To implement continuous and reliable rainfall retrieval, based on the satellite retrieval algorithm of 10-min rain rate, this study proposes an immediate tracking and continuous accumulation technique (ITCAT) of halfhour rainfall retrieval by further combining the cross-correlation method. The ITCAT includes two steps. 1) The cross-correlation method is applied to track cloud-motion currents and establish 10-min-interval image sequences. 2) A continuous retrieval of 10-min rain rates is conducted with the image sequences, and finally a total half-hour rainfall is determined by accumulations. The satellite retrieval tests on the typical precipitation processes in the summer of 2008 show that, compared with the previous direct rainfall retrieval for half-hour to one-hour, this rainfall retrieval technique significantly improves the retrieval accuracy of rainfall scope and rainfall intensity ranging from slight rain to rainstorm for both real-time monitoring or nowcasting processes. This technique is more effective than the previous algorithm, and the fundamental reason lies in its consideration of the movement of cloud clusters. On this basis, coverage duration of rainfall clouds can be reliably estimated. It is of significance to the retrieval of deep convective cloud rainfall with rapid movement speed and drastic intensity variation. This technique also provides a feasible idea for improving the accuracy of rainfall nowcasting.

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The impact of extreme weather on maintaining flight schedules is becoming more pronounced. Currently, radar echo extrapolation technology is widely used in the nowcasting of severe convection, in which the optical flow method is a representative example of traditional extrapolation algorithms. By training a large number of known samples to find the optimal solution, the deep extrapolation models have gradually become better than the traditional algorithms in recent years. In this study, after examining the optical flow method and other deep learning models, a learnable optical flow deep model with a fully convolutional structure is proposed. Using the convolutional deep learning of optical flow information, this new model can overcome the kernel size limitation of traditional convolutional neural networks, and it can correlate the data history further in time and space. The six-year radar mosaics of Guangdong Province, China, were used as the data set to independently train and verify the new model. The results reveal that the new model outperformed the traditional optical flow method and it is also better than other deep learning models.

This study develops a method for both precipitation area and intensity retrievals based on multispectral geostationary satellite images. This method can be applied to continuous observation of large-scale precipitation so as to solve the problem from the measurements of rainfall radar and rain gauge. Satellite observation is instantaneous, whereas the rain gauge records accumulative data during a time interval. For this reason, collocated 10-min rain gauge measurements and infrared (IR) and visible (VIS) data from the FengYun-2C (FY-2C) geostationary satellite are employed to improve the accuracy of satellite rainfall retrieval. First of all, the rainfall probability identification matrix (RPIM) is used to distinguish rainfall clouds from nonrainfall clouds. This RPIM is more efficient in improving the retrieval accuracy of rainfall area than previous threshold combination screening methods. Second, the multispectral segmented curve-fitting rainfall algorithm (MSCFRA) is proposed and tested to estimate the 10-min rain rates. Rainfall samples taken from June to August 2008 are used to assess the performance of the rainfall algorithm. Assessment results show that the MSCFRA improves the accuracy of rainfall estimation for both stratiform cloud rainfall and convective cloud rainfall. These results are practically consistent with rain gauge measurements in both rainfall area division and rainfall intensity grade estimation. Furthermore, this study demonstrates that the temporal resolution of satellite detection is important and necessary in improving the precision of satellite rainfall retrieval.

Shallow geothermal energy reserves are abundant and widely distributed in Shandong Province. Vigorously developing and utilizing shallow geothermal energy will play a significant role in improving energy pressure in Shandong Province. The energy efficiency of ground source heat pumps is closely related to geological and other conditions. However, few studies on geothermal exploitation and utilization have been affected by economic policies. This article will investigate the operation of shallow geothermal engineering in Shandong Province, summarize the current number of operating projects, calculate the engineering annual comprehensive performance coefficient (ACOP), analyze the size characteristics of different cities, and analyze their correlation with economy and policy. Through research, it is found that the number of shallow geothermal energy development and utilization is significantly positively correlated with socioeconomic level and policy orientation, and has a relatively small relationship with ACOP. The research results provide a basis and suggestions for improving and optimizing the energy efficiency coefficient of geothermal heat pumps and promoting the development and utilization of shallow geothermal.