Overview of Ensemble Forecasting

1. Overview of Ensemble Forecasting COMET Faculty 99 Course Presented by Steve Mullen Wednesday, 9 June 1999 Steven L. Mullen Univ. of Arizona

2. Benefactors • Dave Baumhefner, NCAR • Joe Tribbia, NCAR • Ron Errico, NCAR • Tom Hamill, NCAR • Harold Brooks, NSSL • Chuck Doswell, NSSL • Dave Stensrud, NSSL • Eugenia Kalnay, NCEP-UO-UM-? • Steve Tracton, NCEP • Zoltan Toth, NCEP • Ron Gelaro, NRL • Rolf Langland, NRL • Jeff Anderson, GFDL • Mike Harrison, UKMO • Tim Palmer, ECMWF • Roberto Buizza, ECMWF • Peter Houtekamer, AES

3. Presentation Overview • Philosophy and Benefits of Ensembles • Estimate of Initial Uncertainty • Design of Initial Perturbations for EPS • Inclusion of Model Uncertainty in EPS • Ensemble Size • Integration of EPS and Data Assim System • Model Validation • Evaluation and Utility of EPS • Classroom Activities

4. Philosophy and Benefitsof Ensemble Forecasting • Initial Condition Uncertainty (ICU) • Probability Density Function (PDF) of initial conditions about “Truth” • GOAL: predict evolution of PDF • Gives information on 1st & 2nd moments Forecast uncertainty from dispersion • Thought to be most applicable to MRF (6-10 day) and seasonal (30-90 day) forecasts • Beneficial to SRF (06 h-2 day) for QPF • KEY: IC error versus model error More skillful model, more beneficial PIC • Now includes dispersion from uncertainty in initial state and model formulations

6. Univ Utah Ensemble12 km inner grid

7. Univ Utah Ensemble12 km inner grid

9. Precipitation Dispersion32 km NSSL Mixed EnsembleOct 97-Dec 97

10. Perturbation Design • What is the goal?1) Robust estimate of PDF?2) Sample extremes of PDF? 3) Make up for deficiency in EPS? • Requirements 1) Properly constrained by estimates of analysis error 2) Equally-likely probability for each perturbation field • What are some of the attributions of current perturbation schemes for global ensemble models?

11. Dave Baumhefner, in progress

19. Ranked Probability Scoreby Model and Perturbation

20. Ranked ProbabilitySkill ScoreRelative to Climatology

21. Perturbation DesignConclusions • Perturbation methods control dispersion characteristics out to 5-7 days • SV: linear growth 1-3 days • Random: classic error growth curve • Random: project onto SVs 1-5 days • BV: unique, different than analysis error, but has improved with recent changes • Perturb strategy is unimportant after 5-7 days, once growth is strongly nonlinear

23. Model Uncertainties • Specification of Subgrid Scale Processes • GOAL: improve transient variability and increase ensemble dispersion • Methodologies / Philosophies 1) Fixed during model integration: different parameterization schemes change tunable parameters 2) Stochastic element during integration: to a scheme’s tunable parameters to model tendencies directly • What are some of the attributes?

24. Rank Histogram24 h Rain Totals

27. Stochastic Cb Parameterization

28. Model Uncertainties Conclusions • Increases dispersion • Changes predictability estimates • Model validation issues?

29. Model Validation • Major Challenge for Mesoscale LAMs • Inclusion of stochastic dynamics/physics into model requires consideration of amplitude spatial scale temporal scale • Statistics for model and observations are currently lacking, so need for long-term model integrations better utilization of obs network in absence of obs statistics, validate by comparison with explicit models • GOAL: model PDFs match obs PDFs

33. Ensemble Size (N) • Increased N or finer model resolution • Partitioning N among perturbed IC’s and different physics parameterizations • Depend on model, forecast objective etc. • Choice is not always clear Resolution of complex terrain • Larger N always decreases sampling uncertainty Diminishing returns N exceeds 10-20 • N sets limits on resolution of PDF 1% event requires N of 200 or larger • Large N warranted for accurate EPS Model with good climate Ability to simulate phenomenon Sound perturbation strategy

35. EPS and Data Assimilation System • Current status of Data Assimilation 3DVAR and OI techniques homogeneous isotropic flow independent • Kalman filter and 4DVAR can account for these shortcomings Kalman filter expensive 4DVAR lacks cycling • Ensemble of perturbed 6h SRFs may provide an alternative to 4DVAR inexpensive contains cycling • Houtekamer and Mitchell (1998) study

39. Utility of EPS • Challenge: convey info in ensembles Reduce flow dimensionality clusters, EOFs, indices, envelopes User friendly and flexible wide spectrum of needs and abilities “problem of day” changes • Enhance utility by stat. post-processing MLR MOS-techniques Kalman filtering AI-neural networks • Rigorous assessment of stat. significance • Cost-benefit analysis

40. Neural Net Post-ProcessingReliability Diagram 0.25”

41. Cost-Benefit AnalysisPrecipitation

42. Fav SitesReal-Time Ensemble Products • NCEP MRF Ensembles CDC Boulderwww.cdc.noaa.gov/~map/maproom/ENS/ens.html NCEP Ensemble Homepage sgi62.wwb.noaa.gov:8080/ens/enshome.html Univ of Utahwww.met.utah.edu/jhorel/html/models/model_ens.html • MOS for MRF Ensembles Penn Statewww.essc.psu.edu/~rhart/ensemble/ensmos.html • Short-Range Mixed Ensembles NSSL/NOAA vicksburg.nssl.noaa.gov/mm5/ensemble/index_all.html • SAMEX? NCEP ETA/RSM? Ask Kelvin D. and Steve T., respectively!

43. Univ. Utah

44. Univ. Utah

45. MRF Ensemble MOSfrom Penn State

46. NSSL Experiment Ensemble Model Physics/Uncertainty

47. FNMOC/UA Products

48. Classroom ActivitiesAppropriate for Undergrads • Probabilistic Forecasting QPF Use MOS thresholds MAX-MIN Credible Interval Forecasts (e.g. Prob. within 2oF) Be willing to stumble and be humbled! • Hands-On NWP Barotropic Model Experiments