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- Author or Editor: B. L. Cheong x
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Abstract
A three-dimensional radar simulator capable of generating simulated raw time series data for a weather radar has been designed and implemented. The characteristics of the radar signals (amplitude, phase) are derived from the atmospheric fields from a high-resolution numerical weather model, although actual measured fields could be used. A field of thousands of scatterers is populated within the field of view of the virtual radar. Reflectivity characteristics of the targets are determined from well-known parameterization schemes. Doppler characteristics are derived by forcing the discrete scatterers to move with the three-dimensional wind field. Conventional moment-generating radar simulators use atmospheric conditions and a set of weighting functions to produce theoretical moment maps, which allow for the study of radar characteristics and limitations given particular configurations. In contrast to these radar simulators, the algorithm presented here is capable of producing sample-to-sample time series data that are collected by a radar system of virtually any design. Thus, this new radar simulator allows for the test and analysis of advanced topics, such as phased array antennas, clutter mitigation schemes, waveform design studies, and spectral-based methods. Limited examples exemplifying the usefulness and flexibility of the simulator will be provided.
Abstract
A three-dimensional radar simulator capable of generating simulated raw time series data for a weather radar has been designed and implemented. The characteristics of the radar signals (amplitude, phase) are derived from the atmospheric fields from a high-resolution numerical weather model, although actual measured fields could be used. A field of thousands of scatterers is populated within the field of view of the virtual radar. Reflectivity characteristics of the targets are determined from well-known parameterization schemes. Doppler characteristics are derived by forcing the discrete scatterers to move with the three-dimensional wind field. Conventional moment-generating radar simulators use atmospheric conditions and a set of weighting functions to produce theoretical moment maps, which allow for the study of radar characteristics and limitations given particular configurations. In contrast to these radar simulators, the algorithm presented here is capable of producing sample-to-sample time series data that are collected by a radar system of virtually any design. Thus, this new radar simulator allows for the test and analysis of advanced topics, such as phased array antennas, clutter mitigation schemes, waveform design studies, and spectral-based methods. Limited examples exemplifying the usefulness and flexibility of the simulator will be provided.
Abstract
Trends in current weather research involve active phased-array radar systems that have several advantages over conventional radars with klystron or magnetron transmitters. However, phased-array radars generally do not have the same peak transmit power capability as conventional systems so they must transmit longer pulses to maintain an equivalent average power on target. Increasing transmits pulse duration increases range gate size but the use of pulse compression offers a means of recovering the otherwise lost resolution. To evaluate pulse compression for use in future weather radar systems, modifications to a weather radar simulator have been made to incorporate phase-coding into its functionality. Data derived from Barker-coded pulses with matched and mismatched filters were compared with data obtained from uncoded pulses to evaluate the pulse compression performance. Additionally, pulse compression was simulated using data collected from an experimental radar to validate the simulated results. The data derived from both experimental and simulated methods were then applied to a fuzzy logic tornado detection algorithm to examine the effects of the pulse compression process. It was found that the fuzzy logic process was sufficiently robust to maintain high levels of detection accuracy with low false alarm rates even though biases were observed in the pulse-compressed data.
Abstract
Trends in current weather research involve active phased-array radar systems that have several advantages over conventional radars with klystron or magnetron transmitters. However, phased-array radars generally do not have the same peak transmit power capability as conventional systems so they must transmit longer pulses to maintain an equivalent average power on target. Increasing transmits pulse duration increases range gate size but the use of pulse compression offers a means of recovering the otherwise lost resolution. To evaluate pulse compression for use in future weather radar systems, modifications to a weather radar simulator have been made to incorporate phase-coding into its functionality. Data derived from Barker-coded pulses with matched and mismatched filters were compared with data obtained from uncoded pulses to evaluate the pulse compression performance. Additionally, pulse compression was simulated using data collected from an experimental radar to validate the simulated results. The data derived from both experimental and simulated methods were then applied to a fuzzy logic tornado detection algorithm to examine the effects of the pulse compression process. It was found that the fuzzy logic process was sufficiently robust to maintain high levels of detection accuracy with low false alarm rates even though biases were observed in the pulse-compressed data.
Abstract
A case study illustrating the impact of moisture variability on convection initiation in a synoptically active environment without strong moisture gradients is presented. The preconvective environment on 30 April 2007 nearly satisfied the three conditions for convection initiation: moisture, instability, and a low-level lifting mechanism. However, a sounding analysis showed that a low-level inversion layer and high LFC would prevent convection initiation because the convective updraft velocities required to overcome the convective inhibition (CIN) were much higher than updraft velocities typically observed in convergence zones. Radar refractivity retrievals from the Twin Lakes, Oklahoma (KTLX), Weather Surveillance Radar-1988 Doppler (WSR-88D) showed a moisture pool contributing up to a 2°C increase in dewpoint temperature where the initial storm-scale convergence was observed. The analysis of the storm-relative wind field revealed that the developing storm ingested the higher moisture associated with the moisture pool. Sounding analyses showed that the moisture pool reduced or nearly eliminated CIN, lowered the LFC by about 500 m, and increased CAPE by 2.5 times. Thus, these small-scale moisture changes increased the likelihood of convection initiation within the moisture pool by creating a more favorable thermodynamic environment. The results suggest that refractivity data could improve convection initiation forecasts by assessing moisture variability at finer scales than the current observation network.
Abstract
A case study illustrating the impact of moisture variability on convection initiation in a synoptically active environment without strong moisture gradients is presented. The preconvective environment on 30 April 2007 nearly satisfied the three conditions for convection initiation: moisture, instability, and a low-level lifting mechanism. However, a sounding analysis showed that a low-level inversion layer and high LFC would prevent convection initiation because the convective updraft velocities required to overcome the convective inhibition (CIN) were much higher than updraft velocities typically observed in convergence zones. Radar refractivity retrievals from the Twin Lakes, Oklahoma (KTLX), Weather Surveillance Radar-1988 Doppler (WSR-88D) showed a moisture pool contributing up to a 2°C increase in dewpoint temperature where the initial storm-scale convergence was observed. The analysis of the storm-relative wind field revealed that the developing storm ingested the higher moisture associated with the moisture pool. Sounding analyses showed that the moisture pool reduced or nearly eliminated CIN, lowered the LFC by about 500 m, and increased CAPE by 2.5 times. Thus, these small-scale moisture changes increased the likelihood of convection initiation within the moisture pool by creating a more favorable thermodynamic environment. The results suggest that refractivity data could improve convection initiation forecasts by assessing moisture variability at finer scales than the current observation network.
Abstract
The 2007 and 2008 spring refractivity experiments at KTLX investigated the potential utility of high-resolution, near-surface refractivity measurements to operational forecasting. During these experiments, forecasters at the Norman, Oklahoma, National Weather Service Forecast Office (NWSFO) assessed refractivity and scan-to-scan refractivity change fields retrieved from the Weather Surveillance Radar-1988 Doppler weather radar near Oklahoma City—Twin Lakes, Oklahoma (KTLX). Both quantitative and qualitative analysis methods were used to analyze the 41 responses from seven forecasters to a questionnaire designed to measure the impact of refractivity fields on forecast operations. The analysis revealed that forecasts benefited from the refractivity fields on 25% of the days included in the evaluation. In each of these cases, the refractivity fields provided complementary information that somewhat enhanced the forecasters’ capability to analyze the near-surface environment and boosted their confidence in moisture trends. A case in point was the ability to track a retreating dryline after its location was obscured by a weak reflectivity bloom caused by biological scatterers. Forecasters unanimously agreed, however, that the impact of this complementary information on their forecasts was too insignificant to justify its addition as an operational dataset. The applicability of these findings to other NWSFOs may be limited to locations with similar weather situations and access to surface data networks like the Oklahoma Mesonet.
Abstract
The 2007 and 2008 spring refractivity experiments at KTLX investigated the potential utility of high-resolution, near-surface refractivity measurements to operational forecasting. During these experiments, forecasters at the Norman, Oklahoma, National Weather Service Forecast Office (NWSFO) assessed refractivity and scan-to-scan refractivity change fields retrieved from the Weather Surveillance Radar-1988 Doppler weather radar near Oklahoma City—Twin Lakes, Oklahoma (KTLX). Both quantitative and qualitative analysis methods were used to analyze the 41 responses from seven forecasters to a questionnaire designed to measure the impact of refractivity fields on forecast operations. The analysis revealed that forecasts benefited from the refractivity fields on 25% of the days included in the evaluation. In each of these cases, the refractivity fields provided complementary information that somewhat enhanced the forecasters’ capability to analyze the near-surface environment and boosted their confidence in moisture trends. A case in point was the ability to track a retreating dryline after its location was obscured by a weak reflectivity bloom caused by biological scatterers. Forecasters unanimously agreed, however, that the impact of this complementary information on their forecasts was too insignificant to justify its addition as an operational dataset. The applicability of these findings to other NWSFOs may be limited to locations with similar weather situations and access to surface data networks like the Oklahoma Mesonet.
Abstract
A computationally simple cross-correlation model for multiple backscattering from a continuous wave (CW) noise radar is developed and verified with theoretical analysis and brute-force time-domain simulations. Based on this cross-correlation model, a modification of an existing numerical method originally developed by Holdsworth and Reid for spaced antenna (SA) pulsed radar is used to simulate the estimated cross correlation corresponding to atmospheric backscattering using a coherent CW noise radar. Subsequently, coherent radar imaging (CRI) processing comparisons between the CW noise radar and a conventional pulsed radar are presented that verify the potential of CW noise radar for atmospheric imaging.
Abstract
A computationally simple cross-correlation model for multiple backscattering from a continuous wave (CW) noise radar is developed and verified with theoretical analysis and brute-force time-domain simulations. Based on this cross-correlation model, a modification of an existing numerical method originally developed by Holdsworth and Reid for spaced antenna (SA) pulsed radar is used to simulate the estimated cross correlation corresponding to atmospheric backscattering using a coherent CW noise radar. Subsequently, coherent radar imaging (CRI) processing comparisons between the CW noise radar and a conventional pulsed radar are presented that verify the potential of CW noise radar for atmospheric imaging.
Abstract
In this work, the accuracy of the Doppler beam-swinging (DBS) technique for wind measurements is studied using an imaging radar—the turbulent eddy profiler (TEP) developed by the University of Massachusetts, with data collected in summer 2003. With up to 64 independent receivers, and using coherent radar imaging (CRI), several hundred partially independent beams can be formed simultaneously within the volume defined by the transmit beam. By selecting a subset of these beams, an unprecedented number of DBS configurations with varying zenith angle, azimuth angle, and number of beams can be investigated. The angular distributions of echo power and radial velocity obtained by CRI provide a unique opportunity to validate the inherent assumption in the DBS method of homogeneity across the region defined by the beam directions. Through comparison with a reference wind field, calculated as the optimal uniform wind field derived from all CRI beams with sufficient signal-to-noise ratio (SNR), the accuracy of the wind estimates for various DBS configurations is statistically analyzed. It is shown that for a three-beam DBS configuration, although the validity of the homogeneity assumption is enhanced at smaller zenith angles, the root-mean-square (RMS) error increases because of the ill-conditioned matrix in the DBS algorithm. As expected, inhomogeneities in the wind field produce large bias for the three-beam DBS configuration for large zenith angles. An optimal zenith angle, in terms of RMS error, of approximately 9°–10° was estimated. It is further shown that RMS error can be significantly reduced by increasing the number of off-vertical beams used for the DBS processing.
Abstract
In this work, the accuracy of the Doppler beam-swinging (DBS) technique for wind measurements is studied using an imaging radar—the turbulent eddy profiler (TEP) developed by the University of Massachusetts, with data collected in summer 2003. With up to 64 independent receivers, and using coherent radar imaging (CRI), several hundred partially independent beams can be formed simultaneously within the volume defined by the transmit beam. By selecting a subset of these beams, an unprecedented number of DBS configurations with varying zenith angle, azimuth angle, and number of beams can be investigated. The angular distributions of echo power and radial velocity obtained by CRI provide a unique opportunity to validate the inherent assumption in the DBS method of homogeneity across the region defined by the beam directions. Through comparison with a reference wind field, calculated as the optimal uniform wind field derived from all CRI beams with sufficient signal-to-noise ratio (SNR), the accuracy of the wind estimates for various DBS configurations is statistically analyzed. It is shown that for a three-beam DBS configuration, although the validity of the homogeneity assumption is enhanced at smaller zenith angles, the root-mean-square (RMS) error increases because of the ill-conditioned matrix in the DBS algorithm. As expected, inhomogeneities in the wind field produce large bias for the three-beam DBS configuration for large zenith angles. An optimal zenith angle, in terms of RMS error, of approximately 9°–10° was estimated. It is further shown that RMS error can be significantly reduced by increasing the number of off-vertical beams used for the DBS processing.
Abstract
This paper highlights recent results obtained with the Turbulent Eddy Profiler (TEP), which was developed by the University of Massachusetts. This unique 915-MHz radar has up to 64 spatially separated receiving elements, each with an independent receiver. The calibrated raw data provided by this array could be processed using sophisticated imaging algorithms to resolve the horizontal structures within each range gate. After collecting all of the closely spaced horizontal slices, the TEP radar can produce three-dimensional images of echo power, radial velocity, and spectral width. From the radial velocity measurements, it is possible to estimate the three-dimensional wind with high horizontal and vertical resolution. Given the flexibility of the TEP system, various array configurations are possible. In the present work exploitation of the flexibility of TEP is attempted to enhance the rejection of clutter from unwanted biological targets. From statistical studies, most biological clutter results from targets outside the main imaging field of view, that is, the sidelobes and grating lobes (if they exist) of the receiving beam. Because the TEP array's minimum receiver separation exceeds the spatial Nyquist sampling requirement, substantial possibilities for grating-lobe clutter exist and are observed in actual array data. When imaging over the transmit beam volume, the receiving array main lobe is scanned over a ±12.5° region. This scanning also sweeps the grating lobes over a wide angular region, virtually guaranteeing that a biological scatterer outside of the main beam will appear somewhere in the imaged volume. This paper focuses on suppressing pointlike targets in the grating-lobe regions. With a subtle change to the standard TEP array hardware configuration, it is shown via simulations and actual experimental observations (collected in June 2003) that adaptive beamforming methods can subsequently be used to significantly suppress the effects of point targets on the wind field estimates. These pointlike targets can be birds or planes with strong reflectivity. By pointlike the authors mean its appearance is a distinct point (up to the imaging resolution) in the images. The pointlike strong reflectivity signature exploits the capability of adaptive beamforming to suppress the interference using the new array configuration. It should be noted that this same array configuration does not exhibit this beneficial effect when standard Fourier beamforming is employed.
Abstract
This paper highlights recent results obtained with the Turbulent Eddy Profiler (TEP), which was developed by the University of Massachusetts. This unique 915-MHz radar has up to 64 spatially separated receiving elements, each with an independent receiver. The calibrated raw data provided by this array could be processed using sophisticated imaging algorithms to resolve the horizontal structures within each range gate. After collecting all of the closely spaced horizontal slices, the TEP radar can produce three-dimensional images of echo power, radial velocity, and spectral width. From the radial velocity measurements, it is possible to estimate the three-dimensional wind with high horizontal and vertical resolution. Given the flexibility of the TEP system, various array configurations are possible. In the present work exploitation of the flexibility of TEP is attempted to enhance the rejection of clutter from unwanted biological targets. From statistical studies, most biological clutter results from targets outside the main imaging field of view, that is, the sidelobes and grating lobes (if they exist) of the receiving beam. Because the TEP array's minimum receiver separation exceeds the spatial Nyquist sampling requirement, substantial possibilities for grating-lobe clutter exist and are observed in actual array data. When imaging over the transmit beam volume, the receiving array main lobe is scanned over a ±12.5° region. This scanning also sweeps the grating lobes over a wide angular region, virtually guaranteeing that a biological scatterer outside of the main beam will appear somewhere in the imaged volume. This paper focuses on suppressing pointlike targets in the grating-lobe regions. With a subtle change to the standard TEP array hardware configuration, it is shown via simulations and actual experimental observations (collected in June 2003) that adaptive beamforming methods can subsequently be used to significantly suppress the effects of point targets on the wind field estimates. These pointlike targets can be birds or planes with strong reflectivity. By pointlike the authors mean its appearance is a distinct point (up to the imaging resolution) in the images. The pointlike strong reflectivity signature exploits the capability of adaptive beamforming to suppress the interference using the new array configuration. It should be noted that this same array configuration does not exhibit this beneficial effect when standard Fourier beamforming is employed.
Abstract
Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.
Abstract
Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.
