# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## 5 Experimental Results
Rain rate estimates provided by the NEFOCAST SmartLNB-based sensor (solid line) and by the TBR (dashed line). Figure 10: Calculated signal attenuation in Pisa, Italy, 5–10 May 2017. Figure 12 refers to the rain event occurred in Pisa on 9 May and as with the previous figure (albeit with a different scale) compares NEFOCAST real-time rain rate estimates (solid line) with the quantized estimates provided by the TBR (dashed line). Figure 13 refers to the rain event which occurred in Pisa on 6 May 2017 and compares the accumulated rainfall calculated by integrating the NEFOCAST rain rate estimates (solid line) with the measurements provided by the TBR (dashed line). Figure 12: Pisa, 9 May 2017. Rain rate estimates provided by the NEFOCAST SmartLNB-based sensor (solid line) and by the TBR (dashed line). Figure 13: Pisa, 6 May 2017. Accumulated rainfall estimated by the NEFOCAST SmartLNB sensor (solid line) and measured by the TBR (dashed line). Finally, Figure 14 refers to the rain event of 9 May 2017 and, as in the previous Figure 13, compares the accumulated rainfall obtained by integration of the NEFOCAST rain rate estimates (solid line) with the measurements provided by the rain gauge (dashed line). An inspection of Figures 11-14 reveals that, thanks to the very short (20 s) update interval, the NEFOCAST sensor is capable of monitoring and following accurately the fast and random evolution of the rainfall rate during a precipitating event, and warrants a much faster response than a conventional tipping bucket gauge. Furthermore, the latter seems less reliable at medium-to-low rainfall rates, due to its quantized response (10) and resolution, that do not allow the determination of exactly when precipitation starts and when it presents spikes. On the other hand, the NEFOCAST sensor seems less reliable in estimating the cumulated rainfall over long periods, since the attenuation measured by the SmartLNB depends not only on the instantaneous rain rate falling in the installation site, but also on the rain distribution over the entire length of the slant path affected by the rain. This may introduce a nonnegligible random bias when integrating the instantaneous estimated rain rate over long periods of time. In this respect, it is worth recalling that the proposed rainfall estimation technique is affected by many sources of error, a few of which were mentioned earlier. They can be roughly classified as (i) errors in the \(R\)-\(k\) mapping law, i.e., in the choice of the coefficients involved in (5), notably when they are derived by means of global models such as [29]; (ii) errors due to the (unknown) variability of the rainfall rate along the electromagnetic path, resulting in an (averaged) specific attenuation measurement that does not exactly match the desired local specific attenuation at the SmartLNB site; (iii) errors in estimating the liquid precipitation height and other geometric parameters; (iv) errors in the knowledge of those parameters related to the propagation medium and the environment surrounding the receiver, such as climatic conditions, ground temperature and the like. A criterion pursued in the NEFOCAST project and aimed at reducing the impact of the above inaccuracies was to use, wherever possible, measured or reliably estimated (e.g., by short-term forecasts) coefficients and parameters, avoiding the use of global standardized statistical models. The latter are indeed very useful tools to easily obtain predictions valid anywhere in the world but, for a specific site, a better accuracy can be  
Figure 14: Pisa, 9 May 2017. Accumulated rainfall estimated by the NEFOCAST SmartLNB sensor (solid line) and measured by the TBR (dashed line). attained from local experimental data. As mentioned earlier, we adhered to this criterion in Section 5 for the determination of the coefficients in (5) and the 0 \({}^{\circ}\)C isotherm height.