scholarly journals Group Line Energy in Phase-Resolved Ocean Surface Wave Orbital Velocity Reconstructions from X-band Doppler Radar Measurements of the Sea Surface

2019 ◽  
Vol 11 (1) ◽  
pp. 71 ◽  
Author(s):  
Andrew J. Kammerer ◽  
Erin E. Hackett
Keyword(s):  
Dispersion Curve ◽  
Ground Truth ◽  
Orbital Velocity ◽  
Line Energy ◽  
First Order ◽  
Dispersion Relationship ◽  
X Band ◽  
Radar Measurements ◽  
Wave Orbital Velocity ◽  
Order Dispersion

The wavenumber-frequency spectra of many radar measurements of the sea surface contain a linear feature at frequencies lower than the first order dispersion relationship commonly referred to as the “group line”. Plant and Farquharson, showed numerically that the group line is at least partially caused by wave interference-induced breaking of steep short gravity waves. This paper uses two wave retrieval techniques, proper orthogonal decomposition (POD) and FFT-based dispersion curve filtering, to examine two X-band radar datasets, and compare wave orbital velocity reconstructions to ground truth wave buoy measurements within the field of view of the radar. POD allows group line energy to be retained in the reconstruction, while dispersion curve filtering removes all energy not associated with the first order dispersion relationship. Results show that when group line energy is higher or comparable to dispersion curve energy, the inclusion of this group line energy in phase-resolved orbital velocity reconstructions increases the accuracy of the reconstruction. This increased accuracy is demonstrated by higher correlations between POD reconstructed time series with buoy ground truth measurements than dispersion curve filtered reconstructions. When energy lying on the dispersion relationship is much higher than the group line energy, the FFT and POD reconstruction methods perform comparably.

scholarly journals Hail storm hazard in urban areas: identification and probability of occurrence by using a single-polarization X-band weather radar

2016 ◽  
Author(s):  
Vincenzo Capozzi ◽  
Errico Picciotti ◽  
Vincenzo Mazzarella ◽  
Giorgio Budillon ◽  
Frank Silvio Marzano
Keyword(s):  
Urban Areas ◽  
Low Cost ◽  
Weather Radar ◽  
Ground Truth ◽  
Liquid Density ◽  
Data Set ◽  
Area Of Interest ◽  
X Band ◽  
Radar Measurements ◽  
Single Polarization

Abstract. This work exploits the potentiality of hail warning, based on single-polarization X-band weather radar measurements and tested on a large and well-documented data set of thunderstorm events in southern Italy near Naples. Even though X-band radars may suffer of two-way path attenuation especially at long ranges, due to their relatively low cost their use is rapidly increasing for short-range applications such as urban environments. To identify hail through radar measurements, two different methodologies have been selected and adapted to X-band data within the study area: one uses the Waldvogel (WAL) approach, whereas the other one uses the Vertically-Integrated Liquid Density (VIL-Density) product. The study aims at developing a Probability-of-Hail (POH) index in order to support hail risk management at urban scales. In order to find the optimal threshold values to discriminate between hail and severe rain, an extensive intercomparison between outcomes of the two methodologies and ground truth observations of hail has been performed, using a 2 x 2 contingency table and statistical scores. The results show that both methods are accurate for hail detection in the area of interest, although VIL-Density product is less satisfactory than WAL method in terms of false alarm ratio. The relationship between the output of these two methodologies and POH has been derived through a heuristic approach, using a third-order polynomial fitting curve. As an example, the POH indexes have been applied for the thunderstorm event occurred on 21 July 2014, proving to be reliable for hail core detection.

scholarly journals Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

2016 ◽  
Vol 9 (9) ◽  
pp. 4425-4445 ◽  
Author(s):  
Nikola Besic ◽  
Jordi Figueras i Ventura ◽  
Jacopo Grazioli ◽  
Marco Gabella ◽  
Urs Germann ◽  
...  
Keyword(s):  
Polarimetric Radar ◽  
Statistical Framework ◽  
X Band ◽  
Different Types ◽  
Unsupervised Approach ◽  
Radar Measurements ◽  
Supervised Classification Methods ◽  
Operational Potential ◽  
Statistical Clustering

Abstract. Polarimetric radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.

scholarly journals Precipitation at Dumont d'Urville, Adélie Land, East Antarctica: the APRES3 field campaigns dataset

2018 ◽  
Vol 10 (3) ◽  
pp. 1605-1612 ◽  
Author(s):  
Christophe Genthon ◽  
Alexis Berne ◽  
Jacopo Grazioli ◽  
Claudio Durán Alarcón ◽  
Christophe Praz ◽  
...  
Keyword(s):  
Climate Models ◽  
Data Repository ◽  
X Band ◽  
Intensive Observation ◽  
Radar Measurements ◽  
Scientific Results ◽  
Antarctic Precipitation ◽  
Severe Shortage ◽  
Observation Period

Abstract. Compared to the other continents and lands, Antarctica suffers from a severe shortage of in situ observations of precipitation. APRES3 (Antarctic Precipitation, Remote Sensing from Surface and Space) is a program dedicated to improving the observation of Antarctic precipitation, both from the surface and from space, to assess climatologies and evaluate and ameliorate meteorological and climate models. A field measurement campaign was deployed at Dumont d'Urville station at the coast of Adélie Land in Antarctica, with an intensive observation period from November 2015 to February 2016 using X-band and K-band radars, a snow gauge, snowflake cameras and a disdrometer, followed by continuous radar monitoring through 2016 and beyond. Among other results, the observations show that a significant fraction of precipitation sublimates in a dry surface katabatic layer before it reaches and accumulates at the surface, a result derived from profiling radar measurements. While the bulk of the data analyses and scientific results are published in specialized journals, this paper provides a compact description of the dataset now archived in the PANGAEA data repository (https://www.pangaea.de, https://doi.org/10.1594/PANGAEA.883562) and made open to the scientific community to further its exploitation for Antarctic meteorology and climate research purposes.

scholarly journals Estimation of X-Band Polarimetric Radar Attenuation and Measurement Uncertainty Using a Variational Method

2014 ◽  
Vol 53 (4) ◽  
pp. 1099-1119 ◽  
Author(s):  
Wei-Yu Chang ◽  
Jothiram Vivekanandan ◽  
Tai-Chi Chen Wang
Keyword(s):  
Variational Method ◽  
Ad Hoc ◽  
Field Measurements ◽  
Correction Method ◽  
Southwest Monsoon ◽  
Covariance Matrices ◽  
Polarimetric Radar ◽  
Error Covariance ◽  
X Band ◽  
Radar Measurements

AbstractA variational algorithm for estimating measurement error covariance and the attenuation of X-band polarimetric radar measurements is described. It concurrently uses both the differential reflectivity ZDR and propagation phase ΦDP. The majority of the current attenuation estimation techniques use only ΦDP. A few of the ΦDP-based methods use ZDR as a constraint for verifying estimated attenuation. In this paper, a detailed observing system simulation experiment was used for evaluating the performance of the variational algorithm. The results were compared with a single-coefficient ΦDP-based method. Retrieved attenuation from the variational method is more accurate than the results from a single coefficient ΦDP-based method. Moreover, the variational method is less sensitive to measurement noise in radar observations. The variational method requires an accurate description of error covariance matrices. Relative weights between measurements and background values (i.e., mean value based on long-term DSD measurements in the variational method) are determined by their respective error covariances. Instead of using ad hoc values, error covariance matrices of background and radar measurement are statistically estimated and their spatial characteristics are studied. The estimated error covariance shows higher values in convective regions than in stratiform regions, as expected. The practical utility of the variational attenuation correction method is demonstrated using radar field measurements from the Taiwan Experimental Atmospheric Mobile-Radar (TEAM-R) during 2008’s Southwest Monsoon Experiment/Terrain-Influenced Monsoon Rainfall Experiment (SoWMEX/TiMREX). The accuracy of attenuation-corrected X-band radar measurements is evaluated by comparing them with collocated S-band radar measurements.

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 Experimental validation of a dual-receiver radar architecture for snowpack monitoring

2020 ◽  
Vol 12 (6) ◽  
pp. 439-446
Author(s):  
Marco Pasian ◽  
Pedro Fidel Espín-López ◽  
Lorenzo Silvestri ◽  
Massimiliano Barbolini ◽  
Fabio Dell'Acqua
Keyword(s):  
Snow Water Equivalent ◽  
Ground Truth ◽  
Italian Alps ◽  
Advantages And Disadvantages ◽  
Experimental Campaign ◽  
Snow Water ◽  
Working Principle ◽  
Radar Measurements ◽  
Transmitting Antenna ◽  
Non Destructive

AbstractMicrowave radars can be used to monitor the internal structure of the snowpack, delivering real-time and non-destructive measurements. Recently, the working principle of an innovative radar architecture able to identify some of the most important snowpack parameters, without external aids, has been demonstrated. A key point of this new architecture is the use of two independent receiving antennas, and one transmitting antenna. This paper presents a comparison between two different implementations, either based on one physical antenna miming two receiving antennas, or based directly on two physical receiving antennas. The different advantages and disadvantages of both solutions are discussed, highlighting the superior accuracy achieved by the implementation based on two physical receiving antennas. Then, this paper also presents the field results achieved by this type of radar architecture, on the grounds of a 5-day experimental campaign that took place in winter 2019 in the Italian Alps on dry snow. The comparison between the radar measurements and the ground truth (manual snowpit analysis, in terms of snowpack depth, dielectric constant, bulk density, and snow water equivalent) is provided. Overall, a root mean square error of around 3.5 cm, 0.05, 27 kg/m3, and 2.5 cm is achieved, respectively.

A Bayesian Approach for Integrated Raindrop Size Distribution (DSD) Retrieval on an X-Band Dual-Polarization Radar Network

2016 ◽  
Vol 33 (2) ◽  
pp. 377-389 ◽  
Author(s):  
Eiichi Yoshikawa ◽  
V. Chandrasekar ◽  
Tomoo Ushio ◽  
Takahiro Matsuda
Keyword(s):  
Size Distribution ◽  
Estimation Accuracy ◽  
Dual Polarization ◽  
Estimation Errors ◽  
Axis Ratio ◽  
Radar Network ◽  
Raindrop Size Distribution ◽  
X Band ◽  
Radar Measurements ◽  
Raindrop Size

AbstractA raindrop size distribution (DSD) retrieval method for a weather radar network consisting of several X-band dual-polarization radars is proposed. An iterative maximum likelihood (ML) estimator for DSD retrieval in a single radar was developed in the authors’ previous work, and the proposed algorithm in this paper extends the single-radar retrieval to radar-networked retrieval, where ML solutions in each single-radar node are integrated based on a Bayesian scheme in order to reduce estimation errors and to enhance accuracy. Statistical evaluations of the proposed algorithm were carried out using numerical simulations. The results with eight radar nodes showed that the bias and standard errors are −0.05 and 0.09 in log(Nw); and Nw (mm−1 m−3) and 0.04 and 0.09 in D0 (mm) in an environment with fluctuations in dual-polarization radar measurements (normal distributions with standard deviations of 0.8 dBZ, 0.2 dB, and 1.5° in ZHm, ZDRm, and ΦDPm, respectively). Further error analyses indicated that the estimation accuracy depended on the number of radar nodes, the ranges of varying μ, the raindrop axis ratio model, and the system bias errors in dual-polarization radar measurements.

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