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Visualisierung Raum-Zeit Würfel

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Visualisierung Raum-Zeit Würfel

  1. 1. Blockwoche Visualisierung Raum-Zeit-Würfel Raum-Zeit-Würfel Peter Löwe, GFZ Potsdam ploewe@gfz-potsdam.de
  2. 2. Übersicht• Einführung Raum-Zeit-Würfel• Anwendungsbeispiele 2D->3D – Wetterradar – Küstenschutz – Tsunami-Frühwarnsystem• Anwendungsbeispiel 5D->3D – Qualitätssicherung Wetterradar
  3. 3. Motivation„Time is often considered as the fourthcartographic or geographic dimension“ [Wikipedia:“Time“]
  4. 4. Zeitgeographie• 1960er: Torsten Hägerstrand • Raum-Zeit-Würfel: begründet die Zeitgeographie: – X/Y: Geographischer Raum – Z: Zeit – Raum-Zeit-Modell • Zeigt die Beziehungen – Raum-Zeit-Pfad zwischen Zeit, Raum und weiteren Variablen – Raum-Zeit-Würfel • Aufzeigen von Raum-Zeit- Pfaden für Objekte/Individuen • Explorative Datenanalyse • Option: Real Time Monitoring http://www.svgopen.org/2005/papers/abstract_neumann_thematic_navigation_in_space_and_time/
  5. 5. Umsetzung mit GIS„Modelling and visualizing time and spatio-temporal navigation in GIS is truly amultidisciplinary research topic, includingdomains such as• geography,• social and live sciences,• psychology,• philosophy,• GIScience,• GIS,• cartography,• computer science, Substantial input is currently contributed from information visualization, a discipline• information visualization, that deals a lot with interactive graphics,• multimedia design, visualizing large data sets and data mining• mathematics, statistics, etc. issues“ [Card et al 1999 in A. Neumann, 2005]
  6. 6. Beispiel: Minard‘s Karte• Der französische Bauingenieur Charles Joseph Minard veröffentlichte 1869 eine Grafik zu den Verlusten der französischen Armee während Napoleons Russlandfeldzug, die Carte figurative des pertes successives en hommes de lArmée Française dans la campagne de Russie 1812–1813. [Wikipedia]
  7. 7. Carte figurative des pertes successives en hommes de lArmée Française dans la campagne de Russie 1812–1813 [Wikipedia]
  8. 8. 2D -> 3DMinards Karte ist Sankey Diagramm Darstellung als Raum-Zeit-Würfel(Darstellungen mitmengenproportionalen Pfeilen). Kraak 2003
  9. 9. Praxisbeispiele1. Wetterradar2. Küstenentwicklung3. Tsunamimodellierung
  10. 10. Beispiel 1: Wetterradar
  11. 11. Datenbasis• Dreidimensionale Volumenscans der Atmosphäre.• Frequenz: 5 Minuten• Auflösung: 1km• Eingangsdaten: Constant Altitude Plan Position Indicator (CAPPI) (zweidimensionale „Schnitte“)• Maximales Echo über alle Schnittebenen: MaxCAPPI• Produkt: – Niederschlagskarten – „Pluviogramme“• Abgeleitete Größe: Niederschlagserosivität
  12. 12. Processing: High Level View Lower Atmosphere MRL-5 Radar, SAWS Large Amounts of 3D Data 2D Rainfall Rainfall Data Maps Erosivity ModelA complete scan of the loweratmosphere (up to 18km, 200km Erosivityradius) takes 5 minutes: Pulses●288 data sets daily●8,064 – 8928 data sets monthly Visualization Erosivity Maps●195,120 data sets per year
  13. 13. MaxCAPPI Σ Reflectivity 16:18:50 Hours 16:43:30 Hours16:59:56 Hours 24h total Erosivität Σ erosive ErosivityLeft: ReflectivityCentre: RainfallRight: Erosivity
  14. 14. The Challenge● Can we trust the 2D rainfall data ? – Metadata appears correct. – [are the rainfall fields correct ?]● Weather Radar provides 3D data. – [3D->2D transformation: Correctly done?]
  15. 15. Garbage in, Garbage out● Can we trust the rainfall information of the weather radar ?● Model results are based on rainfall data.● Errors and Biases in the rainfall data will affect all derived products.● What about transient biases which might vary in time or space?● One should have a close look at the data ! 3D data Trust „Flattening Overall “ Trust 2D information
  16. 16. From single drawings 1 2 3 „Radar Rain Flip-Book“ „Erosivity Peaks Flip- Book“
  17. 17. Boredom in, Boredom out● Large data archives exist and more data are added every day (288 data sets in our example).● How can we easily identify time intervals when „some interesting weather“ has occurred?● We could watch it all in 4D (3D over time): – Takes too much time, is incredibly boring – Problem to watch the right things at the right time.
  18. 18. Data Errors Flip-book Volume (ground targets) 3 3 2 2 time 1 12D Space: Rainfall field Not real Yellow: Rainfall clouds ! Red: Erosivity
  19. 19. Ce nest pas un nuage! Painting of a pipe
  20. 20. Quality Control Rods of The height of a rainfall track tells eternal us how long it did rain at a soaking: certain location Data errorsA precipitation field and its resulting erosivity pulses shown in side-view.
  21. 21. Beispiel 2: Küstenschutz [Materialien von Prof. Helena Mitasova, 2011] Analysis of barrier islands vulnerability and evolution:  Airborne lidar surveys since 1996  Analysis of DEM time series  Space-time cube
  22. 22. Datenbasis• Datenquelle: LIDAR Scans• Auflösung: 0.3-1.0m• Frequenz: Jährlich
  23. 23. Barrier islands Dynamic topography: Nags Head sand is redistributed by wind, waves, storm surge Vulnerable: coastal erosion, sea level rise, inundation First line of defense against storms Cape Hatteras0 10km N
  24. 24. Vulnerability: Dune ridgelineVulnerability: function of dune ridge and toe positionLeast cost path method for ridgeline extraction:Continuous line, robust to elevation anomalies, highly automated Elevation surface Cost surface
  25. 25. Vulnerability: Dune toelineDune toe extraction: elastic sheet, cost surface and least cost path Cost Surface
  26. 26. Evolution metrics from DEM series t1 Core surface z-min for each cell t2 Envelope surface z-max for each cell tNags Head . 3 Dynamic layer: bounds terrain evolution for a . tn given period Shoreline band: defined by shoreline from core result and envelope, bounds shoreline dynamics for given period0 4km N
  27. 27. Evolution metrics core, envelope, DEM 1999 0 100m 2001 2004Orthophoto and shoreline band 2008 1999 2008 c 1999 2001 min 2004 max 2005 2001 2007 2005 2008 2007 0 50m 2008Time of maximum
  28. 28. Terrain evolution in space-time cubeHow does evolution pattern change with elevation?What is the direction of fastest elevation change?Time series of (x,y,z) point clouds interpolated to voxel model tn ... space-time cube t3 t2 z=f(x,y,t)t1 Time [year] 15 7 Y[m] 0m X[m]
  29. 29. Contour evolution as isosurface Isosurface representation of 10, 11 and 12m elevation contours for time series Elevation: 10 11 12 m 2008 2005 2001 1999 Time Y 0 100m X
  30. 30. Contour evolution with overwash DEM [year] z = 4.5m 0 200m 2005 2005 2003 2001 1999 1997 beach 2003 2005 shoreline 1997 4.6 m contoursTime[year]2005 Time2003 [year]2001 beach Y[m]1999 Elevation 0 200m X[m]1997 4.5m
  31. 31. Dynamics at different elevationsDifferent spatial pattern of dynamics at different elevations:0.3m shoreline, 1.5m upper beach, 4.5m mid-dune, 7.5m dune ridge 2005 2003 2001 1999 z=4.5m 1997 z=1.5m200520032001 2005 dune rebuilt1999 2003 dune overwash1997 2005 2003 sand disposal 2001 1999 z=7.5m 1997 z=0.3m2005200320011999 Time1997 [year] 0 200m stable dune peaks X[m] Y[m]
  32. 32. Beispiel 3: Tsunamiwarnung
  33. 33. Tsunamifrühwarnsysteme• Tsunami Early Warning Systems 1. Erdbeben-Lokation -> Auswahl (TEWS) basieren auf online „passender“ Tsunamimodelle Sensoren und Modelldaten. 2. Reduktion der in Frage kommenden Simulationen• Tsunamiausbreitungs-modelle anhand von online-Sensoren. werden in Bibliotheken für den 3. Informationslogistik auf Basis Ernstfall vorgehalten. des prognostizierten Tsunamiverlaufs.
  34. 34. Datenbasis• Tsunamimodellrechnungen – Vergangenheit – „What-If“• Inhalte: – Wellenhöhenraster – Mareogramme („Fieberkurven“)• Frequenz: 2-5 Minuten• Abgeleitete Daten: – Maximale Wellenhöhen Maximale Wellenhöhen des Tohoku-Tsunami 2011• Kritisch: Validität der (GFZ) Simulation
  35. 35. Validität der Simulation• Wellenausbreitungen sind dynamisch• Verifikation an historischen Testfällen ist „schwierig“• Beurteilung der Stabilität/Belastbarkeit der Simulationen : – Räumliches Verhalten – Zeitliches Verhalten – Informationsgehalt
  36. 36. Beispiel: Kreta 356n.Chr.Wellenaus- breitung Maximale Wellenhöhen
  37. 37. Datenfehler
  38. 38. Tohoku Tsunami 11.3.2011• Magnitude 9 Beben• Bruchlänge: 400 km• 27m Gesamt-Versatz• 7m Vertikalbewegung• „Live-Übertragung“ via KML
  39. 39. Tohoku Raumzeitwürfel
  40. 40. Negative Wellen
  41. 41. Positive Wellen
  42. 42. Einladung: Lange Nacht der Wissenschaften 2. Juni 2011.• Raumzeitwürfel in 3D im Visiolab des GFZ.
  43. 43. 5D -> 3D• Kollabieren höherdimensionaler Daten am Beispiel Wetterradar
  44. 44. Datenkollaps der Höheninformation(Wurde schon gezeigt)The 2D (xy) rainfall field was „squeezed“ out of the 3D (xyz) weather radar data, implicitly „collapsing“ the vertical dimension.The stacking of the time frame „flip-book“ pages substituted the altitude (z) dimension by the time dimension.
  45. 45. Next Step: Spatial CollapseThis approach can be followed further:● In the previous example we collapsed the z- dimension● Now we collapse the horizontal (xy) dimension.● The resulting diagram is a preview format: „Contoured Altitude by Frequency Diagram“ (CFAD).
  46. 46. Contoured Frequency by Altitude Diagrams (CFAD)● CFAD can be created from 3D radar reflectivity data (original airspace radar scan). The 3D data set is sliced vertically.● Histograms of the reflectivities (1D) are generated for each slice/layer.● Stacking the histograms gives us a 2D synopsis of the current situation in the scanned airspace.● This tells us a lot about the weather and potential measurement errors.
  47. 47. CFAD – An Example Largest count of hydrometeorsContoured Frequency by Altitude Diagram (CFAD).Numbers on contour lines give the number of voxels in the observation areawith a given radar reflectivity.The CFAD gives a snapshot of weather intensity at different altitudes in the lower atmosphere.
  48. 48. CFATD = Raum-Zeit-Würfel● Contoured Frequency Altitude by Time Diagram adds the time dimension, resulting in a volume body -> .● The shape of the CFATD makes it easy to identify: ● periods of high radar reflectivity, i.e. intense weather, and ● Errors in the radar or processing chain.
  49. 49. Beispiel Iso Surfaces resemble levels of droplet counts (a few, many,Altitude lots) Critical threshold: If the inner layer (many droplets) of the „loaf“ exceeds it, then there is heavy downpour or even hail.
  50. 50. Visual Quality Control● CFATD gives a convenient and reliable quality measure for observations not to use● If the CFATD structure appears blocky, or „non-organic“: discard the data Faulty data Faulty data
  51. 51. Better data, better models● 4D previews for „Live Quality Control“ in sensor systems: – Weather Radar does „now-casting“ ● It looks into the distance (right now) ● but not into the future – Real-time generation of CFATD „loaves“ could be used for radar system calibration and maintenance. What level of quality do we get RIGHT NOW ?
  52. 52. Fazit• Raum-Zeit-Würfel können in verschiedenen Szenarien eingesetzt werden• Sie vermitteln Übersicht über zeitlich/räumlich fluktuierende Datensätze für Analyse und Diskussion• Möglichkeit zur Analyse von räumlich/zeitlichen Fehlern• Nutzung ist retrospektiv und in „real-time“ möglich.• In Verbindung mit Datenreduktionsmethoden (CFAD) können auch höherdimensionale Daten genutzt werden.
  53. 53. Danke für die Aufmerksamkeit

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