimation (QPE) based on the QPESUMS (Quantitative Precipitation Estimation and Segregation Using Multiple Sensor) Mosaic Radar dataset. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location-specific precipitations. In contrast to QPE methods based on the Z-R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013 ~ 2016. In comparison to the operational QPE scheme used by the Central Weather Bureau (CWB), the volume-to-point framework performed comparably well in general cases and excelled in detecting heavy-rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the QPF in extreme precipitation scenarios.