1. Aviation Weather Hazards
Prediction with Geostationary
Satellites:
Downbursts and Fog
Ken Pryor
NOAA/NESDIS/Center for Satellite
Applications and Research

2. Topics of Discussion
• Thunderstorm Life Cycle
• Thunderstorm downbursts and downburst
prediction technique
– GOES Sounder
– Dual polarization Dopper radar
• GOES Imager Fog Detection
• Case Studies:
– Southern California, August, 2014
– Salt Lake City, Utah, January, 2015
• Conclusions

3. Thunderstorm Life Cycle
• Cumulus Stage:
– vertical growth, updraft
dominated, influenced by
positive buoyant energy
• Mature Stage:
– Maximum updraft
intensity, mixed-phase
precipitation, downdraft
initiation and development
• Dissipating Stage:
– Downdraft dominated,
precipitation diminishes,
cloud debris evaporation

4. Thunderstorm Downburst
• Strong downdraft produced by a
convective storm (or
thunderstorm) that causes
damaging winds on or near the
ground.
• Precipitation loading, sometimes
combined with entrainment of
subsaturated air in the storm
middle level, initiates the
downdraft.
• Melting of hail and sub-cloud
evaporation of rain result in the
cooling and negative buoyancy
that accelerate the downdraft in
the unsaturated layer.
Since 2000, the NTSB has documented ten fatal microburst-related general
aviation aircraft accidents, mostly over the southern and western U.S.

5. Downburst Types
• Macroburst: Outflow size >
4 km, duration 5 to 20
minutes (Fujita 1981)
• Microburst: Outflow size <
4 km, duration 2 to 5
minutes (Fujita 1981)
• Wet Microburst: Heavy
rain observed on the ground.
• Dry Microburst: Little or no
rain observed on the ground.
Courtesy USA TODAY

6. Microburst Windspeed Potential Index
(MWPI)
• Based on factors that promote thunderstorms with potential for strong
winds:
– Convective Available Potential Energy (CAPE): Strong updrafts, large storm
precipitation content (esp. hail, rain)
– Large changes of temperature and moisture (humidity) with height in the lower
atmosphere.
– Index values are positively correlated with downburst wind strength.
• MWPI ≡ CAPE + Γ + [(T – Td)LL - (T – Td)UL]
– Γ = temperature lapse rate (°C km-1) between lower level (LL) and upper level
(UL).
– Based on analysis of 50 downburst events over Oklahoma and Texas, scaling
factors of 1000 J kg–1, 5°C km–1 , and 5°C, respectively, are applied to the
MWPI algorithm to yield a unitless MWPI value that expresses wind gust
potential on a scale from one to five.
/1000 /5°C km –1
/5°C
LL = 850 mb/1500 m UL = 670 mb/3500 m

7. GOES Sounder-MWPI
• Geostationary Operational
Environmental Satellite (GOES)
13-15 Sounder:
• Radiometer that senses specific
data parameters for atmospheric
temperature and moisture
profiles.
• MWPI program ingests the
vertical temperature and
moisture profiles derived from
GOES sounder radiances.
• Generated hourly at the NOAA
Center for Weather and Climate
Prediction (NCWCP).
18 infrared wavelength channels

8. Thunderstorm Wind Prediction
WS = 3.7753(MWPI) + 29.964
30
35
40
45
50
55
60
0 1 2 3 4 5 6
WindGustSpeed(kt)
MWPI
≥50 kt45 – 49 kt
34 – 38 kt 38-42 kt
42 -45 kt
< 34 kt

9. Dual-Polarization Doppler Radar
• Reflectivity factor (Z):
– Power returned to the radar
receiver, proportional to storm
intensity.
– Values > 50 dBZ indicate
strong storms with heavy rain
and possible hail.
• Differential reflectivity (ZDR):
– Ratio of the horizontal
reflectivity to vertical
reflectivity. Ranges from -7.9
to +7.9 in units of decibels (dB)
– ZDR values near zero indicate
hail while values of 2 – 5
indicate melted hail/heavy rain.
NEXRAD: Next Generation Radar

10. Case Study:
18 August 2014
Southern California Downbursts
• During the afternoon of 18 August 2014, clusters of convective storms
developed over the Mogollon Rim of Arizona, and over the San
Bernardino and San Jacinto Mountains of southern California, and
remained nearly stationary.
• The only severe wind event west of the Rocky Mountains recorded by the
National Weather Service/Storm Prediction Center on 18 August occurred
near Daggett, California at Barstow-Daggett Airport at 2215 UTC.
• Between 2000 and 2100 UTC, convective storms developed and
intensified along the moisture discontinuity over the San Bernardino and
San Jacinto Mountains.
• Increasing MWPI gradient between the CaliforniaMexico border and the
Mojave Desert, with the northernmost cluster of convective storms
developing within this gradient between 2000 and 2100 UTC.

11. MWPI: 2000 UTC
18 August 2014

12. Thermodynamic Profile
Comparison
Daggett, CA
Twenty-Nine Palms

13. GOES-15 WV-TIR BTD/NEXRAD PPI:
18 August 2014

14. GOES-15 WV-TIR BTD/dBTD:
2211 UTC

15. GOES-15 WV-TIR BTD/dBTD:
2230 UTC

16. Doppler Radar: 18 August 2014
27 m/s (52 kt) 22 m/s (42 kt)

17. GOES Fog Detection
Case Study: 9 January 2015
Salt Lake City, Utah Fog

18. Current GOES Channels
Band Wavelength (μm) Use
1 (Visible) 0.52-0.77 Cloud detection and
identification
2 (Shortwave IR) 3.76-4.03 Fog identification,
water vs. ice clouds
3 (Water Vapor) 5.77-7.33 Moisture content
4 (Longwave IR) 10.2-11.2 Cloud top temperature
5 (Longwave IR, G-11)
11.5-12.5 Low-level moisture
6 (Longwave IR) 12.96-13.72 Cloud characteristics

19. Next Generation: GOES-R

20. GOES Thermal Infrared Imagery:
9 January 2015

21. Fog vs. Low Cloud Detection with GOES
• Surface temperatures are obtained
from METARs and ship reports
• Surface temperature grid is
generated using a Barnes
interpolation technique and then
calibrated to ch. 4 IR image.
• Barnes parameter: smoothing
factor for the conversion of point
data to grids; a larger number
results in a data point influencing
a greater number of grid points,
and the generation of a smoother
grid.

22. GOES Fog Detection: 9 January 2015

23. GOES Fog Detection: 9 January 2015
METAR KBMC 091255Z AUTO 00000KT M1/4SM OVC001 M03/M05 A3018
METAR KSLC 091300Z 00000KT 1 3/4SM BR FEW002 SCT015 M03/M04 A3017 4000 92 2800 000/00

24. Conclusions
• Downbursts are an important component of
hazardous winds produced by thunderstorms.
• MWPI demonstrates conditional capability to
forecast, with up to three hours lead time,
thunderstorm-generated wind gusts that could
present a hazard to aviation transportation.
• Most intense downburst occurrence is found near
local maxima in MWPI values.
• The GOES MWPI product can be effectively used
with NEXRAD imagery to nowcast downburst
intensity.
• GOES SWIR-LWIR channel BTD technique can
effectively detect fog and low clouds, and show areal
extent.

25. References
Pryor, K. L., 2014: Downburst prediction applications of meteorological
geostationary satellites. Proc. SPIE Conf. on Remote Sensing of the
Atmosphere, Clouds, and Precipitation V, Beijing, China,
doi:10.1117/12.2069283.
Hang, C., Nadeau, D.F., Gultepe, I., Hoch, S.W., Román-Cascón, C., Pryor,
K., Fernando, H.J.S., Creegan, E.D., Leo, L.S., Silver, Z. and Pardyjak,
E.R., 2016. A case study of the mechanisms modulating the evolution of
valley fog. Pure and Applied Geophysics, 173(9), pp.3011-3030.
Pryor, K. L., 2015: Progress and Developments of Downburst Prediction
Applications of GOES. Wea. Forecasting, 30, 1182–1200.
Pryor, K. L., and S. D. Miller, 2016: Downburst Prediction Applications of
GOES over the Western United States. Wea. Forecasting, in review.
Wakimoto, R.M., 1985: Forecasting dry microburst activity over the high
plains. Mon. Wea. Rev., 113, 1131-1143.
Wakimoto, R.M., 2001: Convectively Driven High Wind Events. Severe
Convective Storms, C.A. Doswell, Ed., Amer. Meteor. Soc., 255-298.

26. Thank You!