Unidata software and data usage at University of
- Slides: 57
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 l l l Evolution of UW-Madison AOS involvement with Unidata Ongoing research using Unidata software/data Use in courses
Evolution of Unidata involvement at UW Madison l l 1986 – DIFAX to facsimile machine DDS, PPS to line feed printer 1987 – PC Mc. IDAS 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 l l l 1994 -1995 – GEMPAK installed, replaced Mc. IDAS 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 NMC 2/SPARE/CONDUIT 2000 NEXRAD, FNEXRAD
Evolution of Unidata involvement at UW Madison l l 2002 – archive CONDUIT grid analyses 2003 NIMAGE, CRAFT, IDV
Some uses of Unidata software/data l Products made available on the internet – – l l Surface, Upper Air plots NEXRAD Composites Model plots and animations Lightning strike plots (Restricted) Analysis using NCEP Model Grids used to initialize local mesoscale models
Products on the internet l Surface plots
Products on the internet l Surface plots
Products on the internet l Surface plots
Products on the internet l Upper air analyses
Products on the internet l Upper air analyses
Products on the internet l NEXRAD products and composites – – National and Regional Composites (live link) Individual site products for regional sites
Products on the internet l Model plots and animations – – – Eta on the AWIPS 212 grid Eta on the AWIPS 104 grid GFS on the 1 degree global grid 300 h. Pa 500 h. Pa 850 h. Pa
Products on the internet l GFS/Ensemble 4 -panel plots
1 day forecast Products on the internet l GFS/Ensemble 4 -panel plots
8 day forecast Products on the internet l GFS/Ensemble 4 -panel plots
10 day forecast Products on the internet l GFS/Ensemble 4 -panel plots
Products on the internet l l l Lightning data – plots and loops US region loop WI region loop
Use of NCEP Model Grids l 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 l 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 l 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 l 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 h. Pa 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 l Plots and further explanation available at http: //speedy. aos. wisc. edu/~sgdecker/realtime. html
Ensemble prediction of CAOs Justin Mclay l Daily 00 UTC ensemble initialization is being used in an ongoing assessment of deterministic and ensemble prediction of North American Cold Air Outbreaks (CAOs) l Ensemble forecasts frequently predict “Phantom” or “Sneak” CAOs (Postel 2002, personal communication)
Ensemble prediction of CAOs Justin Mclay l Phantom CAOs – where ensemble suggest a high likelyhood of a CAO, which ultimately does not verify l Sneak CAOs – where ensemble suggests a low, if any likelyhood of a CAO, which ultimately does verify
Ensemble prediction of CAOs Justin Mclay l 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 l Some elements – – – 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 l l l 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 l Initialization for local operational mesoscale modeling - Tripoli – UW-NMS - Morgan/Kleist – MM 5/Adjoint derived forecast sensitivities
Operational UW-NMS Tripoli, Pokrandt, Adams, et. al. l l l 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. l l 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) Vis 5 d animations, GEMPAK output support synoptic lab courses
Operational UW-NMS Tripoli, Pokrandt, Adams, et. al. l Support of various field projects - Lake ICE (Lake Effect Snow over Lake Michigan - Recent Pacific field project – instrument testing – needed heavy precipitation over water
MM 5/Adjoint derived fcst sensitivity Morgan/Kleist l MM 5 Adjoint Modeling System (Zou et al. , 1997) l 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 MM 5 run, initialized with Eta initialization
MM 5/Adjoint derived fcst sensitivity Morgan/Kleist Forecast Model Adjoint Model
MM 5/Adjoint derived fcst sensitivity Morgan/Kleist Real-Time Forecast Sensitivities l l 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: – Sensitivity plots (updated twice daily) for two response functions: l l – 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)
0 h 12 h 24 h 36 h
MM 5/Adjoint derived fcst sensitivity Morgan/Kleist l l l 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 l 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 l l 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 l POLARIS (Photochemical Ozone Loss in the Arctic Region In Summer) – – – Regional scale simulations were run for the campaign area (50 -70 N, 120 W-70 E) 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 l SOLVE (SAGE III Ozone Loss and Validation Experiment) – – 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 (~-80 C), in areas where bouyancy waves induce relatively strong vertical motion
Middle Atmosphere modeling Marcus Buker, Matt Hitchman l SOLVE (SAGE III Ozone Loss and Validation Experiment) – – 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 l TRACE-P (TRansport And Chemical Evolution over the Pacific) – – – 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 l l l 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 l l l GEMPAK/GARP – in class and in research ldm – to get data Maps online Tripoli – storm chasing Synoptic Lab – case studies
The future l l IDV, THREDDS CRAFT
Questions? l Thank you!
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