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

Albert Garcia-Benadi,Mireia Udina,Bernat Codina,Joan Bech,Jean-François Georgis,Sergi Gonzalez

doi:10.3390/rs12244113

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.

Highlights

Precipitation is a key component of the hydrological cycle and a precise knowledge of the precipitating hydrometeor type is essential for remote quantitative precipitation estimates either from scanning or from vertically pointing ground-based or spaceborne radars
This approach has been employed for decades to study fine-scale vertical precipitation characteristics, for instance with the NOAA Aeronomy Laboratory S-band Doppler profiler (Ecklund et al [5]), the X-band Precipitation Occurrence Sensor System (POSS, Sheppard [6]), the K-band Micro Rain Radar (MRR, Löffler-Mang et al [7], Peters et al [8]), and more recently, with shorter wavelength radars traditionally used for cloud studies, such as the Ka-band ARM zenith radar (Chandra et al [9]) or the Milešovka observatory Ka-band cloud radar (Sokol et al [10,11]), used to derive a hydrometeor classification, including four precipitation types
The first part examines a case study to assess the characteristics of Method3, compared with Method1 and Method2

Summary

Introduction

Precipitation is a key component of the hydrological cycle and a precise knowledge of the precipitating hydrometeor type is essential for remote quantitative precipitation estimates either from scanning or from vertically pointing ground-based or spaceborne radars. Precipitation observations of vertically pointing Doppler radars allow us to estimate the fall speed of hydrometeor particles which, in general, are the sum of their terminal fall speed and vertical air velocity (Atlas et al [1], Hauser and Amayenc [2]) As it is well known, radar sensitivity to smaller particle detection increases with shorter wavelengths but, on the other hand, attenuation by intense precipitation, rainfall, increases. A related application has been the use of lidar observations from the NASA MPLNET network to resolve weak precipitation profiles (Lolli et al [12,13]), suited for light rain, drizzle and virga, as if more intense precipitation exists, attenuation becomes too important

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Results