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An Update on Local MM5 Products at NWSFO-Seattle

An Update on Local MM5 Products at NWSFO-Seattle. Eric P. Grimit SCEP NOAA/NWS-Seattle Ph.D. Candidate, University of Washington. Overview. Update on the deterministic MM5 runs Update on the expanded MM5 short-range ensemble forecast system

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An Update on Local MM5 Products at NWSFO-Seattle

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

  2. 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. 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. 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. - 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 State 48hr 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! Frequency Initial State 24hr Forecast State 48hr Forecast 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. analysis PDF Frequency EF Histogram Initial State

  6. Factors to be Considered forMesoscale 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. UW SREF Methodology Overview Analysis pdf cwb avn cwb cmc gsp eta ngp C gsp T ngp cmc avn eta uk Forecast pdf uk 8 “independent” atmospheric analyses, the centroid, plus 8 “mirrored” ICs Analysis pdf : Forecast pdf : 17 divergent, “equally likely” solutions using the same primitive equation model, MM5 phase space 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. 48hr true state 48hr forecast state (core) 48hr forecast state (perturbation)

  8. ICs/LBCs for the Analysis-Centroid Mirroring Ensemble Resolution (~@ 45 N ) Objective Abbreviation/Model/Source Type ComputationalDistributed Analysis avn, Global Forecast System (GFS), Spectral T254 / L64 1.0 / L14 SSI National Centers for Environmental Prediction ~55km ~80km cmcg, Global Environmental Multi-scale (GEM), Spectral T199 / L28 1.25 / L11 3D Var Canadian Meteorological Centre ~100km ~100km eta, Eta limited-area mesoscale model, Finite 12km / L45 90km / L37 SSI National Centers for Environmental Prediction Diff. gasp, Global AnalysiS and Prediction model, Spectral T239 / L29 1.0 / L? 3D Var Australian Bureau of Meteorology ~60km ~80km jma, Global Spectral Model (GSM), Spectral T106 / L21 1.25 / L13OI Japan Meteorological Agency ~135km ~100km ngps, Navy Operational Global Atmos. Pred. System, Spectral T159 / L24 1.0 / L14 OI Fleet Numerical Meteorological & Oceanographic Cntr. ~90km ~80km tcwb, Global Forecast System, Spectral T79 / L18 1.0 / L11 OI Taiwan Central Weather Bureau ~180km ~80km ukmo, Unified Model, Finite 5/65/9/L30 same / L12 3D Var United Kingdom Meteorological Office Diff. ~60km

  9. # of Initial Forecast UW MM5 Name Members Conditions Model(s) Cycle Domain ACME17 8 Ind. Analyses 1 (MM5) 00Z 36km, 12km 1 Centroid 8 Mirrors ACMEcore 8 Independent 1 (MM5) 00Z, 12Z 36km, 12km Analyses ACMEcore+ 8 “ “ 8 (MM5 variations) 00Z 36km, 12km PME 8 “ “ 8 00Z, 12Z 36km NCEP SREF8 Regional 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. 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. Poorman mean Poorman mean C e n t r o i d C e n t r o i d ACME mean ACME mean Validation of the Mirrored Runs (Using centroid analysis verification on 65 cases from Dec01 – Mar02)

  12. 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

  13. Example Ensemble Probability Product valid 2100 UTC today

  14. ACMEcore: 31 Oct 2002 – 20 Jan 2003 BSS: 0.527 0.559 0.474

  15. Example Ensemble Spread Product valid 0000 UTC today 4 mb 7 mb

  16. Variance has a 2 distribution, use the f-statistic: f = s2 / s2 where s2 denotes the climatological median variance ~ ~ Evaluate the CDF: F(s2) Transform to a normal distribution, using the percentile obtained from the f-distribution, where: (F(s2)) Use resulting standard normal rv as a high/low uncertainty index Confidence Products: Visualizing Uncertainty Standardizing the spread valid 0000 UTC today

  17. 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)

  18. 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”

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

  20. Resources & Contact Information • 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 } ensemble runs } deterministic runs

  21. EXTRA SLIDES

  22. analysis pdf Limitations of EF 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) ensemble pdf Frequency Initial State 24hr Forecast State 48hr Forecast State

  23. Value of the Mirrored Runs (Using centroid analysis verification on 65 cases from Dec01 – Mar02) Member Included:

  24. MM5 Configurations for ACMEcore+ ACMEcore+ ACME Total possible combinations: 8  5  3  2  5  3  2  2  8  8 = 921,600

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