Abstract
Abstract. The lower-order moments of the drop size distribution (DSD) have generally been considered difficult to retrieve accurately from polarimetric radar data because these data are related to higher-order moments. For example, the 4.6th moment is associated with a specific differential phase and the 6th moment with reflectivity and ratio of high-order moments with differential reflectivity. Thus, conventionally, the emphasis has been to estimate rain rate (3.67th moment) or parameters of the exponential or gamma distribution for the DSD. Many double-moment “bulk” microphysical schemes predict the total number concentration (the 0th moment of the DSD, or M0) and the mixing ratio (or equivalently, the 3rd moment M3). Thus, it is difficult to compare the model outputs directly with polarimetric radar observations or, given the model outputs, forward model the radar observables. This article describes the use of double-moment normalization of DSDs and the resulting stable intrinsic shape that can be fitted by the generalized gamma (G-G) distribution. The two reference moments are M3 and M6, which are shown to be retrievable using the X-band radar reflectivity, differential reflectivity, and specific attenuation (from the iterative correction of measured reflectivity Zh using the total Φdp constraint, i.e., the iterative ZPHI method). Along with the climatological shape parameters of the G-G fit to the scaled/normalized DSDs, the lower-order moments are then retrieved more accurately than possible hitherto. The importance of measuring the complete DSD from 0.1 mm onwards is emphasized using, in our case, an optical array probe with 50 µm resolution collocated with a two-dimensional video disdrometer with about 170 µm resolution. This avoids small drop truncation and hence the accurate calculation of lower-order moments. A case study of a complex multi-cell storm which traversed an instrumented site near the CSU-CHILL radar is described for which the moments were retrieved from radar and compared with directly computed moments from the complete spectrum measurements using the aforementioned two disdrometers. Our detailed validation analysis of the radar-retrieved moments showed relative bias of the moments M0 through M2 was <15 % in magnitude, with Pearson’s correlation coefficient >0.9. Both radar measurement and parameterization errors were estimated rigorously. We show that the temporal variation of the radar-retrieved mass-weighted mean diameter with M0 resulted in coherent “time tracks” that can potentially lead to studies of precipitation evolution that have not been possible so far.
Highlights
The principal application of polarimetric radar has historically been directed towards more accurate estimation of rain rate (R) that is driven largely by the operational agencies for hydrological applications
It strongly appears that, as a major step forward, the operational algorithm for the US Weather Surveillance Radar – 1988 Doppler (WSR88D) network will be based on specific attenuation because, among other advantages, it is linearly related to rain rate at Published by Copernicus Publications on behalf of the European Geosciences Union
The principal surface-based instruments used in this study are the MPS and thirdgeneration 2DVD, both located within a 2/3-scale Double Fence Intercomparison Reference (DFIR; Rasmussen et al, 2012) wind shield
Summary
The principal application of polarimetric radar has historically been directed towards more accurate estimation of rain rate (R) that is driven largely by the operational agencies for hydrological applications. Using the double-moment approach of L04, Raupach and Berne (2017a, RBa) showed that measured DSDs in stratiform rain with h(x) expressed in the G-G form have shape factors that are sufficiently “invariant” for practical use across different rain climatologies if the reference moments are chosen carefully. Their result essentially validated the “remarkable” stability conclusion of h(x) by Testud et al (2001) which was based on limited data in oceanic rain. The notation E{·} is used for the statistical expectation; · for the average of its argument; (·) for the gamma function; and Im {·} for the imaginary part of its complex argument
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