Time-Series Augmentation Based on Atmospheric Microwave Remote Sensing Data Under the Influence of Pacific Monsoon Climate

Microwave radiometer is a passive remote sensing instrument. The MP3000A microwave radiometer is used in this study. To address the problem of time-series augmentation of contour data, this paper uses a super-resolution network model based on convolutional neural network for time-series augm-entation of 34,568 temperature contour data measured by $M$ P3000A from April 22, 2023 to June 15, 2023 in Harbin. High- resolution branches are added to the original super-resolution network, and the results are compared and analyzed by normal-izing and non-normalizing the original data. The heatmap of the temperature data error shows that the time series amplified temperature data is weakly different from the true value, with a mean square error MSE of 2.4831 and a mean absolute error MAE of 1.3213, and the network model performs well. It is shown that the use of super-resolution network model works well in time series for atmospheric microwave remote sensing data under the influence of the Pacific monsoon climate.