1. UMAC data callpage 1 of 25North American Ensemble Forecast System - NAEFS EMC Operational Models North American Ensemble Forecast System Yuejian Zhu Ensemble Team Leader, Environmental Modeling Center NOAA / NWS / NCEP Yuejian.Zhu@NOAA.gov

2. UMAC data callpage 2 of 25North American Ensemble Forecast System - NAEFS Statement The North American Ensemble Forecast System (NAEFS) combines state of the art weather forecast tools, called ensemble forecasts, developed at the US National Weather Service (NWS) and the Meteorological Service of Canada (MSC). When combined, these tools (a) provide weather forecast guidance for the 1-14 day period that is of higher quality than the currently available operational guidance based on either of the two sets of tools separately; and (b) make a set of forecasts that are seamless across the national boundaries over North America, between Mexico and the US, and between the US and Canada. As a first step in the development of the NAEFS system, the two ensemble generating centers, the National Centers for Environmental Prediction (NCEP) of NWS and the Canadian Meteorological Center (CMC) of MSC started exchanging their ensemble forecast data on the operational basis in September 2004. First NAEFS probabilistic products have been implemented at NCEP in February 2006. The enhanced weather forecast products are generated based on the joint ensemble which has been undergone a statistical post-processing to reduce their systematic errors.

3. UMAC data callpage 3 of 25North American Ensemble Forecast System - NAEFS North American Ensemble Forecast System International project to produce operational multi- center ensemble products Bias correction and combines global ensemble forecasts from Canada & USA Generates products for: Weather forecasters Specialized users End users Operational outlet for THORPEX research using TIGGE archive

4. UMAC data callpage 4 of 25North American Ensemble Forecast System - NAEFS 4 NCEPCMCNAEFS ModelGFSGEMNCEP+CMC Initial uncertaintyETRETKFETR + ETKF Model uncertainty/Stochasti c Yes (Stochastic Pert)Yes (multi-physics and stochastic) Yes Tropical stormRelocationNone Daily frequency00,06,12 and 18UTC00 and 12UTC ResolutionT254L42 (d0-d8)~55km T190L42 (d8-16)~70km About 50km L72 1*1 degree ControlYes Yes (2) Ensemble members20 for each cycle 40 for each cycle Forecast length16 days (384 hours) 16 days Post-processBias correction (same bias for all members) Bias correction for each member Yes Last implementationFebruary 14 th 2012November 18 th 2014 NAEFS Current Status Updated: November 18 th 2014

5. UMAC data callpage 5 of 25North American Ensemble Forecast System - NAEFS NAEFS Milestones Implementations –First NAEFS implementation – bias correction Version 1.00 - May 30 2006 –NAEFS follow up implementation – CONUS downscalingVersion 2.00 - December 4 2007 –Alaska implementation – Alaska downscalingVersion 3.00 - December 7 2010 –Implementation for CONUS/Alaska expansionVersion 4.00 - April 8 2014 –Implementation of 2.5km NDGD for CONUS/AlaskaVersion 5.00 – September 2015 Applications: –NCEP/GEFS and NAEFS – at NWS –CMC/GEFS and NAEFS – at MSC –FNMOC/GEFS – at NAVY –NCEP/SREF – at NWS Publications (or references): –Cui, B., Z. Toth, Y. Zhu, and D. Hou, D. Unger, and S. Beauregard, 2004: “ The Trade-off in Bias Correction between Using the Latest Analysis/Modeling System with a Short, versus an Older System with a Long Archive” The First THORPEX International Science Symposium. December 6-10, 2004, Montréal, Canada, World Meteorological Organization, P281-284. –Zhu, Y., and B. Cui, 2006: “GFS bias correction” [Document is available online]“” –Zhu, Y., B. Cui, and Z. Toth, 2007: “December 2007 upgrade of the NCEP Global Ensemble Forecast System (NAEFS)” [Document is available online] –Cui, B., Z. Toth, Y. Zhu and D. Hou, 2012: "Bias Correction For Global Ensemble Forecast" Weather and Forecasting, Vol. 27 396- 410 –Cui, B., Y. Zhu, Z. Toth and D. Hou, 2015: "Development of Statistical Post-processor for NAEFS" Weather and Forecasting (In process) –Zhu, Y., and B. Cui, 2007: “December 2007 upgrade of the NCEP Global Ensemble Forecast System (NAEFS)” [Document is available online] –Zhu, Y, and Y. Luo, 2014: “Precipitation Calibration Based on Frequency Matching Method (FMM)”. Weather and Forecasting (in press) –Glahn, B., 2013: “A Comparison of Two Methods of Bias Correcting MOS Temperature and Dewpoint Forecasts” MDL office note, 13-1 –Guan, H, B. Cui and Y. Zhu, 2015: “Guan, H., B. Cui and Y. Zhu, 2014: "Improvement of Statistical Post-processing Using GEFS Reforecast Information", (accepted, in press)

6. UMAC data callpage 6 of 25North American Ensemble Forecast System - NAEFS  Bias correction: Bias corrected NCEP/CMC GEFS and NCEP/GFS forecast (up to 180 hrs) Combine bias corrected NCEP/GFS and NCEP/GEFS ensemble forecasts Dual resolution ensemble approach for short lead time NCEP/GFS has higher weights at short lead time  NAEFS products (global) and downstream applications Combine NCEP/GEFS (20m) and CMC/GEFS (20m) Produce Ensemble mean, spread, mode, 10% 50%(median) and 90% probability forecast at 1*1 degree resolution Climate anomaly (percentile) forecasts Wave ensemble forecast system Hydrological ensemble forecast system  Statistical downscaling Use RTMA as reference - NDGD resolution (5km/6km), Extended CONUS and Alaska Generate mean, mode, 10%, 50%(median) and 90% probability forecasts NAEFS Statistical Post-Processing

7. UMAC data callpage 7 of 25North American Ensemble Forecast System - NAEFS NAEFS Bias Correction (Decaying average method) 2). Decaying Average (Kalman Filter method) 1). Bias Estimation: 3). Decaying Weight: w =0.02 in GEFS bias correction (~ past 50-60 days information) 4). Bias corrected forecast: Simple Accumulated Bias Assumption: Forecast and analysis (or observation) is fully correlated

8. UMAC data callpage 8 of 25North American Ensemble Forecast System - NAEFS Variablespgrba_bc fileTotal 51 GHT10, 50, 100, 200, 250, 500, 700, 850, 925, 1000hPa10 TMP2m, 2mMax, 2mMin, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000hPa 13 UGRD10m, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000hPa11 VGRD10m, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000hPa11 VVEL850hPa1 PRESSurface, PRMSL2 FLUX (top)ULWRF (toa - OLR)1 Td and RH2m2 NotesCMC did not process bias correction for Td and RH NAEFS bias corrected variables Last upgrade: April 8 th 2014 - (bias correction)

9. UMAC data callpage 9 of 25North American Ensemble Forecast System - NAEFS Continuous Ranked Probabilistic Skill Scores 5-day forecast 10-day forecast NH 500-hPa Height 30-day running mean GEFS NAEFS

10. UMAC data callpage 10 of 25North American Ensemble Forecast System - NAEFS Statistical downscaling for NAEFS forecast Proxy for truth –RTMA at 5km resolution –Variables (surface pressure, 2-m temperature, and 10-meter wind) Downscaling vector –Interpolate GDAS analysis to 5km resolution –Compare difference between interpolated GDAS and RTMA –Apply decaying weight to accumulate this difference – downscaling vector Downscaled forecast –Interpolate bias corrected 1*1 degree NAEFS to 5km resolution –Add the downscaling vector to interpolated NAEFS forecast Application –Ensemble mean, mode, 10%, 50%(median) and 90% forecasts

11. UMAC data callpage 11 of 25North American Ensemble Forecast System - NAEFS 11 VariablesDomainsResolutionsTotal 10/10 Surface PressureCONUS/Alaska5km/6km1/1 2-m temperatureCONUS/Alaska5km/6km1/1 10-m U componentCONUS/Alaska5km/6km1/1 10-m V componentCONUS/Alaska5km/6km1/1 2-m maximum TCONUS/Alaska5km/6km1/1 2-m minimum TCONUS/Alaska5km/6km1/1 10-m wind speedCONUS/Alaska5km/6km1/1 10-m wind directionCONUS/Alaska5km/6km1/1 2-m dew-point TCONUS/Alaska5km/6km1/1 2-m relative humidityCONUS/Alaska5km/6km1/1 NAEFS downscaling parameters Last Upgrade: April 8 2014 (NDGD resolution) All downscaled products are generated from 1*1 degree bias corrected fcst. globally Products include ensemble mean, spread, 10%, 50%, 90% and mode

12. UMAC data callpage 12 of 25North American Ensemble Forecast System - NAEFS 12hr 2m Temperature Forecast Mean Absolute Error w.r.t RTMA for CONUS Average for September, 2007 GEFS raw forecast NAEFS forecast GEFS bias-corr. & down scaling fcst.

13. UMAC data callpage 13 of 25North American Ensemble Forecast System - NAEFS 13 NCEP/GEFS raw forecast NAEFS final products 4+ days gain from NAEFS From Bias correction (NCEP, CMC) Dual-resolution (NCEP only) Down-scaling (NCEP, CMC) Combination of NCEP and CMC

14. UMAC data callpage 14 of 25North American Ensemble Forecast System - NAEFS 14 From Bias correction (NCEP, CMC) Dual-resolution (NCEP only) Down-scaling (NCEP, CMC) Combination of NCEP and CMC NAEFS final products NCEP/GEFS raw forecast 8+ days gain

15. UMAC data callpage 15 of 25North American Ensemble Forecast System - NAEFS Future Plan for NAEFS Short term plan –Exchange 0.5degree data every three hours for selected variables out to 7 days between NCEP, CMC (and FNMOC). –Exchange wave ensemble data between NCEP, CMC (and FNMOC) –NAEFS (SPP) upgrade (v5.0) – Q4FY15 Improving bias correction process (method) Adding cloud cover variable for bias correction The same algorithm for processing ECMWF ensemble for internal use only Downscaled products from 5km to 2.5km, and extend CONUS domain to cover most of Canada and Mexico Downscaled products from 6km to 3km for Alaska –NAEFS (SPP) upgrade (V6.0) – Q2FY16 Improving bias correction process (method) Adding additional variables (wind gust and wave height) for bias correction and downscaling Adding ECMWF ensembles to NAEFS (for internal use only) Adding new products (EFI and Anomaly Forecast) for extreme weather Expand downscaling products for other domains (Hawaii, Guan and Puerto Rico)

16. UMAC data callpage 16 of 25North American Ensemble Forecast System - NAEFS Future Plan for NAEFS Long term plan –Collaborate with CMC and FNMOC (and MDL) –NAEFS welcomes other international center(s) to join (and/or contribute) –Exchange extend forecasts (out to 30 days) between NCEP, CMC (and FNMOC). –Exchange GEFS reforecast between NCEP, CMC (and FNMOC) –Extend downscaling products to cover whole North American –Improve NAEFS SPP –Develop new guidance for extreme weather events those include weather and week 2, 3 & 4

17. UMAC data callpage 17 of 25North American Ensemble Forecast System - NAEFS Bias correction for each ensemble member + High resolution deterministic forecast Bias correction for each ensemble member + High resolution deterministic forecast Mixed Multi- Model Ensembles (MMME) Mixed Multi- Model Ensembles (MMME) Probabilistic products at 1*1 (and/or).5*.5 degree globally Down-scaling (based on RTMA) Probabilistic products at NSGD resolution (e.g. 2.5km – CONUS) Probabilistic products at NSGD resolution (e.g. 2.5km – CONUS) NCEP Others CMC Reforecast RBMP For blender RBMP For blender Varied decaying weights Auto- adjustment of 2 nd moment Smart initializatio n Smart initializatio n Future NAEFS Statistical Post-Processing

18. UMAC data callpage 18 of 25North American Ensemble Forecast System - NAEFS

19. UMAC data callpage 19 of 25North American Ensemble Forecast System - NAEFS National Unified Operational Prediction Capability NUOPC (National Unified Operational Prediction Capability) is an agreement to coordinate activities between the Department of Commerce (National Oceanic and Atmospheric Administration) and the Department of Defense (Oceanographer and Navigator of the Navy and Air Force Directorate of Weather), in order to accelerate the transition of new technology, eliminate unnecessary duplication, and achieve a superior National global prediction capability. The NUOPC partners determined that the Nation’s global atmospheric modeling capability can be advanced more effectively and efficiently with their mutual cooperation to provide a common infrastructure to perform and support their individual missions. The NUOPC Tri-Agency (NOAA, Navy, Air Force) agreed to work on a collaborative vision through coordinated research, transition and operations in order to develop and implement the next-generation National Operational Global Ensemble modeling system. This NUOPC plan consists of the following elements:

20. UMAC data callpage 20 of 25North American Ensemble Forecast System - NAEFS NCEPCMCFNMOC ModelGFSGEMGlobal Spectrum Initial uncertaintyETREnKF(9) Banded ET Model uncertainty Stochastic Yes (STTP)Yes (multi-physics and Stochastic) None Tropical stormRelocationNone Daily frequency00,06,12 and 18UTC00 and 12UTC ResolutionT254L42 (d0-d8)~55km T190L42 (d8-16)~70km ~ 50km L72 T239L50 ~ 55km ControlYes Ensemble members20 for each cycle Forecast length16 days (384 hours) Post-processBias correction for ensemble mean Bias correction for each member Bias correction for member mean Last implementationFebruary 14 2012November 18 2014May 21 2014 NUOPC Current Status Updated: May 21 2014

21. UMAC data callpage 21 of 25North American Ensemble Forecast System - NAEFS NUOPC (FNMOC) Grid Exchange Variables Update: May 21 2014 21 VariablesPgrba fileTotal 80/73 GHTSurface, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000 hPa11/(11) TMP2m, 2mMax, 2mMin, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000 hPa13/(13) RH2m, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000 hPa11/(11) UGRD10m, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000 hPa11/(11) VGRD10m, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000 hPa11/(11) PRESSurface, PRMSL2/(2) PRCPAPCP, CRAIN, CSNOW, CFRZR, CICEP5/(4) FLUX (surface)LHTFL, SHTFL, DSWRF, DLWRF, USWRF, ULWRF6/(2) FLUX (top)ULWRF (OLR)1/(1) PWATTotal precipitable water at atmospheric column1/(1) TCDCTotal cloud cover at atmospheric column1/(1) CAPEConvective available potential energy, Convective Inhibition2/(2) SOIL/SNOWSOILW(0-10cm), TMP(0-10cm down), WEASD(water equiv. of accum. Snow depth), SNOD(surface) 4/(1) Other850 hPa vertical velocity1/(1) Notes Original NAEFS grids currently being sent to NCEP by FNMOC, Require model change to add. (future plan) Not available FNMOC=72

22. UMAC data callpage 22 of 25North American Ensemble Forecast System - NAEFS National Unified Operational Prediction Capability NAVGEN ensemble (FNMOC) data at NCEP –Twice per day (00UTC, 12UTC) –Every 6 hours, out to 16 days –20+1 ensemble members for each lead time –1*1 degree raw ensembles (72 variables) forecasts Post process – bias correction –NCEP runs bias correction for NAVGEN ensembles –The same algorithm as NCEP and CMC Public data access –NCEP ftp – real time –NCEP NOMADS – real time –NCDC NOMADS – real time?

23. UMAC data callpage 23 of 25North American Ensemble Forecast System - NAEFS Northern Hemisphere 500hPa height (against consensus analysis): Latest 3-month winter scores: CRP score RMS error and ratio of RMS error / spread Anomaly correlation NH CRPS skill scores NH anomaly correlation 500hPa Height NH RMS errors

24. UMAC data callpage 24 of 25North American Ensemble Forecast System - NAEFS Ratio of RMS error over spread Northern Hemisphere 500hPa height (against consensus analysis): 30-day running mean scores of day-5: CRP score RMS error and ratio of RMS error / spread Anomaly correlation NH 500hPa anomaly correlation 5-day forecast NH 500hPa CRP scores Under-dispersion Over-dispersion NH 500hPa RMS errors

25. UMAC data callpage 25 of 25North American Ensemble Forecast System - NAEFS The results demonstrate: 1.BMA could improve 3 ensemble’s mean, but spread could be over if original spread is larger 2.RBMP could keep similar BMA average future, but 2 nd moment will be adjusted internally 3.All important time average quantities are decaying average (or recursive – save storage) The results demonstrate: 1.BMA could improve 3 ensemble’s mean, but spread could be over if original spread is larger 2.RBMP could keep similar BMA average future, but 2 nd moment will be adjusted internally 3.All important time average quantities are decaying average (or recursive – save storage) NUOPCIBC – simple combine three bias corrected ensembles DCBMA – decaying based Bayesian Model Average RBMP – Recursive Bayesian Model Process (built in decaying average and internal 2 nd -moment adjustment) Solid line – RMS error Dash line - Spread Over-dispersion Multi-model Post-processing Multi-model Post-processing