scholarly journals Adaptive and High-Resolution Estimation of Specific Differential Phase for Polarimetric X-Band Weather Radars

2018 ◽  
Vol 35 (3) ◽  
pp. 555-573 ◽  
Author(s):  
Ricardo Reinoso-Rondinel ◽  
Christine Unal ◽  
Herman Russchenberg
Keyword(s):  
Standard Deviation ◽  
Spatial Variability ◽  
Heavy Rain ◽  
Storm Events ◽  
Band Frequency ◽  
Adaptive Approach ◽  
Range Resolution ◽  
Differential Phase ◽  
Weather Radars ◽  
X Band

ABSTRACTOne of the most beneficial polarimetric variables may be the specific differential phase KDP because of its independence from power attenuation and radar miscalibration. However, conventional KDP estimation requires a substantial amount of range smoothing as a result of the noisy characteristic of the measured differential phase ΨDP. In addition, the backscatter differential phase δhv component of ΨDP, significant at C- and X-band frequency, may lead to inaccurate KDP estimates. In this work, an adaptive approach is proposed to obtain accurate KDP estimates in rain from noisy ΨDP, whose δhv is of significance, at range resolution scales. This approach uses existing relations between polarimetric variables in rain to filter δhv from ΨDP while maintaining its spatial variability. In addition, the standard deviation of the proposed KDP estimator is mathematically formulated for quality control. The adaptive approach is assessed using four storm events, associated with light and heavy rain, observed by a polarimetric X-band weather radar in the Netherlands. It is shown that this approach is able to retain the spatial variability of the storms at scales of the range resolution. Moreover, the performance of the proposed approach is compared with two different methods. The results of this comparison show that the proposed approach outperforms the other two methods in terms of the correlation between KDP and reflectivity, and KDP standard deviation reduction.

scholarly journals Improved Estimation of the Specific Attenuation and Backscatter Differential Phase over Short Rain Paths

2018 ◽  
Vol 35 (12) ◽  
pp. 2359-2381
Author(s):  
Ricardo Reinoso-Rondinel ◽  
Christine Unal ◽  
Herman Russchenberg
Keyword(s):  
Accurate Estimation ◽  
Storm Events ◽  
Extended Version ◽  
Range Resolution ◽  
Differential Phase ◽  
Radar Polarimetry ◽  
Specific Attenuation ◽  
X Band ◽  
Raindrop Size ◽  
Improved Technique

AbstractIn radar polarimetry, the differential phase consists of the propagation differential phase and the backscatter differential phase . While is commonly used for attenuation correction (i.e., estimation of the specific attenuation A and specific differential phase ), recent studies have demonstrated that can provide information concerning the dominant size of raindrops. However, the estimation of and is not straightforward given their coupled nature and the noisy behavior of , especially over short paths. In this work, the impacts of estimating on the estimation of A over short paths, using the extended version of the ZPHI method, are examined. Special attention is given to the optimization of the parameter α that connects and A. In addition, an improved technique is proposed to compute from and in rain. For these purposes, diverse storm events observed by a polarimetric X-band radar in the Netherlands are used. Statistical analysis based on the minimum errors associated with the optimization of α and the consistency between and A showed that more accurate and stable α and A are obtained if is estimated at range resolution, which is not possible by conventional range filtering techniques. Accurate estimates were able to depict the spatial variability of dominant raindrop size in the observed storms. By following the presented study, the ZPHI method and its variations can be employed without the need for considering long paths, leading to localized and accurate estimation of A and .

scholarly journals Observations of Severe Local Storms and Tornadoes with the Atmospheric Imaging Radar

2017 ◽  
Vol 98 (5) ◽  
pp. 915-935 ◽  
Author(s):  
James M. Kurdzo ◽  
Feng Nai ◽  
David J. Bodine ◽  
Timothy A. Bonin ◽  
Robert D. Palmer ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Pulse Compression ◽  
Radar Imaging ◽  
Narrow Beam ◽  
Digital Beamforming ◽  
Range Resolution ◽  
Imaging Radar ◽  
Weather Radars ◽  
X Band ◽  
Receive Array

Abstract Mobile radar platforms designed for observation of severe local storms have consistently pushed the boundaries of spatial and temporal resolution in order to allow for detailed analysis of storm structure and evolution. Digital beamforming, or radar imaging, is a technique that is similar in nature to a photograwphic camera, where data samples from different spaces at the same range are collected simultaneously. This allows for rapid volumetric update rates compared to radars that scan with a single narrow beam. The Atmospheric Imaging Radar (AIR) is a mobile X-band (3.14-cm wavelength) imaging weather radar that transmits a vertical, 20° fan beam and uses a 36-element receive array to form instantaneous range–height indicators (RHIs) with a native beamwidth of 1° × 1°. Rotation in azimuth allows for 20° × 90° volumetric updates in under 6 s, while advanced pulse compression techniques achieve 37.5-m range resolution. The AIR has been operational since 2012 and has collected data on tornadoes and supercells at ranges as close as 6 km, resulting in high spatial and temporal resolution observations of severe local storms. The use of atmospheric imaging is exploited to detail rapidly evolving phenomena that are difficult to observe with traditional scanning weather radars.

scholarly journals A Gaussian mixture method for specific differential phase retrieval at X-band frequency

2019 ◽  
Vol 12 (10) ◽  
pp. 5613-5637 ◽  
Author(s):  
Guang Wen ◽  
Neil I. Fox ◽  
Patrick S. Market
Keyword(s):  
Phase Shift ◽  
Linear Regression ◽  
Gaussian Mixture ◽  
Differential Phase Shift ◽  
Band Frequency ◽  
Differential Phase ◽  
X Band ◽  
Gauge Data ◽  
Bow Echo ◽  
Expected Values

Abstract. The specific differential phase Kdp is one of the most important polarimetric radar variables, but the variance σ2(Kdp), regarding the errors in the calculation of the range derivative of the differential phase shift Φdp, is not well characterized due to the lack of a data generation model. This paper presents a probabilistic method based on the Gaussian mixture model for Kdp estimation at X-band frequency. The Gaussian mixture method can not only estimate the expected values of Kdp by differentiating the expected values of Φdp, but also obtain σ2(Kdp) from the product of the square of the first derivative of Kdp and the variance of Φdp. Additionally, the ambiguous phase and backscattering differential phase shift are corrected via the mixture model. The method is qualitatively evaluated with a convective event of a bow echo observed by the X-band dual-polarization radar in the University of Missouri. It is concluded that Kdp estimates are highly consistent with the gradients of Φdp in the leading edge of the bow echo, and large σ2(Kdp) occurs with high variation of Kdp. Furthermore, the performance is quantitatively assessed by 2-year radar–gauge data, and the results are compared to linear regression model. It is clear that Kdp-based rain amounts have good agreement with the rain gauge data, while the Gaussian mixture method gives improvements over the linear regression model, particularly for far ranges.

New pathways for high-resolution weather radar products in the Hamburg metropolitan region

2020 ◽  
Author(s):  
Finn Burgemeister ◽  
Tobias Sebastian Finn ◽  
Tobias Machnitzki ◽  
Marco Clemens ◽  
Felix Ament
Keyword(s):  
Weather Radar ◽  
Real Data ◽  
Potential Method ◽  
Metropolitan Region ◽  
Small Scale ◽  
Radar Observations ◽  
Range Resolution ◽  
Weather Radars ◽  
X Band ◽  
The One

<p>The University of Hamburg operates a single-polarized X-band weather radar to investigate small scale precipitation in Hamburg’s center since 2013. This weather radar provides a temporal resolution of 30 s, a range resolution of 60 m, and a sampling resolution of 1° within a 20 km radius. The X-band observations refine the coarse measurements of the German nationwide C-band radars. On the one hand, the resolution enables new capabilities in research and detection of extreme events, e.g. flash floods or tornadoes in rain events. On the other hand, with the single polarization and small wavelength, attenuation, noise, and non-meteorological echoes become a challenging issue. How can we derive products from disturbed weather radar observations?</p><p>We demonstrate new methods to process X-band weather radar observations effectively using synthetic and real data. Firstly, we present our python package for local weather radars. This package combines all steps of processing our measurements and includes well-established algorithms of image processing and radar meteorology. Secondly, we study machine learning as a new and potential method for our weather radar products. The developed neural network uses raw reflectivity measurements as input and results in data, which is free of noise and non-meteorological echoes. We outline assets and drawbacks of both methods and show possible connections.</p><p>Further X-band weather radar systems are planned for 2020 to monitor precipitation for the Hamburg metropolitan region in a networked environment. The high-quality and -resolution weather radar products will be provided for urban hydrology research within the Cluster of Excellence CLICCS - Climate, Climatic Change, and Society.</p>

scholarly journals A Gaussian Mixture Method for Specific Differential Phase Retrieval at X-band Frequency

2019 ◽  
Author(s):  
Guang Wen ◽  
Neil I. Fox ◽  
Patrick S. Market
Keyword(s):  
Phase Shift ◽  
Linear Regression ◽  
Gaussian Mixture ◽  
Differential Phase Shift ◽  
Band Frequency ◽  
Differential Phase ◽  
X Band ◽  
Gauge Data ◽  
Bow Echo ◽  
Expected Values

Abstract. Specific differential phase Kdp is one of the most important polarimetric radar variables, but the variance σ2(Kdp), regarding the errors in the calculation of the range derivative of differential phase shift Φdp, is not well characterized due to the lack of a data generation model. This paper presents a probabilistic method based on Gaussian mixture model for Kdp estimation at X-band frequency. The Gaussian mixture method can not only estimate the expected values of Kdp by differentiating the expected values of Φdp, but also obtain σ2(Kdp) from the product of the square of the first derivative of Kdp and the variance of Φdp. Additionally, ambiguous Φdp and backscattering differential phase shift are corrected via the mixture model. The method is qualitatively evaluated with a convective event of a bow echo observed by the X-band dual-polarization radar in the University of Missouri. It is concluded that Kdp estimates are highly consistent with the gradients of Φdp in the leading edge of the bow echo, and large σ2(Kdp) occurs with high variation of Kdp. Furthermore, the performance is quantitatively assessed by three-year radar-gauge data, and the results are compared to linear regression model. It is clear that Kdp-based rain amounts have good agreement with the rain gauge data, while the Gaussian mixture method gives improvements over linear regression model, particularly for far ranges.

scholarly journals Improved rainfall estimates in convective storms using polarisation diversity radar

2000 ◽  
Vol 4 (4) ◽  
pp. 555-563 ◽  
Author(s):  
A. J. Illingworth ◽  
T. M. Blackman ◽  
J. W. F. Goddard
Keyword(s):  
Heavy Rainfall ◽  
Phase Measurement ◽  
Heavy Rain ◽  
Absolute Calibration ◽  
Radar Rainfall ◽  
Differential Phase Shift ◽  
Convective Storms ◽  
Differential Phase ◽  
Weather Radars ◽  
Anomalous Propagation

Abstract. Errors arise when using conventional radar reflectivity, Z, to estimate rainfall rate, R, and these can be particularly severe during severe convective storms; the very events when accurate estimates are needed so that action can be taken to mitigate the effects of flooding. Concentration is on three problems associated with heavy rainfall: hail, attenuation and absolute calibration of the radar, and consider how polarisation radar parameters, differential reflectivity, ZDR, and specific differential phase shift KDP, might lead to their alleviation. It is essential to consider the fundamental limits to the accuracy with which these parameters can be estimated. If ZDR can be measured to an accuracy of 0.2 dB, then it provides a measure of mean raindrop shape which is sufficiently precise to improve rainrate estimates. This can be achieved at S-band (10 cm), but seems very difficult for operational C-band (5 cm) radars; differential attenuation by the heavy rain introduces a negative bias into ZDR which increases with range. However, the magnitude of this bias at C-band can then be used to correct for the total attenuation of Z. Differential phase, KDP has the advantage that it is a phase measurement and so is unaffected by attenuation. It only responds to the rainfall and is unaffected by the hail, but KDP is a noisy parameter and is only useful for heavy rainfall above 30 – 60 mm hr-1. Fortuitously, KDP and ZDR are not independent and one use of KDP and ZDR may well be to exploit this redundancy to identify rain areas as opposed to hail, and in rainfall to use the redundancy to provide an automatic calibration of the absolute reflectivity, Z, to 0.5 dB (12%). Finally, the noisy character of both ZDR and KDP together with the low level of the co-polar correlation coefficient provide the first reliable means of detecting and removing anomalous propagation which is a major operational problem for all weather radars. Keywords: polarisation radar, rainfall calibration, attenuation, hail, anomalous propagation

scholarly journals A Simple Method for Attenuation Correction in Local X-Band Radar Measurements Using C-Band Radar Data

2016 ◽  
Vol 33 (11) ◽  
pp. 2315-2329 ◽  
Author(s):  
Katharina Lengfeld ◽  
Marco Clemens ◽  
Claire Merker ◽  
Hans Münster ◽  
Felix Ament
Keyword(s):  
Attenuation Correction ◽  
Radar Data ◽  
Simple Method ◽  
Band Frequency ◽  
Advantages And Disadvantages ◽  
Weather Radars ◽  
Radar Systems ◽  
Radar Networks ◽  
X Band ◽  
Radar Measurements

AbstractThis paper presents a novel, simple method to correct reflectivity measurements of weather radars that operate in attenuation-influenced frequency bands using observations from less attenuated radar systems. In recent years radar systems operating in the X-band frequency range have been developed to provide precipitation fields for areas of special interest in high temporal (≤1 min) and spatial (≤250 m) resolution in complement to nationwide radar networks. However, X-band radars are highly influenced by attenuation. C- and S-band radars typically have coarser resolution (250 m–1 km and 5 min) but are less affected by attenuation.Correcting for attenuation effects in simple (non-Doppler) single-polarized X-band radars remains challenging and is often dependent on restriction parameters, for example, those derived from mountain returns. Therefore, these algorithms are applicable only in limited areas. The method proposed here uses measurements from C-band radars and hence can be applied in all regions covered by nationwide C- (or S-) band radar networks. First, a single scan of X-band radar measurements is used exemplary to identify advantages and disadvantages of the novel algorithm compared to a standard single radar algorithm. The performance of the correction algorithms in different types of precipitation is examined in nine case studies. The proposed method provides very promising results for each type of precipitation. Additionally, it is evaluated in a 5-month comparison with Micro Rain Radar (MRR) observations. The bias between uncorrected X-band radar and MRR data is nearly eliminated by the attenuation correction algorithm, and the RMSE is reduced by 20% while the correlation of ~0.9 between both systems remains nearly constant.

scholarly journals Observing UT1-UTC with VGOS

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Rüdiger Haas ◽  
Eskil Varenius ◽  
Saho Matsumoto ◽  
Matthias Schartner
Keyword(s):  
Standard Deviation ◽  
Reference Frame ◽  
Earth Rotation ◽  
Space Observatory ◽  
International Earth Rotation ◽  
Onsala Space Observatory ◽  
X Band ◽  
First Results

AbstractWe present first results for the determination of UT1-UTC using the VLBI Global Observing System (VGOS). During December 2019 through February 2020, a series of 1 h long observing sessions were performed using the VGOS stations at Ishioka in Japan and the Onsala twin telescopes in Sweden. These VGOS-B sessions were observed simultaneously to standard legacy S/X-band Intensive sessions. The VGOS-B data were correlated, post-correlation processed, and analysed at the Onsala Space Observatory. The derived UT1-UTC results were compared to corresponding results from standard legacy S/X-band Intensive sessions (INT1/INT2), as well as to the final values of the International Earth Rotation and Reference Frame Service (IERS), provided in IERS Bulletin B. The VGOS-B series achieves 3–4 times lower formal uncertainties for the UT1-UTC results than standard legacy S/X-band INT series. The RMS agreement w.r.t. to IERS Bulletin B is slightly better for the VGOS-B results than for the simultaneously observed legacy S/X-band INT1 results, and the VGOS-B results have a small bias only with the smallest remaining standard deviation.

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