Nowcasting Thunderstorms with SIGnificant weather Object Oriented Nowcasting System

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Nowcasting Thunderstorms with SIGnificant
weather Object Oriented Nowcasting S
ystem

• Pascal Brovelli, Stéphane Sénési, Etienne
  Arbogast, Philippe Cau, Sandrine Cazabat, Michel
  Bouzom, Jérôme Reynaud

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SIGOONS principles

• Nowcasting significant weather events (0-4h)
• thunderstorms
• later on, fog areas, heavy rain, snow and icing
  condition, strong wind systems
• Based on an object oriented approach
• SIGOONS manages Significant Weather Objects
  (SWO)
• ? Notice that usual weather conditions are not
  describe
• A hybrid system man-machine mix
• Automated generation of end-user products
  downstream of supervise database

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SIGOONS a hybrid system

• Automated processes
• Diagnostic of thunderstorm SWO guesses

• SWO systematic check against ground observations

• Generation of user-specific warnings

• Forecaster expert input
• Arbitrate discrepancies

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Automated Diagnostic of thunderstorm SWO using
CONO (1)

• CONO tool analyzes radar data in order to
  automaticaly detect convective cells and/or
  systems (Convection Nowcasting Objects, extension
  of the RDT objects, see also Hering et al. and
  Autones et al.)
• Detection by adaptative reflectivity thresholding
  of radar data

• Structure of reflectivity can be complex
  smoothing and morphological  closing  operation
  merges cells and matches the convective system
  scale

• Discrimination of convective systems uses
  lightning data

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Automated Diagnostic of thunderstorm SWO using
CONO (2)

• CONO tool automaticaly tracks convective cells
  and/or systems
• Tracking by overlapping between a cell detected
  in the present image and cells detected in the
  previous image using displacement speed
• Speed estimate blends move of the cell centroïd
  and cross-correlation. After tuning, speed
  diagnostics
• are robust against merges and splits
• have smooth variation
• improve diagnostic of low group speed on backward
  regenerating convective systems
• CONO intialialize thunderstorm SWO attributes
• horizontal envelope, move speed and lightning
  activity
• rain rate and hail risk

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SIGOONS Man-Machine Interaction

• Challenge Minimize input by the forecaster
• SIGOONS is designed to run automatically
• Merge and check automated SWO from the new
   slot  with forecast SWO from the previous
   slot 
• Tracking allows to propagated forward in all
  supervise attributes, provided that there is
  consistency with new observation data
• Discrepancy ? send a specific, relevant
   disagree signal  to the forecaster
• Forecaster input is optional
• monitors the automated initialization of sensible
  weather attributes wind gust, hail risk, rain
  accumulation
• Choose between different automated diagnostic
  values
• Arbitrate discrepancies
• Sets a decay/growth tendency on area, duration,
  attributes
• Creates objects for convective systems or
  thunderstorm prone areas

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SIGOONS display tool
Object with mismatch

• Fully integrated in the operational Synergie
  Workstation

Speed estimate too high
Object with good match
Significant ground obs without supporting object
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Access to rain rate evolution
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Access to ground observations collected over the
SWO trajectory
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SIGOONS human expertise

• A significant effort on ergonomic
• Real-time experiments and case studies using
  man-machine interface prototype (since summer
  2004)
• Ergonomist studies (see Chabaud et al.)
• First study results
• Forecasters feel that the object representation
  is clear
• Forecasters concerns are the workload and their
  ability to exert expertise
• During the first hour SIGOONS should definitively
  favour automation
• For first to fourth hours ahead, expertise apply
  to larger scales thunderstorm systems or
  thunderstorm-prone areas
• Expert input is basically qualitative wind
  gust stronger near the coast

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SIGOONS End-user products

• 2004 experiment findings
• Warning locations are correct and faithfully
  translate the nowcast database contents
• The sensible weather diagnostic (wind gust, rain
  accumulation, hail risk) is still weak
• Time consistency and stability must be improved
• products delivered in push mode, like
  user-specific warnings (e.g. security services)
  are much promising

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SIGOONS Current status

• Automated diagnostic and check run in routine
  mode
• Human expertise
• SWO display tool close to be available for
  operations to all (7) regional offices forecaster
• Experiment on case studies in order to define
  nowcasting specific tasks
• Products
• Test with a few customers of a warning package,
  which includes nowcast warnings, and short range
  forecast
• Assessment of the quality of the thunderstorm
  warnings produced without expert input

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SIGOONS Outlook

• Automated diagnostics
• Use optimal combination of satellite and radar
  tracking
• Improve the conceptual models for automated
  convection diagnostics
• Data fusion with mesoscale analysis
• Identification of convection organization type
• Diagnostics for sensible weather attributes
• Human expertise
• Introduce new objects, better suited to human
  expertise
• thunderstorm-prone area, fog area, surface front
  ( rain and/or wind )
• Products
• Introduce uncertainty on phenomena location and
  intensity
• Design graphical and mobile phone products
• Extend the OO approach to
• Tracking objects in HiRes NWP simulation (a.s.a.p
  re. NWP quality)
• Matching of simulated objects with real objects
  for real-time trend assessment

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Thank you