Unidata software and data usage
at University of Wisconsin Madison
Pete Pokrandt
UW-AOS Computer
Systems Admin
Unidata software and data usage at
UW-AOS
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Evolution of UW-Madison AOS involvement
with Unidata
Ongoing research using Unidata software/data
Use in courses
Evolution of Unidata involvement at
UW Madison
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1986 – DIFAX to facsimile machine
DDS, PPS to line feed printer
1987 – PC McIDAS
1989 – DIFAX to Dot Matrix printer
1992 – DDS, PPS to Sun Workstation
minimal data archiving to Exabyte tape
wxp to plot data
DIFAX to laserprinter
Evolution of Unidata involvement at
UW Madison
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1994-1995 – GEMPAK installed, replaced
McIDAS as primary data analysis/plotting tool
1995 – switch from satellite feed to IDD
DDPLUS, IDS, HDS, MCIDAS, NLDN
1996 – archive DDPLUS, IDS, HDS, MCIDAS
1998 NMC2/SPARE/CONDUIT
2000 NEXRAD, FNEXRAD
Evolution of Unidata involvement at
UW Madison
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2002 – archive CONDUIT grid analyses
2003 NIMAGE, CRAFT, IDV
Some uses of Unidata
software/data
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Products made available on the internet
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Surface, Upper Air plots
NEXRAD Composites
Model plots and animations
Lightning strike plots (Restricted)
Analysis using NCEP Model Grids
NCEP Model Grids used to initialize local
mesoscale models
Products on the internet
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Surface plots
Products on the internet
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Surface plots
Products on the internet
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Surface plots
Products on the internet
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Upper air analyses
Products on the internet
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Upper air analyses
Products on the internet
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NEXRAD products and composites
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National and Regional Composites
(live link)
Individual site products for regional sites
Products on the internet
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Model plots and animations
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Eta on the AWIPS 212 grid
Eta on the AWIPS 104 grid
GFS on the 1 degree global grid
300 hPa 500 hPa 850 hPa
Products on the internet
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GFS/Ensemble 4-panel plots
1 day forecast
Products on the internet
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GFS/Ensemble 4-panel plots
8 day forecast
Products on the internet
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GFS/Ensemble 4-panel plots
10 day forecast
Products on the internet
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GFS/Ensemble 4-panel plots
Products on the internet
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Lightning data – plots and loops
US region
US region loop
WI region
WI region loop
Use of NCEP Model Grids
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Analysis using NCEP Model Grids
- Steve Decker – GFS Energetics plots
- Justin Mclay – Ensemble Verification
- Allison Hoggarth – PV tracking of easterly
waves
GFS Energetics plots
Steven Decker
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Horizontal Kinetic Energy per unit mass (KE) at
a point can be broken into two parts
- Mean KE is derived from the time mean wind
at that point – 28 day time mean
- Eddy KE is derived from current wind minus
mean wind: EKE = (1/2)(u’2 + v’2)
GFS Energetics plots
Steven Decker
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Time tendency of EKE is determined by:
d(EKE)/dt = MAEKE + EAEKE + BTG + BCG +
AGFC + CURV + RES (d/dt is local derivative)
MAEKE is mean advection of EKE
EAEKE is eddy advection of EKE
BTG is barotropic generation
BCG is baroclinic generation
GFS Energetics plots
Steven Decker
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Time tendency of EKE is determined by:
d(EKE)/dt = MAEKE + EAEKE + BTG + BCG +
AGFC + CURV + RES (d/dt is local derivative)
AGFC is ageostrophic geopotential flux conv.
CURV are terms related to earth curvature
RES is a residual, including friction
GFS Energetics plots
Steven Decker
d(EKE)/dt = MAEKE + EAEKE + BTG + BCG +
AGFC + CURV + RES (d/dt is local derivative)
Advection terms move EKE around but do not
create or destroy it
GFS Energetics plots
Steven Decker
d(EKE)/dt = MAEKE + EAEKE + BTG + BCG +
AGFC + CURV + RES (d/dt is local derivative)
Generation terms create or destroy EKE in
various ways
GFS Energetics plots
Steven Decker
d(EKE)/dt = MAEKE + EAEKE + BTG + BCG +
AGFC + CURV + RES (d/dt is local derivative)
AGFC indicates collection (dispersion) of EKE
radiation at (from) a point from (to) elsewhere
in the domain
GFS Energetics plots
Steven Decker
d(EKE)/dt = MAEKE + EAEKE + BTG + BCG +
AGFC + CURV + RES (d/dt is local derivative)
The other terms are usually not important
GFS Energetics plots
Steven Decker
Using GEMPAK and the 1 degree global GFS
data set from the CONDUIT data stream, plots
are created twice daily for EKE with AGF
vectors, EAEKE, BCG, AGFC and a wave
packet envelope function.
GFS Energetics plots
Steven Decker
300 hPa Geo Hgt EKE and AGF vectors
GFS Energetics plots
Steven Decker
Time tendency of EKE due to eddy advection
GFS Energetics plots
Steven Decker
Baroclinic Generation of EKE
GFS Energetics plots
Steven Decker
Wave Packet Envelope function
GFS Energetics plots
Steven Decker
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Plots and further explanation available at
http://speedy.aos.wisc.edu/~sgdecker/realtime/realtime.html
Ensemble prediction of CAOs
Justin Mclay
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Daily 00 UTC ensemble initialization is being
used in an ongoing assessment of
deterministic and ensemble prediction of North
American Cold Air Outbreaks (CAOs)
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Ensemble forecasts frequently predict
“Phantom” or “Sneak” CAOs (Postel 2002,
personal communication)
Ensemble prediction of CAOs
Justin Mclay
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Phantom CAOs – where ensemble suggest a
high likelyhood of a CAO, which ultimately
does not verify
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Sneak CAOs – where ensemble suggests a
low, if any likelyhood of a CAO, which
ultimately does verify
Ensemble prediction of CAOs
Justin Mclay
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Current effort is using GFS ensemble forecasts
via the CONDUIT data stream to document the
performance of the ensemble system with
specific regard to CAOs.
Ensemble prediction of CAOs
Justin Mclay
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Some elements
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Relative frequency of Phantom and Sneak CAOs
Relative skill in predicting moderate vs. extreme
CAO
First and second statistical moments of the
ensemble (mean and covariance) are also being
investigated for incorporation into statistical postprocessing schemes to improve ensemble
prediction of CAOs.
PV Tracking of easterly waves
Allison Hoggarth
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Using 1 degree global GFS analyses and
GEMPAK, evaluate PV (and other quantities)
over the tropical Atlantic basin
Is there a way to categorize whether a wave
will transform into a tropical depression or not?
Tropical depression #2 (June 2003)
Use of NCEP Model Grids
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Initialization for local operational mesoscale
modeling
- Tripoli – UW-NMS
- Morgan/Kleist – MM5/Adjoint derived
forecast sensitivities
Operational UW-NMS
Tripoli, Pokrandt, Adams, et. al.
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Began operational runs in 1992
Data from inside source at NMC, later from
public NMC server
Since 2000, via CONDUIT feed – locally
available sooner than via ftp
“Storm of the Century”, 1993
Mainly lake breeze, lake effect snow – tied to
the terrain/surface characteristics
Operational UW-NMS
Tripoli, Pokrandt, Adams, et. al.
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Cooperation with NWS-Sullivan, studying
predictability of local terrain/topo driven
phenomena (lake breeze, lake effect snow)
Fire Weather index prediction
Supercell Index – supports severe storm
observation class (Storm chasing)
Vis5d animations, GEMPAK output support
synoptic lab courses
Operational UW-NMS
Tripoli, Pokrandt, Adams, et. al.
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Support of various field projects
- Lake ICE (Lake Effect Snow over Lake
Michigan
- Recent Pacific field project – instrument
testing – needed heavy precipitation over water
MM5/Adjoint derived fcst sensitivity
Morgan/Kleist
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MM5 Adjoint Modeling System (Zou et al., 1997)
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All sensitivities to be described were calculated by
integrating the adjoint model “backwards” using dry
dynamics, about a moist basic state generated by the
forward MM5 run, initialized with Eta initialization
MM5/Adjoint derived fcst sensitivity
Morgan/Kleist
'
xin
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R

xin
Forecast
Model
'
 x out
Adjoint
Model
R

x out
x  (u , v ,w ,T,p' ,q v )
R R R R R R
( , , , , )
x
u  v w T p'
MM5/Adjoint derived fcst sensitivity
Morgan/Kleist
Real-Time Forecast Sensitivities
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Goal: To understand the characteristics and sensitivity to initial
conditions of short range numerical weather prediction (NWP)
forecasts and forecast errors over the continental United States
Available:
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Sensitivity plots (updated twice daily) for two response functions:
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36 hour energy-weighted forecast error
36 hour forecast of average temperature over Wisconsin
Adjoint-derived ensemble of forecasts of average temperature over
Wisconsin (soon to be available)
0h
12h
24h
36h
MM5/Adjoint derived fcst sensitivity
Morgan/Kleist
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Sensitivity Based “Ensembles”
Could run several forward models with different
initial conditions (Eta, NGM, GFS,
NOGAPS,etc), get an ensemble of average
temps over WI box
Instead, multiply the sensitivity gradient by
each initial condition to get estimates of the
ensemble members
Use in after-the-fact analysis
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Use of archived datasets for after-the-fact
modeling and analysis
- Hitchman/Buker – UW-NMS/middle
atmosphere modeling
- Martin – GEMPAK libraries to create new
datasets
Middle Atmosphere modeling
Marcus Buker, Matt Hitchman
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Real-time forecasting for flight planning for
various field projects (POLARIS, SOLVE,
TRACE-P)
After-the-fact simulations to interpret
observations
Middle Atmosphere modeling
Marcus Buker, Matt Hitchman
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POLARIS (Photochemical Ozone Loss in the
Arctic Region In Summer)
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Regional scale simulations were run for the
campaign area (50-70N, 120W-70E)
Ozone & passive tracers initialized to monitor
constituent transport across the tropopause
Found ozone is lost from the stratosphere to the
troposphere by stretching/folding of tropopause by
breaking Rossby waves.
Middle Atmosphere modeling
Marcus Buker, Matt Hitchman
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SOLVE (SAGE III Ozone Loss and Validation
Experiment)
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Ozone loss in wintertime boreal polar region is
highly dependent on existence of polar stratospheric
clouds – chemical makeup is conduscive for
photochemical destruction of ozone.
Form in coldest parts of stratosphere (~-80C), in
areas where bouyancy waves induce relatively
strong vertical motion
Middle Atmosphere modeling
Marcus Buker, Matt Hitchman
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SOLVE (SAGE III Ozone Loss and Validation
Experiment)
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Mountain waves are a major contributor to this type
of phenomenon
Hitchman et al. (2003) used UWNMS to show that
non-orographic bouyancy waves can also produce
extensive areas of PSC formation, especially in
early winter
Middle Atmosphere modeling
Marcus Buker, Matt Hitchman
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TRACE-P (TRansport And Chemical Evolution
over the Pacific)
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UW-NMS simulations ongoing for flight dates in March, 2001.
Trying to differentiate between ozone from ground sources and
transport from the stratosphere, to determine contribution of
tropospheric pollution from east Asian sector.
Testing new methodology to get ozone flux between
stratosphere/troposphere in regions of strong tropospheric
activity
GEMPAK to create new data sets
Jon Martin
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Use of GEMPAK libraries and locally written
programs
Read existing data sets, perform calculations,
save out to new data set.
Can be done recursively, or to trim size of a
data set, compute complex functions, etc.
Unidata in UW Courses
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GEMPAK/GARP – in class and in research
ldm – to get data
Maps online
Tripoli – storm chasing
Synoptic Lab – case studies
The future
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IDV, THREDDS
CRAFT
Questions?
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Thank you!