1. 30 January 2003 13:00 NWSFO-Seattle An Update on Local MM5 Products at NWSFO-Seattle Eric P. Grimit SCEP NOAA/NWS-Seattle Ph.D. Candidate, University of Washington

2. 30 January 2003 13:00 NWSFO-Seattle Overview Update on the deterministic MM5 runs Update on the expanded MM5 short- range ensemble forecast system Examples of new products available in AWIPS and IFPS Timing & Availability Forecaster Feedback

3. 30 January 2003 13:00 NWSFO-Seattle Two Deterministic Mesoscale Forecasts New MM5gfs format: AWIPS/IFPS name change from MM5avn to MM5gfs Forecast 0-72 h (36-/12-km); 6-48 h (4-km) Outer 36-km domain nudged toward GFS forecast 4-km nest split out and run separately after 36/12 finishes MM5gfs-36km, MM5gfs-12km at 0000/1200 UTC Delivery: ~10:00 AM/PM (0600/1800 UTC) MM5eta: No changes, still the early run; 0-72 h (36-/12-km) MM5eta-12km at 0000/1200 UTC Delivery: ~9:30 AM/PM (0530/1730 UTC) Future Implementations: MM5gfs-36km will be sent to WRHQ for distribution among all WR FOs; MM5gfs-12km to all Northwest FOs

4. 30 January 2003 13:00 NWSFO-Seattle The Extended Run New MM5ext run: Forecast 72-168 h (36-/12-km) with GFS LBCs Outer 36-km domain nudged toward GFS forecast to prevent synoptic-scale “drift” caused by the limited-area MM5 model MM5ext-36km, MM5ext-12km at 0000/1200 UTC Delivery: ~2 AM/PM (1000/2200 UTC) May need to go to 192 h (day 8) due to lag-time to cover NDFD 7-day forecast requirement; will add ~30 min to delivery time Future Implementations: MM5ext-36km will be sent to WRHQ for distribution among all WR FOs; MM5ext-12km to all Northwest FOs Paired with WOCSS diagnostic model to downscale surface winds to 5-km grid for IFPS

5. Frequency Initial State Ensemble Forecasting, Theory vs. Application - Start with an “analysis PDF” made up of many equally likely analyses of the atmosphere (i.e., initial conditions, ICs). We can think of truth as a random sample from this PDF. - Create “forecast PDFs” by running each IC in a model, producing many possible forecasts. The chaotic nature of the atmosphere leads to non-linear error growth. 24hr Forecast State48hr Forecast State Frequency Initial State24hr Forecast State48hr Forecast State - Difficult to produce the analysis PDF. Errors in mean and spread lead to poor estimates of the forecast PDF. We are therefore uncertain about our uncertainty prediction! analysis PDF EF Histogram

6. 30 January 2003 13:00 NWSFO-Seattle Factors to be Considered for Mesoscale Short-Range Ensembles Compared to medium range EF, successful SREF is elusive: Predominantly linear error growth (Gilmour et al., 2001) so cannot count on perturbations diverging non-linearly Predictability of sensible wx parameters on mesoscale is largely unknown Limited-area domain may constrain error growth Model uncertainty must be included since it likely plays a significant role Local factors for the Pacific NW Uncertainty upstream over the Pacific can be HUGE Significant errors in synoptic wave phase and amplitude Regime and orography may be an advantage Synoptic flow interacts with terrain to create mesoscale features Convection is weak and limited

7. phase space T 48hr forecast state (core) 48hr true state Analysis pdf : Forecast pdf : 8 “independent” atmospheric analyses, the centroid, plus 8 “mirrored” ICs 17 divergent, “equally likely” solutions using the same primitive equation model, MM5 Forecast pdf 48hr forecast state (perturbation) ngp uk eta cmc gsp avn Analysis pdf cwb C ngp eta cmc avn gsp cwb uk UW SREF Methodology Overview A point in phase space completely describes an instantaneous state of the atmosphere. For a model, a point is the vector of values for all parameters (pres, temp, etc.) at all grid points at one time.

8. Resolution ( ~ @ 45  N ) Objective Abbreviation/Model/Source Type Computational Distributed Analysis avn, Global Forecast System (GFS), SpectralT254 / L641.0  / L14 SSI National Centers for Environmental Prediction~55km~80km cmcg, Global Environmental Multi-scale (GEM),SpectralT199 / L281.25  / L113D Var Canadian Meteorological Centre ~100km ~100km eta, Eta limited-area mesoscale model, Finite12km / L45 90km / L37SSI National Centers for Environmental Prediction Diff. gasp, Global AnalysiS and Prediction model,SpectralT239 / L291.0  / L? 3D Var Australian Bureau of Meteorology~60km~80km jma, Global Spectral Model (GSM),SpectralT106 / L211.25  / L13OI Japan Meteorological Agency~135km~100km ngps, Navy Operational Global Atmos. Pred. System,SpectralT159 / L241.0  / L14OI Fleet Numerical Meteorological & Oceanographic Cntr. ~90km~80km tcwb, Global Forecast System,SpectralT79 / L181.0  / L11 OI Taiwan Central Weather Bureau~180km~80km ukmo, Unified Model, Finite5/6  5/9  /L30same / L123D Var United Kingdom Meteorological Office Diff.~60km ICs/LBCs for the Analysis-Centroid Mirroring Ensemble

9. # of Initial Forecast UW MM5 Name Members Conditions Model(s) Cycle Domain ACME 178 Ind. Analyses 1 (MM5)00Z 36km, 12km 1 Centroid 8 Mirrors ACME core 8Independent 1 (MM5)00Z, 12Z 36km, 12km Analyses ACME core+ 8 “ “ 8 (MM5 variations) 00Z 36km, 12km PME 8 “ “ 8 00Z, 12Z 36km NCEP SREF 8Regional 2 (ETA, RSM)00Z, 12Z 36km Breeding Homegrown Imported ACME: Analysis-Centroid Mirroring Ensemble PME: Poor Man’s Ensemble NCEP SREF: National Centers for Environmental Prediction Short Range Ensemble Forecast MM5: PSU/NCAR Mesoscale Modeling System Version 5

10. 30 January 2003 13:00 NWSFO-Seattle Illustration of “Mirroring” Sea-Level Pressure Analyses TCWB CENT C1.1T Disagreement with respect to the southern-most low results in large differences in the position, intensity, and structure of the low Mirrored analysis is still “realistic” or “plausible”

11. 30 January 2003 13:00 NWSFO-Seattle Validation of the Mirrored Runs (Using centroid analysis verification on 65 cases from Dec01 – Mar02) C e n t r o i d ACME mean Poorman mean C e n t r o i d ACME mean Poorman mean

12. 30 January 2003 13:00 NWSFO-Seattle ACME Benefits and Limitations Strengths: Good representation of analysis/observational error Perturbations to synoptic scale disturbances Magnitude of perturbation(s) set by spread among analyses Bigger spread  Bigger perturbations Computationally efficient and affordable Weaknesses: Limited by number and quality of available analyses May miss key features of analysis error Analyses must be independent (i.e., dissimilar biases) Calibration difficult; no stability since analyses may change techniques

14. 30 January 2003 13:00 NWSFO-Seattle Example Ensemble Probability Product valid 2100 UTC today

15. ACME core : 31 Oct 2002 – 20 Jan 2003 BSS: 0.5270.5590.474

16. 30 January 2003 13:00 NWSFO-Seattle 7 mb 4 mb Example Ensemble Spread Product valid 0000 UTC today

17. 30 January 2003 13:00 NWSFO-Seattle Variance has a  2 distribution, use the f-statistic: f = s 2 / s 2 where s 2 denotes the climatological median variance ~ ~ Evaluate the CDF: F(s 2 ) Transform to a normal distribution, using the percentile obtained from the f-distribution, where:  (F(s 2 )) Use resulting standard normal rv as a high/low uncertainty index Standardizing the spread Confidence Products: Visualizing Uncertainty valid 0000 UTC today

18. 30 January 2003 13:00 NWSFO-Seattle MM5ens Product Timing and Availability MM5ens-36km, MM5ens-12km at 0000 UTC Ensemble centroid deterministic forecast Delivery: ~1 AM (0900 UTC) In time for 2 AM AFD & 4:15 AM ZFP MM5ens_prob-36km, MM5ens_prob-12km at 0000 UTC Raw ensemble probabilities for exceedance of selected criteria: 3h, 6h, 12h POP at 5 thresholds 850-hPa T at 2 thresholds 10-m WSPD at 2 thresholds 10-m high and low T at 2 thresholds Delivery: ~5 AM (1300 UTC) In time for 8:30 AM AFD, morning ZFP updates Useful for IFPS POP for at least the first 24-hr period of grids MM5ens_spread-36km at 0000 UTC 500-hPa heights and MSLP standard deviation Delivery: ~5 AM (1300 UTC)

19. 30 January 2003 13:00 NWSFO-Seattle Forecaster Feedback We really want feedback on all new (and old) products to: Improve communication between researchers and operational forecasters Improve the quality of local mesoscale forecast guidance, especially for NWS watch/warning criteria Attempt to reduce the overwhelming nature of too much guidance Create innovative ways of visualizing uncertainty Feedback/reaction to MM5 ensemble products for PNW Workshop ’03 Poster: “Experimental MM5 Short-Range Ensemble Products at NWSFO-Seattle”

20. Innovative Forecast Products/Tools Work with NWS-Seattle, Whidbey NAS forecasters (specialized products for warning criteria) Work with MURI visualization team at UW APL (ways to visualize uncertainty) GOAL: VISUALIZING FORECAST UNCERTAINTY WITHOUT NEEDING A TON OF PRODUCTS

21. 30 January 2003 13:00 NWSFO-Seattle MM5 SREF Webpage: http://www.atmos.washington.edu/~emm5rt/ensemble.cgi Email: epgrimit@atmos.washington.edu teckel@atmos.washington.edu cliff@atmos.washington.edu ovens@atmos.washington.edu I’m at the forecast office on most Fridays 7-3 Resources & Contact Information } deterministic runs } ensemble runs

22. EXTRA SLIDES

23. Difficult to consistently construct the “correct” analysis/forecast pdf. Errors in mean and spread result from: 1) Model error 2) Choice of ICs 3) Under sampling due to limits of computer processing Result: EF products don’t always perform the way they should. (especially a problem for SREF) Limitations of EF Frequency Initial State24hr Forecast State48hr Forecast State analysis pdf ensemble pdf

24. 30 January 2003 13:00 NWSFO-Seattle Value of the Mirrored Runs (Using centroid analysis verification on 65 cases from Dec01 – Mar02) Member Included:

25. 30 January 2003 13:00 NWSFO-Seattle ACME core+ ACME 8  5  3  2  5  3  2  2  8  8 = 921,600 Total possible combinations: MM5 Configurations for ACME core+