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

Reinoso-Rondinel, Ricardo; Unal, C.M.H.; Russchenberg, H.W.J.

doi:10.1175/jtech-d-17-0105.1

Cited by 11 publications

(10 citation statements)

References 43 publications

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“…The residual discrepancy can be due to the small mismatch between the resolution volumes and temporal sampling as well as the used approximations when retrieving DSDs and simulating scattering amplitudes. In the case of IDRA, it is known that measurements of Z DR suffer from a radial pattern that adds a small oscillation to Z DR over time (Reinoso-Rondinel et al 2018). The radial pattern is probably due to a metallic fence that surrounds the IDRA platform.…”

Section: A Examination Of Polarimetric Quantities Derived From Dsdsmentioning

confidence: 99%

Quantitative Evaluation of Polarimetric Estimates from Scanning Weather Radars Using a Vertically Pointing Micro Rain Radar

Reinoso-Rondinel

Schleiss

2021

Journal of Atmospheric and Oceanic Technology

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Conventionally, micro rain radars (MRRs) have been used as a tool to calibrate reflectivity from weather radars, estimate the relation between rainfall rate and reflectivity, and study microphysical processes in precipitation. However, limited attention has been given to the reliability of the retrieved drop size distributions DSDs from MRRs. This study sheds more light on this aspect by examining the sensitivity of retrieved DSDs to the assumptions made to map Doppler spectra into size distributions, and investigates the capability of an MRR to assess polarimetric observations from operational weather radars. For that, an MRR was installed near the Cabauw observatory in the Netherlands, between the IDRA X-band radar and the Herwijnen operational C-band radar. The measurements of the MRR from November 2018 to February 2019 were used to retrieve DSDs and simulate horizontal reflectivity Ze, differential reflectivity ZDR, and specific differential phase KDP in rain. Attention is given to the impact of aliased spectra and right-hand side truncation on the simulation of polarimetric variables. From a quantitative assessment, the correlations of Ze and ZDR between the MRR and Herwijnen radar were 0.93 and 0.70, respectively, while those between the MRR and IDRA were 0.91 and 0.69. However, Ze and ZDR from the Herwijnen radar showed slight biases of 1.07 and 0.25 dB. For IDRA, the corresponding biases were 2.67 and -0.93 dB. Our results show that MRR measurements are advantageous to inspect the calibration of scanning radars and validate polarimetric estimates in rain, provided that the DSDs are correctly retrieved and controlled for quality assurance.

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Section: A Examination Of Polarimetric Quantities Derived From Dsdsmentioning

confidence: 99%

Quantitative Evaluation of Polarimetric Estimates from Scanning Weather Radars Using a Vertically Pointing Micro Rain Radar

Reinoso-Rondinel

Schleiss

2021

Journal of Atmospheric and Oceanic Technology

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“…An adaptive approach that estimates K DP at high spatial resolution while controlling its standard deviation s K (8 km 21 ) is given by Reinoso-Rondinel et al (2018). For notation purposes, the difference of a radar variable V over a given pathlength is expressed as DV.…”

Section: Estimation Techniques For C Dp -Based Variables a Estimation Of K Dpmentioning

confidence: 99%

“…Such a filter design is found suitable for Dr 5 0.03 km. For the estimation of K DP (AHR), values of L on the order of 3 km are associated with theoretical values of s K , 0.58 km 21 for Dr 5 0.03 km (Reinoso-Rondinel et al 2018) and therefore [L min ; L max ] is predefined as [2; 5] km. The z and z dr inputs are corrected for attenuation and differential attenuation, respectively, according to the DP method, in which a linear regression fit of 1 km is applied to C DP profiles.…”

Section: B Comparison Between K Dp and Amentioning

confidence: 99%

“…The purpose of this work is to 1) explore the role and impact of estimated F DP profiles on the performance of the extended ZPHI method at X-band frequencies to improve estimates of a and A over short paths and 2) develop a technique to compute d hv in rain while keeping the spatial variability of drop sizes. For such purpose, two K DP (or F DP ) methods, by Hubbert and Bringi (1995) and Reinoso-Rondinel et al (2018), are reviewed in section 2 as well as three attenuation correction approaches, by Bringi et al (1990), Testud et al (2000), and Bringi et al (2001). In addition, the d hv algorithm is introduced, which integrates estimates of K DP and A.…”

Section: Introductionmentioning

confidence: 99%

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

Reinoso-Rondinel

Unal

Russchenberg

2018

Journal of Atmospheric and Oceanic Technology

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In radar polarimetry, the differential phase ΨDP consists of the propagation differential phase ΦDP and the backscatter differential phase δhv. While ΦDP is commonly used for attenuation correction (i.e., estimation of the specific attenuation A and specific differential phase KDP), recent studies have demonstrated that δhv can provide information concerning the dominant size of raindrops. However, the estimation of ΦDP and δhv is not straightforward given their coupled nature and the noisy behavior of ΨDP, especially over short paths. In this work, the impacts of estimating ΦDP 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 KDP and A. In addition, an improved technique is proposed to compute δhv from ΨDP and ΦDP 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 KDP and A showed that more accurate and stable α and A are obtained if ΦDP is estimated at range resolution, which is not possible by conventional range filtering techniques. Accurate δhv 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 δhv.

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“…In addition, the dp measurements at X-band frequency are affected by the backscattering differential phase shift δ co . The constraints of K dp − Z H − Z DR and δ co − Z DR can be used to improve the estimation of K dp and δ co (Otto and Russchenberg, 2011;Reinoso-Rondinel et al, 2018), although these constraints are only valid in the rain regime.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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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.

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

Cited by 11 publications

References 43 publications

Quantitative Evaluation of Polarimetric Estimates from Scanning Weather Radars Using a Vertically Pointing Micro Rain Radar

Quantitative Evaluation of Polarimetric Estimates from Scanning Weather Radars Using a Vertically Pointing Micro Rain Radar

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

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

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