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Toward an ensemble nowcasting system: describing the steering fields uncertainty in an advection sch

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... steering field's uncertainty in an advection scheme for radar images ... For advection scheme this is mainly due to uncertainty embedded in the ... advection ... – PowerPoint PPT presentation

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Title: Toward an ensemble nowcasting system: describing the steering fields uncertainty in an advection sch


1
Toward an ensemble nowcasting systemdescribing
the steering fields uncertainty in an advection
scheme for radar images
  • Virginia Poli, Pier Paolo Alberoni, Frank S.
    Marzano

2
Motivation
Nowcasting based on advection of radar images
3
Determination of motion field
4
Ensemble nowcasting
IDEA create 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
5
Introduction to the case study
03/04/2006 1300 GMT
6
03/04/2006 1315 GMT
7
03/04/2006 1330 GMT
8
03/04/2006 1345 GMT
9
03/04/2006 1400 GMT
10
03/04/2006 1415 GMT
11
03/04/2006 1430 GMT
12
03/04/2006 1445 GMT
13
03/04/2006 1500 GMT
14
03/04/2006 1515 GMT
15
03/04/2006 1530 GMT
16
03/04/2006 1545 GMT
17
03/04/2006 1600 GMT
18
Random choice of cross-correlation domain
Radar images
Steering vectors spatialization
Steering field
Semi-lagrangian advection
Forecast images
19
Random choice of reflectivity thresholds
Radar images
Steering vectors spatialization
Steering field
Semi-lagrangian advection
Forecast images
20
Random choice of influence radius
Radar images
Cross-correlation analysis
Generation of single motion vectors associated to
each reflectivity level considered
Steering vectors spatialization
Steering field
Semi-lagrangian advection
Forecast images
21
Probabilistic forecast
Lead time 45 minutes Threshold 20 dBZ Ensemble
members 40
Random parameter Z thresholds
  • Observed field forecast time for considered
    threshold

Random parameter influence radius
Total ensemble (120 members)
22
Probabilistic forecast
Lead time 45 minutes Threshold 40 dBZ Ensemble
members 40
Random parameter Z thresholds
Random parameter research domain
  • Observed field forecast time for considered
    threshold

Random parameter influence radius
Total ensemble (120 members)
23
Statistical results
Forecast lead time 45 minutes
Brier score
Brier skill score
24
Statistical results
Total ensemble
Brier score
Brier skill score
25
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

26
Thank you for your attention!
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