Monitoring and Early Management of Emergences: New Instruments

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Brittney Lindsey
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1 Monitoring and Early Management of Emergences: New Instruments Daniele Caviglia, DITEN - University of Genoa Domenico Sguerso, DICCA - University of Genoa Bianca Federici, DICCA - University of Genoa Andrea Caridi, Darts Engineering Tiziano Cosso, Gter LOGO IMPRESA/ENTE

2 Objectives 1. Support the decision maker in the monitoring of meteorological phenomena and evaluation of alerts: GNSS technology, based on a precise satellite positioning, able to assess the content of water vapor present in the atmosphere; A system consisting of a widely distributed set of sensors to estimate and locate rainfall in real time, able to define space-time, high-resolution maps of the precipitation phenomena on a local scale; 2. Offer a tool to the Civil Protection for a timely intervention on the transport system in an emergency.

3 GNSS Concept GNSS positioning is affected by different physical effects, one of these coming from low atmospheric layers that delay the signals. The study of such signal noise allows to obtain information on the precipitable water content in atmosphere Nowaday, meteorological predictive models integrates different technologies: radar very precise but short-term satellite images less precise but longer-term

4 GNSS PWV

5 GNSS Genoa 2001 flood (1/3)

6 GNSS Genoa 2001 flood (2/3)

7 GNSS Genoa 2001 flood (3/3)

8 Smart Rainfall System Estimation and localization of rainfall in real-time Technological innovation: Exploiting the propagation model of microwave signals to estimate the amount and distribution of rainfall at river basin level Deployment of a distributed system of measurement and localization of precipitation in realtime Main purpose: Allow a timely evaluation of the onset of weather-hydrological alarm conditions due to intense rainfall Recipients: Entities responsible for forecasting, monitoring and surveillance of the weather-hydrological risk for civil protection Results: Reduction in the time required by the management and dissemination of alarms in the territory Timely information for a controlled evacuation

9 Smart Rainfall System Estimation and localization of rainfall in real-time Functionalities: Real-time measurement of rainfall rate (in mm/h) for each acquisition sensor Construction of a map of the space-time distribution of rainfall Implemented with: Sensors distributed in the areas to be monitored for the measurement of the precipitation Central system for data integration, and the spatiotemporal identification of the event Uses a TLC infrastructure available on the territory: Parabolic antennas for the reception of satellite DVB Commercial satellite constellations Internet access (3G, ADSL, FTTH) Only requires the installation of the sensor downstream of the dish: Condominium or private antennas Based on components widely available on the market Without any interference with the satellite TV service

10 Smart Rainfall System Sensor Prototype Implementation of the electromagnetic model: Algorithm for estimating precipitation according to interference with the satellite signal (db of attenuation) Design and construction of a prototype of the electronic sensor: Measure of the degradation of the satellite signal due to rain Real time calculation of rainfall rate (in mm/h ) according to the algorithm Communication with the central system Simplified realization of the SW of the central system: Application for collecting and displaying data Technological innovation: Exploiting the propagation model of microwave signals to estimate the amount and distribution of rainfall at river basin level

11 Smart Rainfall System Rain Estimation Approach The output of the antenna (and consequently of the sensor) is related to the rain rate R by the following relationship: v t = v 0 e br t a l v 0 is the baseline a is a parameter depending on location of the RX antenna b is a parameter depending on wave polarization l is the part of the path connecting The path to the zero isotherm can be computed from the knowledge of the geographical positions of RX antenna and satellite, i.e.: h ant θ h 0 0 isotherm Earth projection l = h 0 h ant sin θ Finally the rain rate below the path to the zero isotherm is obtained as: a R t = 1 ln v 0 bl v t Earth path where rain rate is estimated

12 Smart Rainfall System Experimentation and Validation 20 Observed Rain (mm/30 min) Signal Level (dbm) -10 Experimentation: Collecting sets of measures in the UNIGE laboratories in various weather conditions Prototype validation: Through the comparison between the measurements of the Smart Rainfall System and those of a reference rain gauge, under different atmospheric conditions Conclusions: The Smart Rainfall System, operating in real time, anticipates up to 30 mins the results of the rain gauge The measurements of the two instruments are strongly correlated The Smart Rainfall System even detects light rain (tenths of mm/h) :00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 Morning of 3-Sep-2012 The peak of the rain gauge measure (red line) is of 9.6 mm/30 mins Observed Rain (mm/30 min) 0 0.0E+00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12: E E E E E-01 The integral of the estimation from the SRS is consistent with what observed with the rain gauge

13 Smart Rainfall System New Input for Models Map of Precipitation: Geolocation of GNSS antennas using off-the-shelf instrumentation Integration and correlation of real-time data from peripheral sensors High-resolution (at the level of the river basin) maps of precipitation New input for the models needed to estimate risks for the environment Traditional instruments, already used: Measure and transmit the integral of the rain fallen in a temporal interval Evaluate the precipitation on few locations (rain gauges), or over large areas (RADAR) SRS: Allows the operator to take emergency decisions in less time than at present Patentability of the Smart Rainfall System

14 Emergency Management Cartographic tools: Aerial image Technical cartography Openstreetmap Flood risk map: Decide the priority areas Different degrees of hazard Hazard in relation to the river discharge

15 Geo-web Service Near real time data coming from different sensors and sources Radar meteo Idrometer Meteo Model GPS SRS DATA - ASSIMILATION

16 Mobility Use Case The operator can choose an area to close Geowebservice calculates in RT the roads to close It is sent a message to some sensors collocated at the beginning of each roads

17 The Main Functionality VIRTUAL GATE Timely interventions in case of emergency!

18 Conclusions Proposal for a real time system: A. Deploy a SRS sensor network on a pilot area: To monitor the rainfall distribution in real time; B. Obtain the real time observed data from GNSS network To estimate the Precipitable Water Vapour pattern; Enter the results in the decision flow, which may draw a new innovative tool for timely management of potentially flood-prone areas.

Thank You daniele.caviglia@unige.it domenico.sguerso@unige.

19 Thank You

20 LOGO IMPRESA/ENTE

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