Calibration of X-Band Radar for Extreme Events in a Spatially Complex Precipitation Region in North Peru: Machine Learning vs. Empirical Approach

Abstract

Cost-efficient single-polarized X-band radars are a feasible alternative due to their high sensitivity and resolution, which makes them well suited for complex precipitation patterns. The first horizontal scanning weather radar in Peru was installed in Piura in 2019, after the devastating impact of the 2017 coastal El Niño. To obtain a calibrated rain rate from radar reflectivity, we employ a modified empirical approach and draw a direct comparison to a well-established machine learning technique used for radar QPE. For both methods, preprocessing steps are required, such as clutter and noise elimination, atmospheric, geometric, and precipitation-induced attenuation correction, and hardware variations. For the new empirical approach, the corrected reflectivity is related to rain gauge observations, and a spatially and temporally variable parameter set is iteratively determined. The machine learning approach uses a set of features mainly derived from the radar data. The random forest (RF) algorithm employed here learns from the features and builds decision trees to obtain quantitative precipitation estimates for each bin of detected reflectivity. Both methods capture the spatial variability of rainfall quite well. Validating the empirical approach, it performed better with an overall linear regression slope of 0.65 and r of 0.82. The RF approach had limitations with the quantitative representation (slope = 0.44 and r = 0.65), but it more closely matches the reflectivity distribution, and it is independent of real-time rain-gauge data. Possibly, a weighted mean of both approaches can be used operationally on a daily basis.

Generation of Flows Applying a Simple Method of Flood Routing to Monthly Level in La Leche Basin, Peru

“…The results of flood routing for the La Leche basin, as shown in Figure 8, also indicate an increase in flow in an east to southwest direction as the return period progresses. This pattern is characteristic of the North Pacific basins of Peru, as reported by the investigations of Arriola et al (2021), Rollenbeck et al (2021) and Arriola et al (2023). Furthermore, this sequence of hydrological behavior has been influenced in recent years by extreme events due to the El Niño Phenomenon (Aguirre et al, 2019;Carrizales et al, 2022;Peña et al, 2023).…”

Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil

“…Equation (12) calculates the precision of a model. The precision measures the proportion of correct positive predictions.…”

“…Previous studies have suggested that machine learning can be effective in this field, but it is still relatively new, particularly in the development of hybrid machine learning models. Rollenbeck et al [12] showed that machine learning can outperform empirical approaches in calibrating X-band radar for extreme weather events in a region of complex precipitation in North Peru, highlighting the potential of advanced algorithms in such scenarios.…”

Spatiotemporal Land Use and Land Cover Changes and Associated Runoff Impact in Itaperuna, Brazil

“…In the global context, where climate change is exacerbating water-related challenges, the use of technologies such as remote sensing and geographic information systems (GIS) plays a critical role in adapting to and mitigating extreme events [32][33][34][35]. Remote sensing provides detailed insights into key hydrological variables like rainfall, evapotranspiration, and wetland extent [36,37].…”