scholarly journals Calibration of radar differential reflectivity using quasi-vertical profiles

2021 ◽  
Author(s):  
Daniel Sanchez-Rivas ◽  
Miguel A. Rico-Ramirez
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Vertical Profiles ◽  
Weather Radars ◽  
Precipitation Estimates ◽  
Novel Method ◽  
One Year ◽  
The Uk ◽  
Quantitative Precipitation Estimates ◽  
Differential Reflectivity

Abstract. The differential reflectivity (ZDR) is a crucial weather radar measurement that helps to improve quantitative precipitation estimates using polarimetric weather radars. However, a system bias between the horizontal and vertical channels generated by the radar produces an offset in ZDR. Existing methods to calibrate ZDR measurements rely on vertical observations of ZDR taken in rain, in which ZDR values close to 0 dB are expected. However, not all weather radar systems are capable of producing vertical pointing measurements. In this work, we present and analyse a novel method for correcting and monitoring the ZDR offset using quasi-vertical profiles of polarimetric variables. The method is applied to radar data collected through one year of precipitation events by two operational C-band weather radars in the UK. The proposed method proves effective in achieving the required accuracy of 0.1 dB for the calibration of ZDR as the calibration results are consistent with the traditional method based on vertical profiles. Additionally, the method is independently evaluated using disdrometers located near the radar sites. The results showed a good agreement between disdrometer-derived and radar-calibrated ZDR measurements.

scholarly journals Quasi-Vertical Profiles—A New Way to Look at Polarimetric Radar Data

2016 ◽  
Vol 33 (3) ◽  
pp. 551-562 ◽  
Author(s):  
Alexander Ryzhkov ◽  
Pengfei Zhang ◽  
Heather Reeves ◽  
Matthew Kumjian ◽  
Timo Tschallener ◽  
...  
Keyword(s):  
Continuous Monitoring ◽  
Radar Data ◽  
Vertical Profiles ◽  
Remote Sensors ◽  
Weather Radars ◽  
X Band ◽  
Potential Benefits ◽  
High Vertical Resolution ◽  
Microphysical Processes ◽  
Differential Reflectivity

AbstractA novel methodology is introduced for processing and presenting polarimetric data collected by weather surveillance radars. It involves azimuthal averaging of radar reflectivity Z, differential reflectivity ZDR, cross-correlation coefficient ρhv, and differential phase ΦDP at high antenna elevation, and presenting resulting quasi-vertical profiles (QVPs) in a height-versus-time format. Multiple examples of QVPs retrieved from the data collected by S-, C-, and X-band dual-polarization radars at elevations ranging from 6.4° to 28° illustrate advantages of the QVP technique. The benefits include an ability to examine the temporal evolution of microphysical processes governing precipitation production and to compare polarimetric data obtained from the scanning surveillance weather radars with observations made by vertically looking remote sensors, such as wind profilers, lidars, radiometers, cloud radars, and radars operating on spaceborne and airborne platforms. Continuous monitoring of the melting layer and the layer of dendritic growth with high vertical resolution, and the possible opportunity to discriminate between the processes of snow aggregation and riming, constitute other potential benefits of the suggested methodology.

scholarly journals The NCAS mobile dual-polarisation Doppler X-band weather radar (NXPol)

2018 ◽  
Vol 11 (12) ◽  
pp. 6481-6494 ◽  
Author(s):  
Ryan R. Neely III ◽  
Lindsay Bennett ◽  
Alan Blyth ◽  
Chris Collier ◽  
David Dufton ◽  
...  
Keyword(s):  
Weather Radar ◽  
Beam Width ◽  
Cloud Dynamics ◽  
X Band ◽  
Precipitation Estimates ◽  
Technical Specifications ◽  
Research Questions ◽  
Half Power ◽  
The Uk ◽  
Quantitative Precipitation Estimates

Abstract. In recent years, dual-polarisation Doppler X-band radars have become a widely used part of the atmospheric scientist's toolkit for examining cloud dynamics and microphysics and making quantitative precipitation estimates. This is especially true for research questions that require mobile radars. Here we describe the National Centre for Atmospheric Science (NCAS) mobile X-band dual-polarisation Doppler weather radar (NXPol) and the infrastructure used to deploy the radar and provide an overview of the technical specifications. It is the first radar of its kind in the UK. The NXPol is a Meteor 50DX manufactured by Selex-Gematronik (Selex ES GmbH), modified to operate with a larger 2.4 m diameter antenna that produces a 0.98∘ half-power beam width and without a radome. We provide an overview of the technical specifications of the NXPol with emphasis given to the description of the aspects of the infrastructure developed to deploy the radar as an autonomous observing facility in remote locations. To demonstrate the radar's capabilities, we also present examples of its use in three recent field campaigns and its ongoing observations at the NERC Facility for Atmospheric Radio Research (NFARR).

scholarly journals Utilization of Weather Radar Data for the Flash Flood Indicator Application in the Czech Republic

2021 ◽  
Vol 13 (16) ◽  
pp. 3184
Author(s):  
Petr Novák ◽  
Hana Kyznarová ◽  
Martin Pecha ◽  
Petr Šercl ◽  
Vojtěch Svoboda ◽  
...  
Keyword(s):  
Czech Republic ◽  
Flash Flood ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Adjustment Coefficient ◽  
The Czech Republic ◽  
Radar Network ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

In the past few years, demands on flash flood forecasting have grown. The Flash Flood Indicator (FFI) is a system used at the Czech Hydrometeorological Institute for the evaluation of the risk of possible occurrence of flash floods over the whole Czech Republic. The FFI calculation is based on the current soil saturation, the physical-geographical characteristics of every considered area, and radar-based quantitative precipitation estimates (QPEs) and forecasts (QPFs). For higher reliability of the flash flood risk assessment, calculations of QPEs and QPFs are crucial, particularly when very high intensities of rainfall are reached or expected. QPEs and QPFs entering the FFI computations are the products of the Czech Weather Radar Network. The QPF is based on the COTREC extrapolation method. The radar-rain gauge-combining method MERGE2 is used to improve radar-only QPEs and QPFs. It generates a combined radar-rain gauge QPE based on the kriging with an external drift algorithm, and, also, an adjustment coefficient applicable to radar-only QPEs and QPFs. The adjustment coefficient is applied in situations when corresponding rain gauge measurements are not yet available. A new adjustment coefficient scheme was developed and tested to improve the performance of adjusted radar QPEs and QPFs in the FFI.

scholarly journals Fireworks on Weather Radar and Camera

2020 ◽  
Vol 101 (2) ◽  
pp. E90-E108
Author(s):  
D. S. Zrnić ◽  
P. Zhang ◽  
V. Melnikov ◽  
E. Kabela
Keyword(s):  
High Sensitivity ◽  
Weather Radar ◽  
Video Camera ◽  
Radar Data ◽  
Maximum Diameter ◽  
Visible Image ◽  
Weather Radars ◽  
Technical Developments ◽  
Maximum Reflectivity ◽  
Operation Center

Abstract High-sensitivity weather radars easily detect nonmeteorological phenomena characterized by weak radar returns. Fireworks are the example presented here. To understand radar observations, an experiment was conducted in which the National Severe Storms Laboratory (NSSL)’s research (3-cm wavelength) dual-polarization radar and a video camera were located at 1 km from fireworks in Norman, Oklahoma. The fireworks from the 4 July 2017 celebration were recorded by both instruments. The experiment is described. Few bursts recorded by the camera are analyzed to obtain the height of the explosion, its maximum diameter, number of stars, and the duration of the visible image. Radar volume scans are examined to characterize the height of the observation, the maximum reflectivity, and its distribution with height. The fireworks location is close to the Terminal Doppler Weather Radar (TDWR) that operates in single polarization at a 5-cm wavelength and monitors hazardous weather over the Oklahoma City airport. A third radar with data from the event is the Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman. It has a wavelength of 10 cm and supports technical developments at the Radar Operation Center. Reflectivity factors measured by the three radars are compared to infer the size of dominant scatterers. The polarimetric characteristics of fireworks returns are analyzed. Although these differ from those of precipitation, they are indistinguishable from insect returns. Radar observation of larger fireworks in Fort Worth, Texas, with a WSR-88D is included and compared with the observations of the smaller fireworks in Norman. We expect the detectability of explosions would be similar as of fireworks. Pinpointing locations would be useful to first responders, or air quality forecasters. A benefit of fireworks recognition in weather radar data is that it can prevent contamination of precipitation accumulations.

scholarly journals Ground Clutter Mitigation for Weather Radars Using the Autocorrelation Spectral Density

2014 ◽  
Vol 31 (10) ◽  
pp. 2049-2066 ◽  
Author(s):  
Sebastián M. Torres ◽  
David A. Warde
Keyword(s):  
Spectral Density ◽  
Meteorological Data ◽  
Meteorological Variable ◽  
Clutter Suppression ◽  
Environment Analysis ◽  
Weather Radars ◽  
Ground Clutter ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

Abstract Radar returns from the ground, known as ground clutter, can contaminate weather signals, often resulting in severely biased meteorological estimates. If not removed, these contaminants may artificially inflate quantitative precipitation estimates and obscure polarimetric and Doppler signatures of weather. A ground-clutter filter is typically employed to mitigate this contamination and provide less biased meteorological-variable estimates. This paper introduces a novel adaptive filter based on the autocorrelation spectral density, which is capable of mitigating the adverse effects of ground clutter without unnecessarily degrading the quality of the meteorological data. The so-called Clutter Environment Analysis using Adaptive Processing (CLEAN-AP) filter adjusts its suppression characteristics in real time to match dynamic atmospheric environments and meets Next Generation Weather Radar (NEXRAD) clutter-suppression requirements.

scholarly journals Characteristics of the Bright Band Based on Quasi-Vertical Profiles of Polarimetric Observations from an S-Band Weather Radar Network

2020 ◽  
Vol 12 (24) ◽  
pp. 4061
Author(s):  
Jeong-Eun Lee ◽  
Sung-Hwa Jung ◽  
Soohyun Kwon
Keyword(s):  
Signal To Noise Ratio ◽  
Vertical Profiles ◽  
Bright Band ◽  
Weather Radars ◽  
Radar Network ◽  
Temperature Zero ◽  
The Mean ◽  
Microphysical Processes ◽  
Differential Reflectivity ◽  
Self Consistency

Bright band (BB) characteristics obtained via dual-polarization weather radars elucidate thermodynamic and microphysical processes within precipitation systems. This study identified BB using morphological features from quasi-vertical profiles (QVPs) of polarimetric observations, and their geometric, thermodynamic, and polarimetric characteristics were statistically examined using nine operational S-band weather radars in South Korea. For comparable analysis among weather radars in the network, the calibration biases in reflectivity (ZH) and differential reflectivity (ZDR) were corrected based on self-consistency. The cross-correlation coefficient (ρHV) bias in the weak echo regions was corrected using the signal-to-noise ratio (SNR). First, we analyzed the heights of BBPEAK derived from the ZH as a function of season and compared the heights of BBPEAK derived from the ZH, ZDR, and ρHV. The heights of BBPEAK were highest in the summer season when the surface temperature was high. However, they showed distinct differences depending on the location (e.g., latitude) within the radar network, even in the same season. The height where the size of melting particles was at a maximum (BBPEAK from the ZH) was above that where the oblateness of these particles maximized (BBPEAK from ZDR). The height at which the inhomogeneity of hydometeors was at maximum (BBPEAK from the ρHV) was also below that of BBPEAK from the ZH. Second, BB thickness and relative position of BBPEAK were investigated to characterize the geometric structure of the BBs. The BB thickness increased as the ZH at BBBOTTOM increased, which indicated that large snowflakes melt more slowly than small snowflakes. The geometrical structure of the BBs was asymmetric, since the melting particles spent more time forming the thin shell of meltwater around them, and they rapidly collapsed to form a raindrop at the final stage of melting. Third, the heights of BBTOP, BBPEAK, and BBBOTTOM were compared with the zero-isotherm heights. The dry-temperature zero-isotherm heights were between BBTOP and BBBOTTOM, while the wet-bulb temperature zero-isotherm heights were close to the height of BBPEAK. Finally, we examined the polarimetric observations to understand the involved microphysical processes. The correlation among ZH at BBTOP, BBPEAK, and BBBOTTOM was high (>0.94), and the ZDR at BBBOTTOM was high when the BB’s intensity was strong. This proved that the size and concentration of snowflakes above the BB influence the size and concentration of raindrops below the BB. There was no depression in the ρHV for a weak BB. Finally, the mean profile of the ZH and ZDR depended on the ZH at BBBOTTOM. In conclusion, the growth process of snowflakes above the BB controls polarimetric observations of BB.

scholarly journals Statistical and neural classifiers in estimating rain rate from weather radar measurements

2007 ◽  
Vol 10 ◽  
pp. 111-115
Author(s):  
C. I. Christodoulou ◽  
S. C. Michaelides
Keyword(s):  
Electromagnetic Radiation ◽  
Rain Rate ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Meteorological Conditions ◽  
Swiss Alps ◽  
Rain Gauges ◽  
Weather Radars ◽  
Radar Measurements

Abstract. Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to estimate rain rate by using weather radar as an alternative to rain-gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from weather radar and surrounding rain-gauges were collected at the same time. The statistical KNN and the neural SOM classifiers were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The proposed system managed to identify matching pattern waveforms and the rainfall rate on the ground was estimated based on the radar reflectivities with a satisfactory error rate, outperforming the traditional Z/R relationship. It is anticipated that more data, representing a variety of possible meteorological conditions, will lead to improved results. The results in this work show that an estimation of rain rate based on weather radar measurements treated with statistical and neural classifiers is possible.

scholarly journals Radar Applications in Northern Scotland (RAiNS)

2020 ◽  
Author(s):  
Ryan R Neely ◽  
Louise Parry ◽  
David Dufton ◽  
Lindsay Bennett ◽  
Chris Collier
Keyword(s):  
High Resolution ◽  
Weather Radar ◽  
Polarimetric Radar ◽  
Summer Period ◽  
Radar Observations ◽  
X Band ◽  
Precipitation Estimates ◽  
Radar Applications ◽  
Quantitative Precipitation Estimates ◽  
Hydrological Applications

AbstractThe Radar Applications in Northern Scotland (RAiNS) experiment took place from February to August 2016 near Inverness, Scotland. The campaign was motivated by the need to provide enhanced weather radar observations for hydrological applications for the Inverness region. Here we describe the campaign in detail and observations over the summer period of the campaign that show the improvements that high-resolution polarimetric radar observations may have on quantitative precipitation estimates in this region compared to concurrently generated operational radar quantitative precipitation estimates (QPE). We further provide suggestions of methods for generating QPE using dual-polarisation X-band radars in similar regions.

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