Ian Lee NOAA/NWS Albany, NY Dave Fitzjarrald , Jeff Freedman SUNY Albany ASRC NROW XIV

1. The Role of Boundary Layer Variability in Aviation Forecasting Across Eastern New York and Western New England Ian Lee NOAA/NWS Albany, NY Dave Fitzjarrald, Jeff Freedman SUNY Albany ASRC NROW XIV 10 December 2013

2. Motivation • Aviation IFR occurrence key Government Performance and Results Act (GPRA) performance measure • WFO ALY has tried various methodologies and training to improve IFR forecasting • Distance Learning Aviation Course (DLAC) • Utility of MOS/LAMP guidance • Crossover temperature • How important is the planetary boundary layer (PBL) profile? • Specifically, static stability profile

3. Why the Boundary Layer? • Friction results in ~10-50% reduction of kinetic energy in the boundary layer • However, surface fluxes can compensate (turbulence) • Heat transfer, evapotranspiration, diurnal drag • IFR CIG and VIS are tied to boundary layer fluctuations • Fog occurs near surface • Stratus often occurs near the top of a moist boundary layer

4. The Role of Static Stability • So what is the driving force behind IFR? • Static stability profile • Similar in concept to friction • Controls an air parcel’s energy (buoyancy) • Distribution of this profile directly affects PBL orientation of: • Moisture • Wind • Momentum (aka turbulence) well-mixed unstable stable sink rise sink rise Steady unless acted upon by an outside force θ θ θ

5. Static Stability Profile Cont. • Wind effects • Subgeostrophic daytime profile • Supergeostrophic nocturnal low-level jet (LLJ) • Near-surface winds largely follow logarithmic profile • Controls distribution/orientation of turbulent eddies • Bulk Richardson Number Ri = g*(∂θ/ΔZ)/θ*(∂u/Δz)2 *Critical Ri varies depending on resolution • *0.25 used in this research • > 0.25: inhibits turbulence • 0-0.25: turbulence possible • < 0: turbulence favored (Oke 1978)

6. Data • Analyzing 2012 sounding data for KALY • Found every occurrence of IFR VIS and CIG from hourly ASOS data • Multiple IFR occurrences in a single day counted as only 1 event • IFR VIS (< 3 SM) • 102 events • IFR CIG (< 1000 ft) • 88 events

7. Data Resolution • For each occurrence, gathered previous and precluding soundings • KALY hi-res (1 second interval) sounding data • Goal is to recreate what the boundary layer may have “looked” like at time of IFR occurrence • Initially, using only observed soundings • Differing degrees of model unrepresentativeness • Resolution • Parameterization schemes (boundary layer, surface model, radiative transfer)

8. Methodology • Focus on variations in the PBL • Daytime • Convective boundary layer/mixed layer (CBL) • Role of turbulent transport of sensible heat, latent heat, and momentum fluxes • Entrainment effects within the inversion transition zone (ITZ) of the free atmosphere (FA) • Nighttime • Nocturnal stable boundary layer (NBL) and residual layer (RL) • Role of decoupling

9. Diurnal PBL Evolution (Stull 1988)

10. “Normal” PBL Profile - Day (Stull 2000)

11. “Normal” PBL Profile - Night f

12. The Surface Layer – aka NBL • A more stable, “mini” version of the CBL • Turbulence, mixing occur near the surface • Critical for moisture distribution • Fog? Low stratus? Dew? Any clouds? Specific Humidity q Saturation Specific Humidity qs (Fitzjarrald and Lala 1989)

13. Mixed NBL– Radiative Cooling Radiative cooling initially concentrated near the surface, increasing in depth through the night Radiative cooling dispersed through mixed NBL Initial radiative cooling in shallow NBL

14. Determining Top of PBL • Must satisfy three criteria: • Increase in potential temperature • Decrease in mixing ratio • Top must be lower than LCL height • If these criteria coincide = top of PBL • Multiple criteria used to decrease influence of subjective analysis • Top of PBL corresponds to base of ITZ Inflection point

15. Data Analysis/Terms Free Atmosphere • Scaling approach h/zi (day) and h/zr,s (night) h2/zi,r Δθm = zi – hsfc (day) Δθr = zr - zs (night) Δθs= zs- hsfc where… zi = value at top of CBL zr = value at top of RL zs = value at top of NBL h = data level to be scaled h2 = 1.5h (ITZ depth approx) Δθm,r,s = depth of mixed, residual, and surface layers • Statistical/graphical analysis performed using R, Excel software h2 ITZ zi,r Δθr h zs Δθm Adapted from (Stull 2000)

16. Preliminary Results/Applications

17. IFR VIS Occurrence

18. IFR CIG Occurrence

19. Hourly Occurrence • Peak occurrence between 00-12 UTC • Coinciding with stable NBL

20. Example - IFR Increased stability, “spike” in moisture Onset of nocturnal LLJ Onset of NBL

21. Example - VFR Favorable turbulence profile Well-mixed stability profile Super-adiabatic layer

22. Example – IFR Stratus or Fog? Distribution of moisture profile can provide clues to fog or stratus potential

23. Other Potential Applications • Wind Forecasting • Example: Hurricane Sandy • High Wind Warning in effect for much of area from 13 UTC 10/29/12 – 15 UTC 10/30/12 • Did not get as strong of winds as forecasted at KALB and other valley locations (sub-verification criteria) • Peak wind gust at KALB only 37 kts (43 mph) • Higher terrain locations received stronger winds (Hancock, MA in Berkshires had a gust to 67 kts/77 mph) • Why did valley locations not verify, despite intense synoptic-scale pressure gradient?

24. * Top of PBL

25. Critical Ri (0.25) Critical Ri (0.25)

26. Available Tools • Can be utilized operationally using AWIPS • Available through Volume Browser • Load Var vs. Height • Select Point (make sure centered over TAF site) • Potential Temperature, RH, Specific Humidity available in hourly resolution • NAM Bufr • GFS Bufr (3 hourly resolution) • Moisture Flux Divergence can be used as a proxy for turbulence distribution

27. Available Tools – Potential Temp

28. Available Tools – Complete • *Consists of: • Potential temperature • Windspeed • RH • Specific humidity

29. Available Tools - Moisture Temperature Specific Humidity *NWS Albany has an office procedure that incorporates some of these variables: IRLAviation Moisture Flux Divergence Wind Speed

30. BUFKIT

31. Future Work • Continue statistical/graphical analysis • Expand dataset • December 2011 (to capture winter 2011-2012 season) • January 2013 – March 2014 (to capture winter 2013-2014 season) • Differentiate data further • Season, precip vs. non-precip, types of precip

32. Future Work • Creation of forecasting methodology • Develop training/articulate presentations on aviation boundary layer principles • Develop algorithm that computes probability of IFR VIS and/or CIG occurrence • Utilizing NAM/GFS Bufrfiles, KALY soundings

33. References • Banta, R. M., R. K. Newsom, J. K. Lundquist, Y. L. Pichugina, R. L. Coulter, and L. J. Mahrt, 2002: Nocturnal low-level jet characteristics over Kansas during CASES-99. Bound.-Layer Meteor., 105, 221–252. • Fitzjarrald, David R., Michael Garstang, 1981: Boundary-Layer Growth over the Tropical Ocean. Mon. Wea. Rev., 109, 1762–1772. • Fitzjarrald, Dayid R., Michael Garstang, 1981: Vertical Structure of the Tropical Boundary Layer. Mon. Wea. Rev., 109, 1512–1526. • Fitzjarrald, David R., G. Garland Lala, 1989: Hudson Valley Fog Environments. J. Appl. Meteor., 28, 1303–1328. • Hanna, S.R., Burkhart, C.L., Paine, R.J., 1985. Mixing height uncertainties. Proceedings of 7th AMS Symposium on Turbulence and Diffusion, Boulder, pp. 82-85. • Mahrt, L., 1985: Vertical Structure and Turbulence in the Very Stable Boundary Layer. J. Atmos. Sci., 42, 2333–2349. • Martin, C. L., D. Fitzjarrald, M. Garstang, A. P. Oliveira, S. Greco, and E. Browell, 1988: Structure and growth of the mixing layer over the Amazonian rain forest. J. Geophys. Res., 93, 1361–1375. • Oke, T.R., 1978: Boundary Layer Climates, Halsted Press, 372 pp. • Stull, R.B., 1988: An Introduction to Boundary Layer Meteorology. Volume 13. Kluwer Academic Publishers, 670 pp. • Stull, R.B., 1991: Static Stability - An Update. Bull. Amer. Meteorol. Soc. 72, 1521-1529. • Stull R.B., 2000: Meteorology for Scientists and Engineers. 2ndEdition. Cengage Learning, 528 pp.

34. Questions?/Comments?