Titelfoto auf dem Titelmaster einfügen Deutscher Wetterdienst Dr. Paul James, German Weather Service, ECAM/EMS Conference, Reading, 9. Sept. 2013 NowCastMIX

Titelfoto auf dem Titelmaster einfügen

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


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


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


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


NowCastMIXFuzzy Logic Sets


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


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


NowCastMIX Core Technology5. Clustering example


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


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


NowCastMIXOutlook

NowCastMIX

Thanks for your attention !

Questions?


Documents

Titelfoto auf dem Titelmaster einfügen Deutscher Wetterdienst Dr. Paul James, German Weather Service, ECAM/EMS Conference, Reading, 9. Sept. 2013 NowCastMIX