Precipitation Type Classification of Micro Rain Radar Data Using an Improved Doppler Spectral Processing Methodology

Abstract

This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Ze), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (Nw) or the mean mass-weighted raindrop diameter (Dm) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR < 0.30, ORSS > 0.70). The methodology is available as a Python language program called RaProM at the public github repository.

A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations

“…where CC is the calibration constant, TF(n) is the transfer function, δr is the height resolution, n is the number of height gates and i is the number of Doppler bins. From the spectral reflectivity, it is possible to calculate several physical parameters, such as hydrometeor velocity, equivalent radar reflectivity and the precipitation type classification, as described by [31].…”

“…The signal remains until the condition is false and will be considered background noise. More details of the implementation of this first step are detailed in [31].…”

“…Spectral reflectivity aliasing occurs when the target returns a signal outside the unambiguous range interval. A systematic method to correct aliasing in MRR-2 was proposed by [34] and was implemented with some modifications by [31]. The two methods are based on the estimated velocity parameters calculated from equivalent radar reflectivity in [35].…”

“…Essentially, drop size distributions N'(D, n), at each level n, are calculated considering an attenuation factor (the PIA) multiplied by the previous (attenuated) drop size distribution Na(D, n), computed from the Doppler spectra assuming Mie scattering conditions [37,38]. As these calculations are only valid for liquid precipitation particles falling at terminal fall speeds, an additional procedure that provides a hydrometeor classification type for each bin height [31] is applied so that attenuation can be used consistently only for liquid precipitation. As described in Figure 7, the maximum PIA value is 10, because for higher values, the scheme may not work properly.…”

Future Directions in Precipitation Science

“…Disdrometers are likely to be gone by 2045. Radar technology will probably cover the research realm that disdrometers cover today, and more advanced, integrated instruments will likely soon be available [81][82][83]. Rain gauges, however, will certainly continue in use.…”