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Object-based precipitation analysis: application to tropical cyclones and the Slovenian radar data
Mini Workshop on NWP Modelling Research in Slovenia15.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”
• TRMM 3B42
• 3 hourly precipitation accumulations, 0.25 deg
Motivation
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 methodThresholding 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 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
FiT - “Forward in Time” object identification
FiT - “Forward in Time” object identification
FiT - “Forward in Time” object identification
- Merging!- Forced to check into
the past and also perform merger there
FiT - “Forward in Time” object identification
FiT - “Forward in Time” object identification
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
FiT - “Forward in Time” object identification
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” objectsPrecipitation threshold
Secondary thresholdPrecipitation threshold
Estimation of object precipitation by “grown” objects
Estimation of object precipitation by “grown” objectsSequentially grow objects: 1 iteration
Estimation of object precipitation by “grown” objectsSequentially grow objects: 4 iterations
Estimation of object precipitation by “grown” objectsSequentially grow objects: 9 iterations -> end
Estimation of object precipitation by “grown” objectsUnattributed precipitation
Might be more unattributed low intensity precipitation below secondary threshold
In GROWN objects (to 1 mm/3h) now 75 % of all precipitation
IBTrACS database
Identification of TC objectsObject
IBTrACS TC center MATCH? YES
MATCH? YES
Distance smaller than 2.5 deg
MATCH? NO
Distance larger than 2.5 deg
Animation
TC precipitation [mm/day]
Contribution of TC precipitation to all precipitation
[%]
Zonal means of TC precipitation
GLOBAL SEA LAND
Regions
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)
Yearly global TC precipitation
1998 2000 2002 2004 2006 20080
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
11000
12000
13000
14000
15000
16000
pre
c. v
olu
me
[km
^3/y
ea
r]
year
Global Global_sea Global_land NH_Global NH_Global_sea NH_Global_land SH_Global SH_Global_sea SH_Global_land
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
Animation
Animation
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
not grown
grown by 1 km
grown by 2 km
grown by 3 km
Number of objectsNot an exponential distribution
Hail events not a random shortlived process
Results
Trajectories longer than 150 min.Red = eastward, blue = westward
Direction of movement by azimuth(regardless of lifespan)
Thank you
ORIGINAL
SMOOTHING
OBJECTS AFTER THRESHOLDING
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.