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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

50.0

30.0

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[dBZ]

50.0

30.0

0.2

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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

48.0

29.0

0.0

[dBZ]

53.0

30.0

3.0

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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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Reflectivity threshold [dBZ]

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Statistical results

cli

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occur not does event the if 0occurs event the if 1

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event the forecast that members ensemble of percentagePnumber points gridN

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Reflectivity thresholds [dBZ] Number of observations > threshold20 859730 512040 183150 426

Forecast lead time: 45 minutes

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0

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Reflectivity thresholds [dBZ]

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Brier score Brier skill score

Statistical results

Number of observations > threshold for different lead times

Reflectivity thresholds [dBZ] +15’ +30’ +45’ +60’

20 6838 7798 8597 7634

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!