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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.
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Object-based precipitation analysis: application to tropical cyclones and the Slovenian radar dataMini Workshop on NWP Modelling Research in Slovenia15.December 2011 GregorSkokJulio Bacmeister, Joe Tribbia, BenediktStrajnar, JožeRakovec, 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”
Motivation • TRMM 3B42 • 3 hourly precipitation accumulations, 0.25 deg
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
Thresholding only Smoothing Thresholding after smoothing Original MODE method
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
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 BLUE – short lifespan, RED – long lifespan
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. Trajectories for 2001 ORANGE – eastward, GREEN – westward
Number of objects vs. lifespan Straight in a Log-Log graph = Power law
Analysis of tropical cyclone precipitation using satellite dataGregor Skok, Julio Bacmeister, Joe Tribbia • TRMM 3B42 precipitation data • The IBTrACS tropical cyclones track database • 11 years - 1998-2008
FiT -“Forward in Time” object identification • Merging! • Forced to check into the past and also perform merger there
FiT -“Forward in Time” object identification • Don’t allow merging! • Larger “wins” • No need to check into the past -> only forward in time • Side benefit: faster and less memory consuming
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
Estimation of object precipitation by “grown” objects Precipitation threshold Secondary threshold
Estimation of object precipitation by “grown” objects Sequentially grow objects: 1 iteration
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 Might be more unattributed low intensity precipitation below secondary threshold Unattributed precipitation In GROWN objects (to 1 mm/3h) now 75 % of all precipitation
Identification of TC objects Object MATCH?YES IBTrACS TC center Distance smaller than 2.5 deg MATCH?YES Distance larger than 2.5 deg MATCH?NO
Zonal means of TC precipitation GLOBAL SEA LAND
Regions • TCs contribute about 4 % (on average 40 km3/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 dataGregor 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
Number of objects Not an exponential distribution Hail events not a random shortlived process
Results Direction of movement by azimuth (regardless of lifespan) Trajectories longer than 150 min. Red = eastward, blue = westward
OBJECTS AFTER THRESHOLDING