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GOES-R AWG Aviation Team: Tropopause Folding Turbulence

Product (TFTP)June 14, 2011

Presented By: Anthony Wimmers

SSEC/CIMSSUniversity of Wisconsin - Madison

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Outline

Executive Summary (1 slide) Algorithm Description (9 slides) ADEB and IPR Response Summary (1 slide) Requirements Specification Evolution (2 slides) Validation Strategy (5 slides) Validation Results (4 slides) Summary (1 slide)

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Executive Summary This ABI Tropopause Fold Turbulence Product (TFTP) generates the

Option 2 products of tropopause fold-generated turbulence in terms of the upper/lower bounds and the two most hazardous crossing directions

Version 5 was delivered in June. The ATBD (100%) is on track for a June 30 delivery

The product requirements are met. For the accuracy requirement, this means 50% accuracy of predicting turbulence within the volume of a turbulent tropopause fold object

The validation strategy employs a 16-month dataset of automated aircraft Eddy Dissipation Rate (EDR) observations, a commonly accepted proxy for quantifying turbulence intensity.

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Algorithm Description

What Is a Tropopause Fold?

5

14

12

10

8

6

4

150

200

300

400

500

600700

(~100 km)

subtropicalair mass

polar air mass

stratosphere

Pre

ssur

e (h

Pa)

Hei

ght (

km)

tropopause

front

Upper-air front

J

Tropopause folding describes an event at the tropopause break in which the tropopause folds into the troposphere due to ageostrophic flow around the jet stream.

This frequently leads to dynamical instability (enhanced turbulence) because of high levels of vertical shear across the boundary of the tropopause fold, which contains elevated potential vorticity.

Enhanced turbulence

What Is a Tropopause Fold?

6

14

12

10

8

6

4

150

200

300

400

500

600700

(~100 km)

subtropicalair mass

polar air mass

stratosphere

Pre

ssur

e (h

Pa)

Hei

ght (

km)

tropopause

front

Upper-air front

J

Tropopause folding describes an event at the tropopause break in which the tropopause folds into the troposphere due to ageostrophic flow around the jet stream.

This frequently leads to dynamical instability (enhanced turbulence) because of high levels of vertical shear across the boundary of the tropopause fold, which contains elevated potential vorticity.

Enhanced turbulence

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Algorithm Summary

• The Tropopause Folding Turbulence Product (TFTP) generates Option 2 products of regions in which aircraft are prone to Moderate Or Greater turbulence due to passage through tropopause folding events

• The algorithm uses image processing techniques that produce synoptic-scale tropopause fold objects from major water vapor gradients in the hemispheric imagery (rather than a pixel-wise computation).

• Ancillary model fields help to assign height ranges to the tropopause fold objects

• The chosen channel set includes the 6.2 µm channel (ABI ch. 8), and the 7.0 µm channel (ABI ch. 9) as a backup (next slide)

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Motivation for Algorithm Channel

Selection• The TFTP relies on the geostationary retrieval of upper-tropospheric (UT) water vapor

• Major gradients in UT water vapor correspond to air mass boundaries that give rise to tropopause folding and turbulent flow where those boundaries are dynamically unstable

• Any geostationary UT water vapor channel can resolve this feature (GOES-IM, GOES-NOP, Meteosat, MTSAT, etc.)

• GOES-ABI ch. 8 (~6.2µm) is best suited for retrieval in the upper troposphere, and ch. 9 (~7.0µm, midtroposphere) would have only a minor horizontal displacement and image gradient bias as a backup data source

Expected ABI Performance Relative

to Other Sensors• At 2km horizontal resolution, GOES-ABI would present a clear advancement in the product’s horizontal precision.

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TFTP Processing Schematic

Produce tropopause fold objects from image gradients

INPUT: BT8 or BT9, model temperature, pressure

Assign object height relative to model tropopause height

Filter objects for most turbulent sections

OUTPUT: Upper/lower bounds, Directions of most likely turbulence, binary mask

• Used for tropopause height assignment, humidity gradient calculation

• Locates the turbulent areas in 3 dimensions• All directions experience turbulence, but

turbulent eddies are anisotropic, so some directions are more vulnerable

Example TFTP Output

GOES WV channel input

Turbulence lower bounds (kft)

Turbulence upper bounds (kft)

Example TFTP Output

GOES WV channel input

Direction #1 most favorable to turbulence (deg)

Direction #2 most favorable to turbulence (deg)

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Algorithm Changes from 80% to 100%

Product output was filtered more restrictively to meet/exceed the 50% detection requirement

Navigation methodology revised to match latest Geocat updates (to streamline calculations at prime meridian and antimeridian)

Added a binary mask to output

Quality Flags, Product Quality Identifiers completed

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ADEB and IPR Response Summary

All updates w.r.t. coding standards have been addressed

All ATBD errors and clarification requests have been addressed

No feedback required substantive modifications to the approach

Requirements Specification Evolution

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Nam

e

User &

Priority

Geographic

Coverage

(G, H

, C, M

)

Vertical

Resolution

Horizontal

Resolution

Mapping

Accuracy

Measurem

entR

ange

Measurem

entA

ccuracy

Product Refresh

Rate/C

overage Tim

e (Mode 3)

Product Refresh

Rate/C

overage Tim

e (Mode 4)

Vendor A

llocated Ground

Latency

Product Measurem

ent Precision

Tropopause Folding Turbulence Prediction

Tropopause Folding Turbulence Prediction

GOES-R

GOES-R

FD

M

SFC – 100 mb

SFC – 100 mb

2 km

2 km

1 km

1 km

Binary yes/no detection above boundary layer for moderate or greater condition

Binary yes/no detection above boundary layer for moderate or greater condition

50% correct detection of Moderate or Greater turbulence

50% correct detection of Moderate or Greater turbulence

15 min

5 min

5 min 159 sec

159 sec

N/A

N/A

M – Mesoscale FD – Full Disk

Requirements Specification Evolution

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Nam

e

User &

Priority

Geographic

Coverage

(G, H

, C, M

)

Tem

poral Coverage Q

ualifiers

Product Extent Q

ualifier

Cloud C

over Conditions

Qualifier

Product Statistics Qualifier

Tropopause Folding Turbulence Prediction

GOES-R FD, M Day and night Quantitative out to at least 70 degrees LZA and qualitative at larger LZA

Clear conditions down to feature of interest associated with threshold accuracy

Over the lengths of separate flight transects through the region of positive prediction

FD – CONUS M - Mesoscale

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Qualifiers

The following qualifiers apply:

Ancillary model fields are available Sensor zenith angle < 70 degrees Objects must be 300 km away from bad pixels

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Validation Strategy

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Trop Folding Turbulence Approach –

Offline Validation: Data, Methods

Test data» GOES-12 Northern Hemisphere (domain of validation

data)» Corresponding ancillary NWP files: GFS 12 hour

forecasts covering observation times» 1132 images from November 2005 to February 2007

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Trop Folding Turbulence Approach –

Offline Validation: Data, Methods

Truth data» Automated in-situ observations of eddy disturbance from

commercial aircraft (737s and 757s) » Generally limited to the airspace of the continental U.S.» Turbulence metric is “EDR” (Eddy Dissipation Rate)

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Trop Folding Turbulence Approach –

Offline Validation: Data, Methods

Method: Compare Tropopause Fold product with collocated EDR observations

All EDR observations +/- 30 minutes from the image time

Moderate turbulence is yellow/orange.(Light is green)

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Trop Folding Turbulence Approach –

Offline Validation: Data, Methods

Method: Compare Tropopause Fold product with collocated EDR observations

All EDR observations +/- 30 minutes from the image time

EDR observations collocated with Tropopause Fold product (with vertical and directional constraints).

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Trop Folding Turbulence Approach –

Offline Validation: Data, Methods

Method: Compare Tropopause Fold product with collocated EDR observations

EDR observations collocated with Tropopause Fold product (with vertical and directional constraints).

(Sub-sampled to the volume of the tropopause fold)

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Validation Results

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TFTP Validation: Time of year

• Upper air mass boundaries are weak in the summer months and generate very few cases to sample

• Average accuracy: 54% (requirement: 50%)

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TFTP Validation: Image gradient magnitude

• The algorithm uses tropopause fold objects at image gradients above a threshold magnitude value (highlighted). Turbulence generally increases with gradient magnitude.

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TFTP Validation: Direction

• “0” means a direct path of an aircraft across the orientation of the tropopause fold object. “90” means a path along the object orientation

• The higher frequency of turbulence at the “0” bin shows the value of presenting “caution direction” product output

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Validation Results Summary

Parameter Value (spec)

Data set 1132 images

Time period 16 months

Accuracy 53% (50%)

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Summary The Tropopause Folding Turbulence Product is a

unique retrieval of turbulent regions in the atmosphere which will be made more spatially precise with the ABI.

Version 5 is delivered and the 100% ATBD is coming at the end of June.

These products meet the specifications and are ready for operational application.