Cloud identification and property retrieval from Himawari-8 infrared measurements via a deep neural network

Clouds constitute a key component of weather and climate systems, whereas the uniform retrieval of cloud properties, such as cloud top height (CTH) and cloud optical thickness (COT), requires accuracy and computational efficiency improvements. In this study, an image-based deep neural network (DNN) model for cloud identification and simultaneous retrieval of CTH and ice-COT is developed for Himawari-8 satellite infrared measurements. The DNN model is trained with brightness temperature data from four months in 2016 as the input, and cloud properties of an active remote sensing product from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) as the target truth. Supplementary variables, including the vertical temperature profile, the surface elevation, and the geometrical parameters, are added as the input data. DNN model performance is first tested with an independent dataset, and then cases over a CloudSat track and a Himawari-8 granule (85?E-205?E, 60?S-60?N) are selected for further validation of the model by comparing its results with those from two physics-based models. For both the water-and ice-CTH estimates, the DNN model shows high consistency with the target values, with an overall CTH correlation coefficient of 0.90 for high ice clouds with COT >= 0.3. Notably, as an infrared method in nature, the DNN extends the predictable ice-COT to similar to 200, with relative biases of similar to 20% for high ice clouds with COT > 1. The strong accuracy of the DNN model is primarily derived from its ability to learn from the spatial features imprinted on the input brightness temperature image, and its integration of information from neighboring pixels in a three-dimensional space. A single full disk estimation with the DNN model takes about 20 min using one processor; therefore, near-real-time cloud property retrieval that is uniformly available over 24 h can be obtained for severe weather monitoring and mesoscale cloud-system studies.

Automated summary

In this study, an image-based deep neural network (DNN) model is developed for cloud identification and retrieval of cloud top height and cloud optical thickness. The model shows high consistency with the target values and has a strong accuracy derived from learning spatial features and integrating information from neighboring pixels. It can be used for severe weather monitoring and cloud system studies.