Study areas and ground reference datasets

Data from two mobile X-band dual-polarization experimental ground radars were used as reference in our analysis: one located in the Alto Adige region (the National Observatory of Athens radar, hereafter called XPOL), in northeast Italy (during 07/2014-09/2014), and one in the

113 Integrated Precipitation and Hydrology Experiment (IPHEx) experimental region (the National Severe Storm Laboratory radar, hereafter called NOXP), in North Carolina (during 05/2014- 06/2014), as shown in Figure 4.1. As done in this study, GR data are often chosen as “ground truth” because rain gauge measurements are point measurements, while a radar provides horizontal distribution of rainfall field, which can be used to estimate the area average corresponding to satellite measurements. A very dense rain gauge network would be needed for this purpose, especially in a complex terrain area where rainfall field is highly variable. In addition, while for light rain the sampling error (limited resolution) of tipping-bucket gauges may introduce additional errors in comparison with radars (Habib et al. 2001), rainfall estimated by polarimetric radar algorithms may have a good accuracy at the full range of rainfall values (Chen and Chandrasekar 2015). Moreover, due to their smaller size, X-band radars can be deployed in mountainous areas and have quite better coverage of specific areas than permanent installed long-range C or S-band radars.

In both experiments the radars provided plan position indicator (PPI) scans at various antenna elevations (0.5° to 5° for XPol and 0.5° to 19.5° for NOXP) based on which we extracted a hybrid scan consisting of a varying antenna elevation for each location associated with the lowest beam occlusion and bright band effect. The resolution of the NOXP radar is in polar coordinates with 1° angular resolutions, 150-meter range resolution in the vertical, going from 0.5° to 19.5° for every 1° and resolution of the XPOL is 0.7° and 120 meter going from 0.5° to 5° for every 1°. To retrieve precipitation and microphysical estimates, we applied the SCOPE-ME X-band dual-polarization algorithm, consisting of new polarimetric techniques for bright band identification and vertical profile of reflectivity (VPR) correction (Kalogiros et al. 2013; Anagnostou et al. 2010), attenuation

114 correction (Kalogiros et al. 2013; Anagnostou et al. 2009), and rainfall microphysics retrievals (Kalogiros et al. 2014).

Radar rainfall estimates were verified using drop-size distribution and rain rate observations collected by in situ disdrometers and rain gauges. Specifically, disdrometer data were used to verify the local suitability of the parametric techniques used in the dual-polarization radar-rainfall retrievals (Anagnostou et al. 2013). The two radar (i.e., XPOL and NOXP) rainfall estimates were accumulated (in mm) to longer integration periods (e.g., hourly) and compared to rainfall captured by in situ observations (named here as reference) to validate these benchmark datasets (Fig. 4.2). It should be noted that in the case of light rainfall, rain tipping-bucket gauges may introduce additional errors due to their sampling error (limited measurement resolution) in comparison with radar (Fig.4.2). However, this error is significant for temporal scales less than 10 to 15 minutes, while rainfall shown in Fig. 4.2 is accumulated in one-hour time periods. Also, the radar rainfall is estimated by the SCOPE-ME polarimetric algorithm which may have a good accuracy at the full range of rainfall values. The random error in light rain in the comparison between radar and in-situ data (both rain gauge and disdrometer), is significant especially for North Carolina, as shown in Fig.4.2. Ground sites were located up to about 20 km for Italy and up to 70 km (for North Carolina) from the radar, while the radar maximum range was 35 km for Italy site and 110 km for North Carolina site. This scatter for low hourly rainfall values (below 1 mm) is the same for rain gauge and disdrometer data, but it should be noted that comparison of radar rainfall to rain gauges show an overestimation of rain gauges at light rainfall rates in contrast to disdrometers. This bias is actually due to sampling errors of rain gauges at light rain and not error of the radar estimates. Before the application of radar algorithms, disdrometer data at close range from the radar (17 km for Italy site and 7.5 km for North Carolina site) were used for calibration of radar reflectivity

115 (horizontal polarization). Differential reflectivity was calibrated using an average theoretical relationship between reflectivity and differential reflectivity as described in Anagnostou et al. (2009).

The basic statistical metrics for the evaluation of the radar-rainfall estimates include (1) the correlation coefficient between the hourly radar-rainfall estimates and reference rainfall; (2) the bias ratio (BR), which is defined as the ratio of storm event total radar rainfall estimates to the corresponding total reference values, (3) the normalized error (NE) defined as the mean difference of the estimate minus the reference divided by the mean reference values, (4) the least absolute error (LAE), (5) the slope of the least square error (LSE) fit to the data and (6) the normalized mean absolute error (NMAE) (Table 4.2). All the statistical metrics are performed only for liquid precipitation, for less than 5% occlusion from ground clutter and for hourly precipitation greater than or equal to 0.01 mm. It is noted that the XPOL rainfall estimates are almost unbiased (BR = 1.11, LAE = 0.92 and the LSE = 0.87) exhibiting high correlation (0.73) against the in situ reference data. We note consistent performance characteristics for the NOXP rainfall estimates in North Carolina: low bias against the reference in situ observations (BR = 1.04, LAE = 0.81 and LSE = 0.95) and high correlations (0.81). Considering the various error sources in radar data over complex terrain (ground clutter, beam blockage, and melting layer effects due to the use of high antenna elevations to avoid the previous effects), the biases of radar rainfall estimates for both sites against in-situ point sensors (rain gauges and disdrometers) are quite small. Finally, these bias adjustments calculated from both experimental sites were applied to the radar estimates, indicating our best ground reference rainfall, and compared to the satellite retrievals.

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