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Deutscher Wetterdienst
Dr. Paul James, German Weather Service, ECAM/EMS Conference, Reading, 9. Sept. 2013
NowCastMIXAutomatic integrated warnings from continuously monitored nowcasting systems
using spatially clustered fuzzy-logic assessments of storm attributes
NowCastMIX in AutoWARN
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
The AutoWARN process at the DWD monitors several systems automatically for potential warning situations on different time scales.
Warning polygons are sent to duty meteorologists for possible modification before products are generated.
NowCastMIX integrates all data from nowcasting systems
Consistent, optimised and intelligent warning solution
NowCastMIX in AutoWARN
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
NowCastMIX monitors several nowcasting systems on a 5 minutes update cycle
Radar products, lightning, surface obs., NWP model outputs for background
Data mapped onto a 1 x 1 km grid with assessment of cell motion vector field
Storm severity assessments using a fuzzy-logic method
Spatial and temporal optimisation using clustering techniques
Warning polygons covering the next 60 minutes produced and sent on to AutoWARN
Warning events: Thunderstorms / Rain
Different warning events are given an code number (ii) (e.g. „31“)
10 thunderstorm and 3 torrential rain event types need to be monitored in AutoWARN
The thunderstorm severity depends on the presence (and magnitude) of these attributes:
Severe Gusts
Torrential Rain
Hail
The 10 Thunderstorm and 3 Torrential rain events
NowCastMIX - Overview
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
13:15 UTC, 22.06.2011Cell motion vector field
NowCastMIXCell motion vector field
Background cell motion vector field (CVF) is constructed using the following sources:
Pattern-Matching of radar echoes in consecutive images -> motion vectors
Explicit cell tracking vectors* (KONRAD and CellMOS systems)
(*mapped with a Gaussian distribution)
Form weighted mean -> CVF
Loop over all input tracking vectors to remove possible erroneous outliers
Successive improvement of CVF
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
Warning cones are created, opening up in the direction of cell motion
3 possible triggers for creating a cone:
KONRAD Cell ( Radar echoes > 46 dBZ )
CellMOS Cell ( Radar echoes > 37 dBZ + Lightning Strike )
Lightning Strike with weaker radar echoes
Fuzzy Logic applied at the cell centre to estimate storm severity
Attribute strength (Gusts, Hail, Rain) as a function of input data
Weighting function in cases where two or more cones overlap – higher severity preferred
Total cone length = 60 min (as function of cell speed)
Initial radius = 10km, Expansion angle = 7,5 degrees
NowCastMIXConstruction of warning cones
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
NowCastMIXFuzzy Logic Sets
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
In some cases the automatically determined thunderstorm severity may not be able to capture the characteristics of the storm sufficiently
To reduce the risk of missing very severe weather, real-time synoptic station reports are routinely monitored by NowCastMIX
These report e.g. measured gust speeds, hail occurrence, recent rainfall totals etc.
The likely current location of the severe weather event is estimated using the background cell motion vector field
Note that a certain time has typically elapsed since the reported weather started
A certain temporal and spatial uncertainty typically exists, depending on the type of severe weather being reported
The severity levels of warnings in this region can be raised if necessary
NowCastMIXIntegration of station reports
P. James / DWD - ESSL 2013 –
Station
Cell motion vector
Current region of relevance
NowCastMIXOptimization of Warnings
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
Optimal warnings need to find a balance between:
In nowcasting you cannot have both at the same time!
NowCastMIX would tend by nature to be over-precise
An optimal balance is approached via a clustering method
Precision / Accuracy
Realistic, strictly correct
Complex, over detailed and rapidly changing warnings. Hard for duty
meteorologists to assess and process. Hard for customers to understand and
assimilate
Workability / Usefulness
Smoother in space and time. Easier to process for the duty meteorologists,
easier for customers to assimilate.
Locally less precise, some details may be left out. Greater danger of
systematic biases (e.g. over warning)
Clusters (cell groups) are formed with the DBSCAN algorithm
Examining all cells in a time window covering the last 20 minutes
The highest severity value in a cluster is mapped onto all cells in that cluster
High severity levels are thus held up for at least 20 minutes
Results in a certain temporal smoothing
Problem: The clusters themselves sometimes change significantly from one run to the next, 5 minutes later, resulting in a new source of temporal noise on larger scales
A cluster ensemble (up to ~600) is created, using differing randomly perturbed cell positions (up to 6km shifts)
To improve computational efficiency the directions of the random perturbations are successively biased towards more useful sectors as the ensemble members are generated (Adaptive Clustering Ensemble)
The clustering member which is finally used is that which is most similar to the clustering which was used 5 minutes earlier
NowCastMIXClustering
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
NowCastMIX Core Technology5. Clustering example
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
14:05 to 15:05 UTC, 22.06.2011
NowCastMIX – From analysisto warning polygons
Analysis* + Warning areas * 20 minute time window (13:45-14:05)
Clusters + Warning polygons Warning situation for the next hour
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
Further refinement of clustering techniques to optimise warning proposals
Inclusion of further data sources (e.g. rapid-scan based satellite products)
Can early warnings be given before the first lightning or radar signals are occurring?
Feedback from verification results and from assessments by duty meteorologists
Parameter optimisation, further tuning of fuzzy logic sets
Expansion of the warning domain to cover wider area of W. Europe (e.g. FAB EC)
Winter nowcasting (snow, freezing rain)
Radar-based detection (snow, precip.) combined with surface freezing indicators
NWP-based snow forecasts and temperature profiles
Fuzzy logic combinations of such information
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013
NowCastMIXOutlook
NowCastMIX
Thanks for your attention !
Questions?
Dr. P. James (Deutscher Wetterdienst, Offenbach, Germany) – ECAM/EMS Conference, Reading, UK, 9. Sept. 2013