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- Author or Editor: Alexander V. Ryzhkov x
- Journal of Applied Meteorology and Climatology x
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Abstract
Data from polarimetric radars offer remarkable insight into the microphysics of convective storms. Numerous tornadic and nontornadic supercell thunderstorms have been observed by the research polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN); additional storm data come from the Enterprise Electronics Corporation “Sidpol” C-band polarimetric radar in Enterprise, Alabama, as well as the King City C-band polarimetric radar in Ontario, Canada. A number of distinctive polarimetric signatures are repeatedly found in each of these storms. The forward-flank downdraft (FFD) is characterized by a signature of hail observed as near-zero Z DR and high Z HH. In addition, a shallow region of very high Z DR is found consistently on the southern edge of the FFD, called the Z DR “arc.” The Z DR and K DP columns and midlevel “rings” of enhanced Z DR and depressed ρ HV are usually observed in the vicinity of the main rotating updraft and in the rear-flank downdraft (RFD). Tornado touchdown is associated with a well-pronounced polarimetric debris signature. Similar polarimetric features in supercell thunderstorms have been reported in other studies. The data considered here are taken from both S- and C-band radars from different geographic locations and during different seasons. The consistent presence of these features may be indicative of fundamental processes intrinsic to supercell storms. Hypotheses on the origins, as well as microphysical and dynamical interpretations of these signatures, are presented. Implications about storm morphology for operational applications are suggested.
Abstract
Data from polarimetric radars offer remarkable insight into the microphysics of convective storms. Numerous tornadic and nontornadic supercell thunderstorms have been observed by the research polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN); additional storm data come from the Enterprise Electronics Corporation “Sidpol” C-band polarimetric radar in Enterprise, Alabama, as well as the King City C-band polarimetric radar in Ontario, Canada. A number of distinctive polarimetric signatures are repeatedly found in each of these storms. The forward-flank downdraft (FFD) is characterized by a signature of hail observed as near-zero Z DR and high Z HH. In addition, a shallow region of very high Z DR is found consistently on the southern edge of the FFD, called the Z DR “arc.” The Z DR and K DP columns and midlevel “rings” of enhanced Z DR and depressed ρ HV are usually observed in the vicinity of the main rotating updraft and in the rear-flank downdraft (RFD). Tornado touchdown is associated with a well-pronounced polarimetric debris signature. Similar polarimetric features in supercell thunderstorms have been reported in other studies. The data considered here are taken from both S- and C-band radars from different geographic locations and during different seasons. The consistent presence of these features may be indicative of fundamental processes intrinsic to supercell storms. Hypotheses on the origins, as well as microphysical and dynamical interpretations of these signatures, are presented. Implications about storm morphology for operational applications are suggested.
Abstract
The quality of polarimetric radar rainfall estimation is investigated for a broad range of distances from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D). The results of polarimetric echo classification have been integrated into the study to investigate the performance of radar rainfall estimation contingent on hydrometeor type. A new method for rainfall estimation that capitalizes on the results of polarimetric echo classification (EC method) is suggested. According to the EC method, polarimetric rainfall relations are utilized if the radar resolution volume is filled with rain (or rain and hail), and multiple R(Z) relations are used for different types of frozen hydrometeors. The intercept parameters in the R(Z) relations for each class are determined empirically from comparisons with gauges. It is shown that the EC method exhibits better performance than the conventional WSR-88D algorithm with a reduction by a factor of 1.5–2 in the rms error of 1-h rainfall estimates up to distances of 150 km from the radar.
Abstract
The quality of polarimetric radar rainfall estimation is investigated for a broad range of distances from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D). The results of polarimetric echo classification have been integrated into the study to investigate the performance of radar rainfall estimation contingent on hydrometeor type. A new method for rainfall estimation that capitalizes on the results of polarimetric echo classification (EC method) is suggested. According to the EC method, polarimetric rainfall relations are utilized if the radar resolution volume is filled with rain (or rain and hail), and multiple R(Z) relations are used for different types of frozen hydrometeors. The intercept parameters in the R(Z) relations for each class are determined empirically from comparisons with gauges. It is shown that the EC method exhibits better performance than the conventional WSR-88D algorithm with a reduction by a factor of 1.5–2 in the rms error of 1-h rainfall estimates up to distances of 150 km from the radar.
Abstract
Soon, the National Weather Service’s Weather Surveillance Radar-1988 Doppler (WSR-88D) network will be upgraded to allow dual-polarization capabilities. Therefore, it is imperative to understand and identify microphysical processes using the polarimetric variables. Though melting and size sorting of hydrometeors have been investigated, there has been relatively little focus devoted to the impacts of evaporation on the polarimetric characteristics of rainfall. In this study, a simple explicit bin microphysics one-dimensional rainshaft model is constructed to quantify the impacts of evaporation (neglecting the collisional processes) on vertical profiles of polarimetric radar variables in rain. The results of this model are applicable for light to moderate rain (<10 mm h−1). The modeling results indicate that the amount of evaporation that occurs in the subcloud layer is strongly dependent on the initial shape of the drop size distribution aloft, which can be assessed with polarimetric measurements. Understanding how radar-estimated rainfall rates may change in height due to evaporation is important for quantitative precipitation estimates, especially in regions far from the radar or in regions of complex terrain where low levels may not be adequately sampled. In addition to quantifying the effects of evaporation, a simple method of estimating the amount of evaporation that occurs in a given environment based on polarimetric radar measurements of the reflectivity factor ZH and differential reflectivity Z DR aloft is offered. Such a technique may be useful to operational meteorologists and hydrologists in estimating the amount of precipitation reaching the surface, especially in regions of poor low-level radar coverage such as mountainous regions or locations at large distances from the radar.
Abstract
Soon, the National Weather Service’s Weather Surveillance Radar-1988 Doppler (WSR-88D) network will be upgraded to allow dual-polarization capabilities. Therefore, it is imperative to understand and identify microphysical processes using the polarimetric variables. Though melting and size sorting of hydrometeors have been investigated, there has been relatively little focus devoted to the impacts of evaporation on the polarimetric characteristics of rainfall. In this study, a simple explicit bin microphysics one-dimensional rainshaft model is constructed to quantify the impacts of evaporation (neglecting the collisional processes) on vertical profiles of polarimetric radar variables in rain. The results of this model are applicable for light to moderate rain (<10 mm h−1). The modeling results indicate that the amount of evaporation that occurs in the subcloud layer is strongly dependent on the initial shape of the drop size distribution aloft, which can be assessed with polarimetric measurements. Understanding how radar-estimated rainfall rates may change in height due to evaporation is important for quantitative precipitation estimates, especially in regions far from the radar or in regions of complex terrain where low levels may not be adequately sampled. In addition to quantifying the effects of evaporation, a simple method of estimating the amount of evaporation that occurs in a given environment based on polarimetric radar measurements of the reflectivity factor ZH and differential reflectivity Z DR aloft is offered. Such a technique may be useful to operational meteorologists and hydrologists in estimating the amount of precipitation reaching the surface, especially in regions of poor low-level radar coverage such as mountainous regions or locations at large distances from the radar.
Abstract
Although radial velocity data from Doppler radars can partially resolve some tornadoes, particularly large tornadoes near the radar, most tornadoes are not explicitly resolved by radar owing to inadequate spatiotemporal resolution. In addition, it can be difficult to determine which mesocyclones typically observed on radar are associated with tornadoes. Since debris lofted by tornadoes has scattering characteristics that are distinct from those of hydrometeors, the additional information provided by polarimetric weather radars can aid in identifying debris from tornadoes; the polarimetric tornadic debris signature (TDS) provides what is nearly “ground truth” that a tornado is ongoing (or has recently occurred). This paper outlines a modification to the hydrometeor classification algorithm used with the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) network in the United States to include a TDS category. Examples of automated TDS classification are provided for several recent cases that were observed in the United States.
Abstract
Although radial velocity data from Doppler radars can partially resolve some tornadoes, particularly large tornadoes near the radar, most tornadoes are not explicitly resolved by radar owing to inadequate spatiotemporal resolution. In addition, it can be difficult to determine which mesocyclones typically observed on radar are associated with tornadoes. Since debris lofted by tornadoes has scattering characteristics that are distinct from those of hydrometeors, the additional information provided by polarimetric weather radars can aid in identifying debris from tornadoes; the polarimetric tornadic debris signature (TDS) provides what is nearly “ground truth” that a tornado is ongoing (or has recently occurred). This paper outlines a modification to the hydrometeor classification algorithm used with the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) network in the United States to include a TDS category. Examples of automated TDS classification are provided for several recent cases that were observed in the United States.
Abstract
Diabatic cooling from hydrometeor phase changes in the stratiform melting layer is of great interest to both operational forecasters and modelers for its societal and dynamical consequences. Attempts to estimate the melting-layer cooling rate typically rely on either the budgeting of hydrometeor content estimated from reflectivity Z or model-generated lookup tables scaled by the magnitude of Z in the bright band. Recent advances have been made in developing methods to observe the unique polarimetric characteristics of melting snow and the additional microphysical information they may contain. However, to date no work has looked at the thermodynamic information available from the polarimetric radar brightband signature. In this study, a one-dimensional spectral bin model of melting snow and a coupled polarimetric operator are used to study the relation between the polarimetric radar bright band and the melting-layer cooling rate. Simulations using a fixed particle size distribution (PSD) and variable environmental conditions show that the height and thickness of the bright band and the maximum brightband Z and specific differential phase shift
Abstract
Diabatic cooling from hydrometeor phase changes in the stratiform melting layer is of great interest to both operational forecasters and modelers for its societal and dynamical consequences. Attempts to estimate the melting-layer cooling rate typically rely on either the budgeting of hydrometeor content estimated from reflectivity Z or model-generated lookup tables scaled by the magnitude of Z in the bright band. Recent advances have been made in developing methods to observe the unique polarimetric characteristics of melting snow and the additional microphysical information they may contain. However, to date no work has looked at the thermodynamic information available from the polarimetric radar brightband signature. In this study, a one-dimensional spectral bin model of melting snow and a coupled polarimetric operator are used to study the relation between the polarimetric radar bright band and the melting-layer cooling rate. Simulations using a fixed particle size distribution (PSD) and variable environmental conditions show that the height and thickness of the bright band and the maximum brightband Z and specific differential phase shift
Abstract
A new polarimetric melting layer detection algorithm (MLDA) is utilized to estimate the top (melting level) and bottom boundaries of the melting layer and is tailored for operational deployment. Melting layer designations from a polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) in central Oklahoma are validated using radiosonde and model temperature analysis. It is demonstrated that the MLDA estimates the top of the melting layer with a root-mean-square error of about 200 m within 60 km of the radar. There is evidence that the polarimetric radar might yield better spatial and temporal designation of the melting layer within the storm than that obtained from existing numerical model output and soundings.
Abstract
A new polarimetric melting layer detection algorithm (MLDA) is utilized to estimate the top (melting level) and bottom boundaries of the melting layer and is tailored for operational deployment. Melting layer designations from a polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) in central Oklahoma are validated using radiosonde and model temperature analysis. It is demonstrated that the MLDA estimates the top of the melting layer with a root-mean-square error of about 200 m within 60 km of the radar. There is evidence that the polarimetric radar might yield better spatial and temporal designation of the melting layer within the storm than that obtained from existing numerical model output and soundings.
Abstract
Backscatter differential phase δ within the melting layer has been identified as a reliably measurable but still underutilized polarimetric variable. Polarimetric radar observations at X band in Germany and S band in the United States are presented that show maximal observed δ of 8.5° at X band but up to 70° at S band. Dual-frequency observations at X and C band in Germany and dual-frequency observations at C and S band in the United States are compared to explore the regional frequency dependencies of the δ signature. Theoretical simulations based on usual assumptions about the microphysical composition of the melting layer cannot reproduce the observed large values of δ at the lower-frequency bands and also underestimate the enhancements in differential reflectivity Z DR and reductions in the cross-correlation coefficient ρ hυ . Simulations using a two-layer T-matrix code and a simple model for the representation of accretion can, however, explain the pronounced δ signatures at S and C bands in conjunction with small δ at X band. The authors conclude that the δ signature bears information about microphysical accretion and aggregation processes in the melting layer and the degree of riming of the snowflakes aloft.
Abstract
Backscatter differential phase δ within the melting layer has been identified as a reliably measurable but still underutilized polarimetric variable. Polarimetric radar observations at X band in Germany and S band in the United States are presented that show maximal observed δ of 8.5° at X band but up to 70° at S band. Dual-frequency observations at X and C band in Germany and dual-frequency observations at C and S band in the United States are compared to explore the regional frequency dependencies of the δ signature. Theoretical simulations based on usual assumptions about the microphysical composition of the melting layer cannot reproduce the observed large values of δ at the lower-frequency bands and also underestimate the enhancements in differential reflectivity Z DR and reductions in the cross-correlation coefficient ρ hυ . Simulations using a two-layer T-matrix code and a simple model for the representation of accretion can, however, explain the pronounced δ signatures at S and C bands in conjunction with small δ at X band. The authors conclude that the δ signature bears information about microphysical accretion and aggregation processes in the melting layer and the degree of riming of the snowflakes aloft.
Abstract
A new synthesis of information forming the foundation for rule-based systems to deduce dominant bulk hydrometeor types and amounts using polarimetric radar data is presented. The information is valid for a 10-cm wavelength and consists of relations that are based on an extensive list of previous and recent observational and modeling studies of polarimetric signatures of hydrometeors. The relations are expressed as boundaries and thresholds in a space of polarimetric radar variables. Thus, the foundation is laid out for identification of hydrometeor types (species), estimation of characteristics of hydrometeor species (size, concentrations, etc.), and quantification of bulk hydrometeor contents (amounts). A fuzzy classification algorithm that builds upon this foundation will be discussed in a forthcoming paper.
Abstract
A new synthesis of information forming the foundation for rule-based systems to deduce dominant bulk hydrometeor types and amounts using polarimetric radar data is presented. The information is valid for a 10-cm wavelength and consists of relations that are based on an extensive list of previous and recent observational and modeling studies of polarimetric signatures of hydrometeors. The relations are expressed as boundaries and thresholds in a space of polarimetric radar variables. Thus, the foundation is laid out for identification of hydrometeor types (species), estimation of characteristics of hydrometeor species (size, concentrations, etc.), and quantification of bulk hydrometeor contents (amounts). A fuzzy classification algorithm that builds upon this foundation will be discussed in a forthcoming paper.
Abstract
A polarimetric radar–based method for retrieving atmospheric ice particle shapes is applied to snowfall measurements by a scanning Ka-band radar deployed at Oliktok Point, Alaska (70.495°N, 149.883°W). The mean aspect ratio, which is defined by the hydrometeor minor-to-major dimension ratio for a spheroidal particle model, is retrieved as a particle shape parameter. The radar variables used for aspect ratio profile retrievals include reflectivity, differential reflectivity, and the copolar correlation coefficient. The retrievals indicate that hydrometeors with mean aspect ratios below 0.2–0.3 are usually present in regions with air temperatures warmer than approximately from −17° to −15°C, corresponding to a regime that has been shown to be favorable for growth of pristine ice crystals of planar habits. Radar reflectivities corresponding to the lowest mean aspect ratios are generally between −10 and 10 dBZ. For colder temperatures, mean aspect ratios are typically in a range between 0.3 and 0.8. There is a tendency for hydrometeor aspect ratios to increase as particles transition from altitudes in the temperature range from −17° to −15°C toward the ground. This increase is believed to result from aggregation and riming processes that cause particles to become more spherical and is associated with areas demonstrating differential reflectivity decreases with increasing reflectivity. Aspect ratio retrievals at the lowest altitudes are consistent with in situ measurements obtained using a surface-based multiangle snowflake camera. Pronounced gradients in particle aspect ratio profiles are observed at altitudes at which there is a change in the dominant hydrometeor species, as inferred by spectral measurements from a vertically pointing Doppler radar.
Abstract
A polarimetric radar–based method for retrieving atmospheric ice particle shapes is applied to snowfall measurements by a scanning Ka-band radar deployed at Oliktok Point, Alaska (70.495°N, 149.883°W). The mean aspect ratio, which is defined by the hydrometeor minor-to-major dimension ratio for a spheroidal particle model, is retrieved as a particle shape parameter. The radar variables used for aspect ratio profile retrievals include reflectivity, differential reflectivity, and the copolar correlation coefficient. The retrievals indicate that hydrometeors with mean aspect ratios below 0.2–0.3 are usually present in regions with air temperatures warmer than approximately from −17° to −15°C, corresponding to a regime that has been shown to be favorable for growth of pristine ice crystals of planar habits. Radar reflectivities corresponding to the lowest mean aspect ratios are generally between −10 and 10 dBZ. For colder temperatures, mean aspect ratios are typically in a range between 0.3 and 0.8. There is a tendency for hydrometeor aspect ratios to increase as particles transition from altitudes in the temperature range from −17° to −15°C toward the ground. This increase is believed to result from aggregation and riming processes that cause particles to become more spherical and is associated with areas demonstrating differential reflectivity decreases with increasing reflectivity. Aspect ratio retrievals at the lowest altitudes are consistent with in situ measurements obtained using a surface-based multiangle snowflake camera. Pronounced gradients in particle aspect ratio profiles are observed at altitudes at which there is a change in the dominant hydrometeor species, as inferred by spectral measurements from a vertically pointing Doppler radar.
Abstract
Snow sublimating in dry air is a forecasting challenge and can delay the onset of surface snowfall and affect storm-total accumulations. Despite this fact, it remains comparatively less studied than other microphysical processes. Herein, the characteristics of sublimating snow and the potential for nowcasting snowfall reaching the surface are explored through the use of dual-polarization radar. Twelve cases featuring prolific sublimation were analyzed using range-defined quasi-vertical profiles (RDQVPs) and were compared with environmental model analyses. Overall, reflectivity Z significantly decreases, differential reflectivity Z DR slightly decreases, and copolar-correlation coefficient ρ hv remains nearly constant through the sublimation layer. Regions of enhanced specific differential phase K dp were frequently observed in the sublimation layer and are believed to be polarimetric evidence of secondary ice production via sublimation. A 1D bin model was initialized using particle size distributions retrieved from the RDQVPs using numerous novel polarimetric snow retrieval relations for a wide range of forecast lead times, with the model environment evolving in response to sublimation. It was found that the model was largely able to predict the snowfall start time up to 6 h in advance, with a 6-h median bias of just −18.5 min. A more detailed case study of the 8 December 2013 snowstorm in the Philadelphia, Pennsylvania, region was also performed, demonstrating good correspondence with observations and examples of model fields (e.g., cooling rate) hypothetically available from such a tool. The proof-of-concept results herein demonstrate the potential benefits of incorporating spatially averaged radar data in conjunction with simple 1D models into the nowcasting process.
Abstract
Snow sublimating in dry air is a forecasting challenge and can delay the onset of surface snowfall and affect storm-total accumulations. Despite this fact, it remains comparatively less studied than other microphysical processes. Herein, the characteristics of sublimating snow and the potential for nowcasting snowfall reaching the surface are explored through the use of dual-polarization radar. Twelve cases featuring prolific sublimation were analyzed using range-defined quasi-vertical profiles (RDQVPs) and were compared with environmental model analyses. Overall, reflectivity Z significantly decreases, differential reflectivity Z DR slightly decreases, and copolar-correlation coefficient ρ hv remains nearly constant through the sublimation layer. Regions of enhanced specific differential phase K dp were frequently observed in the sublimation layer and are believed to be polarimetric evidence of secondary ice production via sublimation. A 1D bin model was initialized using particle size distributions retrieved from the RDQVPs using numerous novel polarimetric snow retrieval relations for a wide range of forecast lead times, with the model environment evolving in response to sublimation. It was found that the model was largely able to predict the snowfall start time up to 6 h in advance, with a 6-h median bias of just −18.5 min. A more detailed case study of the 8 December 2013 snowstorm in the Philadelphia, Pennsylvania, region was also performed, demonstrating good correspondence with observations and examples of model fields (e.g., cooling rate) hypothetically available from such a tool. The proof-of-concept results herein demonstrate the potential benefits of incorporating spatially averaged radar data in conjunction with simple 1D models into the nowcasting process.
