thunderstorm downburst prediction: an integrated remote sensing approach

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Thunderstorms that are capable of producing intense downdrafts and resulting strong outflow winds at or near the surface have been identified as a serious hazard for aircraft during takeoff and landing and marine transportation, especially passenger vessels and vessels under sail. A downburst, in general, is defined as a strong downdraft that induces an outburst of damaging winds on or near the ground and a microburst is a very small downburst with an outflow diameter of less than 4 km and a lifetime of less than 5 minutes. The National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Applications and Research (STAR) has developed and evaluated a suite of products that assess convective storm-generated downburst potential derived from the current generation of Geostationary Operational Environmental Satellite (GOES) (13–15). The existing suite of downburst prediction algorithms employs the GOES sounder to calculate risk based on conceptual models of favorable environmental thermodynamic profiles for downburst occurrence. A diagnostic nowcasting product, the microburst windspeed potential index (MWPI), is designed to identify attributes of a favorable downburst environment: 1) the presence of large convective available potential energy (CAPE), and 2) the presence of a surface-based or elevated mixed layer with a large temperature lapse rate. In addition to the environmental data derived from a satellite sounder, ground-based Doppler weather radar provides important physical and dynamic attributes of an evolving thunderstorm that can be used to refine downburst wind speed prediction. The presence of a core of heavy precipitation composed of graupel and hail within the thunderstorm is readily detectable by Doppler radar and indicates the potential for enhanced downdraft intensity due to phase-change cooling. This presentation provides a summary of an integrated satellite-radar downburst prediction technique and highlights case studies over the southwestern United States and Florida that demonstrate the effectiveness and adaptability of this integrated approach to thunderstorm downburst prediction. - PowerPoint PPT Presentation

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Thunderstorm Downburst Prediction: An Integrated Remote Sensing Approach

Ken Pryor

Center for Satellite Applications and Research (NOAA/NESDIS)

Topics of Discussion

• Thunderstorm Life Cycle

• Thunderstorm downbursts and downburst prediction technique– GOES Sounder

– Dual polarization Dopper radar

• Case Study: Jacksonville, Florida, June, 2014

• Conclusions

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

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.

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

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

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

Thunderstorm Wind Prediction

0 1 2 3 4 5 630

35

40

45

50

55

60

f(x) = 3.77532973109923 x + 29.9639110712667R² = 0.382964639983185

MWPI

Win

d G

us

t S

pe

ed

(k

t)

≥50 kt45 – 49 kt

34 – 38 kt 38-42 kt

42 -45 kt

< 34 kt

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

Case Study:June 2014

Jacksonville, Florida Downburst

• A confirmed downburst event on 10 June 2014 in Jacksonville, Florida demonstrated an effective application of the MWPI predictive model.

• During the afternoon of 10 June, clusters of strong thunderstorms developed along the Atlantic Coast sea breeze front in east-central Florida and then moved northward toward the Jacksonville area.

• Outflow boundary interaction with the sea breeze front and the subsequent merger of a cluster of thunderstorms over the western portion of the city of Jacksonville during the late afternoon resulted in the development of a large, intense thunderstorm over Jacksonville

• Produced a strong downburst at Whitehouse Naval Outlying Field with a peak wind speed measured at 25 m s–1 (48 kt).

MWPI:10 June 2014

100 km

1745 UTC1747 UTC 1847 UTC 1947 UTC 2047 UTC

McIDAS-V visualization

Barnes analysis

NEXRAD PPI: 10 June 20142101 UTC

50 km

2140 UTC2145 UTC2151 UTC

0 1 2 3 4 5 630

35

40

45

50

55

60

f(x) = 3.77532973109923 x + 29.9639110712667R² = 0.382964639983185

MWPI

Win

d G

us

t S

pe

ed

(k

t)

≥50 kt45 – 49 kt34 – 38 kt 38-42 kt 42 -45 kt1947 UTC 2047 UTC

NEXRAD RHI: 10 June 2014

Liquid water lofting

ΔZDR

Hail core

Conclusions

• Downbursts are an important component of hazardous winds produced by thunderstorms.

• MWPI demonstrates conditional capability to forecast, with up to four 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.

ReferencesAtkins, N.T., and R.M. Wakimoto, 1991: Wet microburst activity

over the southeastern United States: Implications for forecasting. Wea. Forecasting, 6, 470-482.

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.

Pryor, K.L., 2015: Progress and Developments of Downburst Prediction Applications of GOES. Wea. Forecasting. doi:10.1175/WAF-D-14-00106.1, in press.

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.

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