X-band Polarimetric Radar Rainfall Measurements in Keys Area Microphysics Project

2006 ◽  
Vol 63 (1) ◽  
pp. 187-203 ◽  
Author(s):  
Emmanouil N. Anagnostou ◽  
Mircea Grecu ◽  
Marios N. Anagnostou
Keyword(s):  
Phase Shift ◽  
Attenuation Correction ◽  
Doppler Radar ◽  
Model Parameters ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Differential Phase Shift ◽  
Differential Phase ◽  
X Band ◽  
Filter Noise

Abstract The Keys Area Microphysics Project (KAMP), conducted as part of NASA’s Fourth Convective and Moisture Experiment (CAMEX-4) in the lower Keys area, deployed a number of ground radars and four arrays of rain gauge and disdrometer clusters. Among the various instruments is an X-band dual-polarization Doppler radar on wheels (XPOL), contributed by the University of Connecticut. XPOL was used to retrieve rainfall rate and raindrop size distribution (DSD) parameters to be used in support of KAMP science objectives. This paper presents the XPOL measurements in KAMP and the algorithm developed for attenuation correction and estimation of DSD model parameters. XPOL observations include the horizontal polarization reflectivity ZH, differential reflectivity ZDR, and differential phase shift ΦDP. Here, ZH and ZDR were determined to be positively biased by 3 and 0.3 dB, respectively. A technique was also applied to filter noise and correct for potential phase folding in ΦDP profiles. The XPOL attenuation correction uses parameterizations that relate the path-integrated specific (differential) attenuation along a radar ray to the filtered-ΦDP (specific attenuation) profile. Attenuation-corrected ZH and specific differential phase shift (derived from filtered ΦDP profiles) data are then used to derive two parameters of the normalized gamma DSD model, that is, intercept (Nw) and mean drop diameter (D0). The third parameter (shape parameter μ) is calculated using a constrained μ–Λ relationship derived from the measured raindrop spectra. The XPOL attenuation correction is evaluated using coincidental nonattenuated reflectivity fields from the Key West Weather Surveillance Radar-1988 Doppler (WSR-88D), while the DSD parameter retrievals are statistically assessed using DSD parameters calculated from the measured raindrop spectra. Statistics show that XPOL DSD parameter estimation is consistent with independent observations. XPOL estimates of water content and Nw are also shown to be consistent with corresponding retrievals from matched ER-2 Doppler radar (EDOP) profiling observations from the 19 September airborne campaign. Results shown in this paper strengthen the applicability of X-band dual-polarization high resolution observations in cloud modeling and precipitation remote sensing studies.

scholarly journals Characterization of Mediterranean hail-bearing storms using an operational polarimetric X-band radar

2015 ◽  
Vol 8 (11) ◽  
pp. 4681-4698 ◽  
Author(s):  
G. Vulpiani ◽  
L. Baldini ◽  
N. Roberto
Keyword(s):  
Phase Shift ◽  
Attenuation Correction ◽  
Convective System ◽  
Polarimetric Radar ◽  
Differential Phase Shift ◽  
Differential Phase ◽  
X Band ◽  
Radar Measurements ◽  
Storm Characteristics ◽  
The City

Abstract. This work documents the effective use of X-band radar observations for monitoring severe storms in an operational framework. Two severe hail-bearing Mediterranean storms that occurred in 2013 in southern Italy, flooding two important Sicilian cities, are described in terms of their polarimetric radar signatures and retrieved rainfall fields. The X-band dual-polarization radar operating inside the Catania airport (Sicily, Italy), managed by the Italian Department of Civil Protection, is considered here. A suitable processing is applied to X-band radar measurements. The crucial procedural step relies on the differential phase processing, being preparatory for attenuation correction and rainfall estimation. It is based on an iterative approach that uses a very short-length (1 km) moving window, allowing proper capture of the observed high radial gradients of the differential phase. The parameterization of the attenuation correction algorithm, which uses the reconstructed differential phase shift, is derived from electromagnetic simulations based on 3 years of drop size distribution (DSD) observations collected in Rome (Italy). A fuzzy logic hydrometeor classification algorithm was also adopted to support the analysis of the storm characteristics. The precipitation field amounts were reconstructed using a combined polarimetric rainfall algorithm based on reflectivity and specific differential phase. The first storm was observed on 21 February when a winter convective system that originated in the Tyrrhenian Sea, marginally hit the central-eastern coastline of Sicily, causing a flash flood in Catania. Due to an optimal location (the system is located a few kilometers from the city center), it was possible to retrieve the storm characteristics fairly well, including the amount of rainfall field at the ground. Extemporaneous signal extinction, caused by close-range hail core causing significant differential phase shift in a very short-range path, is documented. The second storm, on 21 August 2013, was a summer mesoscale convective system that originated from a Mediterranean low pressure system lasting a few hours that eventually flooded the city of Syracuse. The undergoing physical process, including the storm dynamics, is inferred by analyzing the vertical sections of the polarimetric radar measurements. The high registered amount of precipitation was fairly well reconstructed, although with a trend toward underestimation at increasing distances. Several episodes of signal extinction were clearly manifested during the mature stage of the observed supercells.

scholarly journals Integrated Correction Algorithm for X Band Dual-Polarization Radar Reflectivity Based on CINRAD/SA Radar

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 119
Author(s):  
Chao Wang ◽  
Chong Wu ◽  
Liping Liu ◽  
Xi Liu ◽  
Chao Chen
Keyword(s):  
Attenuation Correction ◽  
Fixed Ratio ◽  
Water Layer ◽  
Resonance Scattering ◽  
Rain Attenuation ◽  
Correction Algorithm ◽  
Dual Polarization ◽  
Differential Phase ◽  
X Band ◽  
Ratio Correction

The values of ratio a of the linear relationship between specific attenuation and specific differential phase vary significantly in convective storms as a result of resonance scattering. The best-linear-fit ratio a at X band is determined using the modified attenuation correction algorithm based on differential phase and attenuation, as well as the premise that reflectivity is unattenuated in S band radar detection. Meanwhile, the systemic reflectivity bias between the X band radar and S band radar and water layer attenuation (ZW) on the wet antenna cover of the X band radar are also considered. The good performance of the modified correction algorithm is demonstrated in a moderate rainfall event. The data were collected by four X band dual-polarization (X-POL) radar sites, namely, BJXCP, BJXFS, BJXSY, and BJXTZ, and a China’s New Generation Weather Radar (CINRAD/SA radar) site, BJSDX, in Beijing on 20 July 2016. Ratio a is calculated for each volume scan of the X band radar, with a mean value of 0.26 dB deg−1 varying from 0.20 to 0.31 dB deg−1. The average values of systemic reflectivity bias between the X band radar (at BJXCP, BJXFS, BJXSY, and BJXTZ) and S band radar (at BJSDX) are 0, −3, 2, and 0 dB, respectively. The experimentally determined ZW is in substantial agreement with the theoretically calculated ones, and their values are an order of magnitude smaller than rain attenuation. The comparison of the modified attenuation correction algorithm and the empirical-fixed-ratio correction algorithm is further evaluated at the X-POL radar. It is shown that the modified attenuation correction algorithm in the present paper provides higher correction accuracy for rain attenuation than the empirical-fixed-ratio correction algorithm.

Investigation of the relationship between differential phase shift and path integrated attenuation in the melting layer of precipitation of X-band radar

2021 ◽  
Author(s):  
Anil Kumar Khanal ◽  
Guy Delrieu ◽  
Brice Boudevillain ◽  
Frédéric Cazenave ◽  
Nan Yu
Keyword(s):  
Phase Shift ◽  
Complex Terrain ◽  
High Mountain ◽  
Differential Phase Shift ◽  
Melting Layer ◽  
Differential Phase ◽  
Quantitative Precipitation Estimation ◽  
X Band ◽  
Precipitation Estimation ◽  
The Relationship

<p>The RadAlp experiment at the Grenoble region in the French Alps aims to advance the radar remote sensing techniques of precipitation in high mountain regions. Since 2016, two dual-polarimetric X-band radars, one on top of Mt Moucherotte (1901 m asl) and another in the Grenoble valley (220 m asl) are operated by Metro France and IGE respectively. High spatio-temporal variability of precipitation (e.g. intensity and phase) in the complex terrain requires high-resolution observations. X-band radar provides high spatial and temporal resolution imagery which makes it ideal for use in complex terrain but also comes with significant attenuation problems during heavy precipitation and in the melting layer (ML). The development of polarimetric techniques, especially differential phase shift (ϕDP) has helped to mitigate the power signal attenuation problem to a certain extent. The ϕDP is immune to attenuation due to rainfall, radar calibration errors and partial beam blockage, making it an attractive parameter for quantitative precipitation estimation (QPE) through attenuation correction of the reflectivity (Z). The ϕDP, however, is quite noisy and requires regularization. An iterative algorithm based on maximum allowed step sizes provides a robust solution in ϕDP regularization. In this study, we aim to understand the relationship between differential phase shift (ϕDP) and path integrated attenuation (PIA) at X-band. This relationship is crucial for quantitative precipitation estimation (QPE) using polarimetric techniques. Furthermore, this relationship is still poorly documented within the melting layer due to the complexity of the hydrometeors' distributions in terms of phase, size, shape and density. We use the mountain reference technique (MRT) for direct PIA estimations associated with the decrease in returns from mountain targets during precipitation events as compared to dry periods. The quasi-vertical profiles from the valley-based radar (XPORT) help to identify, characterize and follow the evolution of the melting layer. For the mountaintop radar (MOUC) stratiform events (59 days between Nov 2016 to Dec 2019) where the O° elevation angle beam passes through the melting layer are considered.  The PIA/ ϕDP ratios at different strata of the ML, snow-ML interface and ML-rain interface are studied. Initial results show that the PIA/ ϕDP ratio peaks at the levels of cross-correlation coefficient (ρHV) minima, remains strong in the upper part of the ML and tends to 0 towards the top of ML. Additionally, its value in rain (0.32 dB per deg) below the ML matches closely with the specific attenuation vs specific phase (k-KDP) relationship (0.29) derived from the disdrometer at ground level.  Its value increases steadily in the lower part of ML (peaks around 0.70 dB per deg), remains strong in the upper part of ML (0.5 - 0.6 dB per degree), and decreases rapidly to 0.13 dB per degree above the ML (in snow).</p>

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.

scholarly journals Evaluation of Attenuation Correction Methodology for Dual-Polarization Radars: Application to X-Band Systems

2005 ◽  
Vol 22 (8) ◽  
pp. 1195-1206 ◽  
Author(s):  
Eugenio Gorgucci ◽  
V. Chandrasekar
Keyword(s):  
Attenuation Correction ◽  
High Frequency ◽  
Lower Cost ◽  
Dual Polarization ◽  
Phase Measurements ◽  
Differential Phase ◽  
High Frequency Radar ◽  
Radar Systems ◽  
X Band ◽  
The Impact

Abstract Monitoring of precipitation using high-frequency radar systems, such as the X band, is becoming increasingly popular because of their lower cost compared to their S-band counterpart. However, at higher frequencies, such as the X band, the precipitation-induced attenuation is significant, and introduces ambiguities in the interpretation of the radar observations. Differential phase measurements have been shown to be very useful for correcting the measured reflectivity for precipitation-induced attenuation. This paper presents a quantitative evaluation of two attenuation correction methodologies with specific emphasis on the X band. A simple differential phase–based algorithm as well as the range-profiling algorithm are studied. The impact of backscatter differential phase on the performance of attenuation correction is evaluated. It is shown that both of the algorithms for attenuation correction work fairly well, yielding attenuation-accurate corrected reflectivities with a negligible bias.

scholarly journals An Extended Kalman Filter Framework for Polarimetric X-Band Weather Radar Data Processing

2012 ◽  
Vol 29 (5) ◽  
pp. 711-730 ◽  
Author(s):  
Marc Schneebeli ◽  
Alexis Berne
Keyword(s):  
Kalman Filter ◽  
Phase Shift ◽  
Extended Kalman Filter ◽  
Rain Rate ◽  
Swiss Alps ◽  
Horizontal Polarization ◽  
Differential Phase Shift ◽  
Differential Phase ◽  
X Band ◽  
Attenuation Corrected

Abstract The different quantities measured by dual-polarization radar systems are closely linked to each other. An extended Kalman filter framework is proposed in order to make use of constraints on individual radar observables that are induced by these relations. This new approach simultaneously estimates the specific differential phase on propagation Kdp, the attenuation-corrected reflectivity at horizontal polarization Zh, and the attenuation-corrected differential reflectivity Zdr, as well as the differential phase shift on backscatter δhυ. In a simulation experiment it is found that Kdp and δhυ can be retrieved with higher accuracy and spatial resolution than existing estimators that solely rely on a smoothed measurement of the differential phase shift Ψdp. Attenuation-corrected Zh was retrieved with an accuracy similar to standard algorithms, but improvements were found for attenuation-corrected Zdr. In addition, the algorithm can be used for radar calibration by comparing the directly retrieved differential phase shift on propagation Φdp with the accumulated Kdp estimates. The extended Kalman filter estimation scheme was applied to data collected with an X-band polarimetric radar in the Swiss Alps in 2010. Radome attenuation appears to be significant (up to 5 dB) in moderate to intense rain events and hence needs to be corrected in order to have reliable quantitative precipitation estimates. Measurements corrected for radome and propagation attenuation were converted into rain-rate R with a newly developed relation between R, Kdp, and Zdr. The good agreement between rain-rate values inferred from ground observations and from the radar measurements confirms the reliability of the proposed radar processing technique.

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 Attenuation Correction Effects in Rainfall Estimation at X-Band Dual-Polarization Radar: Evaluation with a Dense Rain Gauge Network

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Young-A Oh ◽  
DaeHyung Lee ◽  
Sung-Hwa Jung ◽  
Kyung-Yeub Nam ◽  
GyuWon Lee
Keyword(s):  
Attenuation Correction ◽  
Random Error ◽  
Estimation Method ◽  
Rain Gauge ◽  
Spatial Average ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
X Band ◽  
Rain Gauge Network

The effects of attenuation correction in rainfall estimation with X-band dual-polarization radar were investigated with a dense rain gauge network. The calibration bias in reflectivity (ZH) was corrected using a self-consistency principle. The attenuation correction ofZHand the differential reflectivity (ZDR) were performed by a path integration method. After attenuation correction,ZHandZDRwere significantly improved, and their scatter plots matched well with the theoretical relationship betweenZHandZDR. The comparisons between the radar rainfall estimation and the rain gauge rainfall were investigated using the bulk statistics with different temporal accumulations and spatial averages. The bias significantly improves from 70% to 0% withR(ZH). However, the improvement withR(ZH,ZDR)was relatively small, from 3% to 1%. This indicated that rainfall estimation using a polarimetric variable was more robust at attenuation than was a single polarimetric variable method. The bias did not show improvement in comparisons between the temporal accumulations or the spatial averages in either rainfall estimation method. However, the random error improved from 68% to 25% with different temporal accumulations or spatial averages. This result indicates that temporal accumulation or spatial average (aggregation) is important to reduce random error.

Sign in / Sign up

Export Citation Format

Share Document