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NCEP Ensemble Prediction System

NCEP Ensemble Prediction System. Yuejian Zhu Ensemble team leader EMC/NCEP/NWS/NOAA Acknowledgements: EMC ensemble team members. Responsibilities of Ensemble Development (NCEP). - Assess, model, communicate uncertainty in numerical forecasts Present uncertainty in numerical forecasting

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NCEP Ensemble Prediction System

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  1. NCEP Ensemble Prediction System Yuejian Zhu Ensemble team leader EMC/NCEP/NWS/NOAA Acknowledgements: EMC ensemble team members

  2. Responsibilities of Ensemble Development (NCEP) - Assess, model, communicate uncertainty in numerical forecasts • Present uncertainty in numerical forecasting • Tasks • Design, implement, maintain, and continuously improve ensemble systems • Sciences • Initial value related uncertainty • Model related forecast uncertainty • Ensemble systems • Global – GEFS / NAEFS / NUOPC • Regional – SREF / HREF / NARRE-TL / HWAF ensemble • Climate – Contributions to future coupling CFS configuration • NAEFS/GEFS downscaled • Ocean wave ensemble (MMA/EMC) • Statistical correction of ensemble forecasts • Current tasks • Correct for systematic errors on model grid, correct ensemble spread. • Downscale information to fine resolution grid (NDFD) • Combine all forecast info into single ensemble/probabilistic guidance • Probabilistic product generation / user applications • Contribute to design of probabilistic products • Support use of ensembles by • Internal users (NCEP Service Center, WFOs, OHD/RFC forecasters and et al.) • External users (research, development, and applications)

  3. Uncertainties & disagreements Ensemble forecast is widely used in daily weather forecast

  4. December 2012 was 20 anniversary of both NCEP and ECMWF global ensemble operational implementation

  5. Description of the main ensemble systems Each ensemble member evolution is given by integrating the following equation where ej(0) is the initial condition, Pj(ej,t) represents the model tendency component due to parameterized physical processes (model uncertainty), dPj(ej,t) represents random model errors (e.g. due to parameterized physical processes or sub-grid scale processes – stochastic perturbation) and Aj(ej,t) is the remaining tendency component (different physical parameterization or multi-model). Reference: Buizza, R., P. L. Houtekamer, Z. Toth, G. Pellerin, M. Wei, Y. Zhu, 2005: "A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems“ Monthly Weather Review, Vol. 133, 1076-1097 Initial problem Model problem Operation: NCEP-1992; ECMWF-1992; MSC-1998

  6. Evolution of NCEP GEFS configuration

  7. Estimating and Sampling Initial Errors:The Breeding Method - 1992 • DATA ASSIM: Growing errors due to cycling through NWP forecasts • BREEDING: - Simulate effect of obs by rescaling nonlinear perturbations • Sample subspace of most rapidly growing analysis errors • Extension of linear concept of Lyapunov Vectors into nonlinear environment • Fastest growing nonlinear perturbations • Not optimized for future growth – • Norm independent • Is non-modal behavior important? • References • Toth and Kalnay: 1993 BAMS • Tracton and Kalnay: 1993 WAF • Toth and Kalnay: 1997 MWR Courtesy of Zoltan Toth

  8. Ensemble Transform with Rescaling (2006 ) Bred Vector (2006) 2006 Rescaling Rescaling P1 forecast P2 forecast P1 ANL ANL N1 2Dmask P3 forecast P4 forecast t=t0 t=t1 t=t2 t=t0 t=t1 t=t2 P#, N# are the pairs of positive and negative P1 and P2 are independent vectors Simple scaling down (no direction change) P1, P2, P3, P4 are orthogonal vectors No pairs any more To centralize all perturbed vectors (sum of all vectors are equal to zero) Scaling down by applying mask, The direction of vectors will be tuned by ET. P2 ANL N2 • References: • Wei and et al: 2006 Tellus • Wei and et al: 2008 Tellus

  9. Tropical Storm Relocation 2005 Ensemble TS relocation GFS TS relocation 6hrs fcst Fcst/guess 3hrs 6hrs 9hrs P N C Use GFS track information Use ens. track information Use ens. track information Relocate TS to Observed position Use GFS track information To separate into env. flow (EF) Strom, and storm perturbation (SP) Relocate TS to observed position GDAS (SANL) Ens. rescaling For EF (p+n) Ens. rescaling For SP (p+n) Combined FCST Reference: Liu and et al: 2006 AMS conference extended paper FCST

  10. Example of Hurricane Track Improvement Ivan (09/14) Without relocation With relocation

  11. Stochastic Total Tendency Perturbation (STTP)(Hou, Toth and Zhu, 2006)NCEP operation – Feb. 2010 Formulation: Simplification: Use finite difference form for the stochastic term Modify the model state every 6 hours: Where w is an evolving combination matrix, and g is a rescaling factor. Reference: 1. Hou and et al: 2008 AMS conference extended paper 2. Hou and et al: 2010 in review of Tellus

  12. STTP (continuous) Generation of Stochastic Combination Coefficients: • Matrix Notation (N forecasts at M points) S (t) = P(t) W(t) MxNMxNNxN • As P is quasi orthogonal, an orthonormal matrix W ensures orthogonality for S. • Generation of W matrix: (Methodology and software provided by James Purser). • a) Start with a random but orthonormalized matrix W(t=0); • b) W(t)=W(t-1) R0 R1(t) • R0, R(t) represent random but slight rotation in N-Dimensional space wij(t) for i=14, and j=1,14 Random walk (R1) superimposed on a periodic Function (R0)

  13. What do we expect from ensemble forecast? What is relatively good ensemble forecast?

  14. The Relative Measure of Predictability (RMOP) Application of theory To illustrate the use of RMOP, consider the graphic of RMOP from the ensemble run of 00 UTC 17 December 2003 valid 00 UTC 22 December 2003 (a 120-hr forecast), found right: The shading indicates the RMOP of the ensemble mean 500-hPa height at each grid point, compared to ensemble forecasts of 500-hPa height over the previous 30 days. These are in 10% increments as indicated by the color bar at the bottom of the graphic. Shading at 90% indicates that at least 9 of 10 ensemble forecasts in the past 30 days had fewer ensemble members in the same "bin" as the ensemble mean than the present forecast. In this case, the trough in the eastern US is 90% predictable relative to ensemble forecasts in the past 30 days. The blue numbers over each box represents the percentage of time that a forecast with the given degree of predictability has verified over the past 30 days. Here, over the 90% predictability box we see that only 72% of the forecasts with 90% relative predictability at 120 hours have verified in the same climatological bin as the observed 500-hPa height at 120 hours over the past 30 days. Note that in general, the values are generally lower than the RMOP numbers below the bar. This is because: The underlying forecast model is imperfect, The initial conditions are imprecise, and The atmosphere behaves chaotically. We can expect verification percentages to decrease with Increasing forecast lead time, During the warm season, and During relatively unpredictable regimes in all seasons. In this example, we see that only 72% of the forecasts with 90% relative predictability at 120 hours have verified in the same climatological bin as the observed 500-hPa height, over the past 30 days of ensemble forecasts. Reference: Toth, Zhu and Marchok, 2001: WAF

  15. NAEFS products – Metagram (examples)

  16. An experimental multi-model product Dot area is proportional to the weighting applied to that member •= ens. mean position* = observed position Courtesy of Tom Hamill

  17. Day at which forecast loses useful skill (AC=0.6) N. Hemisphere 500hPa height calendar year means

  18. NH Anomaly Correlation for 500hPa HeightPeriod: January 1st – December 31st 2012 GFS – 8.0d GEFS – 9.5d NAEFS – 9.85d

  19. Which ensemble is better? NCEP/GEFS CMC/GEFS CMC - multi-physics (parameterizations) NCEP - single model

  20. NH 1000hPa height 53 50 57 50 =1.14 =1.06 10-day forecast: Spring 2011 Spring 2012 Improvement of ensemble spread NH 500hPa height 84 70 =1.2 78 72 =1.08 Spring 2011 Spring 2012 Last implementation: Reduced RMS errors Increasing spread

  21. Ensemble Spread Now Then Courtesy of Dr. Trevor Alcott

  22. Ensemble Range Now Then Courtesy of Dr. Trevor Alcott

  23. Atlantic, AL01~19 (06/01~12/31/2011) Atlantic, AL01~19 (05/01~11/27/2012) GEFS-11 GEFS T190L28 (operational run) GEFS-12 GEFS T254L42 (operational run) Track error(NM) Forecast hours CASES-2011: 393 356 314 274 238 182 141 98 71 49 CASES-2012: 439 399 357 315 277 219 178 143 119 94

  24. Track Forecast Error for Atlantic 2012 Season GFS – NCEP deterministic forecast GEFS – NCEP ensemble forecast EC_det – ECWMF deterministic forecast EC_ens – ECMWF ensemble forecast NCEP made better forecast FCST HOURS 0 12 24 36 48 72 96 120 Cases 170 151 137 123 110 90 73 57

  25. Interactions between DA and EPS Ideally, EPS and DA systems should be consistent for best performance of both. DA provides best estimates of initial uncertainties, i.e. analysis error covariance, for EPS. EPS produces accurate flow dependent forecast (background) covariance for DA. Best analysis error variances EPS DA Accurate forecast error covariance

  26. Next GEFS Configurations • Time for implementation – Q2FY14 • Model • NCEP Global Forecast System (GFS) version 10 – will be implemented in the same time with GEFS • Semi-lagrangian scheme for dynamics • Check the track page (milestone has all listed items) • Horizontal resolution • Current T254 (~50km) for 0-192 hours, T190 (~70km) for 192-384 hours • Plan for T574 SL (dynamic) and T382 (physics) (~34km) for all 16 days forecast • Vertical resolution • Current 42 hybrid levels • Plan for 64 hybrid levels to match with hybrid DA and GFS deterministic forecast • Memberships and runs • 21 members per run • 4 runs per day at 00, 06, 12, 18UTC • Time step for integration • 900s for dynamic and 450s for physics • Resource required for this configuration • Current operation on WCOSS – 84 nodes for integration and 12 nodes for post process (about 46 minutes) • This configuration will use 252 nodes for integrations and 30 nodes for post process (about 65 minutes) • Note: WCOSS has totally 640 nodes for maximum usage. • Output • Additional: output every 3hr and 0.5 degree pgrb files for first 8-day for NAEFS data exchanges and downstream application, such as SREF

  27. 20121023 20121024 High resolution SL testing ~33km Operation ~50km 00UTC 00UTC New high resolution ensemble has about 12 hours in advance to predict Sandy turned to North-west 06UTC 06UTC 12UTC 12UTC 18UTC 18UTC

  28. Background !!!

  29. Random Model Error – Stochastic Schemes • Stochastically-perturbed physics tendencies (SPPT) – operational ECMWF scheme. • Stochastic total tendency perturbation (STTP) – operational NCEP scheme • Vorticity confinement (VC) – under development at UKMet and ECMWF • Stochastically-perturbed boundary-layer humidity (SHUM) • Perturbed convective trigger on SAS • Testing on HWRF ensemble and global system • Questions and issues • Will these schemes help to increase ensemble spread? • Will these schemes help to reduce missing forecast for extreme weather events? • What is the future of physical parameterizations? • Deterministic or probabilistic?

  30. In general, breeding method is more conception, and SV is more practical. Since we don’t know the size of initial uncertainties, we believe that smaller initial perturbations will be better (if it grows faster and catch up forecast errors) Early study from ZoltanToth: BAMS 1992

  31. Two-scale Lorenz ‘96 model slow large-scale variables xi (i=1,2,…I) fast small-scale variables yi,j (i=1,2,…I; j=1,2,…J) yi,j LetI=36 and J=10 in this study. Thus, the slow large-scale variables xi could be thought of some atmospheric quantity in 36 sectors of a latitude circle, so that each large sector covers 10 degrees of longitude, while the fast small-scale variables yi,jcan represent the values of some other quantity in 36*10 sectors, so that each small sector covers 1 degree of longitude in one large sector. Courtesy of Jessie Ma

  32. Results from Lorenz experiments No interaction High interaction Courtesy of Dr. Jessie Ma

  33. Experiments for 2009 Operational ImplementationT126L28 vs. T190L28 resolution, Nov. 2007 CasesSPS works with both resolutions ROC CRPSS --- T126L28 --- T126L28 + SP --- T190L28 --- T190L28 + SP

  34. Ensemble size test from 5 to 80 membersComparing T126L28 with 80 members to T190L28 with 20 members An Effect Configurations of Ensemble Size and Horizontal Resolution for NCEP GEFS Juhui Ma, Yuejian Zhu, Panxin Wang and Richard Wobus AAS 2012

  35. Northern Hemisphere 500hPa Geopotential Height CRPSS PAC RMSE/SPREAD Significant test for RMSE

  36. Northern Hemisphere 500hPa Geopotential Height RMSE/SPREAD AC CRPSS

  37. Latest ECMWF ensemble forecast system Initial Condition = UnperturbedAnalysis + EDA-based perturbation + SV-based perturbation Unperturbed Analysis - 4D-VAR (TL1279L91) EDA-based perturbation - the difference between the perturbed (perturb all obs and sea-surface T and use SPPT to simulate random model error) and unperturbed first-guesses (TL399L91) SV-based perturbation - initial singular vectors (T42L62) mem1 SV6 SV1 mem2 SV7 SV2 + mem3 EDA1 SV8 SV3 SV4 SV9 SV10 SV5 + EDA2 …… …… …… mem50 EDA10

  38. Similar to EnKF, growing slowly, good CRPS scores Buizza R, Leutbecher M, Isaksen L, et al. 2010. Combined use of EDA- and SV-based perturbations in the EPS. Newsletter n. 123, ECMWF, Shinfield Park, Reading RG2-9AX, UK, pg 22–28.

  39. CMC’s Multi-model EPS for the assimilation (current) P. Houtekamer, ARMA

  40. Changes to the 16 day forecast systemP. Houtekamer, ARMA/MSC, Canada • Like in the EnKF: • Use of a more recent version of the model and the model physics, • Removing an old surface scheme, • 20 minute time step, • Use of a topography filter, • No perturbation of model physics when convection is active, • No longer ramping down the stochastic physics in the tropics. • With these changes the system is a lot more robust (on occasion with the currently operational system we have to rerun an integration). • Implementation: Jan/Feb 2013

  41. Evolution of NCEP SREF configuration

  42. SREF system upgrade (Aug. 21, 2012) • Model Change 1. Model adjustment (eliminate Eta and RSM legacy models and add new NEMS-based NMMB model) 2. Model upgrade (two existing WRF cores from v2.2 to version 3.3) 3. Resolution increase (from 32km/35km to 16km) 4. All models run with 35 levels in the vertical and 50 mb model top. • IC diversity improvement 1. More control ICs (NDAS -> NMMB, GDAS -> NMM, RAP blended @ edges w/GFS -> ARW) 2. More IC perturbation diversity (a mix of Breeding, ETR as well as a Blending of the two) 3. Diversity in land surface initial states (NDAS, GFS, and RAP). • Physics diversity improvement 1. More diversity of physics schemes (flavors from NAM, GFS, NCAR and RAP)

  43. List of the physics schemes

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