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Ensemble Forecasting

Ensemble Forecasting. Yuejian Zhu Ensemble & Probabilistic Guidance Team Environmental Modeling Center December 7 th 2010 Acknowledgment for: Jun Du, Dingchen Hou, Geoff DiMego, John Ward, Hendrik Tolman Bill Lapenta and Steve Lord. Outline. Service for users

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Ensemble Forecasting

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  1. Ensemble Forecasting Yuejian Zhu Ensemble & Probabilistic Guidance Team Environmental Modeling Center December 7th 2010 Acknowledgment for: Jun Du, Dingchen Hou, Geoff DiMego, John Ward, Hendrik Tolman Bill Lapenta and Steve Lord

  2. Outline • Service for users • Requirements / decision support • Ensemble’s responsibilities • Upgrades and Plans for • SREF • Ocean Wave Ensemble • GEFS • HREF, VSREF, HWAFE • NAEFS/NUOPC, NAEFS-LAM • Ensemble processing plans • Bias correction • Downscaling • Products

  3. User Requirements - Simplified • Applications affected by (extreme/high impact) weather • Must consider information on weather to • Minimize losses due to adverse weather • Optimal user decision threshold equals • Probability of adverse weather exceeding • Cost / loss ratio of decision situation (simplified decision theory) • Probability of weather events must be provided • Only option in past, based on error statistics of single value forecasts • e.g., MOS POP • Now can be based on ensemble statistical information (e.g., RMOP) • Users act when forecast probability exceeds their cost/loss ratio (example) • Advantages • A set of products (e.g., 10 / 50 / 90 percentile forecast , metagram, mean and mode) • Advanced - Problems??? • Proliferation of number of products • For different variables, probability / weather element thresholds, joint probabilities • Limited usage • Downstream applications severely limited (e. g., wave, streamflow, etc, ensembles not possible)

  4. User Requirements - Advanced • Advanced information • Statistical reliable ensemble forecast products • Ensemble statistical data – historical information • 6D-cube – space (3D) + time + variables + ensemble • Expanded NDFD – future official NWS weather / climate / water forecast database • Joint probabilities • Many variables, different probabilities / critical value decision thresholds • Some (or many) of forecast events are related joint probabilities. • Probability of significant convection • Fire weather • User application model (UAM) • Must be easy operating with quick information access • Simulating optimal weather related operations • Simulating different user procedures for multiple plausible weather scenarios • Able to tell – what are actions / costs / benefits? - assuming weather is known • Application • Run UAM n x n times with multiple weather scenarios from each ensemble member (n) and user procedures (n) • Weather scenario from each ensemble - generated from optimized user procedures • Take ensemble mean of economic outcome (costs + losses) for each set of user procedures • Choose operating procedures to minimize costs and losses in expected sense. • Make optimizing weather related decisions • Challenge • Requires - storage / telecom bandwidth • Requires - smart sub-setting & interrogation tools – can derive any weather related information include joint probabilities

  5. Responsibilities of Ensemble Team - Assess, model, communicate uncertainty in numerical forecasts • Present uncertainty in numerical forecasting • Tasks • Design, implement, maintain, and continuously improve ensemble systems • Topics • Initial value related uncertainty • Model related forecast uncertainty • Ensemble systems • Global – GEFS / NAEFS • Regional – SREF / HREF / VSREF / HWAF ensemble • Climate – Contributions to future CFS configuration • NAEFS/GEFS downscaled • Ocean wave ensemble (MMA/EMC) • Statistical correction of ensemble forecasts • Tasks • Correct for systematic errors on model grid • 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

  6. Review SREF Implementation (Oct. 27th 2009) • Upgrade model versions • WRF-NMM from v2.0+ to v2.2+ • WRF-ARW from v2.0+ to v2.2+ • RSM from v2007 to v2009 • Increase horizontal resolution • WRF-NMM from 40km to 32km • WRF-ARW from 45km to 35km • RSM from 45km to 32km • Adjust membership – total membership = 21 • Replace 2 Eta (BMJ-sat) members with 2 WRF-NMM members • Replace 2 Eta (KF-det) members with 2 WRF-ARW members • Enhancement physics diversity of RSM: replace Zhao cloud scheme with Ferrier cloud scheme for 3 SAS members • Enhance initial perturbation diversity: Replace regional bred perturbations with global ET perturbations for 10 WRF members • Improvements (T2m, precipitation) • Add/fix/unify variables in SREF output • Increase output frequency from every 3hr to hourly for 1st 39hr (for SPC, AWC) • Wind variation products (for DTRA) • Radar (composite reflectivity + echo top) (for FAA) • Etc …

  7. Next SREF Implementation Plan (Q4FY2011)- Geoff DiMego and Jun Du Models and configurations • Add NMMB members (7), WRF-ARW (2) and WRF-NMM (2) to replace RSM (5) and Eta members (6) • Future configuration of SREF will be • 7 NEMS_NMMB, 7 WRF_NMM and 7 WRF-ARW – totally 21 members • Update WRF-NMM and WRF-ARW model versions • NMMB version is under NEMS infrastructure • Resolutions (versions): • NEMS_NMMB – 22km • WRF_NMM – 22km • WRF_ARW – 22km • Downscaled ETR perturbations from global ensemble • Output same fields as the current operational SREF • Performance evaluation Post process (and regional ensemble related) • Precipitation calibration (starting NCO evaluation now) • HREF – dynamical downscaling from NMM and ARW, 44m, 4km, out to 48 hours • VSREF – lag ensemble from RUC and NMM, 11m, 12km, hourly out to 12hours

  8. SREF Implementation Plan for FY12-15- Geoff DiMego and Jun Du North America Ensemble Forecast System’s extension to regional ensemble (NAEFS_LAM) • Need - Both U.S. and Canada need to run high-resolution regional ensembles for high-impact weather. • Benefit - More resource can be spent on increasing model resolution but not on increasing ensemble membership as well as increasing forecast diversity. • Canadian REPS: 20 members with GEM, downscaled GEFS IC perturbations, 30km, 48hr, NA domain, later 2010 implementation (B08RDP, VC2010, Haiti earth quake relief effort) (21+20=41 member SREF) • Problem - Both countries are big in domain and don’t have enough computing resources to run such a high-resolution ensemble with large enough ensemble size. NRRE - NAM Rapid Refresh Ensemble, NA domain, 12km, hourly, 24hr for aviation forecast. HRRE – Hi-res Rapid Refresh Ensemble, smaller domains, 3km, hourly, 24hr, for storm scale events HWRF ensemble – in testing – supported by HFIP program Others • Stochastic physics • Convective parameterization of Teixeira et al - NRL • Ensemble transform with rescaling (ETR) initial perturbations • Consistent with boundary perturbations from GEFS • Resolution • Looking for higher resolution

  9. Ocean Wave Ensemble System- Hendrik Tolman • Configuration of ocean wave ensemble system Wave ensemble has been running since 2008 • Running on 1°×1° wave model grid as the control. • 20 wave members generated through GEFS using ETR method • Cycling initial conditions for individual members to introduce uncertainty in swell results. • 10 day forecast using the GEFS bias corrected 10m wind (future operation) • Improving forecast uncertainties through • Introducing ensemble initial perturbations from previous model cycle • Introducing bias corrected ensembles as external forcing. • Example of comparison (wave heights) • Plans • Work towards a combined NCEP-FNMOC ensemble • Analyze the role of swell played in the wave ensemble

  10. Review GEFS Implementation (Feb. 23rd 2010) • Using current operational GFS version 8.0 • Upgrade horizontal resolution from T126 to T190 • 4 cycles per day, 20+1 members per cycle • Up to 384 hours (16 days) • Use 8th order horizontal diffusion for all resolutions • Improved forecast skills and ensemble spread • Introduce ESMF (Earth System Modeling Framework) for GEFS • Version 3.1.0rp2 • Allows concurrent generation of all ensemble members • Needed for efficiency of stochastic perturbation scheme • Add stochastic perturbation scheme to account for random model errors • Increased ensemble spread and forecast skill (reliability) • Add new variables (28 more) to pgrba files • Based on user request • From current 52 (variables) to future 80 (variables) • For NAEFS ensemble data exchange • What do we expect from coming GEFS implementation? • For large scale (see 500hPa height AC for NH) • For ensemble distributions (850hPa temperature CRPS and RMS/SPREAD) • For tropical storms (track errors)

  11. Next GEFS Implementation Plan (Q4 FY11) According to total resource distribution for each model (Jigsaw puzzle) GEFS has 40% of total CPUs (52 of 130) during +4:35 and +6:00 for main integration and main post-process Current: GEFS and GEFS/NAEFS post processing T190L28 for all 384 hours lead-time 20+1 members per cycle, 4 cycles per day Computation usage: average 20 nodes (22 high mark) for 50 minutes Next GEFS and GEFS/NAEFS post processing (Q4FY2011): T254L42 (0-192hr) – increasing both horizontal and vertical resolutions Factor of 3.6 by comparing T190L28 T190L42 (192-384hr) – increasing vertical resolution Factor of 1.5 by comparing T190L28 20+1 members per cycle, 4 cycles per day Total cost for integration and post processing Factor of 3.6 for 0-192hrs, factor of 1.5 for 192-384 Average factor for processing (0-384hrs) is 2.55 51 nodes for 50 minutes (start: +4:35 end: +5:25) Products will be delayed by approximately 20 minutes because CCS can’t offer 51 nodes 40 nodes for 70 minutes (start +4:35 end: +5:45) Why do we make this configurations? Considering the limited resources Resolution makes difference (T126 .vs T190) What do we expect from this implementation? Preliminary results (NH 500hPa and SH 500hPa height)

  12. GEFS Implementation Plan for FY12-15 Hybrid data assimilation based GEFS initializations Using 6-hr EnKF forecast combined improved ETR (without cycling) (Schematic diagram) Improved ETR Adaptive modification of initial and stochastic model perturbation variances Based on recursive average monitoring of forecast errors and ensemble spread Avoid having to tune perturbation size after each analysis/model/ensemble changes Improving performance and easy maintenance Real time generation of hind-casts (plan) Make control forecast once every ~5th day (6 runs for each cycle) T254L42 (0-192) and T190L42 (192-384), and use new reanalysis (~30y) Increasing sample of analysis – forecast pairs for statistic corrections Improving bias correction beyond 5-d Potential for regime/situation dependent bias correction Coupled ocean-land-atmosphere ensemble Couple MOM4/HYCOM with land-atmosphere component using ESMF Depending on skill, extend integration to 35 days Merge forecasts with CFS ensemble for seamless weather climate interface Land perturbation and surface perturbation (later) Explore predictability in intra-seasonal time scale (week 3-4) Potential skill beyond 15 days Hydro-meteorological (river flow) ensemble forecasting Pending on operational LDAS/GLDAS

  13. Ensemble Post Processing Steps • Bias correction / combination of all information • Remove lead time dependent model bias on model grid • Calibrate higher moments of ensemble • Combine information from all sources into single set of ensemble • Forecaster modification • Subjective changes to ensemble data (over US only? Pending on?) • Proxy for truth • Create observationally based fine resolution analysis for use in downscaling • Downscaling • Interpret bias corrected ensemble on user relevant grid – NDFD • Additional variables • Derive further variables from bias corrected / downscaled NWP output • Visualization / Derived products • Interrogate bias corrected / downscaled ensemble dataset

  14. Bias Correction / Combination of All Information • Method (current and plan) • Kaman filter method (decaying average) – current in NAEFS operation • Multi-model multi-center ensembles • Based on Bayesian principle (future plan – THORPEX proposal) • Combines information from all sources • Fuses forecast data with climatological distribution (“prior”) • Adjusts “spread” according to skill observed in forecast sample • Outputs statistically corrected distribution (“posterior”) • Ensemble members adjusted to represent posterior distribution • Data sources • Reanalysis as prior (use new reanalysis when available) • Sample of past forecasts - most recent 60-90 days • Include control hind-casts when available • Sample of reforecast – Tom Hamill’s approach • Latest analyzed or observed data • Current status • 35 (will be 49 for NAEFS - table ) NAEFS & SREF variables bias corrected • 1st moment corrected only • NAEFS & SREF processed separately • CMC + NCEP ensembles + GFS hires control combined • Plan • Bias correct all model output variables on model native grid (2-3 yrs) • Include precipitation (use observationally based analysis as truth – 1-2 yrs) • Add hind-casts for NCEP ensemble calibration (1-2 yrs) • Combine all forecasts into single guidance (2-3 yrs) • Ensemble & hires from NCEP (GEFS and SREF), CMC, FNMOC, ECMWF

  15. Downscaling (statistic) • Method • Perfect prognostic • Establish relationship (“donwscaling vector”) between • Proxy for truth (high resolution observationally based analysis) & • NWP analysis (used as reference in bias correction step) • Level of sophistication • Climatological (statistical) • Regime dependent (statistical) • Case dependent (dynamical, using LAM) – most expensive • Sub-NWP-grid resolution variance • Need to be stochastically added in statistical methods • Outputs ensemble members statistically consistent with • Bias corrected forecasts on NWP grid • Proxy for truth on fine resolution grid • Data sources • Sample of • Fine resolution observationally based analysis fields • Corresponding NWP analysis fields • Current status (table) • RTMA used proxy for truth • 4 NDFD variables downscaled using regime dependent downscaling vector for COUNS • Surface pressure, T2m, U10m and V10m • 8 NDFD variables downscaled for Alaska (Q1FY11) • Surface pressure, T2m, Tmax, Tmin, U10m, V10m, Wdir and Wspd • Plan • Add more NDFD variables by • Expanding RTMA analysis & using derived variables (e.g., dew point temperature and etc…) • Using SMARTINIT + downscaled NDFD variables

  16. High-Resolution Ensemble Forecast (HREF) HREF is a dynamically downscaled ensemble using the dual-resolution “Hybrid Ensembling” method (Du 2004) to superimpose forecast variances from a low-res ensemble onto a hi-res single run. In this case, the 32km 21-member SREF was combined with 4km Hi-res window’s NMM and ARW two single runs to produce a 44-member 4km ensemble over US East/US West/Alaska three domains. Below shows an ensemble mean precipitation forecast before and after downscaled. To be implemented together with Hi-Res window run package (Q2FY11, Matt Pyle).

  17. Very-Short Range Ensemble Forecast (VSREF) • VSREF is a time-lagged ensemble based on 12km NAM and RUC) with 11 members for the purpose of aviation forecasts. It runs up to 12 hour with hourly update cycle over the continental U.S. domain. The products are listed in Table 1 and display at the following web for real time use: http://www.emc.ncep.noaa.gov/mmb/SREF_avia/FCST/VSREF/web_site/html/icing.html.

  18. NAEFS upgrade and NUOPC-IOC • Multi-model, multi-center ensemble • NCEP GFS high resolution deterministic • NCEP GFS based ensembles (GEFS) • CMC global ensembles • Current status • NCEP 20+1+1 members per cycle, 4 cycle per day • CMC 20+1 members per cycle, twice per day • Every 6 hours, out to 16 days • Products and Benefits • Bias corrected at 1*1 degree (35 variables, will be 49) • Hybrid with NCEP bias corrected GFS forecast • Combined (with adjusted) bias corrected CMC’s ensemble • Probabilistic products (10%, 50%, 90%, mean, mode and spread) • Downscaled products for CONUS (T2m MAE and CRPS) and Alaska (Q1FY11 – verification , HPC’s evaluation, Tmax and Windspeed) • Improving track skills from NAEFS (tracks, compare to GFS) • Experimental multi-model ensemble • Track skills from super-ensemble (AL+EP, WP) • Guidance of track forecast: strike probability, fuzzy map • NAEFS upgrade / NUOPC-IOC - include FNMOC ensemble • What do we get from this inclusion directly? (see T2m skills) • What do we get from downscaling? ( T2m skills)

  19. Precipitation Calibration (Plan) Background (QPF bias correction in NCEP) Implemented May 2004 (HPC, CPC endorsed) Bias corrected GFS/GEFS forecasts At 2.5 degree resolution, every 24 hours, using Gauge (12UTC-12UTC) Using decay average (or Kalman Filter) method for sampling Using frequency match algorithm for CDF of OBS/FCST Climatological Calibrated Precipitation Analysis (CCPA) – Q3FY10 Use 30year CPC unified analysis at 1/8 degree, daily, global land - reliability Use 8year RFC/QPE (stage IV) 5km resolution, 6-h(CONUS) – resolution Use regression method to generate a and b from above two datasets Produce CCPA analysis ( CCPA = a*QPErfc + b) Resolution is 5km (NDFD) grid (and subsets) for CONUS (verify 1 and 2) Update QPF bias correction from #1 – Q4FY11 Bias corrected GFS/GEFS forecasts at 1.0 degree and 6 hours (example) Bias corrected NAM/SREF forecasts at 30km and 1 hours (optional) (example) Statistical downscaling to 5km – Q4 FY11 Proxy of truth - CCPA at NDFD grid (5km) or RTMA (if it creates) Decaying average (or Kalman filter) methods to generate downscaling (DS) vectors Downscaled forecasts Based on bias corrected forecasts (#3), interpolated to 5km, applying DS vectors Jointed development with ESRL/GSD through THORPEX Final calibrated precipitation forecast with 2nd moment adjustment (FY12-13) Multi-model ensemble after bias correction Bayesian Process of Ensemble (BPE) (joined with GSD/ESRL through THORPEX)

  20. End-to-end Forecast Process HOW WHO / WHAT WHY Dynamical Prediction Numerical modeling NCEP, other centers Ensemble generation Statistical consistency Statistical calibration MDL/NCEP, partners Ensemble processing USERS OBSERVATIONS Human intervention NCEP/SC  WFOs QC, enhancements Added value Interpretation Decision models USERS Applications

  21. BACKGROUND

  22. Optimal Threshold = 15% Decision Theory Example Forecast? YES NO Critical Event: sfc winds > 50kt Cost (of protecting): $150K Loss (if damage ): $1M Hit False Alarm Miss Correct Rejection YES NO $150K $1000K Observed? $150K $0K back

  23. back

  24. NAEFS products – Metagram (examples) back

  25. Ranked Probabilistic Skill Score CONUS 2 meter temperature 02 February – 10 August 2009 new old New SREF is more skillful than the old SREF back

  26. Warm season 24h Accumulated Precip from EMC parallel (Mar. 12 – Aug. 30, 2009) Red = new SREF Black = old SREF ETS BIAS back

  27. Comparison of the ensemble systems old cycle Old ensemble setup, ensemble with cycling of initial conditions and wind bias correction (BC). Mean wave height (contours) and spread (shading) 2008/03/28 t06z 120h forecast cycle, BC back

  28. NH Anomaly Correlation for 500hPa HeightPeriod: August 1st – September 30th 2007 GEFSg is better than GFS at 48 hours GEFSg could extend skillful forecast (60%) for 9+ days 24 hours better than current GEFS 48 hours better than current GFS back

  29. What do we expect from next GEFS impl. ? RMSE / SPREAD 500 hPa Height CRPS NH 850 hPa Temp Extend current 5-day skill to 6.5-day back

  30. STATS for all basins 00UTC only 2 months (August and September 2008) back

  31. Resolution makes difference for Typhoon Morakot Ini: 2009080600 T126 ensemble T190 ensemble Most models do not make right forecasts Ini: 2009080700 T126 ensemble T190 ensemble back

  32. FCST +4:35 --- +5:15 Current Future FCST +4:35 --- +5:35 FNMOC_ENS_DEBIAS +7:20 --- +7:30 20m late finish NCEP_POST +4:37 --- +5:17 NAEFS products start NCEP_POST (PGB) +4:37 --- +5:37 +7:35 +5:00 +6:00 +7:00 +8:00 +9:00 CMC_ENS_PREP CMC_ENS_POST +7:20 --- +7:22 ENS_DEBIAS +4:40 --- +5:24 ENS_DEBIAS +5:00 --- +5:44 CMC_ENS_DEBIAS +7:22 --- +7:26 PROB_PRODUCTS +5:24 --- +6:45 NAEFS_PROB_PROD (1) +7:33 -- +8:08 PROB_PRODUCTS +5:44 --- +6:40 20m late start NAEFS products (2) +8:08 --- +9:08 5m early finish GEFS/NAEFS 6-hr window flow chart back

  33. 11 days Skill line 10.5 days back Courtesy of Jessie Ma

  34. 8.25d 7.75d back Courtesy of Jessie Ma

  35. Flow Chart for Hybrid Variation and Ensemble Data Assimilation System (HVEDAS) - concept EnKF assimilation t=j EnKF assimilation t=j+1 Ensemble fcst (1) t=j-1  j Ensemble fcst t=j,  j+1 Lower resolution Ensemble fcst (2) t=j  16d Two-way hybrid Replace Ensemble Mean Estimated Background Error Covariance from Ensemble Forecast (6 hours) Estimated Background Error Covariance from Ensemble Forecast (6 hours) 3DVAR/4DVAR t=j 3DVAR/4DVAR t=j+1 Hybrid Analysis? back Higher resolution

  36. Real Time Generation of Hind-cast dataset Today’s Julian Date TJD TJD + 30 TJD - 30 Actual ensemble generated today 2010 Time 2009 2008 2007 1968 back 1967 Hind-casts for TJD+30 generated today Hind-casts (or its statistics) for TJD+/- 30 saved on disc

  37. Future seamless forecast system NCEP/GEFS will plan for T254L42 (2010 GFS version) resolution with tuned ETR initial perturbations and adjusted STTP scheme for 21 ensemble members, forecast out to 16 days and 4 cycles per day. Extended to 45 days at T126L28/42 resolution, 00UTC only (coupling is still a issue?) NAEFS will include FNMOC ensemble in 2011, with improving post process which include bias correction, dual resolution and down scaling Main products: ENSO predictions??? Seasonal forecast??? Main event MJO GEFS/NAEFS service CFS service week-1 week-2 one month Weather/Climate linkage SEAMLESS • Main products: • Probabilistic forecasts for every 6-hr out to 16 days, 4 times per day: 10%, 50%, 90%, ensemble mean, mode and spread. • D6-10, week-2 temperature and precipitation probabilistic mean forecasts for above, below normal and normal forecast • MJO forecast (week 3 & 4 … ) Next Operational CFS will plan to be implemented by Q2FY2011 with T126L64 atmospheric model resolution (CFSv2, 2010version) which is fully coupled with land, ocean and atmosphere (GFS+MOM4+NOAH), 4 members per day (using CFS reanalysis as initial conditions, one day older?), integrate out to 9 months. Future: initial perturbed CFS back

  38. Development of Statistical Post-Processing for NAEFS • Opportunities for improving the post-processor • Utilization of additional input information • More ensemble, high resolution control forecasts (hybrid?) • Using reforecast information to improve week-2 forecast and precipitation • Analysis field (such as RTMA and etc..) • Improving calibration technique • Calibration of higher moments (especially spread) • Use of objective weighting in input fields combination • Processing of additional variables with non-Gaussian distribution • Improve downscaling methods Future Configuration of EMC Ensemble Post-Processor back

  39. NEXT NAEFS pgrba_bc files (bias correction) back

  40. NAEFS downscaling parameters and products Last update: May 1st 2010 (NDGD resolutions) All products at 1*1 (lat/lon) degree globally Ensemble mean, spread, 10%, 50%, 90% and mode back

  41. 2-m temp 10/90 probability forecast verificationNorthern Hem, period of Dec. 2007 – Feb. 2008 90% 3-month verifications 10% Top: 2-m temperature probabilistic forecast (10% and 90%) verification red: perfect, blue: raw, green: NAEFS Left: example of probabilistic forecasts (meteogram) for Washington DC, every 6-hr out to 16 days from 2008042300 back

  42. NCEP/GEFS raw forecast 4+ days gain from NAEFS NAEFS final products From Bias correction (NCEP, CMC) Dual-resolution (NCEP only) Down-scaling (NCEP, CMC) Combination of NCEP and CMC back

  43. NCEP/GEFS raw forecast 8+ days gain NAEFS final products From Bias correction (NCEP, CMC) Dual-resolution (NCEP only) Down-scaling (NCEP, CMC) Combination of NCEP and CMC back

  44. Application for Alaska region and HPC Alaska desk 10-m U Max Temp Solid – RMS error Dash - spread Bias (absolute value) 10-m wind speed Max Temp Bias (absolute value) CRPS – small is better back

  45. Objective Evaluation Mean, 50th, and HPC best back 45 Courtesy of Dave Novak

  46. 50th (median) and mean are best back Courtesy of Dave Novak

  47. Track forecast error for 2009 season (AL+EP+WP) Cases 240 223 196 169 144 110 75 42 NAEFS is combined NCEP (NCEPbc) and CMC’s (CMCbc) bias corrected ensemble and bias corrected GFS back Contributed by Dr. Jiayi Peng (EMC/NCEP)

  48. Track forecast error for 2009 season (AL+EP+WP) Cases 240 223 196 169 144 110 75 42 NAEFS is combined NCEP (NCEPbc) and CMC’s (CMCbc) bias corrected ensemble and bias corrected GFS back Contributed by Dr. Jiayi Peng (EMC/NCEP)

  49. NEMN – NCEP raw ensemble 3EMN – NCEP + CMC + EC raw ensemble AVN0 – NCEP GFS S6MN – 3EMN + NCEPgfs + CMCgfs + ECgfs OFCL – NHC official forecast back Courtesy of Jiayi Peng

  50. NEMN – NCEP raw ensemble 3EMN – NCEP + CMC + EC raw ensemble AVN0 – NCEP GFS S6MN – 3EMN + NCEPgfs + CMCgfs + ECgfs OFCL – JTWC official forecast back Courtesy of Jiayi Peng

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