Unidata software and data usage at University of Wisconsin - Madison

1. Unidata software and data usage at University of Wisconsin - Madison Pete Pokrandt UW-AOS Computer Systems Admin

2. Unidata software and data usage at UW-AOS • Evolution of UW-Madison AOS involvement with Unidata • Ongoing research using Unidata software/data • Use in courses

3. Evolution of Unidata involvement at UW Madison • 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

4. Evolution of Unidata involvement at UW Madison • 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

5. Evolution of Unidata involvement at UW Madison • 2002 – archive CONDUIT grid analyses • 2003 NIMAGE, CRAFT, IDV

6. Some uses of Unidata software/data • Products made available on the internet • 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

7. Products on the internet • Surface plots

8. Products on the internet • Surface plots

9. Products on the internet • Surface plots

10. Products on the internet • Upper air analyses

11. Products on the internet • Upper air analyses

12. Products on the internet • NEXRAD products and composites • National and Regional Composites(live link) • Individual site products for regional sites

13. Products on the internet • Model plots and animations • Eta on the AWIPS 212 grid • Eta on the AWIPS 104 grid • GFS on the 1 degree global grid300 hPa 500 hPa 850 hPa

14. Products on the internet • GFS/Ensemble 4-panel plots

15. 1 day forecast Products on the internet • GFS/Ensemble 4-panel plots

16. 8 day forecast Products on the internet • GFS/Ensemble 4-panel plots

17. 10 day forecast Products on the internet • GFS/Ensemble 4-panel plots

18. Products on the internet • Lightning data – plots and loops • US region • US region loop • WI region • WI region loop

19. Use of NCEP Model Grids • Analysis using NCEP Model Grids- Steve Decker – GFS Energetics plots- Justin Mclay – Ensemble Verification- Allison Hoggarth – PV tracking of easterly waves

20. GFS Energetics plotsSteven Decker • 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)

21. GFS Energetics plotsSteven Decker • 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 EKEEAEKE is eddy advection of EKEBTG is barotropic generationBCG is baroclinic generation

22. GFS Energetics plotsSteven Decker • 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 curvatureRES is a residual, including friction

23. GFS Energetics plotsSteven 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

24. GFS Energetics plotsSteven 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

25. GFS Energetics plotsSteven 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

26. GFS Energetics plotsSteven Decker d(EKE)/dt = MAEKE + EAEKE + BTG + BCG + AGFC + CURV + RES (d/dt is local derivative)The other terms are usually not important

27. GFS Energetics plotsSteven 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.

28. GFS Energetics plotsSteven Decker 300 hPa Geo Hgt EKE and AGF vectors

29. GFS Energetics plotsSteven Decker Time tendency of EKE due to eddy advection

30. GFS Energetics plotsSteven Decker Baroclinic Generation of EKE

31. GFS Energetics plotsSteven Decker Wave Packet Envelope function

32. GFS Energetics plotsSteven Decker • Plots and further explanation available athttp://speedy.aos.wisc.edu/~sgdecker/realtime/realtime.html

33. Ensemble prediction of CAOsJustin Mclay • Daily 00 UTC ensemble initialization is being used in an ongoing assessment of deterministic and ensemble prediction of North American Cold Air Outbreaks (CAOs) • Ensemble forecasts frequently predict “Phantom” or “Sneak” CAOs (Postel 2002, personal communication)

34. Ensemble prediction of CAOsJustin Mclay • Phantom CAOs – where ensemble suggest a high likelyhood of a CAO, which ultimately does not verify • Sneak CAOs – where ensemble suggests a low, if any likelyhood of a CAO, which ultimately does verify

35. Ensemble prediction of CAOsJustin Mclay • 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.

36. Ensemble prediction of CAOsJustin Mclay • 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 post-processing schemes to improve ensemble prediction of CAOs.

37. PV Tracking of easterly wavesAllison Hoggarth • 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)

38. Use of NCEP Model Grids • Initialization for local operational mesoscale modeling- Tripoli – UW-NMS- Morgan/Kleist – MM5/Adjoint derived forecast sensitivities

39. Operational UW-NMSTripoli, Pokrandt, Adams, et. al. • 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

40. Operational UW-NMSTripoli, Pokrandt, Adams, et. al. • 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

41. Operational UW-NMSTripoli, Pokrandt, Adams, et. al. • Support of various field projects- Lake ICE (Lake Effect Snow over Lake Michigan- Recent Pacific field project – instrument testing – needed heavy precipitation over water

42. MM5/Adjoint derived fcst sensitivityMorgan/Kleist • MM5 Adjoint Modeling System (Zou et al., 1997) • 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

43. MM5/Adjoint derived fcst sensitivityMorgan/Kleist ForecastModel Adjoint Model

44. MM5/Adjoint derived fcst sensitivityMorgan/Kleist Real-Time Forecast Sensitivities • 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: • 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)

45. 0h 12h 24h 36h

46. MM5/Adjoint derived fcst sensitivityMorgan/Kleist • 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

47. Use in after-the-fact analysis • 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

48. Middle Atmosphere modelingMarcus Buker, Matt Hitchman • Real-time forecasting for flight planning for various field projects (POLARIS, SOLVE, TRACE-P) • After-the-fact simulations to interpret observations

49. Middle Atmosphere modelingMarcus Buker, Matt Hitchman • POLARIS (Photochemical Ozone Loss in the Arctic Region In Summer) • Regional scale simulations were run for thecampaign 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.

50. Middle Atmosphere modelingMarcus Buker, Matt Hitchman • 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 (~-80C), in areas where bouyancy waves induce relatively strong vertical motion