1 GOES-R AWG Aviation Team: Tropopause Folding Turbulence Product (TFTP) June 14, 2011 Presented By: Anthony Wimmers SSEC/CIMSS University of Wisconsin - Madison

2 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)

3 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.

4 Algorithm Description

What Is a Tropopause Fold? (~100 km) subtropical air mass polar air mass stratosphere Pressure (hPa) Height (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? (~100 km) subtropical air mass polar air mass stratosphere Pressure (hPa) Height (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

7 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)

8 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.

10 TFTP Processing Schematic Produce tropopause fold objects from image gradients INPUT: BT 8 or BT 9, 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)

13 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

14 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 15 NameUser &PriorityGeographicCoverage(G, H, C, M)VerticalResolutionHorizontalResolutionMappingAccuracyMeasurementRangeMeasurementAccuracyProduct RefreshRate/CoverageTime (Mode 3)Product RefreshRate/CoverageTime (Mode 4)Vendor AllocatedGround LatencyProductMeasurementPrecision Tropopause Folding Turbulence Prediction GOES- R FD M SFC – 100 mb 2 km 1 km Binary yes/no detection above boundary layer for moderate or greater condition 50% correct detection of Moderate or Greater turbulence 15 min 5 min 159 sec N/A M – Mesoscale FD – Full Disk

Requirements Specification Evolution 16 NameUser &PriorityGeographicCoverage(G, H, C, M)TemporalCoverageQualifiersProductExtentQualifierCloud CoverConditionsQualifierProductStatisticsQualifier Tropopause Folding Turbulence Prediction GOES-RFD, MDay and nightQuantitative 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

17 Qualifiers The following qualifiers apply:  Ancillary model fields are available  Sensor zenith angle < 70 degrees  Objects must be 300 km away from bad pixels

18 Validation Strategy

19 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

20 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)

21 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)

22 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).

23 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)

24 Validation Results

25 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%)

26 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.

27 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

28 Validation Results Summary ParameterValue (spec) Data set1132 images Time period16 months Accuracy53% (50%)

29 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.