Virginia Poli, Pier Paolo Alberoni, Frank S. Marzano
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Transcript of Virginia Poli, Pier Paolo Alberoni, Frank S. Marzano
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Toward an ensemble nowcasting system:
describing the steering field’s uncertainty in an advection scheme for radar images
Virginia Poli, Pier Paolo Alberoni, Frank S. Marzano
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r MotivationForecast depends on the knowledge of initial conditions
Lack and/or error in initial conditions propagate on the predicted fields
Nowcasting based on advection of radar images
Explore the uncertainty originated by steering field generation mechanism and understand
how it propagates in the prediction
For advection scheme this is mainly due to uncertainty embedded in the steering fieldSteering field generation
Radar images
Numerical advection scheme
“Dynamical” uncertainties“Evolution” uncertainties
See poster 9.3
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r Determination of motion field
Vectors associated at each reflectivity level are merged using a method based on successive correction analysis. Retrieved vectors are associated to a radius that circumscribes an area in which they are supposed to have a certain influence
Parameters involved: • influence radius
Radar images
Semi-lagrangian advection
Generation of single motion vectors
associated to each reflectivity level
considered
Cross-correlation analysis
Forecast images
Steering vectors spatialization
Steering field
First step of cross-correlation is made on an area centered on radar image
Parameters involved: • dimension and position of cross-correlation domain• reflectivity value of the first layer
Geometrical segmentation of two subsequent radar reflectivity fields in order to track different targets at different scales examined
Parameters involved: • reflectivity values used to bound reflectivity areas
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r Ensemble nowcasting
IDEAcreate an ensemble changing each fixed parameter apart
trying to understand what is the one that better represents forecast field variability
Modifications are performed by a random selection of parameters themselves
Generation of a large set of motion fields starting from these different configurations
Ensemble generated by running semi-lagrangian advection algorithm with these different input steering
fields
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r Introduction to the case study03/04/2006 13:00 GMT
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03/04/2006 13:15 GMT
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03/04/2006 13:30 GMT
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03/04/2006 13:45 GMT
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03/04/2006 14:00 GMT
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03/04/2006 14:15 GMT
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03/04/2006 14:30 GMT
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03/04/2006 14:45 GMT
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03/04/2006 15:00 GMT
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03/04/2006 15:15 GMT
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03/04/2006 15:30 GMT
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03/04/2006 15:45 GMT
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03/04/2006 16:00 GMT
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rRadar images
Semi-lagrangian advection
Forecast images
Generation of single motion vectors
associated to each reflectivity level
considered
Cross-correlation analysis
Steering vectors spatialization
Steering field
Random choice of cross-correlation domain
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rRadar images
Semi-lagrangian advection
Forecast images
Generation of single motion vectors
associated to each reflectivity level
considered
Cross-correlation analysis
Steering vectors spatialization
Steering field
Random choice of reflectivity thresholds
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rRadar images
Semi-lagrangian advection
Forecast images
Generation of single motion vectors
associated to each reflectivity level
considered
Cross-correlation analysis
Steering vectors spatialization
Steering field
Random choice of influence radius
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Random parameter: Z thresholds
Random parameter: influence radius
Random parameter: research domain
Total ensemble (120 members)
— Observed field forecast time for considered threshold
Probabilistic forecastLead time: 45 minutesThreshold: 20 dBZEnsemble members: 40
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Random parameter: Z thresholds
Random parameter: influence radius
Random parameter: research domain
Total ensemble (120 members)
— Observed field forecast time for considered threshold
Probabilistic forecastLead time: 45 minutesThreshold: 40 dBZEnsemble members: 40
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Brier score Brier skill score
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Statistical results
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Reflectivity thresholds [dBZ] Number of observations > threshold20 859730 512040 183150 426
Forecast lead time: 45 minutes
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Statistical results
Number of observations > threshold for different lead times
Reflectivity thresholds [dBZ] +15’ +30’ +45’ +60’
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30 4058 4633 5120 3920
40 1144 1484 1831 977
50 159 376 426 162
Total ensemble
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r Comments and conclusion
At the present moment:
• Preliminary analysis on a convective event with its rainfall structures characterised by different direction and speed of motion
• Every changed parameter has a different impact on results
• Changing verification threshold results maintain their tendency
• Algorithm has an higher sensitivity to the use of random reflectivity levels (better impact on forecasts)
• Brier score trend is rapidly decreasing: one of the causes resides in the structures characterized by high reflectivity. They are very localized and following their motion becomes very difficult
Future work:
• Extend the verification of this probabilistic approach to a larger number of cases analysing different typologies of evens
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Thank you for your attention!