NSSL Staff Profiles

Application of 2-Dimensional Video Disdrometer (2DVD) data, collected in central Oklahoma, to the problem of convective-stratiform rain separation is presented. The partition into convective (CO) and stratiform (ST) periods is achieved by applying a multi-variable Bayesian classification algorithm to the 2DVD dataset. It turns out that the CO-ST separation methods developed for measurements with one type of disdrometer may not work optimally on measurements with a different type of disdrometer. Similarly, single/dual parameter, or simple threshold separation methods may not be able to adequately separate CO and ST rain types. The corresponding shapeslope (μ-Λ) relations of the constrained gamma distribution are derived for these two rain classes. These constrained gamma relations are then used for rain drop size distribution (DSD) retrievals, and the results are compared with those obtained from the exponential distribution and the unified μ-Λ constraint previously proposed. It is demonstrated that the results based on the convective-stratiform separation yield more accurate DSD retrievals with respect to the exponential distribution and moderate improvements in comparison to unified μ-Λ constraint.

Observations and analysis of an ice–liquid phase precipitation event, collected with an S-band polarimetric KOUN radar and a two-dimensional video disdrometer (2DVD) in central Oklahoma on 20 January 2007, are presented. Using the disdrometer measurements, precipitation is classified either as ice pellets or rain/freezing rain. The disdrometer observations showed fast-falling and slow-falling particles of similar size. The vast majority (>99%) were fast falling with observed velocities close to those of raindrops with similar sizes. In contrast to the smaller particles (<1mm in diameter), bigger ice pellets (>1.5 mm) were relatively easy to distinguish because their shapes differ from the raindrops. The ice pellets were challenging to detect by looking at conventional polarimetric radar data because of the localized and patchy nature of the ice phase and their occurrence close to the ground. Previously published findings referred to cases in which ice pellet areas were centered on the radar location and showed a ringlike structure of enhanced differential reflectivity ZDR and reduced copolar correlation coefficient rhv and horizontal reflectivity ZH in PPI images. In this
study, a new, unconventional way of looking at polarimetric radar data is introduced: slanted vertical profiles (SVPs) at low (0–1 deg) radar elevations. From the analysis of the localized and patchy structures using SVPs, the polarimetric refreezing signature, reflected in local enhancement in ZDR and reduction in ZH and rhv, became much more evident. Model simulations of sequential drop freezing using Marshall–Palmer DSDs along with the observations suggest that preferential freezing of small drops may be responsible for the
refreezing polarimetric signature, as suggested in previous studies.

Accurate measurements of snow amounts by radar are very difficult to achieve. The inherent uncertainty in
radar snow estimates that are based on the radar reflectivity factor Z is caused by the variability of snow
particle size distributions and snow particle density as well as the large diversity among snow growth habits. In
this study, a novel method for snow quantification that is based on the joint use of radar reflectivity Z and
specific differential phase KDP is introduced. An extensive dataset of 2D-video-disdrometer measurements of
snow in central Oklahoma is used to derive polarimetric relations for liquid-equivalent snowfall rate S and ice
water content IWC in the forms of bivariate power-law relations S = g1xKDP^a1xZ^b1 and IWC = g2xKDP^a2xZ^b2 , along
with similar relations for the intercept N0s and slope Ls of the exponential snow size distribution. The physical
basis of these relations is explained. Their multipliers are sensitive to variations in the width of the canting angle
distribution and to a lesser extent the particles’ aspect ratios and densities, whereas the exponents are practically
invariant. This novel approach is tested against the S(Z) relation using snow disdrometer measurements in three
geographical regions (Oklahoma, Colorado, and Canada). Significant improvement in snow estimates relative to
the traditional Z-based methods is demonstrated.

In a 2018 paper by Bukovcic´ et al., polarimetric bivariate power-law relations for estimating snowfall rate S and ice water content (IWC), S(KDP, Z) = γKDPαZβ and IWC(KDP, Z) = γ2KDPα2Zβ2 , were developed utilizing 2D video disdrometer snow measurements in Oklahoma. Herein, these disdrometer-based relations are generalized for the range of particle aspect ratios from 0.5 to 0.8 and the width of the canting angle distribution from 0° to 40° and are validated via analytical/theoretical derivations and simulations. In addition, a novel S(KDP, Zdr) polarimetric relation utilizing the ratio between specific differential phase KDP and differential reflectivity Zdr, KDP/(1 – Zdr-1), is derived. Both KDP and (1 - Zdr-1) are proportionally affected by the ice particles’ aspect ratio and width of the canting angle distribution; therefore, the variables’ ratio tends to be almost invariant to the changes in these parameters. The S(KDP, Z) and S(KDP, Zdr) relations are applied to the polarimetric S-band WSR-88D data obtained from three geographical locations in Virginia, Oklahoma, and Colorado, and their performance is compared with estimations from the standard S(Z) relations and ground snow measurements. The polarimetric estimates of snow accumulations from the three cases exhibit smaller bias in comparison with the S(Z), indicating good potential for more reliable radar snow measurements.

The intrinsic uncertainty of radar-based retrievals in snow originates from a large diversity of snow growth, habits, densities, and particle size distributions, all of which can make interpreting radar measurements of snow very challenging. The application of polarimetric radar for snow measurements can mitigate some of these issues. In this study, a novel polarimetric method for quantification of the extinction coefficient and visibility in snow, based on the joint use of radar reflectivity at horizontal polarization Z and specific differential phase KDP, is introduced. A large 2D-videodisdrometer snow dataset from central Oklahoma is used to derive a polarimetric bivariate power-law relation for the extinction coefficient, σe(KDP,Z) = γKDPαZβ. The relation is derived for particle aspect ratios ranging from 0.5 to 0.8 and the width of the canting angle distribution ranging from 0° to 40°, values typical of aggregated snow, and validated via theoretical and analytical derivations/simulations. The multiplier of the relation is sensitive to variations in particles’ densities, the width of the canting angle distribution, and particles’ aspect ratios, whereas the relation’s exponents are practically invariant to changes in the latter two parameters. This novel approach is applied to polarimetric S-band WSR-88D data and verified against previous studies and in situ measurements of the extinction coefficient for four snow events in the eastern United States. The polarimetric radar estimates of the extinction coefficient exhibit smaller biases in comparison to previous studies concerning the ground measurements. The results indicate that there is good potential for reliable radar estimates of visibility from polarimetric weather radars, a parameter inversely proportional to the extinction coefficient.

This study evaluates ice particle size distribution and aspect ratio (φ) Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed Kdp and ZDR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using ZDR and Kdp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded -2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean
φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals.

KEYWORDS: radar, snow, dual polarization, microphysics, in situ, MRMS, radar retrievals, IMPACTS, differential reflectivity, specific differential phase, dendritic growth layer, winter storms

Rain drop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN)
technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field
Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006−2017. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods -- physics-based inversion, empirical formula, and DNN -- were applied to two different temporal domains (instantaneous and rain-event-average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root mean squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain rate estimate bias of the DNN was significantly reduced (3.3% in DNN versus 50.1% in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations.

KEYWORDS: Artificial intelligence, deep neural network, drop size distribution retrieval, error analysis, polarimetric radar data, rain estimation, two-dimensional video disdrometer

Radar quantitative precipitation estimation (QPE) is one of the primary tasks of weather radars. The QPE quality was substantially improved after polarimetric upgrade of the radars. This study provides an overview of existing polarimetric methodologies for rain and snow estimation and their operational implementation. The variability of drop size distributions (DSDs) is a primary factor affecting the quality of rainfall estimation, and its impact on the performance of various radar rainfall relations at S, C, and X microwave frequency bands is one of the focuses of this review. The radar rainfall estimation algorithms based on the use of specific attenuation A and specific differential phase KDP are the most efficient. Their brief description is presented and possible ways for their further optimization are discussed. Polarimetric techniques for the vertical profile of reflectivity (VPR) correction at longer distances from the radar are also summarized. Radar quantification of snow is particularly challenging and it is demonstrated that polarimetric methods for snow measurements show good promise. Finally, the article presents a summary of the latest operational radar QPE products available in the US by integration of the information from the WSR-88D radars via the Multi-Radar Multi-Sensor (MRMS) platform.

After decades of research and development, the WSR-88D (NEXRAD) network in the United States had been upgraded with dual-polarization capability, providing polarimetric radar data (PRD) that has the potential to improve weather observations, quantification, forecasting, and warnings. The weather radar networks in China (CINRAD) and other countries are also being upgraded with the dual-polarization capability. Now, with radar polarimetry technology matured and polarimetric radar data (PRD) available both nationally and globally, it is important to understand current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction (NWP) models. In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation (QPE) and forecast (QPF) based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We will also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.