Developments in echo tracking - enhancing TITAN 1 Nowcasting Techniques 7.6 ERAD 2014 2 September 2014 Mike Dixon 1 and Alan Seed 2 1 National Center for.

Developments in echo tracking - enhancing TITAN

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Nowcasting Techniques7.6

ERAD 20142 September 2014

Mike Dixon1 and Alan Seed2

1National Center for Atmospheric Research, Boulder, Colorado2Centre for Australian Weather and Climate Research, Melbourne, Australia

Current work on TITAN enhancementsCurrent work on TITAN enhancements

Separating convective regions from stratiform areas

prior to storm identification

Applying spatial scaling to storm objects appropriately

for forecast lead time

Correcting tracking errors using Optical Flow

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Example case: convective outbreak in Colorado, Example case: convective outbreak in Colorado, 2014/06/082014/06/08

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Handling mixed convective / stratiform situationsHandling mixed convective / stratiform situations

(a) Identify the convective regions within the radar volume(a) Identify the convective regions within the radar volume

(b) Constrain the storm identification to the convective regions only(b) Constrain the storm identification to the convective regions only

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Example of scene with large regions of stratiform / bright-band,Example of scene with large regions of stratiform / bright-band,along with embedded convectionalong with embedded convection

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Vertical section along line 1-2

Column-max reflectivity Bright-band Convection

Convectivearea

Stratiformarea

TITAN tends to merge both the convective and stratiform regionsTITAN tends to merge both the convective and stratiform regionsinto a single storm identification.into a single storm identification.

Therefore we need to isolate the convective regions.Therefore we need to isolate the convective regions.

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Merged convective andstratiform regions

The Steiner et. al (1995) method for convective partitioning was tested.The Steiner et. al (1995) method for convective partitioning was tested.However, it seemed to over-identify convective areas.However, it seemed to over-identify convective areas.

The Steiner method computes the difference betweenthe reflectivity at a point and the ‘background’ reflectivity

defined as the mean within 11 km of that point.

The method identifies the convective regions based on the reflectivity difference, determining the radius of convective

influence as a function of the background value.

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Stratiformarea

A modified method was developed, based on the ‘texture’ of reflectivity A modified method was developed, based on the ‘texture’ of reflectivity surrounding a grid point. surrounding a grid point.

‘Mean texture’ of reflectivity – mean over the column oftexture = sqrt(sdev(dbz2))

computed over a circular kernel 5km in radius,for each CAPPI height.

Convective (cyan) vs Stratiform (blue)partition computed by thresholding

texture at 15 dBZ

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Storm identification on all regionsStorm identification on all regionscompared with using the convective regions onlycompared with using the convective regions only

Storms identified using a 35 dBZ threshold.The storms include the regions of bright-band,

leading to erroneously large storm areas

Storms using the same 35 dBZ thresholdbut including only the convective regions

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Example of convective partitioning for single radar with Example of convective partitioning for single radar with extensive bright-bandextensive bright-band

1 degree PPI for radar near Sydney Australia.1 degree PPI for radar near Sydney Australia.Extensive stratiform region to the NE of the radar.Extensive stratiform region to the NE of the radar.

Vertical section (1-2) showing bright-band near the radar Vertical section (1-2) showing bright-band near the radar and convection further away and convection further away

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Stratiformregion

Bright-band

Computing the texture and creating the partition for the Computing the texture and creating the partition for the single-radar casesingle-radar case

Mean reflectivity texture over all levelsMean reflectivity texture over all levelsConvective areas shown in gray,Convective areas shown in gray,

with TITAN storm trackswith TITAN storm tracks

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TITAN storms for all areas (left)TITAN storms for all areas (left)and convective areas only (right)and convective areas only (right)

TITAN storms including stratiform areasTITAN storms including stratiform areas TITAN storms on convective areas onlyTITAN storms on convective areas only

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Spatial scaling appropriate for longer-term nowcasts - Spatial scaling appropriate for longer-term nowcasts - investigating approaches for a 2-hour lead time.investigating approaches for a 2-hour lead time.

For nowcasts of 30 to 60 minutes, the scale of storms as For nowcasts of 30 to 60 minutes, the scale of storms as measured by the radars is appropriate.measured by the radars is appropriate.

For longer lead time forecasts, say 1 hour to 2 hours, we For longer lead time forecasts, say 1 hour to 2 hours, we want to identify and track only larger scale features, so we want to identify and track only larger scale features, so we need a technique to isolate those features.need a technique to isolate those features.

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From Seed (2003) event lifetime vs. spatial scaleFrom Seed (2003) event lifetime vs. spatial scalebased on computed median correlation time for precipitation eventsbased on computed median correlation time for precipitation events

1414A. Seed, J Appl Meteor, Vol 42, No 3, March 2003.

~50km

2 hrs

30 mins

~12km

From Germann et. al (2006), for an expected lifetime of 2 hours,From Germann et. al (2006), for an expected lifetime of 2 hours,the spatial scale should be between 32 and 64 km.the spatial scale should be between 32 and 64 km.

We choose to test with a spatial scale of 50 km.We choose to test with a spatial scale of 50 km.

1515Germann et. al, J Atmos, Vol 63, No 8, August 2006.

2 hr lifetime~50 kmspatial scale

30 min lifetime~8 kmspatial scale

Computing the spectrum of the reflectivity field shows the Computing the spectrum of the reflectivity field shows the spatial frequency of the scenespatial frequency of the scene

Reflectivity over a 1200km x 1200 km gridReflectivity over a 1200km x 1200 km grid 2D FFT-based spectrum of reflectivity field2D FFT-based spectrum of reflectivity field

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Computing the spectrum of the reflectivity field shows the Computing the spectrum of the reflectivity field shows the spatial frequency of the phenomenonspatial frequency of the phenomenon

Reflectivity filtered for features 50 km and largerReflectivity filtered for features 50 km and largerSpectrum filtered to retain features of 50 km Spectrum filtered to retain features of 50 km

scale and largerscale and larger

1717This includes the stratiform regions. What if we use this procedure on the convective areas only?

Applying the 50km spatial filter to the convective regions Applying the 50km spatial filter to the convective regions highlights the larger scale convective featureshighlights the larger scale convective features

Convective reflectivity regionsConvective reflectivity regionsConvective reflectivity filtered for features 50 km Convective reflectivity filtered for features 50 km

and largerand larger

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Comparing convective storm identificationComparing convective storm identificationat different scalesat different scales

Identification of smaller-scale convective Identification of smaller-scale convective features, minimum size 30 kmfeatures, minimum size 30 km22

Identification of features at the 50km spatial Identification of features at the 50km spatial scale, minimum size 2500 kmscale, minimum size 2500 km22

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How well did we do with forecasting the lineHow well did we do with forecasting the linefiltered using a 50 km spatial filter?filtered using a 50 km spatial filter?

Forecast at 23:05 UTC on 2014/06/08. Shown areForecast at 23:05 UTC on 2014/06/08. Shown are4 x 30 minute forecasts, to 2 hours.4 x 30 minute forecasts, to 2 hours.

2-hour verification at 01:05 UTC on 2014/06/09.2-hour verification at 01:05 UTC on 2014/06/09.This demonstrates that we can have some success This demonstrates that we can have some success

forecasting large-scale features at longer lead times.forecasting large-scale features at longer lead times.

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IMPROVING STORM TRACKING USING IMPROVING STORM TRACKING USING OPTICAL FLOWOPTICAL FLOW

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Sometimes we get tracking errors in challenging situationsSometimes we get tracking errors in challenging situations

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Example of radar scanning at 10 minute intervals, with fast moving storms.This can lead to problems with correct tracking.

Using a field tracked such as Optical Flow allows us to estimate the Using a field tracked such as Optical Flow allows us to estimate the ‘background’ movement of the echoes.‘background’ movement of the echoes.

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Example of tracking errors.Example of tracking errors.Neither storm in the NE quadrant is correctly tracked.Neither storm in the NE quadrant is correctly tracked.

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In this case no overlap occurs because of small storm sizes, long time between scans In this case no overlap occurs because of small storm sizes, long time between scans and fast movement.and fast movement.

By applying the Optical Flow vectors to storms with short histories,By applying the Optical Flow vectors to storms with short histories,we can improve both tracking the forecast accuracy.we can improve both tracking the forecast accuracy.

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Using TITAN, you can have some fun and animate the Using TITAN, you can have some fun and animate the event as it unfoldsevent as it unfolds

2727Thank you

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THANK YOUTHANK YOU