1. LiveE! Project weather sensor network Seiichi X. Kato (Hyogo University of Health Sciences) 1

2. Weather hazard information Global Scale – Global Warming – Large scale hurricane Local Scale – Urban heat island – Urban squalls – Flood It is important to observe the weather information in detail in order to predict these phenomena Squalls FloodHurricane 2

3. Scale of meteorological phenomena Meteorological phenomena occurs in a variety of time/ spatial scale time scale spatial scale Tornado Heat Island Typhoon Low pressure minutehourday week 100m 1km 10km 100km 1000km Warm/cold front meso/micro -scale Synoptic-scale Squalls Our target 3

4. Grass-roots Weather Observing System Background – Low-cost weather sensors are marketed – Broadband network is in widespread use Some companies and individuals have weather station. It will be possible to observe the weather information in detail if these weather station are connected each other by internet. 4

5. Live E! Project Founded by WIDE project & some industries in Japan (2005) – WIDE project is a research consortium on the internet technology among industry and academia We’ll establish the platform to share all the digital weather information and devices by individuals and organizations in order to recognize the environment of the Earth. If you have some weather sensors and are interested in this project, please contact me. 5

6. Live E! sensor map(May. 2009) http://www.map-asp.net/Spatial_Gateway/pl/Gate_100.html 6 Tokyo Kurashiki ～ 230 sensor sites

7. What kind of sensors we use. Weather sensors that can read... – Temperature – Humidity – Pressure – RainFall – WindDir – WindSpeed Cost – US$200 ～ 3000 Vaisala WXT510 WM918WMR968 VantagePRO2 One-Wire Weather Station 7

8. Live E! server architecture Java VM SOAP Web Service (Axis) Database (PostgreSQL) Live E! Service 8

9. Live E! global system. jp. th. wide.jp.hoge.jp.hoge2.jp.ku.th.ait.th. DNS like architecture Control information for this system (profile, schema, query etc.) are exchanged by link Delegation of sub-authorities Metadata of the sensor data are replicated in all server. 9

10. Live E! service architecture Link Manager Data Manager Resolver & Retriever Archive Profile Schema Sensor Data Upload Profile Management User Link to Other Sites Live E! service 10

11. Current Web service API Get Observation data – GetCurrentDataAll – GetCurrentData – GetDataByTimespan – GetCurrentDataOf – GetCurrentDataByType – GetCurrentDataByAreaRect Ger detail profile of each sensor – GetProfileAll – GetProfile – GetProfileByType – GetProfileByAreaRect 11

12. Live E! data (xml) 10.2 … Profile – Sensor_Id – Sensor_type Temperature, Humidity etc. – Location information Longitude,latitude etc. Data – Observation value 12

13. > Provisions for natural disaster Kurashiki-city, Okayama, JAPAN – Rainfall has a locality; i.e., many sensors are needed to correctly monitor the area. – About 30 sensors on schools – Weather sensor mesh 3km × 3km – The local government uses these sensor data for flood prediction. http://live-e.naist.jp/map/ 13 75km Kurashiki

14. Web service -> Overlay network Current system – Server-client model – Single point of failure – Load of server will be enormous if the number of sensor become enormous. Next system – P2P(We use PIAX developed by Osaka Univ.) – Load distribution system – Realtime alarm for disaster 14

15. AR Model for forecast AR model is one the model of time series analysis, and can forecast future value by validating it from the past data Example of AR Model Validation results AIC minimization AIC ( Akaike’s Information Criterion) is one of the index that selects the best order of the model, and the minimum AIC model is the best model. PARCOR Method PARCOR means Partical Autocorrelation Cooefficient,and the following expression consists in PARCOR and model’s AR parameter. From this expression, if PARCOR 1,..,m is obtained, all AR parameters can be calculated. ● observed values ○ forecasted values 15

16. Example of our application for weather forecast 16 Historical data Prediction Obs. data Prediction by AR(auto-regressive) model Prediction by AR model is independent of the data of the other points.

17. Test application: contour map Temperature Barometric pressure humidity Contour map on Google Map 17

18. Current problem The number of sensor is small – The accuracy of our interpolation is incredible Not suitable for long-term forecast We’d like to combine the satellite data with our local sensor data. – Check the accuracy of interpolation and the value of each sensor(discovery of failure) – Get the information of valuable phenomena 18

19. Future work Collaborate with GeoGRID – Now we are implementing web service in order to convert Live E! xml data to SensorML by OGC (Open Geospatial Consortium). – To use the satellite data in order to check the error data in Live E! data and predict more accurately. 19