Transcript WDSS-II Development

High resolution radar data and
products over the Continental
United States
Valliappa.Lakshmanan@noaa.gov
National Severe Storms Laboratory
Norman OK, USA
http://www.wdssii.org/
Evolution of WDSS
19931998
Single-radar
SCIT, MDA, TDA
Now part of RPG
19952000
Single-radar with multisensor input
NSE inputs
Scheduled for ORPG-8
2003
Multi-radar multi-sensor
over regional domain
(1000km x 1000 km)
Gridded products
Shipped to select WFOs
Used in Storm Pred. Center
Product gen. for AWIPS?
2005
Multi-radar multi-sensor
over CONUS
CONUS 1km grids
Available on the Internet
Used in SPC
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What products? How often?
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Products:
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Spatial Resolution:
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Gridded hail products
Reflectivity at constant temperature levels and layer averages
Low-level and mid-level shear and rotation tracks
Short-term forecast fields
Lightning Density
More …
0.01 deg x 0.01 deg [x 1km] resolution
Approximately 1km x 1km throughout Continental United States.
1km in height
Temporal resolution:
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2D reflectivity mosaics every 2 minutes
3D and derived products every 5 minutes
lakshman@ou.edu
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How does it work?
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The process for creating
2D composites:
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Ingest Level-II radar
data tilt by tilt
QC reflectivity data
(Lak06, JAM, review)
Create virtual volume
composites
Merge composites from
all the CONUS radars
(Lak06, WF, accepted)
2nd level of QC -- using
satellite and surface
temperature data.
lakshman@ou.edu
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Virtual volume composite
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In a traditional composite,
 Process volume-by-volume.
 Take maximum of all tilts.
 Need to wait for end of
volume.
In a virtual volume composite:
 Process tilt-by-tilt.
 Keep a running volume.
 Replace older data each time.
 Take maximum of most
current tilts.
 No need to wait for end of
volume scan.
A virtual volume provides more
timely data.
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lakshman@ou.edu
at19.5
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Why do QC?
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On a single-radar
product, users may:
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want to see clear-air
returns.
tolerate more clutter
tolerate test patterns,
etc.
On a multi-radar
product, clutter and
clear-air returns are
distracting.
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Impact of QC
raw
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With QC’ed composites
Left: What we would get if directly combined raw (virtual volume)
reflectivity composite data
 Clear-air return, sun strobes, test patterns
Right: combining QCed virtual volume reflectivity composite
The QC is performed radar-by-radar
 Takes into account terrain, texture and vertical structure.
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Second level of QC
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The radar QC is
conservative
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Doesn’t always remove
non-precipitation echo
Especially if it is
biological i.e. moving.
A second level of QC
looks at satellite and
surface temperature and
retains echo where there
is likely to be clouds.
8 July 2015
lakshman@ou.edu
Bad
data
(bloom)
No
clouds
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What do we do with the composite?
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The 2D radar mosaic is created every 2 minutes at
1km resolution.
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Converted to Grib2 and sent to the SPC.
Put on the Internet:
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Snapshots with map background
Converted to Geotiff
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http://wdssii.nssl.noaa.gov
Not 24x7
The software is licensed by some private companies
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Loadable with Google Earth or any GIS software.
Google Earth does real-time loading
Talk in IIPS on Tuesday
They run it on their own machines.
They take care of 24x7 reliability.
lakshman@ou.edu
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2D vs 3D
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The 2D composite is cheap to
create
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Need to compute in 3D
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5 dual-Xeon machines with 6
GB RAM
But always provides an
underestimate of true values.
Height of dBZ value important!
Can incorporate NSE
information by height
A lot more products!
The 3D products need:
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5 dual-Xeon with 6 GB RAM
2 dual-Xeon with 16 GB RAM
64-bit architecture
 composite from 2D:
 composite from 3D:
8 July 2015
lakshman@ou.edu
45 dBZ
50 dBZ
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The 3D flow
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Not just
reflectivity.
 Compute
shear
(Smith05)
and lowlevel shear.
 Process
lightning
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3D processing
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Combine QC’ed
reflectivity in 3D
Combine
AzShear in 3D
Compute hail
diagnosis and
layer averages.
Compute storm
motion from
composite.
Use it to advect
storms for
short-term
forecast.
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Example products
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Extracted from the real-time generation
on Jan. 11, 2006
The day I created this presentation!
 We haven’t run the CONUS system in
Spring yet, so the severe weather products
may be underwhelming.
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Reflectivity products
Composite from 2D
Height of Max Ref
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Composite from 3D
Which radars?
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Azimuthal shear products
Azimuthal shear 0-3km MSL
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30 minute rotation tracks
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Severe weather diagnosis
Also:
Probability of
Severe Hail
Reflectivity at temp. levels
VIL
Maximum
Expected Hail
Size
VIL_Density
VIL_of_the_
day
Other echo
top dBZ levels
Convection
Echo top (18 dBZ)
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Short-term forecast
Reflectivity at T=0
Clusters
Reflectivity at T=30 (forecast)
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Southward motion
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Precipitation estimates
Instantaneous precip rate
Ref closest to ground
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Just the 88D algorithm on CONUS
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Uses hybrid scan reflectivity
Convective/stratiform
segregration based on presence
of hail
88D Z/R relationships.
Not multi-sensor
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QPESUMS-II under development
at NSSL.
2hr precip accum
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What do we do with these products?
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The 3D products are created every 5 minutes
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1km resolution (0.01deg x 0.01deg x 1km)
Converted to Grib2 and sent to the SPC.
Put on the Internet (not all of them):
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Snapshots with map background
Converted to Geotiff
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Loadable with Google Earth or any GIS software.
Google Earth does real-time loading
Talk in IIPS on Tuesday
http://wdssii.nssl.noaa.gov
Looking for the NWS to pick this up!
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lakshman@ou.edu
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