Thunderstorm Nowcasting

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Thunderstorm Nowcasting

• Presented by Brian Vant-Hull
• CREST team Arnold Gruber, Shayesteh Mahani, Reza
  Khanbilvardi
• CREST Students Nasim Norouzi, Bernard Mhando
• NOAA Collaborators Robert Rabin, Mamoudou Ba,
  Robert Kuligowski, Stephan Smith
• Meteo-France Contributors Frederic Autones,
  Stephane Senesi

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Outline

• Nowcasting what and why?
• Principles of satellite identification of
  thunderstorms
• Tracking and forecasting
• Comparison of two identification algorithms
• Ideas for improving detection
• Ideas for improving forecasting

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What is Thunderstorm Nowcasting?

• Identify and track thunderstorms by
  satellite/radar.
• B. Project storm development and motion into
  the future.

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Why is nowcasting needed?
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Who would use this?

• Air traffic control already uses radar based
  nowcasting, but can only track a storm once
  precipitation develops. Satellites can see storm
  signatures in clouds before rain develops.
• Flood forecasts require accurate, high resolution
  precipitation forecasts which numerical/statistica
  l models cannot provide.

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Identifying Storms by Satellite

• Rapid growth vertical
• and horizontal

• Rounded tops

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Storm Trajectories Past and Future
Object based past motion predicts
future motion.
Field based future motion predicted by
surrounding motion.
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Comparison of Two Storm Identification Algorithms

• Rapidly Developing Thunderstorm (RDT) model
• Developed at Meteo-France, used operationally
  throughout Europe.
• Identifies and tracks individual thunderstorms.
• Does not project future development of storms,
  but provides statistics
• that may be used for that purpose.
• Hydro-Estimator (HE) model
• Developed at NOAA/NESDIS, run operationally on
  site.
• Estimates precipitation based on local cloud
  statistics.
• HE is the core of a nowcasting module that
  projects the development
• of precipitation fields.

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The RDT model
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Hydro-Estimator/Nowcaster
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Comparison Jul7 27, 2005
RDT Contours HE rainfall
RDT Contours Radar rainfall
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Comparison Aug 21, 2004
RDT Contours HE rainfall
RDT Contours Radar rainfall
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Improving Storm Detection 1
Water vapor channel
Visible channel
Overshooting tops stand out more in water vapor
imagery.
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Improving Storm Detection 2
Upper air divergence can often be detected in
water vapor images.
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Improving Storm Forecasting 1
Storm development in the tropics follows a fairly
predictable pattern which is easily extrapolated.
Is this also true for temperate zones?
growth gt gt gt gt gt gt maturity gt gt gt gt
gt gt gt gt decay
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(No Transcript)
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Improving Storm Forecasting 2
If sufficient moisture is added to bottom of an
otherwise stable layer, it can become Absolutely
Unstable.

• 3 2 1 0
• Elevation (km)

Dry
Moist
-20 -10 0 10
20 30 Temperature (C)
Predicting such situations is possible by
numerical models, but recent work by Ralph
Petersen at CIMMS has demonstrated simpler,
observation based approaches.
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CREST CCNY Satellite Direct Feed

• Reduces processing and distribution time
• Allows customized data products

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Summary

• We are at the beginning of a multi-year project
  to produce thunderstorm nowcasting for the New
  York area.
• We are in the testing phase to determine the best
  parts of existing models to combine for our own
  model.
• A direct satellite feed increases the utility of
  the eventual product, which will be made
  available via the web.