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.