Tony Wimmers, Wayne Feltz 5.32 Estimating regions of tropopause folding and clear-air turbulence with the GOES water vapor channel Tony Wimmers, Wayne Feltz Cooperative Institute for Meteorological Satellite Studies (CIMSS), UW-Madison World Weather Research Symposium on Nowcasting and Very Short Range Forecasting Toulouse, France, 5-9 Sept, 2005 My title and objective is… Saves lives, prevents damage and injury for anyone who gets on a plane. Basically, there’s a lot of time and money that goes into this kind of safety. The tie-in to multispectral will be evident later Those of you who do midwave IR water vapor applications should be particularly interested in this. The rest of you, I don’t know.

CAT and tropopause folds Abstract: Clear-air turbulence remains a significant aviation hazard, yet by its nature it is very difficult to detect. One of the sources of clear-air turbulence is the dynamic instability associated with “tropopause folding”, which describes the entrainment of stratospheric air into tropospheric levels at upper-level fronts. We describe a near real-time satellite product that estimates areas of tropopause folding in regions of strong humidity gradients in the GOES midwave infrared (water vapor) channel. Using an empirical relationship between upper tropospheric humidity gradients and tropopause breaks, the algorithm estimates that turbulence-generating tropopause folds protrude from some of these tropopause breaks. This product is validated over the United States with manual pilot reports as well as newer automated aircraft reports of turbulence. Upper-air front 150 stratosphere 14 Although this graph shows the chemical mixing, it was coincident with CAT So we need a tool to estimate tropopause height 200 12 subtropical air mass 10 Pressure (hPa) 300 Height (km) tropopause 8 400 front 6 500 600 polar air mass 4 700 (~100 km)

Vertical component of the fold Building a statistical model Vertical component of the fold subtropical air mass polar air mass stratosphere tropopause Upper-air front +15K -5K  surface longitude latitude Major issues for multispectral sounding: What position do we assign to the fold? Can we obtain a tropopause height? Can we make out multiple layers? ***

Web product: Real-time pirep validation Pirep data is provided courtesy of NCAR Aviation Digital Data Service (ADDS) Notes: This latest version, which uses RUC grids to assign a height to the folds, was completed last Friday. The base image is the specific humidity product – blue and purple are dry, polar subsiding air, yellow and orange are moist, subtropical air, and convection Gray are the folds Turbulent pireps are red, non-turbulent pireps are in blue Red dots increase in size with the severity of the turbulence Labels are in hundreds of feet, but reports that are in, above or below the folds show the difference in potential temperature between the pirep and the fold

Web product: Real-time TAMDAR validation TAMDAR (Tropospheric Airborne Meteorological Data Report) is part of the Great Lakes Field Experiment Unfortunately, it is mostly lower and midtroposphere Notes:

Web pages: http://cimss.ssec.wisc.edu/asap/exper/tfoldsVer2/pirepSep.html http://cimss.ssec.wisc.edu/asap/exper/tfoldsVer2/tamdarDisplay.html

Validation: Details April 8-30, 2005 1500-2300 UTC (peak time) Eastern U.S. (away from mountain wave turbulence) Above 15,000 feet (mid- and upper troposphere) Areas of strong convection are filtered out (no C.A.T.) If the pirep is in a modeled fold and reports turbulence, then this is a correctly classified “Yes” report. If the pirep is outside a modeled fold and reports no turbulence, this is a correctly classified “No” report. 2,293 pirep observations, 62% of ALL observations are turbulent. Now, I only have conclusions I can draw from an inspection of the performance of the model so far, but since we’re just here to share ideas, why don’t I jump the gun and run these by you, and take questions anyway?

Validation: Method Find the model’s “Probability of Detection” for turbulence Next, search for any further constraints on the model that improve the Probability of Detection Now, I only have conclusions I can draw from an inspection of the performance of the model so far, but since we’re just here to share ideas, why don’t I jump the gun and run these by you, and take questions anyway?

Statistics for tropopause fold turbulence prediction (N=2293, “background” rate of success=0.64) Number of Yes reports Proportion of Yes reports correctly classified Proportion of No reports mis-classified* 1. Initial model 296 0.77 0.63 2. Revised model: Longer folds 240 0.78 3. Revised model (#2): Longer folds, higher gradients 138 0.82 * Does not purport to classify all negative reports

Preliminary conclusions: Trop folding + CAT The tropopause folding model shows significant skill at predicting upper-tropospheric turbulence The model increases in accuracy significantly as it is made more selective (Prob of Detection = 82%) Predicted turbulence is predominantly “light” or “moderate” Now, I only have conclusions I can draw from an inspection of the performance of the model so far, but since we’re just here to share ideas, why don’t I jump the gun and run these by you, and take questions anyway? stratosphere subtropical air mass polar air mass Upper-air front