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

Vulpiani, G.; Baldini, Luca; Roberto, Nicoletta

doi:10.5194/amt-8-4681-2015

Cited by 28 publications

(23 citation statements)

References 53 publications

(57 reference statements)

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“…Past studies [22][23][24][25][26][27][28][29] have shown that locally deployed dual-polarization X-band radar systems can contribute higher resolution rain rate estimations and improved rainfall quantification accuracies than the lower frequency (C-band and S-band) long-range operational radar systems. These short-range radar systems could potentially be used to fill in coverage gaps of operational weather radar networks, which is particularly important for advancing early warning of precipitation driven hydrological hazards (flash floods, landslides, debris flows, etc.)…”

Section: Introductionmentioning

confidence: 99%

Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

et al. 2018

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Abstract:In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10-35 km) mountainous basin (64 km 2 ). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h −1 . The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50-70%) and lower correlations (0.48-0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response.

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Section: Introductionmentioning

confidence: 99%

Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

et al. 2018

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“…It is mostly based on [17] and is tailored to identify meteorological targets, with a particular emphasis on the hydrometeors involved in convective events. Recent updates are described in [9,27], for X-band and C-band, respectively, that show the membership functions used. Such functions are not so different from those reported in the literature and therefore the results obtained should not be significantly influenced by the choice of FL classifier.…”

Section: Support Vector Machine Implementation Of Hcamentioning

confidence: 99%

“…The case studies considered occurred on 21 February and 21 August 2013 in Sicily and were observed by the Selex-Gematronik 50 DX system (with a 3 dB antenna beamwidth of 1.3 • and a transmit peak power of 50 kW) of the Department of Civil Protection of Italy, operational at the airport of Catania [27]. The radar performed a volume scan made of 12 sweeps at different antenna elevation angles ranging from 1 • to 21.6 • , and an additional one at vertical incidence.…”

Section: X-band Datasetmentioning

confidence: 99%

A Support Vector Machine Hydrometeor Classification Algorithm for Dual-Polarization Radar

et al. 2017

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Abstract:An algorithm based on a support vector machine (SVM) is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively) and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather.

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“…The conversion coefficients a q , b q , and c q are defined so that the resulting rain rate R q is in (mm·h −1 ), where the subscript q indicates one of the three estimation formulas through the labels "Z", "K", and "DR". Finally, Equation (4) (with label q = KZ), adapted from the formulation proposed in [28], is a weighted combination of R K and R Z estimates from Equations (2) to (3) proposed by [29,30], respectively. In this case, the value of the weight (w) is set up so that the rain estimation that comes out using K dp , i.e., R K , weighs more for higher precipitation rates than that when making use of Z hh and Z dr only, i.e., R DR .…”

Section: Qpe From Radar Observablesmentioning

confidence: 99%

Investigation of Weather Radar Quantitative Precipitation Estimation Methodologies in Complex Orography

et al. 2017

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Near surface quantitative precipitation estimation (QPE) from weather radar measurements is an important task for feeding hydrological models, limiting the impact of severe rain events at the ground as well as aiding validation studies of satellite-based rain products. To date, several works have analyzed the performance of various QPE algorithms using actual and synthetic experiments, possibly trained by measurement of particle size distributions and electromagnetic models. Most of these studies support the use of dual polarization radar variables not only to ensure a good level of data quality but also as a direct input to rain estimation equations. One of the most important limiting factors in radar QPE accuracy is the vertical variability of particle size distribution, which affects all the acquired radar variables as well as estimated rain rates at different levels. This is particularly impactful in mountainous areas, where the sampled altitudes are likely several hundred meters above the surface. In this work, we analyze the impact of the vertical profile variations of rain precipitation on several dual polarization radar QPE algorithms when they are tested in a complex orography scenario. So far, in weather radar studies, more emphasis has been given to the extrapolation strategies that use the signature of the vertical profiles in terms of radar co-polar reflectivity. This may limit the use of the radar vertical profiles when dual polarization QPE algorithms are considered. In that case, all the radar variables used in the rain estimation process should be consistently extrapolated at the surface to try and maintain the correlations among them. To avoid facing such a complexity, especially with a view to operational implementation, we propose looking at the features of the vertical profile of rain (VPR), i.e., after performing the rain estimation. This procedure allows characterization of a single variable (i.e., rain) when dealing with vertical extrapolations. In this work, a definition of complex orography is also given, introducing a radar orography index to objectively quantify the degree of terrain complexity when dealing with radar QPE in heterogeneous environmental scenarios. Three case studies observed by the research C-band polarization agility Doppler radar named Polar 55C, managed by the Institute of Atmospheric Sciences and Climate (ISAC) at the National Research Council of Italy (CNR), were used to prove the concept of VPR. Our results indicate that the combined algorithm, which merges together differential phase shift (K dp ), single polarization reflectivity factor (Z hh ), and differential reflectivity (Z dr ), once accurately processed, in most cases performs better among those tested and those that make use of Z hh alone, K dp alone, and Z hh , and Z dr . Improvements greater than 25% are found for the total rain accumulations in terms of normalized bias when the VPR extrapolation is applied.

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

Cited by 28 publications

References 53 publications

Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

A Support Vector Machine Hydrometeor Classification Algorithm for Dual-Polarization Radar

Investigation of Weather Radar Quantitative Precipitation Estimation Methodologies in Complex Orography

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