presentation

1st European Hail Workshop – Bern, Switzerland – 25 - 27 June 2014
Dr. Tanja Winterrath
From Algorithms to Applications –
Deutscher Wetterdienst
an Overview of Hail Nowcasting
Climate and Environment
at Deutscher Wetterdienst
Hydrometeorology Department
The DWD Radar Network
DWD Radar Network (effective: June 2014)
17 Doppler C-band radar systems
plus research radar at Hohenpeißenberg
German radar mosaic products:
based on terrain-following
precipitation scan with 150 km range
Temporal resolution: 5 min
Grid size: 1km x 1km
Currently, the radar systems are
being replaced by
dual-polarization technique
single-pol
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
dual-pol
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From Algorithms to Applications –
an Overview of Hail Nowcasting
at Deutscher Wetterdienst
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Radar-based Nowcasting Algorithms
Applications and Warnings
Hail in Numerical Weather Prediction
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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From Algorithms to Applications –
an Overview of Hail Nowcasting
at Deutscher Wetterdienst
1
2
3
Radar-based Nowcasting Algorithms
Applications and Warnings
Hail in Numerical Weather Prediction
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms
The DWD Hail Nowcasting Modules
RADVOR = Radar-based precipitation forecasting
Gridded hail information for Germany
every 15 min, 2 hours lead time
KONRAD = Radar-based cell tracking and forecasting
Hail flag for individual cell objects
every 5 min, 1 hour lead time
CellMOS = Radar-based „model output statistics“
Hail probability fields for individual cells
every 5 min, 2 hours lead time
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – RADVOR
RADVOR – nowcasting of precipitation and hail
Pattern recognition and tracking in 2D radar mosaic
Semi-Langrangian forecasting of precipitation patterns
Analysis plus forecasts up to two hours lead time
Hail detection by an empirical combination of:
High radar reflectivity values (>= 37 dBz)
Intensive Lightning:
Assign single flashes pixel-wise to a defined area of
25 to 400 km2 depending on the lightning‘s amplitude
>= 40 events summed-up over a period of 15 minutes
Hail flag („yes/no“) marks an enhanced (however `unquantified´)
probability for the occurence of hail.
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – RADVOR
Radar-based Precipitation
Lightning
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – RADVOR
Radar-based Precipitation
Lightning
+ RADVOR (+1h forecast)
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – KONRAD
Input data
German 1x1km reflectivity mosaic from 2D
precipitation scan
KONvektive Entwicklung in RADarprodukten
=
CONvective development in RADar products
(by Peter Lang and Otto Plörer)
Cell detection
Search for contiguous data regions with
reflectivity ≥ 46 dBZ (heavy precip.) and
therein contained pixels ≥ 55 dBZ (hail)
Classification
Categories 0-3, color coding:
depending on core dimension,
existence of hail
Tracking
Closest forecast position, cell core
similarity, otherwise: new cell
Forecasting
Linear displacement, +30 / +60 min
track extrapolation (forecast)
Manuel Werner, Meteorological Applications Development
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – KONRAD
Input data
German 1x1km reflectivity mosaic from 2D
precipitation scan
Cell detection
Search for contiguous data regions with
reflectivity ≥ 46 dBZ (heavy precip.) and
therein contained pixels ≥ 55 dBZ (hail)
Classification
Categories 0-3, color coding:
depending on core dimension,
existence of hail
Tracking
Closest forecast position, cell core
similarity, otherwise: new cell
Forecasting
Linear displacement, +30 / +60 min
track extrapolation (forecast)
Cell category
1 pixel ≈ 1 km²
Pixel ≥ 46 dBZ:
Pixel ≥ 55 dBZ:
- Level 0
≥ 15 km² and < 32 km² and
no pixel
- Level 1
≥ 32 km² or
1-12 pixel (Hail possible!)
- Level 2
≥ 80 km² or
13-30 pixel (Hail!)
- Level 3
≥ 210 km² or
more than 30 pixel (Hail!)
Manuel Werner, Meteorological Applications Development
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – KONRAD
Input data
German 1x1km reflectivity mosaic from 2D
precipitation scan
Cell detection
Search for contiguous data regions with
reflectivity ≥ 46 dBZ (heavy precip.) and
therein contained pixels ≥ 55 dBZ (hail)
Classification
Categories 0-3, color coding:
depending on core dimension,
existence of hail
Tracking
Closest forecast position, cell core
similarity, otherwise: new cell
Forecasting
Linear displacement, +30 / +60 min
track extrapolation (forecast)
Hail warning
1 km²
≥ 55 dBZ
12 km² ≥ 55 dBZ
Manuel Werner, Meteorological Applications Development
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – KONRAD
Radar-based Precipitation
Lightning
+ RADVOR
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – KONRAD
Radar-based Precipitation
Lightning
+ RADVOR
+ KONRAD
cat. 3
hail
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – CellMOS
CellMOS – cell detection and statistical forecasting
Forecast of cell track and characteristics with model output statistics (MOS)
Input data:
DWD radar reflectivities, 1x1 km², every 5 min
Lightning observations (LINET, continuously)
DWD global NWP model (GME) model predictions (00, 12 UTC)
Detection of a thunderstorm cell:
Radar reflectivity > 37 dBZ over at least 9 km2
Observed lightning
Hail detection and forecast:
Maximum reflectivity and area of high (> 53(46) dBz) reflectivity
Regression equation based on CellMOS hail size climatology
Sebastian Trepte, Meteorological Applications Development
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – CellMOS
Radar-based Precipitation
Lightning
+ RADVOR
+ KONRAD
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms – CellMOS
Radar-based Precipitation
Lightning
+ RADVOR
+ KONRAD
+ CellMOS (probability for hail > 15 mm,
+1h forecast)
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms
Single / Dual Polarization
Water droplet, diameter = 5 mm
Szakáll, 2009, University of Mainz
Patrick Tracksdorf, Meteorological Applications Development
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms
Choice of polarimetric measurements for the hydrometeor classification
differential reflectivity
(shape of the hydrometeors)
The modern polarimetric weather radar systems
provide additional measurement parameters
allowing a classification of the hydrometeors.
The distinction between the most probable
hydrometeor classes rain, hail, bright band
(melting particles), graupel and snow
is based on a fuzzy logic approach.
copolar correlation
(variance of the hydrometeors)
Classification
horizontal reflectivity
(backscattering)
Jörg Steinert, Meteorological Applications Development
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms
Radar-based Precipitation
Lightning
+ RADVOR
+ KONRAD
+ CellMOS (probability for hail > 15 mm)
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Radar-based Nowcasting Algorithms
Detection of rain/hail in ~ 3 km height
Jörg Steinert, Meteorological Applications Development
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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From Algorithms to Applications –
an Overview of Hail Nowcasting
at Deutscher Wetterdienst
1
2
3
Radar-based Nowcasting Algorithms
Applications and Warnings
Hail in Numerical Weather Prediction
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Applications and Warnings – NowCastMIX
Fuzzy Set
NowCastMIX
Fuzzy Logic
Hierarchy 2014
Rain Enhancement
Forecast 1h-Rain
(RADVOR)
Observed 1h-Rain
(RADOLAN)
Cell speed
Fuzzy Set
Torrential Rain
fpRE
fpRI
Fuzzy Set
Local Rain Intensity
Fuzzy Probabilities
Cell size (KONRAD)
Fuzzy Set
Precip Water (NWP)
Local Intensity of
Convection
fpLIC
VIL
Fuzzy Set
Hail
Lightning Density
Fuzzy Set
Gust Potential
(from horiz. motions)
Cell speed
Max. wind speed
below 700hPa (NWP)
Hail Flag (KONRAD)
Torrential Rain
15-25, 25-40, >40 mm/h
Hail
Categorical
Storm Warning
Small hail, Large hail
for next 60 minutes
Fuzzy Probabilities
fpLIC
Fuzzy Set
Severe Gusts
fpLIC
fpGP
Fuzzy Probabilities
Severe Gusts
Bft 8-10, Bft 11-12
Input data source
fpX = Fuzzy Potential for X
Paul James, Meteorological Applications Development
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Applications and Warnings – NowCastMIX
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Applications and Warnings – www.dwd.de
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Applications and Warnings – Aviation
Warning of
thunderstorm
with hail
•
•
Warnings
in aviation
applications
are issued for
specific areas
airports
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Applications and Warnings – Aviation
ESWD
(14 UTC):
COSMO-DE model temp (12 UTC)
Model temp shows
significant lability
indices
expected
hail diameter
= 1.2 cm
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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From Algorithms to Applications –
an Overview of Hail Nowcasting
at Deutscher Wetterdienst
1
2
3
Radar-based Nowcasting Algorithms
Applications and Warnings
Hail in Numerical Weather Prediction
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Hail in Numerical Weather Prediction
Hail forecast with the COSMO-model
Operational COSMO-DE high-resolution DWD NWP model
Hail forecasting based on environmental conditions
Grid spacing = 2.8 km, sometimes too coarse for explicit hail forecasting
1-moment bulk cloud microphysical scheme without hail processes
Research mode (applied in this study)
Explicit simulation of hail formation
Smaller grid spacing possible, 1 km in this study
Seifert-Beheng 2-moment bulk cloud microphysical scheme (2MS)
extended by the additional hydrometeor class `hail´
Currently, too expensive for operational applications
Needs further testing and refinements (e.g., melting of hail)
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Hail in Numerical Weather Prediction
Case study: Hail storm with reported damage to vineyards
Hail cell artificially triggered by "warm bubble"
Radar Karlsruhe
Ulrich Blahak, Research and Development,
presented work conducted at KIT
COSMO (research mode)
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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Contact:
Dr. Tanja Winterrath
Deutscher Wetterdienst
Hydrometeorology Department
Frankfurter Straße 135
63067 Offenbach am Main
Germany
www.dwd.de/radvor-op
Thanks to my DWD colleagues:
Ulrich Blahak, Paul James,
Wolfgang Rosenow, Carina Seidel,
Jörg Steinert, Sebastian Trepte,
Manuel Werner
Dr. Tanja Winterrath, 27 June 2014, 1st European Hail Workshop, Bern (Switzerland)
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