Speakers
Description
Rainfall data retrieved from opportunistic sensors, such as Commercial Microwave Links (CMLs) and Satellite Microwave Links (SMLs), must be validated against the ground truth (e.g., rain gauge or weather radar measurements) using rigorous procedures and standardized metrics, to enable meaningful comparisons across datasets and processing schemes. However, existing validation studies adopt heterogeneous Key Performance Indicators (KPIs) and procedures, which limit cross-study comparisons. This contribution provides guidelines for selecting and calculating appropriate KPIs, when validating CML/SML rainfall estimates against the ground truth. Two research problems are addressed here: rainfall identification and rainfall intensity quantification. Both are affected by the peculiar characteristics of rainfall as a random process: rainfall occurs at a small percentage of time and exhibits a highly intermittent behavior. For the binary identification problem, it is recommended to use aggregate performance indicators such as the Harmonic Mean (HM), the Mean Detection Error (MDE) or the Matthews Correlation Coefficient (MCC), which account for the imbalance between wet and dry observations. For the intensity quantification problem,dimensionless KPIs, such as Relative Bias (RB), Coefficient of Variation (CV), or Normalized bias-corrected Root Mean Square Error (NRMSE), are preferable, as they enable fair performance comparisons across datasets of different sizes, different precipitation regimes, or in meta-analyses. The calculation of KPIs is also impacted by the different spatial and temporal sampling between CML/SML observations and reference measurements from rain gauges and radars, as well as by rainfall variability. In this respect, temporal sampling adjustments, such as hourly or daily integration, can mitigate the above effects. The proposed strategy promotes standardized and reproducible validation of CML/SML rainfall products, improving comparability across studies.