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- Author or Editor: Iwan Holleman x
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Abstract
Weather radar wind profiles (WRWPs) have been retrieved from Doppler volume scans using different implementations of the velocity–azimuth display (VAD) and volume velocity processing (VVP) methods. An extensive quality control of the radial velocity data and the retrieved wind vectors has been applied. The quality and availability of the obtained wind profiles have been assessed by comparisons with collocated radiosonde observations and numerical weather prediction (NWP) data over a 9-month period. The comparisons reveal that the VVP methods perform better than the VAD methods, and that the simplest implementation of the VVP (VVP1) method performs the best of all. The availability fraction of VVP1 wind vectors is about 0.39 at ground level and drops below 0.16 at a 6-km altitude. The observation minus background statistics of the VVP1 wind profiles against the High Resolution Limited Area Model (HIRLAM) NWP model are at least as good as those of the radiosonde profiles. This result clearly demonstrates the high quality of (quality controlled) weather radar wind profiles.
Abstract
Weather radar wind profiles (WRWPs) have been retrieved from Doppler volume scans using different implementations of the velocity–azimuth display (VAD) and volume velocity processing (VVP) methods. An extensive quality control of the radial velocity data and the retrieved wind vectors has been applied. The quality and availability of the obtained wind profiles have been assessed by comparisons with collocated radiosonde observations and numerical weather prediction (NWP) data over a 9-month period. The comparisons reveal that the VVP methods perform better than the VAD methods, and that the simplest implementation of the VVP (VVP1) method performs the best of all. The availability fraction of VVP1 wind vectors is about 0.39 at ground level and drops below 0.16 at a 6-km altitude. The observation minus background statistics of the VVP1 wind profiles against the High Resolution Limited Area Model (HIRLAM) NWP model are at least as good as those of the radiosonde profiles. This result clearly demonstrates the high quality of (quality controlled) weather radar wind profiles.
Abstract
A method to determine the elevation and azimuth biases of the radar antenna using solar signals observed by a scanning radar is presented. Data recorded at low elevation angles where the atmospheric refraction has a significant effect on the propagation of the radio wave are used, and a method to take the effect of the refraction into account in the analysis is presented. A set of equations is given by which the refraction of the radio waves as a function of the relative humidity can easily be calculated. Also, a simplified model for the calculation of the atmospheric attenuation is presented. The consistency of the adopted models for the atmospheric refraction and atmospheric attenuation is confirmed by data collected at a single elevation pointing, but over a long observing time. Finally, the method is applied to datasets based on operational measurements at the Finnish Meteorological Institute (FMI) and Royal Netherlands Meteorological Institute (KNMI), and elevation and azimuth biases of the radars are shown.
Abstract
A method to determine the elevation and azimuth biases of the radar antenna using solar signals observed by a scanning radar is presented. Data recorded at low elevation angles where the atmospheric refraction has a significant effect on the propagation of the radio wave are used, and a method to take the effect of the refraction into account in the analysis is presented. A set of equations is given by which the refraction of the radio waves as a function of the relative humidity can easily be calculated. Also, a simplified model for the calculation of the atmospheric attenuation is presented. The consistency of the adopted models for the atmospheric refraction and atmospheric attenuation is confirmed by data collected at a single elevation pointing, but over a long observing time. Finally, the method is applied to datasets based on operational measurements at the Finnish Meteorological Institute (FMI) and Royal Netherlands Meteorological Institute (KNMI), and elevation and azimuth biases of the radars are shown.
Abstract
The dual pulse repetition frequency (dual PRF) technique for extension of the unambiguous velocity interval is available on many operational Doppler weather radars. Radial velocity data obtained from a C-band Doppler radar running in dual PRF mode have been analyzed quantitatively. The standard deviation of the velocity estimates and the fraction of dealiasing errors are extracted and related using a simple model. A postprocessing algorithm for dual PRF velocity data, which removes noise and corrects dealiasing errors, has been developed and tested. It is concluded that the algorithm is very efficient and produces high quality velocity data.
Abstract
The dual pulse repetition frequency (dual PRF) technique for extension of the unambiguous velocity interval is available on many operational Doppler weather radars. Radial velocity data obtained from a C-band Doppler radar running in dual PRF mode have been analyzed quantitatively. The standard deviation of the velocity estimates and the fraction of dealiasing errors are extracted and related using a simple model. A postprocessing algorithm for dual PRF velocity data, which removes noise and corrects dealiasing errors, has been developed and tested. It is concluded that the algorithm is very efficient and produces high quality velocity data.
Abstract
Weather radars give quantitative precipitation estimates over large areas with high spatial and temporal resolutions not achieved by conventional rain gauge networks. Therefore, the derivation and analysis of a radar-based precipitation “climatology” are highly relevant. For that purpose, radar reflectivity data were obtained from two C-band Doppler weather radars covering the land surface of the Netherlands (≈3.55 × 104 km2). From these reflectivities, 10 yr of radar rainfall depths were constructed for durations D of 1, 2, 4, 8, 12, and 24 h with a spatial resolution of 2.4 km and a data availability of approximately 80%. Different methods are compared for adjusting the bias in the radar precipitation depths. Using a dense manual gauge network, a vertical profile of reflectivity (VPR) and a spatial adjustment are applied separately to 24-h (0800–0800 UTC) unadjusted radar-based precipitation depths. Further, an automatic rain gauge network is employed to perform a mean-field bias adjustment to unadjusted 1-h rainfall depths. A new adjustment method is developed (referred to as MFBS) that combines the hourly mean-field bias adjustment and the daily spatial adjustment methods. The record of VPR gradients, obtained from the VPR adjustment, reveals a seasonal cycle that can be related to the type of precipitation. A verification with automatic (D ≤ 24 h) and manual (D = 24 h) rain gauge networks demonstrates that the adjustments remove the systematic underestimation of precipitation by radar. The MFBS adjustment gives the best verification results and reduces the residual (radar minus rain gauge depth) standard deviation considerably. The adjusted radar dataset is used to obtain exceedance probabilities, maximum rainfall depths, mean annual rainfall frequencies, and spatial correlations. Such a radar rainfall climatology is potentially valuable for the improvement of rainfall parameterization in weather and climate models and the design of hydraulic structures.
Abstract
Weather radars give quantitative precipitation estimates over large areas with high spatial and temporal resolutions not achieved by conventional rain gauge networks. Therefore, the derivation and analysis of a radar-based precipitation “climatology” are highly relevant. For that purpose, radar reflectivity data were obtained from two C-band Doppler weather radars covering the land surface of the Netherlands (≈3.55 × 104 km2). From these reflectivities, 10 yr of radar rainfall depths were constructed for durations D of 1, 2, 4, 8, 12, and 24 h with a spatial resolution of 2.4 km and a data availability of approximately 80%. Different methods are compared for adjusting the bias in the radar precipitation depths. Using a dense manual gauge network, a vertical profile of reflectivity (VPR) and a spatial adjustment are applied separately to 24-h (0800–0800 UTC) unadjusted radar-based precipitation depths. Further, an automatic rain gauge network is employed to perform a mean-field bias adjustment to unadjusted 1-h rainfall depths. A new adjustment method is developed (referred to as MFBS) that combines the hourly mean-field bias adjustment and the daily spatial adjustment methods. The record of VPR gradients, obtained from the VPR adjustment, reveals a seasonal cycle that can be related to the type of precipitation. A verification with automatic (D ≤ 24 h) and manual (D = 24 h) rain gauge networks demonstrates that the adjustments remove the systematic underestimation of precipitation by radar. The MFBS adjustment gives the best verification results and reduces the residual (radar minus rain gauge depth) standard deviation considerably. The adjusted radar dataset is used to obtain exceedance probabilities, maximum rainfall depths, mean annual rainfall frequencies, and spatial correlations. Such a radar rainfall climatology is potentially valuable for the improvement of rainfall parameterization in weather and climate models and the design of hydraulic structures.
Solar Monitoring of the NEXRAD WSR-88D Network Using Operational Scan DataAbstract
Solar monitoring is a method in which solar interferences, recorded during operational scanning of a radar, are used to monitor antenna pointing, identify signal processor issues, track receiver chain stability, and check the balance between horizontal and vertical polarization receive channels. The method is used by EUMETNET to monitor more than 100 radars in 20 European countries and it has been adopted by many national weather services across the world. NEXRAD is a network of 160 similar S-band weather radars (WSR-88Ds), which makes it most suitable for assessing the capability of the solar monitoring method on a continental scale. The NEXRAD level-II data contain radial-by-radial noise power estimates. An increase in this estimate is observed when the antenna points close to the sun. Our decoding software extracts these noise power estimates for the horizontal and vertical receive channels (converted to solar flux units) and other relevant metadata, including azimuth, elevation, observation time, and radar location. Here we present results of analyzing one year of solar monitoring data generated by 142 radars from the contiguous United States. We show monitoring results, geographical maps, and statistical outcomes on antenna pointing, solar fluxes, and differential reflectivity biases. We also assess the quality of the radars by defining a figure of merit, which is calculated from the solar monitoring results. The results demonstrate that the solar method provides great benefit for routine monitoring and harmonization of national and transnational operational radar networks.
Abstract
Solar monitoring is a method in which solar interferences, recorded during operational scanning of a radar, are used to monitor antenna pointing, identify signal processor issues, track receiver chain stability, and check the balance between horizontal and vertical polarization receive channels. The method is used by EUMETNET to monitor more than 100 radars in 20 European countries and it has been adopted by many national weather services across the world. NEXRAD is a network of 160 similar S-band weather radars (WSR-88Ds), which makes it most suitable for assessing the capability of the solar monitoring method on a continental scale. The NEXRAD level-II data contain radial-by-radial noise power estimates. An increase in this estimate is observed when the antenna points close to the sun. Our decoding software extracts these noise power estimates for the horizontal and vertical receive channels (converted to solar flux units) and other relevant metadata, including azimuth, elevation, observation time, and radar location. Here we present results of analyzing one year of solar monitoring data generated by 142 radars from the contiguous United States. We show monitoring results, geographical maps, and statistical outcomes on antenna pointing, solar fluxes, and differential reflectivity biases. We also assess the quality of the radars by defining a figure of merit, which is calculated from the solar monitoring results. The results demonstrate that the solar method provides great benefit for routine monitoring and harmonization of national and transnational operational radar networks.
Solar Monitoring of the NEXRAD WSR-88D Network Using Operational Scan DataAbstract
Solar monitoring is a method in which solar interferences, recorded during operational scanning of a radar, are used to monitor antenna pointing, identify signal processor issues, track receiver chain stability, and check the balance between horizontal and vertical polarization receive channels. The method is used by EUMETNET to monitor more than 100 radars in 20 European countries and it has been adopted by many national weather services across the world. NEXRAD is a network of 160 similar S-band weather radars (WSR-88Ds), which makes it most suitable for assessing the capability of the solar monitoring method on a continental scale. The NEXRAD level-II data contain radial-by-radial noise power estimates. An increase in this estimate is observed when the antenna points close to the sun. Our decoding software extracts these noise power estimates for the horizontal and vertical receive channels (converted to solar flux units) and other relevant metadata, including azimuth, elevation, observation time, and radar location. Here we present results of analyzing one year of solar monitoring data generated by 142 radars from the contiguous United States. We show monitoring results, geographical maps, and statistical outcomes on antenna pointing, solar fluxes, and differential reflectivity biases. We also assess the quality of the radars by defining a figure of merit, which is calculated from the solar monitoring results. The results demonstrate that the solar method provides great benefit for routine monitoring and harmonization of national and transnational operational radar networks.
Abstract
Solar monitoring is a method in which solar interferences, recorded during operational scanning of a radar, are used to monitor antenna pointing, identify signal processor issues, track receiver chain stability, and check the balance between horizontal and vertical polarization receive channels. The method is used by EUMETNET to monitor more than 100 radars in 20 European countries and it has been adopted by many national weather services across the world. NEXRAD is a network of 160 similar S-band weather radars (WSR-88Ds), which makes it most suitable for assessing the capability of the solar monitoring method on a continental scale. The NEXRAD level-II data contain radial-by-radial noise power estimates. An increase in this estimate is observed when the antenna points close to the sun. Our decoding software extracts these noise power estimates for the horizontal and vertical receive channels (converted to solar flux units) and other relevant metadata, including azimuth, elevation, observation time, and radar location. Here we present results of analyzing one year of solar monitoring data generated by 142 radars from the contiguous United States. We show monitoring results, geographical maps, and statistical outcomes on antenna pointing, solar fluxes, and differential reflectivity biases. We also assess the quality of the radars by defining a figure of merit, which is calculated from the solar monitoring results. The results demonstrate that the solar method provides great benefit for routine monitoring and harmonization of national and transnational operational radar networks.
The operational weather radar network in Europe covers more than 30 countries and contains more than 200 weather radars. The radar network is heterogeneous in hardware, signal processing, transmit/receive frequency, and scanning strategy, thus making it fundamentally different than the Next Generation Weather Radar (NEXRAD) network. Another difference is that the density of the European weather radar network is roughly twice that of the NEXRAD network. Within the European National Meteorological Services (EUMETNET), a grouping of services, the Operational Program for Exchange of Weather Radar Information (OPERA) has been working since 1999 on improving the harmonization of radars and their measurements. In addition, OPERA has facilitated and stimulated the exchange of radar data between its members, among others, by the development of a radar data information model and jointly agreed data formats. Since 2011, a radar data center (“Odyssey”) has been in operation, producing network-wide radar mosaics from volumetric data. An essential part of the OPERA work is the documentation of the members' best practices in radar operation and data production and the making of joint recommendations: for example, on the interferences caused by other microwave sources and the disturbances caused by wind turbines. Hence, the expertise of the most experienced members is made available to all members supporting the development of the network as a whole. Recent work has produced reports on best practices for production of radar data, on quality indicators, and on experiences with the use of polarimetric radars. All of these reports and recommendations are publicly available on the OPERA website, for use by the wider meteorological community.
The operational weather radar network in Europe covers more than 30 countries and contains more than 200 weather radars. The radar network is heterogeneous in hardware, signal processing, transmit/receive frequency, and scanning strategy, thus making it fundamentally different than the Next Generation Weather Radar (NEXRAD) network. Another difference is that the density of the European weather radar network is roughly twice that of the NEXRAD network. Within the European National Meteorological Services (EUMETNET), a grouping of services, the Operational Program for Exchange of Weather Radar Information (OPERA) has been working since 1999 on improving the harmonization of radars and their measurements. In addition, OPERA has facilitated and stimulated the exchange of radar data between its members, among others, by the development of a radar data information model and jointly agreed data formats. Since 2011, a radar data center (“Odyssey”) has been in operation, producing network-wide radar mosaics from volumetric data. An essential part of the OPERA work is the documentation of the members' best practices in radar operation and data production and the making of joint recommendations: for example, on the interferences caused by other microwave sources and the disturbances caused by wind turbines. Hence, the expertise of the most experienced members is made available to all members supporting the development of the network as a whole. Recent work has produced reports on best practices for production of radar data, on quality indicators, and on experiences with the use of polarimetric radars. All of these reports and recommendations are publicly available on the OPERA website, for use by the wider meteorological community.
Abstract
Wind profiles from an operational C-band Doppler radar have been combined with data from a bird tracking radar to assess the wind profile quality during bird migration. The weather radar wind profiles (WRWPs) are retrieved using the well-known volume velocity processing (VVP) technique. The X-band bird radar performed range–height scans perpendicular to the main migration direction and bird densities were deduced by counting and normalizing the observed echoes. It is found that the radial velocity standard deviation (σr ) obtained from the VVP retrieval is a skillful indicator of bird migration. Using a threshold of 2 m s−1 on σr , more than 93% of the bird-contaminated wind vectors are rejected while over 70% of the true wind vectors are accepted correctly. For high bird migration densities the raw weather radar wind vectors have a positive speed bias of 8.6 ± 3.8 m s−1, while the quality-controlled wind vectors have a negligible speed bias. From the performance statistics against a limited area numerical weather prediction model, it is concluded that all (significant) bird contamination is removed and that high-quality weather radar wind profiles can be obtained, even during the bird migration season.
Abstract
Wind profiles from an operational C-band Doppler radar have been combined with data from a bird tracking radar to assess the wind profile quality during bird migration. The weather radar wind profiles (WRWPs) are retrieved using the well-known volume velocity processing (VVP) technique. The X-band bird radar performed range–height scans perpendicular to the main migration direction and bird densities were deduced by counting and normalizing the observed echoes. It is found that the radial velocity standard deviation (σr ) obtained from the VVP retrieval is a skillful indicator of bird migration. Using a threshold of 2 m s−1 on σr , more than 93% of the bird-contaminated wind vectors are rejected while over 70% of the true wind vectors are accepted correctly. For high bird migration densities the raw weather radar wind vectors have a positive speed bias of 8.6 ± 3.8 m s−1, while the quality-controlled wind vectors have a negligible speed bias. From the performance statistics against a limited area numerical weather prediction model, it is concluded that all (significant) bird contamination is removed and that high-quality weather radar wind profiles can be obtained, even during the bird migration season.
Abstract
A method for operational monitoring of a weather radar receiving chain, including the antenna gain and the receiver, is presented. The “online” method is entirely based on the analysis of sun signals in the polar volume data produced during operational scanning of weather radars. The method is an extension of that for determining the weather radar antenna pointing at low elevations using sun signals, and it is suited for routine application.
The solar flux from the online method agrees very well with that obtained from “offline” sun tracking experiments at two weather radar sites. Furthermore, the retrieved sun flux is compared with data from the Dominion Radio Astrophysical Observatory (DRAO) in Canada. Small biases in the sun flux data from the Dutch and Finnish radars (between −0.93 and +0.47 dB) are found. The low standard deviations of these sun flux data against those from DRAO (0.14–0.20 dB) demonstrate the stability of the weather radar receiving chains and of the sun-based online monitoring.
Results from a daily analysis of the sun signals in online radar data can be used for monitoring the alignment of the radar antenna and the stability of the radar receiver system. By comparison with the observations from a sun flux monitoring station, even the calibration of the receiving chain can be checked. The method presented in this paper has great potential for routine monitoring of weather radars in national and international networks.
Abstract
A method for operational monitoring of a weather radar receiving chain, including the antenna gain and the receiver, is presented. The “online” method is entirely based on the analysis of sun signals in the polar volume data produced during operational scanning of weather radars. The method is an extension of that for determining the weather radar antenna pointing at low elevations using sun signals, and it is suited for routine application.
The solar flux from the online method agrees very well with that obtained from “offline” sun tracking experiments at two weather radar sites. Furthermore, the retrieved sun flux is compared with data from the Dominion Radio Astrophysical Observatory (DRAO) in Canada. Small biases in the sun flux data from the Dutch and Finnish radars (between −0.93 and +0.47 dB) are found. The low standard deviations of these sun flux data against those from DRAO (0.14–0.20 dB) demonstrate the stability of the weather radar receiving chains and of the sun-based online monitoring.
Results from a daily analysis of the sun signals in online radar data can be used for monitoring the alignment of the radar antenna and the stability of the radar receiver system. By comparison with the observations from a sun flux monitoring station, even the calibration of the receiving chain can be checked. The method presented in this paper has great potential for routine monitoring of weather radars in national and international networks.
Abstract
A method for the daily monitoring of the differential reflectivity bias for polarimetric weather radars is presented. Sun signals detected in polar volume data produced during operational scanning of the radar are used. This method is an extension of that for monitoring the weather radar antenna pointing at low elevations and the radar receiving chain using the sun. This “online” method is ideally suited for routine application in networks of operational radars.
The online sun monitoring can be used to check the agreement between horizontal and vertical polarization lobes of the radar antenna, which is a prerequisite for high-quality polarimetric measurements. By performing both online sun monitoring and rain calibration at vertical incidence, the differential receiver bias and differential transmitter bias can be disentangled. Results from the polarimetric radars in Trappes (France) and Bornholm (Denmark), demonstrating the importance of regular monitoring of the differential reflectivity bias, are discussed.
It is recommended that the online sun-monitoring method, preferably in combination with rain calibration, is routinely performed on all polarimetric weather radars because accurate calibration is a prerequisite for most polarimetric algorithms.
Abstract
A method for the daily monitoring of the differential reflectivity bias for polarimetric weather radars is presented. Sun signals detected in polar volume data produced during operational scanning of the radar are used. This method is an extension of that for monitoring the weather radar antenna pointing at low elevations and the radar receiving chain using the sun. This “online” method is ideally suited for routine application in networks of operational radars.
The online sun monitoring can be used to check the agreement between horizontal and vertical polarization lobes of the radar antenna, which is a prerequisite for high-quality polarimetric measurements. By performing both online sun monitoring and rain calibration at vertical incidence, the differential receiver bias and differential transmitter bias can be disentangled. Results from the polarimetric radars in Trappes (France) and Bornholm (Denmark), demonstrating the importance of regular monitoring of the differential reflectivity bias, are discussed.
It is recommended that the online sun-monitoring method, preferably in combination with rain calibration, is routinely performed on all polarimetric weather radars because accurate calibration is a prerequisite for most polarimetric algorithms.
