Speaker
Description
The benefit of using Commercial Microwave Links (CMLs) as opportunistic sensors for rainfall estimation is greatest in those regions that lack dedicated rainfall sensors, notably large parts of the tropics and mountainous areas. The lack of dedicated rainfall sensors, however, also means that for these regions calibration of CML rainfall retrieval algorithms is a challenge. The core of these retrieval algorithms is based on the near power law relation between rainfall intensity R and signal attenuation k through $R=ak^b$. In this equation the coefficient a and exponent b are dependent on rainfall climatology and associated drop size distributions (DSDs), raindrop temperature, CML frequency, and CML polarization. Drop size distributions are key in determining the propagation of microwave signals through the atmosphere, and with that the scattering and absorption mechanisms by rain drops. However, information regarding drop size distributions globally is extremely sparse, and where available often not accessible. Therefore, most retrieval algorithms have so far employed a single (global) a and b parameter set provided by the International Telecommunication Union (ITU), or have relied on drop size distributions collected in a completely different climate.
In this work we present the installation of two Thies Laser Precipitation Monitors (LPM) in Rwanda. One in the city of Kigali, with a tropical savannah climate, and the other in Northern Rwanda at the foot of the Volcanoes National Park with a temperate climate. The disdrometers are equipped with a custom data collection software DisdroDL, and the data is processed using disdroDB, an open-source tool to process and archive disdrometer data. Here we show the first results of using local empirical drop size distributions to calibrate rainfall estimates from CMLs. We compare these rainfall estimates to those estimated using DSDs from the Netherlands, and to those using the a and b parameters provided by ITU, as well as another tropical DSD data set collected in Ghana. These results serve to understand the sensitivity of CML derived rainfall rates to drop size distributions in a tropical climate, and such determine the uncertainty in CML rainfall estimates when local calibration is not feasible due to lack of DSDs.