Classification of Precipitation Types during Transitional Winter Weather Using the RUC Model and Polarimetric Radar Retrievals

2012 ◽  
Vol 51 (4) ◽  
pp. 763-779 ◽  
Author(s):  
Terry J. Schuur ◽  
Hyang-Suk Park ◽  
Alexander V. Ryzhkov ◽  
Heather D. Reeves
Keyword(s):  
Numerical Models ◽  
Radar Data ◽  
Storm Event ◽  
Model Output ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Precipitation Type ◽  
Microphysical Processes ◽  
Using Data

AbstractA new hydrometeor classification algorithm that combines thermodynamic output from the Rapid Update Cycle (RUC) model with polarimetric radar observations is introduced. The algorithm improves upon existing classification techniques that rely solely on polarimetric radar observations by using thermodynamic information to help to diagnose microphysical processes (such as melting or refreezing) that might occur aloft. This added information is especially important for transitional weather events for which past studies have shown radar-only techniques to be deficient. The algorithm first uses vertical profiles of wet-bulb temperature derived from the RUC model output to provide a background precipitation classification type. According to a set of empirical rules, polarimetric radar data are then used to refine precipitation-type categories when the observations are found to be inconsistent with the background classification. Using data from the polarimetric KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman, Oklahoma, the algorithm is tested on a transitional winter-storm event that produced a combination of rain, freezing rain, ice pellets, and snow as it passed over central Oklahoma on 30 November 2006. Examples are presented in which the presence of a radar bright band (suggesting an elevated warm layer) is observed immediately above a background classification of dry snow (suggesting the absence of an elevated warm layer in the model output). Overall, the results demonstrate the potential benefits of combining polarimetric radar data with thermodynamic information from numerical models, with model output providing widespread coverage and polarimetric radar data providing an observation-based modification of the derived precipitation type at closer ranges.

scholarly journals A Microphysical Bulk Formulation Based on Scaling Normalization of the Particle Size Distribution. Part I: Description

2005 ◽  
Vol 62 (12) ◽  
pp. 4206-4221 ◽  
Author(s):  
Wanda Szyrmer ◽  
Stéphane Laroche ◽  
Isztar Zawadzki
Keyword(s):  
Particle Size ◽  
High Resolution ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Numerical Models ◽  
Radar Data ◽  
Retrieval Algorithm ◽  
Microphysical Processes ◽  
The Moment ◽  
Microphysical Parameterization

Abstract The authors address the problem of optimization of the microphysical information extracted from a simulation system composed of high-resolution numerical models and multiparameter radar data or other available measurements. As a tool in the exploration of this question, a bulk microphysical scheme based on the general approach of scaling normalization of particle size distribution (PSD) is proposed. This approach does not rely on a particular functional form imposed on the PSD and naturally leads to power-law relationships between the PSD moments providing an accurate and compact PSD representation. To take into account the possible evolution of the shape/curvature of the distribution, ignored within standard one- and two-moment microphysical schemes, a new three-moment scheme based on the two-moment scaling normalization is proposed. The methodology of the moment retrieval included in the three-moment scheme can also be useful as a retrieval algorithm combining different remote sensing observations. The developed bulk microphysical scheme presents a unified formulation for microphysical parameterization using one, two, or three independent moments, suitable in the context of data assimilation. The effectiveness of the scheme with different combinations of independent moments is evaluated by comparison with a very high resolution spectral model within a 1D framework on representative microphysical processes: rain sedimentation and evaporation.

scholarly journals Classification of Rainfall Types Using Parsivel Disdrometer and S-Band Polarimetric Radar in Central Korea

2020 ◽  
Vol 12 (4) ◽  
pp. 642 ◽  
Author(s):  
Jui Le Loh ◽  
Dong-In Lee ◽  
Mi-Young Kang ◽  
Cheol-Hwan You
Keyword(s):  
Vertical Profile ◽  
Drop Size ◽  
Classification Scheme ◽  
Radar Data ◽  
Number Concentration ◽  
Polarimetric Radar ◽  
Retrieval Method ◽  
Median Volume ◽  
Better Than

Tools to identify and classify stratiform and convective rains at various times of the 12 days from June 2015 to March 2016 in Jincheon, Korea, were developed by using a Parsivel disdrometer and S-band polarimetric (S-POL) radar data. Stratiform and convective rains were identified using three different methods (vertical profile of reflectivity (VPR), the method proposed by Bringi et al. (BR03), and a combination of the two (BR03-VPR)) by using a Parsivel disdrometer for its applications to radar as a reference. BR03-VPR exhibits a better classification scheme than the VPR and BR03 methods. The rain types were compared using the drop size distribution (DSD) retrieved from polarimetric variables and reflectivity only. By using the DSD variables, a new convective/stratiform classification line of the log-normalized droplet number concentration ( log 10 N w ) − median volume diameter ( D 0 ) was derived for this area to classify the rainfall types using DSD variables retrieved from the polarimetric radar. For the radar variables, the method by Steiner et al. (SHY95) was found to be the best method, with 0.00% misclassification of the stratiform rains. For the convective rains, the DSD retrieval method performed better. However, for both stratiform and convective rains, the fuzzy method performed better than the SHY95 and DSD retrieval methods.

scholarly journals On the Use of Radiosondes in Freezing Precipitation

2018 ◽  
Vol 35 (3) ◽  
pp. 459-472 ◽  
Author(s):  
Sean Waugh ◽  
Terry J. Schuur
Keyword(s):  
Response Times ◽  
Radar Data ◽  
Lower Atmosphere ◽  
Radiosonde Data ◽  
Storm Event ◽  
Polarimetric Radar ◽  
Winter Storm ◽  
Winter Storms ◽  
Model Initialization ◽  
Source Of Error

AbstractRadiosonde observations are used the world over to provide critical upper-air observations of the lower atmosphere. These observations are susceptible to errors that must be mitigated or avoided when identified. One source of error not previously addressed is radiosonde icing in winter storms, which can affect forecasts, warning operations, and model initialization. Under certain conditions, ice can form on the radiosonde, leading to decreased response times and incorrect readings. Evidence of radiosonde icing is presented for a winter storm event in Norman, Oklahoma, on 24 November 2013. A special sounding that included a particle imager probe and a GoPro camera was flown into the system producing ice pellets. While the iced-over temperature sensor showed no evidence of an elevated melting layer (ML), complementary Particle Size, Image, and Velocity (PASIV) probe and polarimetric radar observations provide clear evidence that an ML was indeed present. Radiosonde icing can occur while passing through a layer of supercooled drops, such as frequently found in a subfreezing layer that often lies below the ML in winter storms. Events that have warmer/deeper MLs would likely melt any ice present off the radiosonde, minimizing radiosonde icing and allowing the ML to be detected. This paper discusses the hypothesis that the absence of an ML in the radiosonde data presented here is more likely to occur in winter storms that produce ice pellets, which tend to have cooler/shallower MLs. Where sounding data do appear to be compromised by icing, polarimetric radar data might be used to identify MLs for nowcasting purposes and numerical model initialization.

scholarly journals X-Band Polarimetric Weather Radar Observations of a Hailstorm

2013 ◽  
Vol 30 (9) ◽  
pp. 2143-2151 ◽  
Author(s):  
Jordi Figueras i Ventura ◽  
Françoise Honoré ◽  
Pierre Tabary
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Complementary Information ◽  
Radar Observations ◽  
National Network ◽  
X Band ◽  
Polarimetric Weather Radar

Abstract This paper presents an analysis of a hail event that occurred 27 May 2012 over Brignoles, located in southeastern France. The event was observed by an X-band polarimetric radar located in Mont Maurel, 75 km northeast of the hailstorm. Lightning data from the French national network (owned and operated by Météorage) are also used in the study. The analysis highlights that the lightning and radar data provide complementary information that may allow a better microphysical interpretation of the hailstorm and potentially increase the probability of its detection.

scholarly journals Bulk-Density Representations of Branched Planar Ice Crystals: Errors in the Polarimetric Radar Variables

2018 ◽  
Vol 57 (2) ◽  
pp. 333-346 ◽  
Author(s):  
Robert S. Schrom ◽  
Matthew R. Kumjian
Keyword(s):  
Bulk Density ◽  
Cross Sections ◽  
Low Frequency ◽  
Resonance Scattering ◽  
Ice Crystals ◽  
Model Output ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Aspect Ratios ◽  
W Band

AbstractRecent interest in interpreting polarimetric radar observations of ice and evaluating microphysical model output with these observations has highlighted the importance of accurately computing the scattering of microwave radiation by branched planar ice crystals. These particles are often represented as spheroids with uniform bulk density, reduced from that of solid ice to account for the complex, nonuniform structure of natural branched crystals. In this study, the potential errors that arise from this assumption are examined by comparing scattering calculations of branched planar crystals with those of homogeneous, reduced-density plate crystals and spheroids with the same mass, aspect ratio, and maximum dimension. The results show that this assumption leads to significant errors in backscatter cross sections at horizontal and vertical polarization, specific differential phase (KDP), and differential reflectivity (ZDR), with the largest ZDR errors for ice crystals with the most extreme aspect ratios (<0.01) and effective densities < 250 kg m−3. For example, the maximum errors in X-band ZDR are 4.5 dB for 5.6-mm branched planar crystals. However, substantial errors are present at all weather radar frequencies, with resonance scattering effects at Ka and W band amplifying the low-frequency errors. The implications of these results on the interpretation of polarimetric radar observations and forward modeling of the polarimetric radar variables from microphysical model output are discussed.

scholarly journals Parameterization of the Spatial Variability of Rain for Large-Scale Models and Remote Sensing

2015 ◽  
Vol 54 (10) ◽  
pp. 2027-2046 ◽  
Author(s):  
Z. J. Lebo ◽  
C. R. Williams ◽  
G. Feingold ◽  
V. E. Larson
Keyword(s):  
Spatial Variability ◽  
Large Scale ◽  
Rain Rate ◽  
Numerical Models ◽  
Spatial Scales ◽  
Warm Pool ◽  
Radar Data ◽  
Model Output ◽  
Wide Range ◽  
Averaged Model

AbstractThe spatial variability of rain rate R is evaluated by using both radar observations and cloud-resolving model output, focusing on the Tropical Warm Pool–International Cloud Experiment (TWP-ICE) period. In general, the model-predicted rain-rate probability distributions agree well with those estimated from the radar data across a wide range of spatial scales. The spatial variability in R, which is defined according to the standard deviation of R (for R greater than a predefined threshold Rmin) σ(R), is found to vary according to both the average of R over a given footprint μ(R) and the footprint size or averaging scale Δ. There is good agreement between area-averaged model output and radar data at a height of 2.5 km. The model output at the surface is used to construct a scale-dependent parameterization of σ(R) as a function of μ(R) and Δ that can be readily implemented into large-scale numerical models. The variability in both the rainwater mixing ratio qr and R as a function of height is also explored. From the statistical analysis, a scale- and height-dependent formulation for the spatial variability of both qr and R is provided for the analyzed tropical scenario. Last, it is shown how this parameterization can be used to assist in constraining parameters that are often used to describe the surface rain-rate distribution.

Observed Bulk Hook Echo Drop Size Distribution Evolution in Supercell Tornadogenesis and Tornadogenesis Failure

2021 ◽  
Author(s):  
Kristofer S. Tuftedal ◽  
Michael M. French ◽  
Darrel M. Kingfield ◽  
Jeffrey C. Snyder
Keyword(s):  
Drop Size ◽  
Thermodynamic Characteristics ◽  
Radar Data ◽  
Number Concentration ◽  
Size Distributions ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Near Surface ◽  
Microphysical Processes ◽  
Drop Size Distributions

AbstractThe time preceding supercell tornadogenesis and tornadogenesis “failure” has been studied extensively to identify differing attributes related to tornado production or lack thereof. Studies from the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) found that air in the rear-flank downdraft (RFD) regions of non- and weakly tornadic supercells had different near-surface thermodynamic characteristics than that in strongly tornadic supercells. Subsequently, it was proposed that microphysical processes are likely to have an impact on the resulting thermodynamics of the near-surface RFD region. One way to view proxies to microphysical features, namely drop size distributions (DSDs), is through use of polarimetric radar data. Studies from the second VORTEX used data from dual-polarization radars to provide evidence of different DSDs in the hook echoes of tornadic and non-tornadic supercells. However, radar-based studies during these projects were limited to a small number of cases preventing result generalizations. This study compiles 68 tornadic and 62 non-tornadic supercells using Weather Surveillance Radar–1988 Doppler (WSR-88D) data to analyze changes in polarimetric radar variables leading up to, and at, tornadogenesis and tornadogenesis failure. Case types generally did not show notable hook echo differences in variables between sets, but did show spatial hook echo quadrant DSD differences. Consistent with past studies, differential radar reflectivity factor (ZDR) generally decreased leading up to tornadogenesis and tornadogenesis failure; in both sets, estimated total number concentration increased during the same times. Relationships between DSDs and the near-storm environment, and implications of results for nowcasting tornadogenesis, also are discussed.

scholarly journals Rainfall Estimates with Respect to Rainfall Types Using S-Band Polarimetric Radar in Korea

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 773
Author(s):  
Cheolhwan You ◽  
Miyoung Kang ◽  
Dong-In Lee
Keyword(s):  
Polarimetric Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rainfall Events ◽  
Stratiform Rain ◽  
Rainfall Variation ◽  
Precipitation Type ◽  
Rainfall Type ◽  
The Impact

To investigate the impact of rainfall type on rainfall estimation using polarimetric variables, rainfall relations such as those between rain rate (R) and specific differential phase (KDP), between R and KDP/differential reflectivity (ZDR), and between R and reflectivity (Z)/ZDR, were examined with respect to the precipitation type classified using drop size distributions (DSDs) measured by a disdrometer. The classification of rainfall type was assessed using four different methods: temporal rainfall variation; and the relations between intercept parameter (N0) and R; normalized intercept parameter (Nw) and median diameter (D0); and slope parameter (Λ) and R. The logN0–R relation discriminated between convective and stratiform rain with less standard deviation than the other methods as shown by the Z–ZDR scatter with respect to the rainfall types. The transition type from convective to stratiform and vice versa occurred in the stratiform rain region for all methods. To apply the classified rainfall relations to radar rainfall estimation, logNw and D0 were retrieved from polarimetric variables to discriminate the rainfall types in the radar domain. The DSD classification was verified with the vertical profile of reflectivity extracted at two positions corresponding to gage sites. Statistical analysis of four different rainfall events showed that rainfall estimation using the relations with precipitation type were better than those obtained without classification. The R(KDP,ZDR) relation with classification performed best on rainfall estimation for all rainfall events. The greatest improvement in rainfall estimation was obtained from R(Z,ZDR) with classification. We conclude that the classification of rainfall type leads to more accurate rainfall estimation. The different relations R(KDP), R(KDP,ZDR), and R(Z,ZDR) with respect to the rain types using polarimetric radar show improvement compared to estimation without consideration of rainfall type, in Korea.

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