X-Band Mini Radar for Observing and Monitoring Rainfall Events

Allegretti, Marco; Bertoldo, Silvano; Prato, Andrea; Lucianaz, Claudio; Rorato, Oscar; Notarpietro, Riccardo; Gabella, Marco

doi:10.4236/acs.2012.23026

Cited by 28 publications

(20 citation statements)

References 18 publications

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“…Although there are many studies about X-band radar adjusted by using rain gauge networks [79][80][81][82], most of them do not consider polarimetric X-band radar. Additionally, there are many other studies demonstrating that the use of polarimetric X-band radar does not require any absolute calibration, including rain gauge adjustments [5,[83][84][85][86][87].…”

Section: Discussionmentioning

confidence: 99%

Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

Paz

Willinger

Gires

et al. 2018

Water

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This paper presents a comparison between C-band and X-band radar data over an instrumented and regulated catchment of the Paris region. We study the benefits of polarimetry and the respective hydrological impacts with the help of rain gauge and flow measurements using a semi-distributed hydrological model. Both types of radar confirm the high spatial variability of the rainfall down to their space resolution (1 km and 250 m, respectively). Therefore, X-band radar data underscore the limitations of simulations using a semi-distributed model with sub-catchments of an average size of 2 km. The use of the polarimetric capacity of the Météo-France C-band radar was limited to corrections of the horizontal reflectivity, and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, no absolute calibration and scanning optimisation were performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (hereafter referred to as the ENPC X-band radar). In spite of this and the fact that the catchment is much closer to the C-band radar than to the X-band radar (average distance of 15 km vs. 35 km, respectively), the latter seems to perform at least as well as the former, but with a higher spatial resolution. This was best highlighted with the help of a multifractal analysis, which also shows that the X-band radar was able to pick up a few rainfall extremes that were smoothed out by the C-band radar.

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Section: Discussionmentioning

confidence: 99%

Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

Paz

Willinger

Gires

et al. 2018

Water

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“…Short-range radars, such as X-band, are quite efficient for monitoring localized rain fall events [37]. Rain data collected from MicroRadarNet presented in [2,3] is used in this investigation.…”

Section: Discussionmentioning

confidence: 99%

Storm Identification, Tracking and Forecasting Using High-Resolution Images of Short-Range X-Band Radar

2015

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Rain nowcasting is an essential part of weather monitoring. It plays a vital role in human life, ranging from advanced warning systems to scheduling open air events and tourism. A nowcasting system can be divided into three fundamental steps, i.e., storm identification, tracking and nowcasting. The main contribution of this work is to propose procedures for each step of the rain nowcasting tool and to objectively evaluate the performances of every step, focusing on two-dimension data collected from short-range X-band radars installed in different parts of Italy. This work presents the solution of previously unsolved problems in storm identification: first, the selection of suitable thresholds for storm identification; second, the isolation of false merger (loosely-connected storms); and third, the identification of a high reflectivity sub-storm within a large storm. The storm tracking step of the existing tools, such as TITANand SCIT, use only up to two storm attributes, i.e., center of mass and area. It is possible to use more attributes for tracking. Furthermore, the contribution of each attribute in storm tracking is yet to be investigated. This paper presents a novel procedure called SALdEdA (structure, amplitude, location, eccentricity difference and areal difference) for storm tracking. This work also presents the contribution of each component of SALdEdA in storm tracking. The second order exponential smoothing strategy is used for storm nowcasting, where the growth and decay of each variable of interest is considered to be linear. We evaluated the major steps of our method. The adopted techniques for automatic threshold calculation are assessed with Atmosphere 2015, 6 580 a 97% goodness. False merger and sub-storms within a cluster of storms are successfully handled. Furthermore, the storm tracking procedure produced good results with an accuracy of 99.34% for convective events and 100% for stratiform events.

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“…For example in [10] it is shown the importance of high-resolution rainfall data using X-band radar network for what concern monitoring operation of extreme intense events and in complex orography environment.…”

Section: Rain Monitoringmentioning

confidence: 99%

Cars as a Diffuse Network of Road-Environment Monitoring Nodes

Allegretti¹,

Bertoldo²

2014

WSN

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The present paper aims to describe the conceptual idea to use cars as sensors to measure and acquire data related road environment. The parameters are collected using only standard equipment commonly installed and operative on commercial cars. Real sensors and car sub-systems (e.g. thermometers, accelerometers, ABS, ESP, and GPS) together with other "implicit" sensors (e.g. fog lights, windscreen wipers) acquire and contain information. They are shared inside an in-vehicle communication network using mainly the standard CAN bus and can be collected by a simple central node. This node can also be available on the market without too expensive costs thanks to some companies which business is devoted to car fleet monitoring. All the collected data are then geolocalized using a standard GPS receiver and sent to a remote elaboration unit, exploiting mobile network technologies such as GPRS or UMTS. A large number of cars, connected together in a diffuse Wireless Sensor Network, allow the elaboration unit to realize some info-layers put at the disposal of a car driver. Traffic, state of the road and other information about the weather can be received by car drivers using an ad hoc developed mobile application for smartphone which can give punctual information related to a specific route, previously set on the mobile phone navigator. The description of some experimental activities is presented, some technical points will be addressed and some examples of applications of the network of cars "as sensors" will be given.

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X-Band Mini Radar for Observing and Monitoring Rainfall Events

Cited by 28 publications

References 18 publications

Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

Storm Identification, Tracking and Forecasting Using High-Resolution Images of Short-Range X-Band Radar

Cars as a Diffuse Network of Road-Environment Monitoring Nodes

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