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Raindrop 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 2006-17. 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 vs 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. [ABSTRACT FROM AUTHOR]

Research has shown that dual-polarization (dual-pol) data currently available to National Weather Service forecasters could provide important information about changes in a storm’s structure and intensity. Despite these new data being used gradually by forecasters more over time, they are still not used extensively to inform warning decisions because it is unclear how to apply dual-pol radar data to specific warning decisions. To address this knowledge gap, rapid-update (i.e., volumetric update time of 2.3 min or less) radar data of 45 storms in Oklahoma are used to examine one dual-pol signature, known as the differential reflectivity (ZDR) column, to relate this signature to warning decisions. Base data (i.e., ZDR, reflectivity, velocity) are used to relate ZDR columns to storm intensity, radar signatures such as upper-level reflectivity cores, and scientific conceptual models used by forecasters during the warning decision process. Analysis shows that 1) differences exist between the ZDR columns of severe and nonsevere storms, 2) ZDR columns develop and evolve prior to upper-level reflectivity cores, 3) rapid-update radar data provide a more complete picture of ZDR column evolution than traditional-update radar data (i.e., volumetric update time of about 5 min), and 4) ZDR columns provide a clearer and earlier indication of changes in updraft strength compared to reflectivity signatures. These findings suggest that ZDR columns can be used to inform warning decisions, increase warning confidence, and potentially increase warning lead time especially when they are integrated into existing conceptual models about a storm’s updraft and intensity. [ABSTRACT FROM AUTHOR]

Achieving accurate storm-scale analyses and reducing the spinup time of modeled convection is a primary motivation for the assimilation of radar reflectivity data. One common technique of reflectivity data assimilation is using a cloud analysis, which inserts temperature and moisture increments and hydrometeors deduced from radar reflectivity via empirical relations to induce and sustain updraft circulations. Polarimetric radar data have the ability to provide enhanced insight into the microphysical and dynamic structure of convection. Thus far, however, relatively little has been done to leverage these data for numerical weather prediction. In this study, the Advanced Regional Prediction System's cloud analysis is modified from its original reflectivity-based formulation to providemoisture and latent heat adjustments based on the detection of differential reflectivity columns, which can serve as proxies for updrafts in deep moist convection and, subsequently, areas of saturation and latent heat release. Cycled model runs using both the original cloud analysis and above modifications are performed for two high-impact weather cases: the 19 May 2013 central Oklahoma tornadic supercells and the 25 May 2016 north-central Kansas tornadic supercell. The analyses and forecasts of convection qualitatively and quantitatively improve in both cases, including more coherent analyzed updrafts, more realistic forecast reflectivity structures, a better correspondence between forecast updraft helicity tracks and radar-derived rotation tracks, and improved frequency biases and equitable threat scores for reflectivity. Based on these encouraging results, further exploration of the assimilation of dual-polarization radar data into storm-scale models is warranted. [ABSTRACT FROM AUTHOR]

With the development of multimoment bulk microphysical schemes and polarimetric radar forward operators, one can better examine convective storms simulated in high-resolution numerical models from a simulated polarimetric radar perspective. Subsequently, relationships between observable and unobservable quantities can be examined that may provide useful information about storm intensity and organization that otherwise would be difficult to obtain. This paper, Part I of a two-part sequence, describes the bulk microphysics scheme, polarimetric radar forward operator, and numerical model configuration used to simulate supercells in eight idealized, horizontally homogenous environments with different wind profiles. The microphysical structure and evolution of copolar cross-correlation coefficient (ρhv) rings associated with simulated supercells are examined in Part I, whereas Part II examines ZDR columns, ZDR rings, and KDP columns. In both papers, some systematic differences between the signature seen at X and S bands are discussed. The presence of hail is found to affect ρhv much more at X band than at S band (and is found to affect ZDR more at S band than at X band), which corroborates observations. The ρhv half ring is found to be associated with the presence of large, sometimes wet, hail aloft, with an ~20-min time lag between increases in the size of the ρhv ring aloft and the occurrence of a large amount of hail near the ground in some simulations. [ABSTRACT FROM AUTHOR]

A high-resolution numerical model and polarimetric forward operator allow one to examine simulated convective storms from the perspective of observable polarimetric radar quantities, enabling a better comparison of modeled and observed deep moist convection. Part I of this two-part study described the model and forward operator used for all simulations and examined the structure and evolution of rings of reduced copolar cross-correlation coefficient (i.e., ρhv rings). The microphysical structure of upward extensions of enhanced differential reflectivity ( ZDR columns and ZDR rings) and enhanced specific differential phase ( KDP columns) near and within the updrafts of convective storms serve as the focus of this paper. In general, simulated ZDR columns are located immediately west of the midlevel updraft maximum and are associated with rainwater lofted above the 0°C level and wet hail/graupel, whereas ZDR rings are associated with wet hail located near and immediately east of the midlevel updraft maximum. The deepest areas of ZDR > 1 dB aloft are associated with supercells in the highest shear environments and those that have the most intense updrafts; the upper extent of the ZDR signatures is found to be positively correlated with the amount and mean-mass diameter of large hail aloft likely as a by-product of the shared correlations with updraft intensity and wind shear. Large quantities of rain compose the KDP columns, with the size and intensity of the updrafts directly proportional to the size and depth of the KDP columns. [ABSTRACT FROM AUTHOR]

Observations and recent high-resolution numerical model simulations indicate that liquid water and partially frozen hydrometeors can be lofted considerably above the environmental 0°C level in the updrafts of convective storms owing to the warm thermal perturbation from latent heating within the updraft and to the noninstantaneous nature of drop freezing. Consequently, upward extensions of positive differential reflectivity (i.e., ZDR ≥ 1 dB)-called ZDR columns-may be a useful proxy for detecting the initiation of new convective storms and examining the evolution of convective storm updrafts. High-resolution numerical simulations with spectral bin microphysics and a polarimetric forward operator reveal a strong spatial association between updrafts and ZDR columns and show the utility of examining the structure and evolution of ZDR columns for assessing updraft evolution. This paper introduces an automated ZDR column algorithm designed to provide additional diagnostic and prognostic information pertinent to convective storm nowcasting. Although suboptimal vertical resolution above the 0°C level and limitations imposed by commonly used scanning strategies in the operational WSR-88D network can complicate ZDR column detection, examples provided herein show that the algorithm can provide operational and research-focused meteorologists with valuable information about the evolution of convective storms. [ABSTRACT FROM AUTHOR]