Browse
Retrieval of Peak Thunderstorm Wind Velocities Using WSR-88D Weather RadarsAbstract
The current study develops a variant of the VAD method to retrieve thunderstorm peak event velocities using low-elevation WSR-88D radar scans. The main challenge pertains to the localized nature of thunderstorm winds, which complicates single-Doppler retrievals as it dictates the use of a limited spatial scale. Since VAD methods assume constant velocity in the fitted section, it is important that retrieved sections do not contain background flow. Accordingly, the current study proposes an image processing method to partition scans into regions, representing events and the background flows, that can be retrieved independently. The study compares the retrieved peak velocities to retrievals using another VAD method. The proposed technique is found to estimate peak event velocities that are closer to measured ASOS readings, making it more suitable for historical analysis. The study also compares the results of retrievals from over 2600 thunderstorm events from 19 radar–ASOS station combinations that are less than 10 km away from the radar. Comparisons of probability distributions of peak event velocities for ASOS readings and radar retrievals showed good agreement for stations within 4 km from the radar while more distant stations had a higher bias toward retrieved velocities compared to ASOS velocities. The mean absolute error for velocity magnitude increases with height ranging between 1.5 and 4.5 m s−1. A proposed correction based on the exponential trend of mean errors was shown to improve the probability distribution comparisons, especially for higher velocity magnitudes.
Abstract
The current study develops a variant of the VAD method to retrieve thunderstorm peak event velocities using low-elevation WSR-88D radar scans. The main challenge pertains to the localized nature of thunderstorm winds, which complicates single-Doppler retrievals as it dictates the use of a limited spatial scale. Since VAD methods assume constant velocity in the fitted section, it is important that retrieved sections do not contain background flow. Accordingly, the current study proposes an image processing method to partition scans into regions, representing events and the background flows, that can be retrieved independently. The study compares the retrieved peak velocities to retrievals using another VAD method. The proposed technique is found to estimate peak event velocities that are closer to measured ASOS readings, making it more suitable for historical analysis. The study also compares the results of retrievals from over 2600 thunderstorm events from 19 radar–ASOS station combinations that are less than 10 km away from the radar. Comparisons of probability distributions of peak event velocities for ASOS readings and radar retrievals showed good agreement for stations within 4 km from the radar while more distant stations had a higher bias toward retrieved velocities compared to ASOS velocities. The mean absolute error for velocity magnitude increases with height ranging between 1.5 and 4.5 m s−1. A proposed correction based on the exponential trend of mean errors was shown to improve the probability distribution comparisons, especially for higher velocity magnitudes.
A Comparison of Beamforming and Direction Finding Algorithms (Beamscan and MUSIC) on a Linear Array HF Radar in a Medium to Low Wave Energy EnvironmentAbstract
We assess the performance of three different algorithms for estimating surface ocean currents from two linear array HF radar systems. The delay-and-sum beamforming algorithm, commonly used with beamforming systems, is compared with two direction-finding algorithms: Multiple Signal Classification (MUSIC) and direction finding using beamforming (Beamscan). A 7-month dataset from two HF radar sites (CSW and GTN) on Long Bay, South Carolina (United States), is used to compare the different methods. The comparison is carried out on three locations (midpoint along the baseline and two locations with in situ Eulerian current data available) representing different steering angles. Beamforming produces surface current data that show high correlation near the radar boresight (R 2 ≥ 0.79). At partially sheltered locations far from the radar boresight directions (59° and 48° for radar sites CSW and GTN, respectively) there is no correlation for CSW (R 2 = 0) and the correlation is reduced significantly for GTN (R 2 = 0.29). Beamscan performs similarly near the radar boresight (R 2 = 0.8 and 0.85 for CSW and GTN, respectively) but better than beamforming far from the radar boresight (R 2 = 0.52 and 0.32 for CSW and GTN, respectively). MUSIC’s performance, after significant tuning, is similar near the boresight (R 2 = 0.78 and 0.84 for CSW and GTN) while worse than Beamscan but better than beamforming far from the boresight (R 2 = 0.42 and 0.27 for CSW and GTN, respectively). Comparisons at the midpoint (baseline comparison) show the largest performance difference between methods. Beamforming (R 2 = 0.01) is the worst performer, followed by MUSIC (R 2 = 0.37) while Beamscan (R 2 = 0.76) performs best.
Abstract
We assess the performance of three different algorithms for estimating surface ocean currents from two linear array HF radar systems. The delay-and-sum beamforming algorithm, commonly used with beamforming systems, is compared with two direction-finding algorithms: Multiple Signal Classification (MUSIC) and direction finding using beamforming (Beamscan). A 7-month dataset from two HF radar sites (CSW and GTN) on Long Bay, South Carolina (United States), is used to compare the different methods. The comparison is carried out on three locations (midpoint along the baseline and two locations with in situ Eulerian current data available) representing different steering angles. Beamforming produces surface current data that show high correlation near the radar boresight (R 2 ≥ 0.79). At partially sheltered locations far from the radar boresight directions (59° and 48° for radar sites CSW and GTN, respectively) there is no correlation for CSW (R 2 = 0) and the correlation is reduced significantly for GTN (R 2 = 0.29). Beamscan performs similarly near the radar boresight (R 2 = 0.8 and 0.85 for CSW and GTN, respectively) but better than beamforming far from the radar boresight (R 2 = 0.52 and 0.32 for CSW and GTN, respectively). MUSIC’s performance, after significant tuning, is similar near the boresight (R 2 = 0.78 and 0.84 for CSW and GTN) while worse than Beamscan but better than beamforming far from the boresight (R 2 = 0.42 and 0.27 for CSW and GTN, respectively). Comparisons at the midpoint (baseline comparison) show the largest performance difference between methods. Beamforming (R 2 = 0.01) is the worst performer, followed by MUSIC (R 2 = 0.37) while Beamscan (R 2 = 0.76) performs best.
Abstract
Variation of the Kuroshio path south of Japan has an important impact on weather, climate, and ecosystems due to its distinct features. Motivated by the ever-popular deep learning methods using neural network architectures in areas where more accurate reference data for oceanographic observations and reanalysis are available, we build four deep learning models based on the long short-term memory (LSTM) neural network, combined with the empirical orthogonal function (EOF) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), namely, the LSTM, EOF–LSTM, CEEMDAN–LSTM, and EOF–CEEMDAN–LSTM. Using these models, we conduct long-range predictions (120 days) of the Kuroshio path south of Japan based on 50-yr ocean reanalysis and nearly 15 years of satellite altimeter data. We show that the EOF–CEEMDAN–LSTM performs the best among the four models, by attaining approximately 0.739 anomaly correlation coefficient and 0.399° root-mean-square error for the 120-day prediction of the Kuroshio path south of Japan. The hindcasts of the EOF–CEEMDAN–LSTM are successful in reproducing the observed formation and decay of the Kuroshio large meander during 2004/05, and the formation of the latest large meander in 2017. Finally, we present predictions of the Kuroshio path south of Japan at 120-day lead time, which suggest that the Kuroshio will remain in the state of the large meander until November 2022.
Abstract
Variation of the Kuroshio path south of Japan has an important impact on weather, climate, and ecosystems due to its distinct features. Motivated by the ever-popular deep learning methods using neural network architectures in areas where more accurate reference data for oceanographic observations and reanalysis are available, we build four deep learning models based on the long short-term memory (LSTM) neural network, combined with the empirical orthogonal function (EOF) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), namely, the LSTM, EOF–LSTM, CEEMDAN–LSTM, and EOF–CEEMDAN–LSTM. Using these models, we conduct long-range predictions (120 days) of the Kuroshio path south of Japan based on 50-yr ocean reanalysis and nearly 15 years of satellite altimeter data. We show that the EOF–CEEMDAN–LSTM performs the best among the four models, by attaining approximately 0.739 anomaly correlation coefficient and 0.399° root-mean-square error for the 120-day prediction of the Kuroshio path south of Japan. The hindcasts of the EOF–CEEMDAN–LSTM are successful in reproducing the observed formation and decay of the Kuroshio large meander during 2004/05, and the formation of the latest large meander in 2017. Finally, we present predictions of the Kuroshio path south of Japan at 120-day lead time, which suggest that the Kuroshio will remain in the state of the large meander until November 2022.
Abstract
Stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. We developed a bispectral filter (BSF) to reduce the stripe noise. The BSF is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. The Second-Generation Global Imager (SGLI) is an optical sensor on board the Global Change Observation Mission–Climate (GCOM-C) satellite. We applied the BSF to SGLI data and validated the retrieved SSTs. The validation results demonstrate the effectiveness of BSF, which reduced stripe noise in the retrieved SGLI SSTs without blurring SST fronts. It also improved the accuracy of the SSTs by about 0.04 K (about 13%) in the robust standard deviation.
Significance Statement
This method reduces stripe noise and improves the accuracy of SST data with minimal compromise of spatial resolution. The method assumes the relationship between the brightness temperature and the brightness temperature difference in the split window based on the physical background of atmospheric radiative transfer. The physical background of the data provides an easy solution to a complex problem. Although destriping generally requires a complex algorithm, our approach is based on a simple Gaussian filter and is easy to implement.
Abstract
Stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. We developed a bispectral filter (BSF) to reduce the stripe noise. The BSF is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. The Second-Generation Global Imager (SGLI) is an optical sensor on board the Global Change Observation Mission–Climate (GCOM-C) satellite. We applied the BSF to SGLI data and validated the retrieved SSTs. The validation results demonstrate the effectiveness of BSF, which reduced stripe noise in the retrieved SGLI SSTs without blurring SST fronts. It also improved the accuracy of the SSTs by about 0.04 K (about 13%) in the robust standard deviation.
Significance Statement
This method reduces stripe noise and improves the accuracy of SST data with minimal compromise of spatial resolution. The method assumes the relationship between the brightness temperature and the brightness temperature difference in the split window based on the physical background of atmospheric radiative transfer. The physical background of the data provides an easy solution to a complex problem. Although destriping generally requires a complex algorithm, our approach is based on a simple Gaussian filter and is easy to implement.
Reconstruction of the Rheological Parameters in a Sea Ice Model with Viscoplastic RheologyAbstract
We analyzed the feasibility of the reconstruction of the spatially varying rheological parameters through the four-dimensional variational data assimilation of the sea ice velocity, thickness, and concentration into the viscoplastic (VP) sea ice model. The feasibility is assessed via idealized variational data assimilation experiments with synthetic observations configured for a 1-day data assimilation window in a 50 × 40 rectangular basin forced by the open boundaries, winds, and ocean currents and should be viewed as a first step in the developing the similar algorithms which can be applied for the more advanced sea ice models. It is found that “true” spatial variability (∼5.8 kN m−2) of the internal maximum ice strength parameter
Abstract
We analyzed the feasibility of the reconstruction of the spatially varying rheological parameters through the four-dimensional variational data assimilation of the sea ice velocity, thickness, and concentration into the viscoplastic (VP) sea ice model. The feasibility is assessed via idealized variational data assimilation experiments with synthetic observations configured for a 1-day data assimilation window in a 50 × 40 rectangular basin forced by the open boundaries, winds, and ocean currents and should be viewed as a first step in the developing the similar algorithms which can be applied for the more advanced sea ice models. It is found that “true” spatial variability (∼5.8 kN m−2) of the internal maximum ice strength parameter
Abstract
The adjoint assimilation method has been widely used in various ocean models, and a series of technical schemes have been developed at the same time. Open boundary conditions (OBCs) in the two-dimensional (2D) tidal model of the M2 tidal constituent in the Bohai and the Yellow Seas (BYS) were inverted successfully using the adjoint assimilation methods in previous studies. However, the cost function in the adjoint assimilation method usually used the L2 norm in the past, which is difficult to maintain the robustness of the method when there are outliers. Meanwhile, using the L1 norm with strong robustness will shield the outliers’ information fully. Therefore, we propose a new scheme that replaces the L2 norm with the Huber function to improve the robustness of the adjoint assimilation method and absorb the data’s useful information to some extent. This scheme was verified in the ideal experiments in which magnitudes of the misfit vector were significantly reduced and the quality control (QC) process was simplified consequently. In the practical experiments, the introduction of the Huber function improved the accuracy of inversion in the inshore area using mixed data containing tide gauges and satellite altimetry. With this scheme, the root-mean-square errors (RMSEs) between the estimation and the observed values at tide gauge stations were reduced from ∼8 cm with the original scheme to ∼6 cm. Testing the new scheme in more complex models and how it might be affected remains a topic for future study.
Significance Statement
The adjoint assimilation method has been effectively applied in various ocean models. The cost function in the adjoint assimilation is usually in the form of the L2 norm, which presents poor robustness. By using the Huber function instead of the L2 norm as the cost function, we proposed a new scheme that can perfectly handle the potential outliers in data and noticeably improve the robustness of the adjoint assimilation method. The new method was applied to the inversion of tidal open boundary conditions of the M2 constituent in the Bohai and the Yellow Seas. Both the ideal and practical experiments verified the effectiveness of the developed scheme.
Abstract
The adjoint assimilation method has been widely used in various ocean models, and a series of technical schemes have been developed at the same time. Open boundary conditions (OBCs) in the two-dimensional (2D) tidal model of the M2 tidal constituent in the Bohai and the Yellow Seas (BYS) were inverted successfully using the adjoint assimilation methods in previous studies. However, the cost function in the adjoint assimilation method usually used the L2 norm in the past, which is difficult to maintain the robustness of the method when there are outliers. Meanwhile, using the L1 norm with strong robustness will shield the outliers’ information fully. Therefore, we propose a new scheme that replaces the L2 norm with the Huber function to improve the robustness of the adjoint assimilation method and absorb the data’s useful information to some extent. This scheme was verified in the ideal experiments in which magnitudes of the misfit vector were significantly reduced and the quality control (QC) process was simplified consequently. In the practical experiments, the introduction of the Huber function improved the accuracy of inversion in the inshore area using mixed data containing tide gauges and satellite altimetry. With this scheme, the root-mean-square errors (RMSEs) between the estimation and the observed values at tide gauge stations were reduced from ∼8 cm with the original scheme to ∼6 cm. Testing the new scheme in more complex models and how it might be affected remains a topic for future study.
Significance Statement
The adjoint assimilation method has been effectively applied in various ocean models. The cost function in the adjoint assimilation is usually in the form of the L2 norm, which presents poor robustness. By using the Huber function instead of the L2 norm as the cost function, we proposed a new scheme that can perfectly handle the potential outliers in data and noticeably improve the robustness of the adjoint assimilation method. The new method was applied to the inversion of tidal open boundary conditions of the M2 constituent in the Bohai and the Yellow Seas. Both the ideal and practical experiments verified the effectiveness of the developed scheme.
Abstract
To calculate a turbulent kinetic energy dissipation rate from the microstructure vertical shear of the horizontal velocity via a spectral analysis, shear spectra need first to be cleaned from vibrations of the moving vehicle. Unambiguously, this study shows that the spectral cleaning must be applied all over the frequency range and not only at frequencies larger than 10 Hz, as a recent study suggested. For a Vertical Microstructure Profiler (VMP-6000), not correcting for vehicle vibrations below 10 Hz leads to overestimated dissipation rates from 50% to 700% for usual downcast velocities and for weak dissipation rates (ε < 1 × 10−9 W kg−1). Vibrations concern all vehicles, but the exact vibrational frequency signature depends on the vehicle shape and its downcast velocity. In any case, a spectral cleaning over the whole frequency range is strongly advised. This study also reports on a systematic low bias of inferred dissipation rates induced by the spectral cleaning when too few degrees of freedom are available for the cleaning, which is usually the default of the standard processing. Whatever the dissipation rate level, not correcting for the bias leads to underestimated dissipation rates by a factor 1.4–2.7 (with usual parameters), the exact amplitude of the bias depending on the number of degrees of freedom and on the number of independent accelerometer axes used for the cleaning. It is strongly advised that such a bias be taken into account to recompute dissipation rates of past datasets and for future observations.
Abstract
To calculate a turbulent kinetic energy dissipation rate from the microstructure vertical shear of the horizontal velocity via a spectral analysis, shear spectra need first to be cleaned from vibrations of the moving vehicle. Unambiguously, this study shows that the spectral cleaning must be applied all over the frequency range and not only at frequencies larger than 10 Hz, as a recent study suggested. For a Vertical Microstructure Profiler (VMP-6000), not correcting for vehicle vibrations below 10 Hz leads to overestimated dissipation rates from 50% to 700% for usual downcast velocities and for weak dissipation rates (ε < 1 × 10−9 W kg−1). Vibrations concern all vehicles, but the exact vibrational frequency signature depends on the vehicle shape and its downcast velocity. In any case, a spectral cleaning over the whole frequency range is strongly advised. This study also reports on a systematic low bias of inferred dissipation rates induced by the spectral cleaning when too few degrees of freedom are available for the cleaning, which is usually the default of the standard processing. Whatever the dissipation rate level, not correcting for the bias leads to underestimated dissipation rates by a factor 1.4–2.7 (with usual parameters), the exact amplitude of the bias depending on the number of degrees of freedom and on the number of independent accelerometer axes used for the cleaning. It is strongly advised that such a bias be taken into account to recompute dissipation rates of past datasets and for future observations.
Abstract
The presence of internal waves (IWs) in the ocean alters the isotropic properties of sound speed profiles (SSPs) in the water column. Changes in the SSPs affect underwater acoustics since most of the energy is dissipated into the seabed due to the downward refraction of sound waves. In consequence, variations in the SSP must be considered when modeling acoustic propagation in the ocean. Empirical orthogonal functions (EOFs) are regularly employed to model and represent SSPs using a linear combination of basis functions that capture the sound speed variability. A different approach is to use dictionary learning to obtain a learned dictionary (LD) that generates a nonorthogonal set of basis functions (atoms) that generate a better sparse representation. In this paper, the performance of EOFs and LDs are evaluated for sparse representation of SSPs affected by the passing of IWs. In addition, an LD-based supervised framework is presented for SSP classification and is compared with classical learning models. The algorithms presented in this work are trained and tested on data collected from the Shallow Water Experiment 2006. Results show that LDs yield lower reconstruction error than EOFs when using the same number of bases. In addition, overcomplete LDs are demonstrated to be a robust method to classify SSPs during low, medium, and high IW activity, reporting accuracy that is comparable to and sometimes higher than that of standard supervised classification methods.
Abstract
The presence of internal waves (IWs) in the ocean alters the isotropic properties of sound speed profiles (SSPs) in the water column. Changes in the SSPs affect underwater acoustics since most of the energy is dissipated into the seabed due to the downward refraction of sound waves. In consequence, variations in the SSP must be considered when modeling acoustic propagation in the ocean. Empirical orthogonal functions (EOFs) are regularly employed to model and represent SSPs using a linear combination of basis functions that capture the sound speed variability. A different approach is to use dictionary learning to obtain a learned dictionary (LD) that generates a nonorthogonal set of basis functions (atoms) that generate a better sparse representation. In this paper, the performance of EOFs and LDs are evaluated for sparse representation of SSPs affected by the passing of IWs. In addition, an LD-based supervised framework is presented for SSP classification and is compared with classical learning models. The algorithms presented in this work are trained and tested on data collected from the Shallow Water Experiment 2006. Results show that LDs yield lower reconstruction error than EOFs when using the same number of bases. In addition, overcomplete LDs are demonstrated to be a robust method to classify SSPs during low, medium, and high IW activity, reporting accuracy that is comparable to and sometimes higher than that of standard supervised classification methods.
Abstract
Properties of the surface mixed layer (ML) are critical for understanding and predicting atmosphere–sea ice–ocean interactions in the changing Arctic Ocean. Mooring measurements are typically unable to resolve the ML in the Arctic due to the need for instruments to remain below the surface to avoid contact with sea ice and icebergs. Here, we use measurements from a series of three moorings installed for one year in the Beaufort Sea to demonstrate that upward-looking acoustic Doppler current profilers (ADCPs) installed on subsurface floats can be used to estimate ML properties. A method is developed for combining measured peaks in acoustic backscatter and inertial shear from the ADCPs to estimate the ML depth. Additionally, we use an inverse sound speed model to infer the summer ML temperature based on offsets in ADCP altimeter distance during open-water periods. The ADCP estimates of ML depth and ML temperature compare favorably with measurements made from mooring temperature sensors, satellite SST, and from an autonomous Seaglider. These methods could be applied to other extant mooring records to recover additional information about ML property changes and variability.
Abstract
Properties of the surface mixed layer (ML) are critical for understanding and predicting atmosphere–sea ice–ocean interactions in the changing Arctic Ocean. Mooring measurements are typically unable to resolve the ML in the Arctic due to the need for instruments to remain below the surface to avoid contact with sea ice and icebergs. Here, we use measurements from a series of three moorings installed for one year in the Beaufort Sea to demonstrate that upward-looking acoustic Doppler current profilers (ADCPs) installed on subsurface floats can be used to estimate ML properties. A method is developed for combining measured peaks in acoustic backscatter and inertial shear from the ADCPs to estimate the ML depth. Additionally, we use an inverse sound speed model to infer the summer ML temperature based on offsets in ADCP altimeter distance during open-water periods. The ADCP estimates of ML depth and ML temperature compare favorably with measurements made from mooring temperature sensors, satellite SST, and from an autonomous Seaglider. These methods could be applied to other extant mooring records to recover additional information about ML property changes and variability.
Impacts of Vertical Nonuniform Beam Filling on the Observability of Secondary Ice Production due to SublimationAbstract
Quasi-vertical profiles (QVPs) of polarimetric radar data have emerged as a powerful tool for studying precipitation microphysics. Various studies have found enhancements in specific differential phase K dp in regions of suspected secondary ice production (SIP) due to rime splintering. Similar K dp enhancements have also been found in regions of sublimating snow, another proposed SIP process. This work explores these K dp signatures for two cases of sublimating snow using nearly collocated S- and Ka-band radars. The presence of the signature was inconsistent between the radars, prompting exploration of alternative causes. Idealized simulations are performed using a radar beam-broadening model to explore the impact of nonuniform beam filling (NBF) on the observed reflectivity Z and K dp within the sublimation layer. Rather than an intrinsic increase in ice concentration, the observed K dp enhancements can instead be explained by NBF in the presence of sharp vertical gradients of Z and K dp within the sublimation zone, which results in a K dp bias dipole. The severity of the bias is sensitive to the Z gradient and radar beamwidth and elevation angle, which explains its appearance at only one radar. In addition, differences in scanning strategies and range thresholds during QVP processing can constructively enhance these positive K dp biases by excluding the negative portion of the dipole. These results highlight the need to consider NBF effects in regions not traditionally considered (e.g., in pure snow) due to the increased K dp fidelity afforded by QVPs and the subsequent ramifications this has on the observability of sublimational SIP.
Significance Statement
Many different processes can cause snowflakes to break apart into numerous tiny pieces, including when they evaporate into dry air. Purported evidence of this phenomenon has been seen in data from some weather radars, but we noticed it was not seen in data from others. In this work we use case studies and models to show that this signature may actually be an artifact from the radar beam becoming too big and there being too much variability of the precipitation within it. While this breakup process may actually be occurring in reality, these results suggest we may have trouble observing it with typical weather radars.
Abstract
Quasi-vertical profiles (QVPs) of polarimetric radar data have emerged as a powerful tool for studying precipitation microphysics. Various studies have found enhancements in specific differential phase K dp in regions of suspected secondary ice production (SIP) due to rime splintering. Similar K dp enhancements have also been found in regions of sublimating snow, another proposed SIP process. This work explores these K dp signatures for two cases of sublimating snow using nearly collocated S- and Ka-band radars. The presence of the signature was inconsistent between the radars, prompting exploration of alternative causes. Idealized simulations are performed using a radar beam-broadening model to explore the impact of nonuniform beam filling (NBF) on the observed reflectivity Z and K dp within the sublimation layer. Rather than an intrinsic increase in ice concentration, the observed K dp enhancements can instead be explained by NBF in the presence of sharp vertical gradients of Z and K dp within the sublimation zone, which results in a K dp bias dipole. The severity of the bias is sensitive to the Z gradient and radar beamwidth and elevation angle, which explains its appearance at only one radar. In addition, differences in scanning strategies and range thresholds during QVP processing can constructively enhance these positive K dp biases by excluding the negative portion of the dipole. These results highlight the need to consider NBF effects in regions not traditionally considered (e.g., in pure snow) due to the increased K dp fidelity afforded by QVPs and the subsequent ramifications this has on the observability of sublimational SIP.
Significance Statement
Many different processes can cause snowflakes to break apart into numerous tiny pieces, including when they evaporate into dry air. Purported evidence of this phenomenon has been seen in data from some weather radars, but we noticed it was not seen in data from others. In this work we use case studies and models to show that this signature may actually be an artifact from the radar beam becoming too big and there being too much variability of the precipitation within it. While this breakup process may actually be occurring in reality, these results suggest we may have trouble observing it with typical weather radars.
