Very Large Rain Drops from 2D Video Disdrometers and Concomitant Polarimetric Radar Observations

Drop size distribution (DSD) measurements using ground-based disdrometers (point measurements) have often been used to derive equations to relate radar observations to the integral rainfall parameters (Atlas et al. 1999, Bringi et al., 2003, Kozu et al., 2006, Tokay and Short, 1996, Ajayi and Owolabi, 1987, Battan, 1973). Disdrometers such as JWD, MRR and several others have a major limitation in measuring drops with equi-volume diameters (D(sub eq)) larger than 5 mm because they often rely on the velocity-diameter relationship which plateaus beyond this diameter range (Atlas et al., 1973, Gunn & Kinzer, 1949). Other disdrometers such as Parsivel also lack accuracy beyond this diameter range. The 2D video disdrometer (2DVD: Schnhuber et al., 2008) on the other hand gives drop-shape contours and velocities for each individual drop/hydrometeor falling through its sensor area; this provides a unique opportunity to study the role of very-large drops on radar measurements in particular those with polarimetric radar capability where DSDs with a significant component of very large drops may require special consideration given that the differential reflectivity and other polarimetric radar parameters including attenuation-correction methods will be sensitive to the concentrations of these large drops. A recent study on the occurrence of large drops by Gatlin et al. (2014) has compiled a large and diverse set of measurements made with the 2D video disdrometers from many locations around the globe. Some of the largest drops found in this study were 9 mm D(sub eq) and larger, and in this paper, we report on three such events, with maximum D(sub eq's) of 9.0, 9.1 and 9.7 mm, which occurred in Colorado, Northern Alabama, and Oklahoma, respectively. Detailed examination of the 2DVD data - in terms of shapes and fall velocities - has confirmed that these are fully-melted hydrometeors, although for the last case in Oklahoma, a bigger and non-fully-melted hydrometeor was also observed. All three events were also captured by polarimetric radars, namely the S-band CHILL radar operated by Colorado State University (Brunkow et al., 2000), the C-band ARMOR radar (Petersen et al., 2007) operated by University of Alabama in Huntsville, and NEXRADKVNX, operated by the US National Weather Service, respectively. For the last event, several other radar observations were also made, including two X-band radars operated by the US Dept. of Energy. Analyses of 2DVD data in conjunction with the corresponding radar observations are presented, along with some discussion on sampling issues related to the measurements of such large rain drops. The latter is addressed using maximum diameter D(sub max) measurements from 1-minute DSDs using two collocated 2DVDs for 37 events in Huntsville

Measurement and Modeling of the Precipitation Particle Size Distribution

Precipitation plays a vital role within the Earth system. Precipitation consists of a collection of liquid or solid water particles, which are referred to as hydrometeors, that begin within a cloud [1]. When these hydrometeors grow too heavy to continue being suspended by vertical motions in the cloud, they fall towards the ground in the form of rain, snow/ice or a mixture and ultimately replenish the oceans and lakes, either directly or indirectly via surface hydrological processes such as runoff and discharge. Hence, understanding how hydrometeors are distributed within the atmosphere and near the ground enables a more accurate depiction of a principle component of the water cycle. It also enables a more accurate depiction of precipitation&rsquo;s erosive effects on the soil important for modeling runoff and on human-made structures important for defining building standards and codes [2,3,4]. &nbsp;</p

Retrieval of lower-order moments of the drop size distribution using CSU-CHILL X-band polarimetric radar: a case study

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 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

Observing the Full Spectrum of the Rain Drop Size Distribution

GPM DSD retrievals exhibit inconsistencies between GV, DPR and Combined algorithm retrievals. Development of positive bias in convective Dm rain DSD noted, and strongest in KuPR retrieval. Associated epsilons are too low and result in markedly reduced convective rain rates (a current issue in the retrievals). Source may be NUBF. Issues with the large end of the DSD not withstanding, on the small end of the DSD, combined MPS and 2DVD measurements fit with generalized gamma functions exhibit strong potential for representing the entire spectrum of the DSD and subsequently the whole rain rate spectrum

Consistent Measurement and Physical Character of the DSD: Disdrometer to Satellite

Objective: Validate GPM (Global Precipitation Measurement) Drop Size Distribution Retrievals: Drop size distributions (DSD) are critical to GPM DPR (Dual-frequency Precipitation Radar)-based rainfall retrievals; NASA GPM Science Requirements stipulate that the GPM Core observatory radar estimation of D (sub m) (mean diameter) shall be within plus or minus 0.5 millimeters of GV (Ground Validation); GV translates disdrometer measurements to polarimetric radar-based DSD and precipitation type retrievals (e.g., convective vs. stratiform (C/S)) for coincident match-up to GPM core overpasses; How well do we meet the requirement across product versions, rain types (e.g., C/S partitioning), and rain rates (heavy, light) and is behavior physically and internally consistent

A New Perspective on the Raindrop Size Distribution and Its Implications for Retrievals of Light Rain

No abstract availabl

Spatial Correlation of Rain Drop Size Distribution from Polarimetric Radar and 2D-Video Disdrometers

Spatial correlations of two of the main rain drop-size distribution (DSD) parameters - namely the median-volume diameter (Do) and the normalized intercept parameter (Nw) - as well as rainfall rate (R) are determined from polarimetric radar measurements, with added information from 2D video disdrometer (2DVD) data. Two cases have been considered, (i) a widespread, long-duration rain event in Huntsville, Alabama, and (ii) an event with localized intense rain-cells within a convection line which occurred during the MC3E campaign. For the first case, data from a C-band polarimetric radar (ARMOR) were utilized, with two 2DVDs acting as ground-truth , both being located at the same site 15 km from the radar. The radar was operated in a special near-dwelling mode over the 2DVDs. In the second case, data from an S-band polarimetric radar (NPOL) data were utilized, with at least five 2DVDs located between 20 and 30 km from the radar. In both rain event cases, comparisons of Do, log10(Nw) and R were made between radar derived estimates and 2DVD-based measurements, and were found to be in good agreement, and in both cases, the radar data were subsequently used to determine the spatial correlations For the first case, the spatial decorrelation distance was found to be smallest for R (4.5 km), and largest fo Do (8.2 km). For log10(Nw) it was 7.2 km (Fig. 1). For the second case, the corresponding decorrelation distances were somewhat smaller but had a directional dependence. In Fig. 2, we show an example of Do comparisons between NPOL based estimates and 1-minute DSD based estimates from one of the five 2DVDs

Accurate characterization of winter precipitation using multi-angle snowflake camera, visual hull, advanced scattering methods and polarimetric radar

Includes bibliographical references (pages 28-31).This article proposes and presents a novel approach to the characterization of winter precipitation and modeling of radar observables through a synergistic use of advanced optical disdrometers for microphysical and geometrical measurements of ice and snow particles (in particular, a multi-angle snowflake camera-MASC), image processing methodology, advanced method-of-moments scattering computations, and state-of-the-art polarimetric radars. The article also describes the newly built and established MASCRAD (MASC + Radar) in-situ measurement site, under the umbrella of CSU-CHILL Radar, as well as the MASCRAD project and 2014/2015 winter campaign. We apply a visual hull method to reconstruct 3D shapes of ice particles based on high-resolution MASC images, and perform "particle-by-particle" scattering computations to obtain polarimetric radar observables. The article also presents and discusses selected illustrative observation data, results, and analyses for three cases with widely-differing meteorological settings that involve contrasting hydrometeor forms. Illustrative results of scattering calculations based on MASC images captured during these events, in comparison with radar data, as well as selected comparative studies of snow habits from MASC, 2D video-disdrometer, and CHILL radar data, are presented, along with the analysis of microphysical characteristics of particles. In the longer term, this work has potential to significantly improve the radar-based quantitative winter-precipitation estimation.Published with support from the Colorado State University Libraries Open Access Research and Scholarship Fund

The Retrieval of Drop Size Distribution Parameters Using a Dual-Polarimetric Radar

The raindrop size distribution (DSD) is vital for applications such as quantitative precipitation estimation, understanding microphysical processes, and validation/improvement of two-moment bulk microphysical schemes. We trace the history of the DSD representation and its linkage to polarimetric radar observables from functional forms (exponential, gamma, and generalized gamma models) and its normalization (un-normalized, single/double-moment scaling normalized). The four-parameter generalized gamma model is a good candidate for the optimal representation of the DSD variability. A radar-based disdrometer was found to describe the five archetypical shapes (from Montreal, Canada) consisting of drizzle, the larger precipitation drops and the ‘S’-shaped curvature that occurs frequently in between the drizzle and the larger-sized precipitation. Similar ‘S’-shaped DSDs were reproduced by combining the disdrometric measurements of small-sized drops from an optical array probe and large-sized drops from 2DVD. A unified theory based on the double-moment scaling normalization is described. The theory assumes the multiple power law among moments and DSDs are scaling normalized by the two characteristic parameters which are expressed as a combination of any two moments. The normalized DSDs are remarkably stable. Thus, the mean underlying shape is fitted to the generalized gamma model from which the ‘optimized’ two shape parameters are obtained. The other moments of the distribution are obtained as the product of power laws of the reference moments M3 and M6 along with the two shape parameters. These reference moments can be from dual-polarimetric measurements: M6 from the attenuation-corrected reflectivity and M3 from attenuation-corrected differential reflectivity and the specific differential propagation phase. Thus, all the moments of the distribution can be calculated, and the microphysical evolution of the DSD can be inferred. This is one of the major findings of this article

A Robust error-based rain estimation method for polarimetric radar. Part I: Development of a method

The algorithms used to estimate rainfall from polarimetric radar variables show significant variance in error characteristics over the range of naturally occurring rain rates. As a consequence, to improve rainfall estimation accuracy using polarimetric radar, it is necessary to optimally combine a number of different algorithms. In this study, a new composite method is proposed that weights the algorithms by the inverse of their theoretical error. A number of approaches are discussed and are investigated using simulated radar data calculated from disdrometer measurements. The resultant algorithms show modest improvement over composite methods based on decision-tree logic-in particular, at rain rates above 20 mm h-1.12 page(s