Using a novel Hail Sensor to Optimize Weather Radar

Transcription

1 Using a novel Hail Sensor to Optimize Weather Radar By: Christian Ruckstuhl, Justin D Atri, Serge Mattli, Martin Loeffler-Mang, Dominik Schoen, Edgar Wetzel, and Urs Germann

2 2 World s first online network of ground-based hail measurement combined with modern dual-polarization weather radar TODAY S AGENDA Research Motivations What were our goals? Intro to Consortium Who was involved? Experimental Approach Can forecasts be improved using new hail measurement tools? Automatic Hail Sensor What is the HailSens and how does it work? Hail Event Results What does a typical hail event look like? Future Plans How does MeteoSwiss use the data and what s next?

3 Research Motivation Grossversuch Hail Measurement Network of Hail Pads in Central Switzerland from early-mid 1980 s 5km 3 In order to exploit the great potential within modern radar, on-the-ground hail measurement is needed. Data behind hail detection radar algorithms are based on a climate and technology that are vastly different than today. This data is based on Hailpad studies from years ago that are correlated with previous generation radar technology. Some hail prediction data is even based from studies from Australia. Source from Study call Grossversuch Federer B. (1986) Main results of Grossversuch IV. Journal of Climate and Applied Meteorology, 25,

4 Past Present Future 4 Switzerland Map Switzerland Map Rapidly increasing user demands wrong (heavy rainfall erroneously classified as hail)? + old hail risk map (low high) frequency of hail > 3cm + App reports + sophisticated analyses Input: maximum radar reflectivity, hail damage claims Output: hail risk map with errors OPPORTUNITY FOR NEW ERA OF HAIL SERVICES Input: 3D radar reflectivity, hail damage claims (no ground truth for calibration) Output: empirical climatology Input: 3D radar reflectivity, hail damage claims, + dual-polarisation radar + hail sensors on ground + App reports (crowdsourcing) Output: a new era of hail services (climatology, real-time monitoring, nowcasts/warning, forecasts)

5 5 Our Portfolio Research Projects Hypothesis 03 Can dual polarization radar algorithms be improved using new hail measurement tools? If these new tools provide accurate measurements, they can enable the foundation for the next generation of hail products for leading weather service providers. HailSens: Automatic Hail Sensor Crowd-Sourced Hail Observations

6 6 Intro Research Consortium Urs Germann 1 Martin Löffler-Mang 2 MeteoSwiss 1 Switzerland s National Weather Agency University of Applied Sciences Saarbrucken 2 Laboratory for Optical Measurement & Laser Technology KISTERS AG 3 Leading SW/HW solutions provider for the sustainable management of energy, water, & air Edgar Wetzel 3 Dominik Schön 4 dimeto GmbH 4 German R&D Service Provider for Intelligent Sensors innet Monitoring AG 5 Swiss Environmental Monitoring Service Provider and Innovative ICT Systems Integrator Serge Mattli 5

7 7 Our Research Approach Two-year Pilot study of Central Switzerland s Hail Hotspot Region Solothurn Sursee Aadorf Yellow = 2cm hail event per year Switzerland Map 50km 10 HailSens network built near hail hotspots RICK RHONSON Rooftop Installation of HailSens

8 8 HailSens: an automatic hail sensor Designed to automatically measure time-series based hail data Large measurement surface of.5m 2 ensures a representative measurement sample Uses a Piezo Based measurement principle to measure: 1. Hailstone Impact Energy 2. Hailstone Size & Distribution 3. Event Intensity and Duration Enables the classification of Hail Classes & Hail damage potential

9 HailSens: Measurement Principle Cloud-Based Intelligence 9 On-Device Intelligence Microcontroller JL Sanchez et al v 1. Was that vibration hail? Piezo Signal Piezo Microphone & Microcontroller Time (ms) GPRS Piezo Signal Calibration Regression Curve Kinetic Energy of Hail Impact 2. How big was the Hailstone? KISTERS Cloud DB Application

10 Sensor Development & Hail Canon Lab Setup Laboratory Calibration Method Hailpad was initially used as a comparison for developing the HailSens measurement sensitivity range & hail detection algorithm 10 Löffler Mang, M., Schön, D. and Landry, M. (2011) Characteristics of a New Automatic Hail Recorder. Atmospheric Research, 100,

11 11 Calibration in the Field Ball Drop Method Piezo Signal Calibration Regression Line Kinetic Energy of Hail Impact The correlation between the Piezo Signal & the Kinetic Energy is defined by dropping 4 different sized balls (with the same density as ice) 10 times from the same height. Every HailSens has its own Regression Line

12 Example Hail Event Results Figure 3: Radar Data Figure 1: Mobile Phone confirmation Aadorf, CH Hail Event HailSens confirmed maximum hailstone size detected by MeteoSwiss radar. Figure 2: Hailstone Size Duration Profile of Aadorf Hail Event (redline is average) Figure 4: HailSens Estimated Hailstone Size based on Kinetic Energy Hailstone Size in mm :15:07 18:15:50 18:16:34 18:17:17 18:18:00 18:18:43 18:19:26 18:20:10 18:20:53 18:21:36 Time UTC on

13 13 What Portfolio have Projects we 03learned? Can dual polarization radar algorithms be improved using new hail measurement tools? HailSens is a promising on-the-ground verification tool & its hail measurement data will form the foundation of radar algorithm enhancements. HailSens: Automatic Hail Sensor Excellent for on-the-ground verification of radar predicted hail events Time-series based data enable the foundation for algorithm improvement Difficult to cover the entire country-side High Data Quality but Low Quantity Crowd-Sourced Hail Observations Useful for Quality Assurance of Radar predictions > Did it actually hail? Useful for Marketing the MeteoSwiss App Human Reporting Error & Lack of Time Series limits use for improving radar algorithms High Data Quantity but Low Quality

14 14 Using the HailSens to improve MeteoSwiss Hail Products Polarmetric Hydrometer Classifcation Algorithm 5 yrs. Data Collection Data from operational scan of Albis radar, 12 June 2015, 1735, RHI181 Hyrdometeor classification through statistical clustering of polarimetric rada measurement: a semi-supervised approach» (2016) Atmospheric Measurement Techniques, Vol 9, N Besic, M Gabella, J Grazioli, U Germann and A Berne Reserch Project between MeteoSwiss + EPFL Algorithm Enhancement R&D Projects New Era of Hail Products climatology, forecasts, real-time monitoring, nowcasts/warnings

15 15 Interested in Measuring Hail? Edgar Wetzel KISTERS AG Business Development Manager Justin D Atri innet Monitoring AG Partnership Coordinator datri.justin@innetag.ch Come visit the HailSens at stand #6040 KISTERS AG Source: Sean Heavey (Bing United States)

16 16 APPENDIX: Citations Besic, N., Gabella, M., Grazioli, J., Germann, U. and Berne, A. (2016) Hyrdometeor classification through statistical clustering of polarimetric radar measurements: a semi supervied approach. Atmospheric Measurment Techniques, 9, Federer B. (1986) Main results of Grossversuch IV. Journal of Climate and Applied Meteorology, 25, Löffler Mang, M., Schön, D. and Landry, M. (2011) Characteristics of a New Automatic Hail Recorder. Atmospheric Research, 100, Sanchez, J., Lopez, L., Bustos, C., Marcos, J., and Garcia Ortega, E., (2008) Short term forecast of thunderstorms in Argentina, Atmospheric Research, 88:1, 36 45