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

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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.