Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

Abstract

This paper presents a comparison between C-band and X-band radar data over an instrumented and regulated catchment of the Paris region. We study the benefits of polarimetry and the respective hydrological impacts with the help of rain gauge and flow measurements using a semi-distributed hydrological model. Both types of radar confirm the high spatial variability of the rainfall down to their space resolution (1 km and 250 m, respectively). Therefore, X-band radar data underscore the limitations of simulations using a semi-distributed model with sub-catchments of an average size of 2 km. The use of the polarimetric capacity of the Météo-France C-band radar was limited to corrections of the horizontal reflectivity, and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, no absolute calibration and scanning optimisation were performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (hereafter referred to as the ENPC X-band radar). In spite of this and the fact that the catchment is much closer to the C-band radar than to the X-band radar (average distance of 15 km vs. 35 km, respectively), the latter seems to perform at least as well as the former, but with a higher spatial resolution. This was best highlighted with the help of a multifractal analysis, which also shows that the X-band radar was able to pick up a few rainfall extremes that were smoothed out by the C-band radar.

Multi-hydro hydrological modelling of a complex peri-urban catchment with storage basins comparing C-band and X-band radar rainfall data

“…QGIS tools were used to perform the intersection between the mesh of the model and the meshes of both the X-and C-band radar data. These rainfall files are generated by calculating the contribution of each radar data pixel's pluviometric index to the model pixels, as follows (Paz et al 2018):…”

Radar Data Analyses for a Single Rainfall Event and Their Application for Flow Simulation in an Urban Catchment Using the SWMM Model

“…In an analysis carried out in Malaysia using hourly rainfall data, more than 80% of data obtained from the radar were overestimated when compared to rain gauge observations [31]. As described in the work [23], when comparing the C-band and X-band rainfall totals to those resulting from tipping bucket rain gauges, one may note that the X-band radar tends to underestimate, while C-band radar generally overestimates them. In spite of the greatly improved quality of the operational C-band radar estimates, the average differences between the radar estimates (without calibration with gauges) and ground observations vary between 28% and 54%, increasing with distance [32].…”

“…Weather radars do not measure rainfall directly but rather the back-scattered energy from precipitation particles from elevated volumes, and thus an algorithm should be developed and calibrated against the rain gauge network [22]. As pointed out in paper [23], in order to quantify the uncertainty on accumulated rainfall, the authors usually perform either a comparison of different radar products or compare ground measurements and precipitation estimates on radar pixels where rain gauges are located [24,25]. Different Z-R relationships used in hydrometeorology imply different properties of resulting radar rainfall products [26].…”

“…Adjusting radar data son that it more closely resembles the observations of rain gauges will consequently improve the results obtained with distributed simulation models [42]. Moreover, numerous studies showed a strong influence of rainfall spatiotemporal variability on model response, especially in urban areas, where response times are shorter due to high levels of imperviousness and smaller catchments [23,[43][44][45][46].…”

Radar Data Analyses for a Single Rainfall Event and Their Application for Flow Simulation in an Urban Catchment Using the SWMM Model

“…In an analysis carried out in Malaysia using hourly rainfall data, more than 80% of data 261 obtained from the radar were overestimated when compared to rain gauge observations [44]. As 262 described in the work [45], when comparing the C-band and X-band rainfall totals to those resulting 263 from tipping bucket rain gauges, one may note that the X-band radar tends to underestimate, while…”