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Group 3 Karen Simpson Paul Fomenky Roman Sizov Sameh Ebeid

Automated Cyclone Discovery and Tracking using Knowledge Sharing in Multiple Heterogeneous Satellite Data. Group 3 Karen Simpson Paul Fomenky Roman Sizov Sameh Ebeid. Authors Shen-Shyang Ho Ashit Talukder Jet Propulsion Laboratory California Institute of Technology. Assignment 1

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Group 3 Karen Simpson Paul Fomenky Roman Sizov Sameh Ebeid

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  1. Automated Cyclone Discovery and Tracking using Knowledge Sharing in Multiple Heterogeneous Satellite Data Group 3 Karen Simpson Paul Fomenky Roman Sizov Sameh Ebeid Authors Shen-Shyang Ho Ashit Talukder Jet Propulsion Laboratory California Institute of Technology Assignment 1 02/22/2010

  2. Outline • Introduction • Previous Work • Data Description • Issues and Challenges • Heterogeneous Remote Satellite-Based Detection and Tracking Approach • Experimental Results • Lessons Learned and Conclusions Automated Cyclone Discovery and tracking

  3. Introduction What is Cyclone • An area of closed, circular fluid motion rotating in the same direction as the Earth • Low pressure areas, their center is the lowest atmospheric pressure in the region Automated Cyclone Discovery and tracking

  4. Introduction Surface-based Types • Polar cyclone • Polar low • Extra-tropical • Sub-tropical • Tropical • Mesoscale Automated Cyclone Discovery and tracking

  5. Introduction Extra-tropical • Synoptic scale low pressure weather system that has neither tropical nor polar characteristics • Often described as depressions or lows by weather forecasters Automated Cyclone Discovery and tracking

  6. Introduction Tropical • Storm characterized by a low pressure center and numerous thunderstorms that produce strong winds and flooding rain • Referred to by other names such as hurricane, typhoon, tropical storm • Develop over large bodies of warm water, and lose strength if they move over land Automated Cyclone Discovery and tracking

  7. Introduction Tropical • An average 86 tropical cyclones of tropical storm intensity form annually worldwide, 47 reaching hurricane/typhoon strength, and 20 becoming intense tropical cyclones Automated Cyclone Discovery and tracking

  8. Automated Cyclone Discovery and tracking

  9. Introduction Cyclone detection and tracking • The tropical prediction center / National Hurricane Center (TPC/NHC) use conventional surface and upper-air observations and reconnaissance aircraft report • In recent years, some studies have used satellite images that are manually retrieved and analyzed to improve the accuracy of cyclone tracking Automated Cyclone Discovery and tracking

  10. Introduction Cyclone detection and tracking • A new automated global cyclone discovery and tracking approach on a truly global basis using near real-time (NRT) and historical sensor data from multiple satellite • This implementation employs two types of satellite sensor measurements • QuikSCAT wind satellite data • Merged precipitation data using TRMM and other satellites Automated Cyclone Discovery and tracking

  11. Introduction Cyclone detection and tracking • Challenges pertaining to mining data from orbiting satellites • Each orbiting satellite cannot monitor a region continuously and the measurements are instantaneous Automated Cyclone Discovery and tracking

  12. Introduction Cyclone detection and tracking • Challenges pertaining to mining data from orbiting satellites • Each orbiting satellite cannot monitor a region continuously and the measurements are instantaneous Can minimize their effects by using data from multiple satellite Automated Cyclone Discovery and tracking

  13. Introduction Cyclone detection and tracking • Challenges pertaining to mining data from orbiting satellites • Each orbiting satellite cannot monitor a region continuously and the measurements are instantaneous • Different satellites provide different measurements • Different satellites sensors acquire measurements at different spatial and temporal resolution Automated Cyclone Discovery and tracking

  14. Introduction Cyclone detection and tracking These problems make mining heterogeneous data from multiple orbiting satellites extremely challenging and remains as a now primarily an unsolved problem • Challenges pertaining to mining data from orbiting satellites • Each orbiting satellite cannot monitor a region continuously and the measurements are instantaneous • Different satellites provide different measurements • Different satellites sensors acquire measurements at different spatial and temporal resolution Automated Cyclone Discovery and tracking

  15. Introduction Cyclone detection and tracking • Challenges related to the problem of detection and tracking of cyclones • Cyclone events are dynamic in nature • There is lack of annotated negative (non-cyclone) examples by experts • A single satellite sensor may miss a cyclone event due to a pre-defined orbiting trajectory Automated Cyclone Discovery and tracking

  16. Outline • Introduction • Previous Work • Data Description • Issues and Challenges • Heterogeneous Remote Satellite-Based Detection and Tracking Approach • Experimental Results • Lessons Learned and Conclusions Automated Cyclone Discovery and tracking

  17. Previous work Previous work • No solution currently exists that uses heterogeneous sensor measurement to automatically detect and track cyclones • The current solutions involve human interference and decision Automated Cyclone Discovery and tracking

  18. Outline • Introduction • Previous Work • Data Description • Issues and Challenges • Heterogeneous Remote Satellite-Based Detection and Tracking Approach • Experimental Results • Lessons Learned and Conclusions Automated Cyclone Discovery and tracking

  19. Data description QuikSCAT Wind Data • The QuikSCAT (Quick Scatterometer) mission provide important high quality ocean wind data set • Recent research showed QuikSCAT data is useful for early detection of tropical cyclones Automated Cyclone Discovery and tracking

  20. Data description Precipitation Data from TRMM satellite • The Tropical Rainfall Measurement Mission (TRMM) is a joint mission between NASA and JAXA designed to monitor and study tropical rainfall • The (Level) 3b-42 TRMM data product used in this paper is produced using a combined instrument rain calibration algorithm Automated Cyclone Discovery and tracking

  21. Outline • Introduction • Previous Work • Data Description • Issues and Challenges • Heterogeneous Remote Satellite-Based Detection and Tracking Approach • Experimental Results and Conclusions Automated Cyclone Discovery and tracking

  22. Issues and Challenges • Main issues and challenges • Non-Continuous Region Monitoring • Event Occlusion • Varying Temporal and Spatial Resolution • Lack of Annotated Negative Examples Automated Cyclone Discovery and tracking

  23. Main issues and challenges • Satellite measurements are instantaneous; hence, satellites cannot measure sustained winds. Remember, a leading characteristic of cyclones is sustained winds • TRMM 3B42 data is known to underestimate rainfall, which might lead to false negatives Automated Cyclone Discovery and tracking

  24. Non-Continuous Region Monitoring – Problem • Geostationary Operational Environmental Satellites (GOES) monitor specific area at all times, helping identify “sustained” winds etc. Unfortunately, most countries do not have these. • Because QuikSCAT and TRMM are motile, this monitoring is “lost.” This results in “invisible” swaths. Automated Cyclone Discovery and tracking

  25. print Non-Continuous Region Monitoring – Problem Evidence Automated Cyclone Discovery and tracking

  26. Non-Continuous Region Monitoring – Operational Weather Satellite System • Satellite systems consist of two types • Geostationary Operational Environmental Satellites are static and throw light on current and short term weather trends. • Orbiting satellites like QuikSCAT and TRMM help with longer term forecasting. Automated Cyclone Discovery and tracking

  27. Non-Continuous Region Monitoring – Solution • Usage of multiple satellites produces a higher temporal density hence helping alleviate the problem. • A group of complementary satellites can make this problem almost insignificant. Automated Cyclone Discovery and tracking

  28. Event Occlusion - Problem • Satellite swath can partially (or worst case, totally) miss events of interest. • Though in continuous orbit, event can be gone by time satellite comes back. Automated Cyclone Discovery and tracking

  29. print Event Occlusion – Problem Evidence 1 • QuikSCAT showing only a small part of event of interest. • Hurricane Dean – Aug 17th 2007, 0900 Automated Cyclone Discovery and tracking

  30. print Event Occlusion – Problem Evidence 2 • Next QuikSCAT swath shows a bit more. • Hurricane Dean – Aug 17th 2007, 1041 Automated Cyclone Discovery and tracking

  31. print Event Occlusion – Problem Evidence 3 • Another QuikSCAT swath shows much more, but missing eye of storm. • Hurricane Dean – Aug 17th 2007, 2310 Automated Cyclone Discovery and tracking

  32. print Event Occlusion – Problem Evidence 4 • QuikSCAT swath from previous day showed more! • Hurricane Dean – Aug 16hth 2007, 2156 Automated Cyclone Discovery and tracking

  33. Event Occlusion – Solution • Clearly, multiple orbits of the same satellite can produce more information on the event being examined. • Also, as in continuity monitoring issue, numerous satellites working together are less likely to miss important events. Automated Cyclone Discovery and tracking

  34. Varying Temporal and Spatial Resolution – Problem • Different aspects influence the temporal resolution of measurements: • Satellite orbit time (QuikSCAT 101 minutes, TRMM 92.5mins) • Swath width of measuring instrument (SeaWinds on QuikSCAT 1800km; PR, TMI and VIRS on TRMM 247km, 878km, 873km respectively) • Geographic coverage (QuikSCAT – global; TRMM – 50N to 50S) Automated Cyclone Discovery and tracking

  35. Varying Temporal and Spatial Resolution – Problem Cont’d • Spatial resolution depends on • Sensor instruments (PR, TMI and VIRS on TRMM 5.1km, 5.0km, 2.4km respectively) • Satellite orbital altitude ((TRMM Pre-boost (350km) (TMI): 4.4km to 5.1km (Post-boost (403 km)) • Processing algorithm (operational QuikSCAT data has spatial resolutions of 12.5km and 25km ) Automated Cyclone Discovery and tracking

  36. Varying Temporal and Spatial Resolution – Problem Cont’d 2 • In addition to inter satellite differences, there are some intra satellite tempo-spatial differences. • TRMM Level 3 data has lower temporal resolution than levels 1 and 2. Automated Cyclone Discovery and tracking

  37. Varying Temporal and Spatial Resolution – Solution • On TRMM, mine areas QuikSCAT showed events of interest on. • Also, because of different swath sizes, latitudes and longitudes were used to identify locations. • Temporal tracking done on TRMM as temporal resolution higher than in QuikSCAT. Automated Cyclone Discovery and tracking

  38. Lack of Annotated Negative examples - Problem • Scientists have not clearly shown what a “non-event” is despite the large archives of events. Automated Cyclone Discovery and tracking

  39. Lack of Annotated Negative examples - Solution • Random “non-event” days were monitored and fed to system as examples of non event. Automated Cyclone Discovery and tracking

  40. Introduction • Previous Work • Data Description • Issues and Challenges • Heterogeneous Remote Satellite-Based Detection and Tracking Approach • Experimental Results and Conclusions Automated Cyclone Discovery and tracking

  41. Heterogeneous Remote Satellite-Based Detection and Tracking Approach Heterogeneous Remote Satellite-Based Detection and Tracking Approach • QuikSCAT Feature Selection • Ensemble Classifier for Cyclone Detection • Knowledge Sharing between TRMM and QuikSCAT data for Cyclone Tracking Automated Cyclone Discovery and tracking

  42. Heterogeneous Remote Satellite-Based Detection and Tracking Approach QuikSCAT Feature Selection • Features that characterize and identify a cyclone are selected from QuikSCAT satellite data • The QuikSCAT Level 2B data that consist of ocean wind vector information are utilized • The Level 2B data are grouped by rows of wind vector cells (WVC) which are squares of dimension 25 km or 12.5 km Automated Cyclone Discovery and tracking

  43. Heterogeneous Remote Satellite-Based Detection and Tracking Approach QuikSCAT Feature Selection (cont`d) • 1624 WVC rows at 25 km or 3248WVC rows at 12.5 are required to cover the earth circumference • Out of 25 fields in the data structure for the Level 2B data we are interested only in latitude, longitude, wind speed(WS) and wind direction (WD) Automated Cyclone Discovery and tracking

  44. Heterogeneous Remote Satellite-Based Detection and Tracking Approach QuikSCAT Feature Selection (cont`d) Table 1. The fields of interest from Level 2B data structure Automated Cyclone Discovery and tracking

  45. Heterogeneous Remote Satellite-Based Detection and Tracking Approach QuikSCAT Feature Selection (cont`d) • The Level 2B data needs to be interpolated on a uniformly gridded surface due to the non-uniformity in the measurements taken by the QuikSCAT satellite on a spherical surface • The nearest neighbor rule is used for this pre-processing procedure for both wind speed (WS) and wind direction (WD) Automated Cyclone Discovery and tracking

  46. Heterogeneous Remote Satellite-Based Detection and Tracking Approach QuikSCAT Feature Selection (cont`d) • Histograms are constructed to estimate probability density of the wind speed (WS) and wind direction (WD) within a predefined bounding box extracted from a QuikSCAT image Automated Cyclone Discovery and tracking

  47. Heterogeneous Remote Satellite-Based Detection and Tracking Approach QuikSCAT Feature Selection (cont`d) • WS(i,j),WD(i,j) – wind speed and wind direction at location (i,j) • DSR(i,j) – the direction to speed ratio at (i,j) Automated Cyclone Discovery and tracking

  48. Heterogeneous Remote Satellite-Based Detection and Tracking Approach QuikSCAT Feature Selection (cont`d) • When there is a strong wind with wind circulation, the DSR at a WVC will be small • DSR histogram will have a skewed distribution towards the smaller value • When there is weak or no wind with no circulation, DSR histogram does not have the skewed characteristics Automated Cyclone Discovery and tracking

  49. Heterogeneous Remote Satellite-Based Detection and Tracking Approach QuikSCAT Feature Selection (cont`d) • When a region contains a cyclone, the WS histogram shows a density estimate skewed towards the larger values and WD histogram shows a “near uniform” distribution • A cyclone is defined as a “warm-core non-frontal synoptic-scale” system, with “organized deep convection and a closed surface wind circulation about a well-defined center” Automated Cyclone Discovery and tracking

  50. Heterogeneous Remote Satellite-Based Detection and Tracking Approach QuikSCAT Feature Selection (cont`d) • To discriminate between cyclone and non-cyclone events based on the circulation property two additional features are used: (1) a measure of relative strength of the dominant wind direction (DOWD) (2) the relative wind vorticity (RWV) Automated Cyclone Discovery and tracking

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