Presentation on theme: "Object-based precipitation analysis: application to tropical cyclones and the Slovenian radar data Mini Workshop on NWP Modelling Research in Slovenia."— Presentation transcript:
Object-based precipitation analysis: application to tropical cyclones and the Slovenian radar data Mini Workshop on NWP Modelling Research in Slovenia 15.December 2011 Gregor Skok Julio Bacmeister, Joe Tribbia, Benedikt Strajnar, Jože Rakovec, Anton Zgonc, Mark Žagar
Overview Object based analysis Analysis of tropical cyclone precipitation using satellite data Hail-area tracking algorithm using radar data
Object based analysis Doctoral thesis: “Object-Based Analysis And Verification Of Precipitation Over Low- And Mid-Latitudes”
Object identification method Based on MODE - Method for Object-based Diagnostic Evaluation developed by Devis et al (2006a,b) Part of Model Evaluation Tools (MET) verification package developed by NCAR 3 steps: – Smoothing – Thresholding – Identification of self-enclosed areas as objects The method tries to simulate what a human forecaster or analyst might infer by a more subjective visual evaluation of a field => (Objective) simulation of a subjective evaluation
Original MODE method Thresholding only Smoothing Thresholding after smoothing
Methodology - Extended method Time evolution of objects “tri-dimensional” objects Enables study of properties: location, size, shape movement, lifespan, total object precipiation, ….
Doctoral thesis: Pacific, 6-years of 3-hourly TRMM 3B42 precipitation data
Trajectories for 2001 BLUE – short lifespan, RED – long lifespan highest density of objects with a longer lifespan (red) is in the ICTZ and in the low-latitudes in the west eastern tip of the ITCZ – mainly objects with short lifespan Central America – mostly shortlived objects …….
Trajectories for 2001 ORANGE – eastward, GREEN – westward Movement in the northern and southern parts of domain is predominantly eastward In the ITCZ region, movement in both directions is present although westward movement (green) is more frequent In the eastern and western part of the ITCZ the westward movement is clearly dominant.
Number of objects vs. lifespan Straight in a Log-Log graph = Power law
Analysis of tropical cyclone precipitation using satellite data Gregor Skok, Julio Bacmeister, Joe Tribbia TRMM 3B42 precipitation data The IBTrACS tropical cyclones track database 11 years - 1998-2008
The problem of “missed” precipitation Inside objects (threshold 7mm/3h) there is only 50 % of all precipitation. The other 50 % is located in a dislocated self- enclosed areas of low-intensity precipitation or just outside the borders of objects. We want to include nearby low-intensity precipitation for TC analysis
Estimation of object precipitation by “grown” objects Precipitation threshold
Secondary thresholdPrecipitation threshold Estimation of object precipitation by “grown” objects
Estimation of object precipitation by “grown” objects Sequentially grow objects: 4 iterations
Estimation of object precipitation by “grown” objects Sequentially grow objects: 9 iterations -> end
Estimation of object precipitation by “grown” objects Unattributed precipitation Might be more unattributed low intensity precipitation below secondary threshold In GROWN objects (to 1 mm/3h) now 75 % of all precipitation
TCs contribute about 4 % (on average 40 km 3 /day) This percentage is on average higher for oceans than for land (4.8 % vs. 1.4 %). NH the TCs contribute around 5.1 % and in SH about 2.8 % precipitation Compared to the oceans, the land sub-regions have much smaller TC precipitation volumes. some land regions get over 3 %: Australia, Maritime continent with E Asian islands and E Asia some seasons TCs contribute more precipitation; i.e. N America (6 % in SON), Australia (4 and 5.5 % in DJF and MAM), Maritime continent with E Asian islands (5,5 % in JJA and SON), E Asia (3 and 6 % in JJA and SON) and S Asia (4 % in SON)
Hail-area tracking algorithm using radar data Gregor Skok, Benedikt Strajnar, Jože Rakovec, Anton Zgonc, Mark Žagar Using volumetric radar data from Lisca – 8 years 2002-2010 Areas with hail precipitation identified using a combination of two methods: Waldwogel et al. (1979) and Gmoser et al.(2006). This produces a 2D binary field – hail yes/no. Radar scan is performed every 10 minutes. A sequence of 2D binary “hail” fields is fed into the object identification algorithm The movement of objects represent the movement of areas with hail precipitation
Hail area tracking No smoothing/thresholding possible since the field is binary The hail areas are relatively small and move fast – they often do not overlap in 10 minute intervals To overcome this problem the objects are artificially grown in all directions This improves the overlap but can merge nearby objects The value of parameter describing the “extent” of growth has to be selected carefully – sensitivity analysis
Success of TC matching Total IBTrACS TCs = 1144 Total IBTrACS TC trajectory points = 67362 Matched TCs = 1141 Matched TC trajectory points = 54919 Matching success of trajectory points = 81.5 % Non-matching happens when precipitation amounts in the TC are very low (usually below threshold so that no object is identified) and therefore a smaller amount of global TC precipitation is “missed” because of this reason.