Speaker
Description
Opportunistic sensing (OS) using commercial microwave links (CMLs) has gained increasing attention as a complementary observation source for hydrological applications. Most existing methods rely on a single type of transmission data protocol - e.g., instantaneous vs. min-max sampling at a specific sampling rate, due to the single source available (the network operator). However, when access to multiple cellular networks within the same region is available, a unique opportunity to analyze diverse measurement types with distinct temporal and statistical characteristics might become jointly available.
In this study, we investigate how a weighted integration of a number of different CML transmission protocols can improve the rainfall estimation performance, relative to each protocol when used independently. To be more specific, we address how such integration of both CML protocols from different providers can yield superior results compared to each protocol used independently.
We analyze the ISRaCML dataset (December 2016 to August 2017), which includes two types of protocols from two different providers: (i) instantaneous signal levels at 15 minute intervals and (ii) 15 minute minimum–maximum values.
For each protocol, rainfall intensities are retrieved using attenuation based methods.. A weighted fusion framework is then applied to combine the protocol specific rainfall estimates.
The resulting accumulated rainfall fields are evaluated against reference observations from rain gauges operated by the Israel Meteorological Service (IMS).
The findings of this study are expected to demonstrate the potential of multi-protocol integration for enhancing the accuracy and reliability of CML-derived rainfall maps.