Automatic Precipitation Measurement Based on Raindrop Imaging and Artificial Intelligence

Rainfall measurement is subjected to various uncertainties due to the complexity of measurement techniques and atmosphere characteristics associated with weather type. Thus, this article presents a video-based disdrometer to analyze raindrop images by introducing artificial intelligence technology for the rainfall rate. First, a high-speed CMOS camera is integrated into a planar LED as a backlight source for appropriately acquiring falling raindrops in different positions. The falling raindrops can be illuminated and used for further image analysis. Algorithms developed for raindrop detection and trajectory identification are employed. In a field test, a rainfall event of 42 continuous hours has been measured by the proposed disdrometer that is validated against a commercial PARSIVEL² disdrometer and a tipping bucket rain gauge at the same area. In the evaluation for 5-min rainfall images, the results of the trajectory identification are within the precision of 87.8%, recall of 98.4%, and F1 score of 92.8%, respectively. Furthermore, the performance exhibits that the rainfall rate and raindrop size distribution (RSD) obtained by the proposed disdrometer are remarkably consistent with those of PARSIVEL² disdrometer. The results suggest that the proposed disdrometer based on the continuous movements of the falling raindrops can achieve accurate measurements and eliminate the potential errors effectively in the real-time monitoring of rainfall

Realizing future intelligent networks via spatial and multi-temporal data acquisition in disdrometer networks

Abstract: Data acquisition and qualitative precipitation estimation (QPE) via disdrometers play an important role in estimating rain-induced attenuation in wireless networks. However, existing disdrometer observations do not provide sufficient information for modelling intelligent wireless networks. The design of intelligent wireless networks requires that QPE parameters for a location be known at different epochs. This requires that disdrometers with spatial variability should be capable of multi-temporal QPE observations. A disdrometer architecture that addresses this challenge is presented in this paper. The proposed multi–temporal disdrometer incorporates a computing payload for storing QPE related data at multiple epochs. Performance evaluation shows that the use of the proposed multi–temporal disdrometer in QPE related data acquisition increases data suitable for QPE related modelling by up to 52.2% and 49.4% in the short term and long term respectively