Rainfall Nowcasting Using Commercial Microwave Links

Abstract

Accurate and timely precipitation forecasts are crucial for early warning. Rainfall nowcasting, the process of statistically extrapolating recent rainfall observations, is increasingly used for short-term forecasting. Nowcasts are generally constructed with high-resolution radar observations. As a proof of concept, we construct nowcasts with country-wide rainfall maps estimated from signal level data of commercial microwave links (CMLs) for 12 summer days in the Netherlands. CML nowcasts compare well to radar rainfall nowcasts. Provided well-calibrated CML rainfall estimates are employed, CML nowcasts can outperform unadjusted real-time radar nowcasts for high rainfall rates, which are underestimated as compared to a reference. Care should be taken with the sensitivity of the advection field derivation to areas with low CML coverage and the inherent measurement scale of CML data, which can be larger than the application scale. We see potential for rainfall nowcasting with CML data, for example, in regions where weather radars are absent. Plain Language Summary Capturing the amount, timing and location of rainfall are of key importance for water management and the agricultural sector. Nowcasting of rainfall, a computational approach where the most recent rainfall observations are spatially extrapolated, is increasingly used for short-term rainfall forecasts. The required rainfall observations normally originate from weather radars, but it has previously been demonstrated that rainfall can also be estimated from rain-induced signal attenuation from the commercial microwave links (CMLs) in cellular telecommunication networks, which are used worldwide. In this study, we use such estimates to construct rainfall nowcasts for 12 summer events in the Netherlands, which we compare to nowcasts using operational radars. The results demonstrate that there is potential for using rainfall estimates from CML data to nowcast rainfall up to a few hours ahead, for example, in (low-and middle-income) regions where high-resolution rainfall observations or weather radars are absent.

Evaluation of an early flood warning system in Bamako (Mali): Lessons learned from the flood of May 2019

“…In addition, simulations showed its relevance in hydrological applications at the city scale (Turko et al, 2021; Pastorek et al, 2019; Fencl et al, 2013; Fenicia et al, 2012). The technique allows real‐time country‐wide nowcasting, as experienced in Germany and the Netherlands (Chwala et al, 2019; Imhoff et al, 2020). In dense zones like Bamako CMLs may improve rainfall sampling and flood simulation.…”

Missing Rainfall Extremes in Commercial Microwave Link Data Due To Complete Loss of Signal

“…CML rainfall estimates were derived for many countries around the globe, for example, the Netherlands (Overeem et al., 2016), Sri Lanka (Overeem et al., 2021), Burkina Faso (Doumounia et al., 2014) and Germany (Graf et al., 2020). CML‐derived rainfall information can be used for applications like streamflow prediction, urban drainage modeling, agricultural purposes and rainfall nowcasting (Brauer et al., 2016; Fencl et al., 2013; Imhoff et al., 2020; Stransky et al., 2018). Especially for flash flood prediction, precise precipitation maxima are of great importance (Cristiano et al., 2017).…”

Reconstructing and Nowcasting the Rainfall Field by a CML Network

“…After obtaining the rainfall intensity, to reconstruct rainfall fields, spatial interpolation algorithms (Messer et al., 2008), machine learning algorithms (Zhao et al., 2022), and tomographic methods (D’Amico et al., 2016) are adopted. Finally, deep learning approaches are employed to nowcast the rainfall fields (Imhoff et al., 2020; Jacoby et al., 2020).…”