NCEP GEFS System and Ensemble Based Probabilistic Guidance

Transcription

1 NCEP GEFS System and Ensemble Based Probabilistic Guidance Yuejian Zhu Ensemble Team Leader EMC/NCEP/NWS/NOAA May

2 Evolution of NCEP GEFS configuration Initial uncertainty TS relocation Model uncertainty Resolution Forecast length Ensemble members Daily frequency BV None None T62L UTC T62L (00UTC) 4(12UTC) 00,12UTC T126L28(0-2.5) T62L28(2.5-16) T126(0-3.5) T62L28(3.5-16) T126L28(0-7.5) T62L28(7.5-16) 00,06,12,1 8UTC TSR T126L ETR STTP T190L T254L42 (0-8) T190L42 (8-16)

3 Proposal Next GEFS Changes Model and initialization Using GFS V9.01 instead of GFS V8.00 Improved Ensemble Transform with Rescaling (ETR) initialization Improved Stochastic Total Tendency Perturbation (STTP) Configurations T254 (55km) horizontal resolution for hours (from T190 70km) T190 (70km horizontal resolution for hours (same as current opr) L42 vertical levels for hours (from L28) Part of products will be delayed by approximately 20 minutes Due to limit CCS resources 40 nodes for 70 minutes (start +4:35 end: +5:45) Unchanged: 20+1 members per cycle, 4 cycles per day pgrb file output at 1*1 degree every 6 hours GEFS and NAEFS post process output data format Why do we make this configurations? Considering the limited resources Resolution makes difference What do we expect from this implementation? Preliminary results (NH 500hPa and SH 500hPa height and tracks)

4 11.00d Anomaly Correlation Winter 2 months Skillful line 10.25d SH 500hPa height NH 500hPa height GFS V8.0.vs V9.0 NH 850hPa temperature SH 850hPa temperature

5 Spread/Error (NM) GEFS-T254 next implementation in 2011 GFS-T574 T190-Error T190-Spread T254-Error T254-Spread GFS T574: GFSv9.01, current GFS operation GEFS T190: GFSv8.0, current operation GEFS T254: GFSv9.01, next implementation CASES Forecast Hours Tropical Storm Tracks (Aug. Sep. 2010, for AL, EP and WP)

6 NCEP CMC FNMOC Model GFS GEM Global Spectrum Initial uncertainty Model uncertainty Stochastic ETR EnKF Banded ET (STTP) (multi-physics) None Tropical storm Relocation None None Daily frequency 00,06,12 and 18UTC 00 and 12UTC 00 and 12UTC Resolution T190L28 ~70km 1.0 degree T119L30 ~1.0degree Control Ensemble members 20 for each cycle 20 for each cycle 20 for each cycle Forecast length 16 days (384 hours) 16 days (384 hours) 16 days (384 hours) Post-process Last implementation NAEFS/NUOPC Configuration (Q2FY2011) Bias correction for ensemble mean Updated: November 2010 Bias correction for each member Bias correction for member mean February 23 rd 2010 July 10 th 2007 May 2010

7 New Version of CMC s GEFS Peter Houtekamer (ARMA) June (or July) 2011, a new version of the Canadian GEPS will be implemented Modification to analysis configuration: 192 ensemble members (instead of 96), Version of GEM-DM model including staggered vertical coordinates (Charney-Phillips), Model top at 2 hpa (instead of 10 hpa), New SST analysis Modification to forecast part: Increased horizontal resolution: 600x300 (66km) vs 400x200 (100km) Staggered vertical levels Increased vertical resolution: L40 vs L28 Higher model top: 2 hpa vs 10 hpa Reduced time step: 1800s (30 min.) vs 2700s (45 min.) 7

8 Statistical Post-Processing GOAL Improve reliability while maintaining resolution in NWP forecasts Reduce systematic errors (improve reliability) while Not increasing random errors (maintaining resolution) Retain all useful information in NWP forecast METHODOLOGY Use bias-free estimators of systematic error Need methods with fast convergence using small sample APPROACH Computational efficiency Bias Correction : remove lead-time dependent bias on model grid Working on coarser model grid allows use of more complex methods Feedback on systematic errors to model development Downscaling: downscale bias-corrected forecast to finer grid Further refinement/complexity added No dependence on lead time 8

9 Current NCEP/EMC Statistical Post-Processing System Bias corrected NCEP/CMC GEFS and NCEP/GFS forecast (up to 180 hrs), same bias correction algorithm 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 Combine NCEP/GEFS (20m) and CMC/GEFS (20m), FNMOC ens. will be in soon Produce Ensemble mean, spread, mode, 10% 50%(median) and 90% probability forecast at 1*1 degree resolution Climate anomaly (percentile) forecasts also generated for ens. mean Statistical downscaling Use RTMA as reference - NDGD resolution (5km/6km), CONUS and Alaska Generate mean, mode, 10%, 50%(median) and 90% probability forecasts 9

10 Bias Correction Method & Application Bias Correction Techniques array of methods Estimate/correct bias moment by moment (e.g., D. Unger et al.). Simple approach, implemented partially May be less applicable for extreme cases Bayesian approach (e.g., Roman Krzysztofovicz) Allows simultaneous adjustment of all modes considered, under development Moment-based method at NCEP: apply adaptive (Kalman Filter type) algorithm decaying averaging mean error = (1-w) * prior t.m.e + w * (f a) For separated cycles, each lead time and individual grid point, t.m.e = time mean error 6.6% 3.3% Test different decaying weights. 0.25%, 0.5%, 1%, 2%, 5% and 10%, respectively Toth, Z., and Y. Zhu, % Decide to use 2% (~ 50 days) decaying accumulation bias estimation 10

11 NAEFS bias correction variables (bias correction) Variables pgrba_bc file Total 49 (13) GHT 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000hPa 10 (3) TMP 2m, 2mMax, 2mMin, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000hPa 13 (3) UGRD 10m, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000hPa 11 (3) VGRD 10m, 10, 50, 100, 200, 250, 500, 700, 850, 925, 1000hPa 11 (3) VVEL 850hPa 1(1) PRES Surface, PRMSL 2(0) FLUX (top) ULWRF (toa - OLR) 1 (0) 13 new vars Notes 11

12 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 12

13 NAEFS downscaling parameters and products Last update: May 1 st 2010 (NDGD resolutions) Variables Domains Resolutions Total 4/8 Surface Pressure CONUS/Alaska 5km/6km 1/1 2-m temperature CONUS/Alaska 5km/6km 1/1 10-m U component CONUS/Alaska 5km/6km 1/1 10-m V component CONUS/Alaska 5km/6km 1/1 2-m maximum T Alaska 6km 0/1 2-m minimum T Alaska 6km 0/1 10-m wind speed Alaska 6km 0/1 10-m wind direction Alaska 6km 0/1 Note: Alaska products implementation: Dec. 7, 2010 All products at 1*1 (lat/lon) degree globally Ensemble mean, spread, 10%, 50%, 90% and mode 13

14 NCEP/GEFS raw forecast 4+ days gain from NAEFS NAEFS final products From Bias correction (NCEP, CMC) Dual-resolution (NCEP only) Combination of NCEP and CMC Down-scaling (NCEP, CMC) 14

15 NCEP/GEFS raw forecast 8+ days gain NAEFS final products From Bias correction (NCEP, CMC) Dual-resolution (NCEP only) Combination of NCEP and CMC Down-scaling (NCEP, CMC) 15

16 Alaska Downscaling 6km resolution Implemented by December 7 th

17 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 CRPS small is better Bias (absolute value) 17

18 MAE (Degrees F) Objective Evaluation Alaska NAEFS Maximum Temperature MAE July-October Mean, 50 th, and HPC best HPC NDFD GEMODE GEAVG GE50PT GMOS00 GMOS Day 4 Day 5 Day 6 Day 7 Day 8 Forecast Day Courtesy of Dave Novak 18

19 MAE (m/s) 3.5 Alaska NAEFS Wind Speed MAE July-October th (median) and mean are best HPC NDFD GEMODE GEAVG GE50PT GMOS00 GMOS D4 06Z D4 12Z D4 18Z D4 00Z D5 06Z D5 12Z D5 18Z D5 00Z D6 06Z D6 12Z D6 18Z Forecast time D6 00Z D7 06Z D7 12Z D7 18Z D7 00Z D8 06Z D8 12Z D8 18Z D8 00Z 19 Courtesy of Dave Novak

20 Upgrade for CONUS downscaling Plan for implementation - Q4FY

21 NAEFS downscaling parameters and products Plan: Q4FY2011 (NDGD resolutions) Variables Domains Resolutions Total 10/8 Surface Pressure CONUS/Alaska 5km/6km 1/1 2-m temperature CONUS/Alaska 5km/6km 1/1 10-m U component CONUS/Alaska 5km/6km 1/1 10-m V component CONUS/Alaska 5km/6km 1/1 2-m maximum T CONUS/Alaska 5km/6km 1/1 2-m minimum T CONUS/Alaska 5km/6km 1/1 10-m wind speed CONUS/Alaska 5km/6km 1/1 10-m wind direction CONUS/Alaska 5km/6km 1/1 2-m dew-point T CONUS 5km 1/0 2-m relative humidity CONUS 5km 1/0 Total cloud cover? All downscaled products based on 1*1 (lat/lon) degree globally Products include ensemble mean, spread, 10%, 50%, 90% and mode 21

22 Process to Downscale Tmax & Tmin for CONUS Based on 1*1 degree 6-hr bias corrected Tmax, Tmin and down-scaling vectors (DV) for T2m at each 6-hr cycle Definition of Tmax and Tmin for Conus region Tmax period: 11/12UTC(7am-local) 23/00UTC(7pm-local) EAST Tmin period: 23/00UTC(7pm-local) 12/13UTC(8am-local) EAST Definition of approximated period for Tmax and Tmin for giving initial cycle Mean DV of T2m for 6-hr period: weighted average of two instantaneous DVs Interpolating bias corr. 6-hr Tmax & Tmin (1X1) to 6km NDGD grid for Conus Detailed Process Apply mean DV to each grid point, each ens. member, and each 6-hr lead-time period, to produce down-scaled Tmax and Tmin for each 6-hr lead-time period Find out highest Tmax and lowest Tmin for approximated period For different grid points, different ens. members, highest Tmax could be in different 6-hr period, the same for lowest Tmin Only one down-scaled Tmax and Tmin for every 24-hr. fcst, up to 384 hours Calculate the mean, spread, mode, 10%, 50% and 90% based on above step 22

23 Process to Downscale Dew Point Temperature Output products: DPT, RH2m NCEP & CMC bias corrected DPT, RH2m on 1 degree NAEFS probabilistic forecasts on 1 degree: mean, spread, mode, 10%, 50% and 90% fcst Downscaled probabilistic forecasts on NDGD 5km NCEP/CMC Bias correction process on 1 degree Calculate DPT fcst. & analysis from T2m and RH2m, accumulate bias by applying decaying weight Calculated DPT from T2m and RH2m forecast, bias correct DPT Adjust bias corrected DPT, comparing with bias corrected T2m, smaller value as the DPT Bias correct RH2m by removing RH2m bias accumulation NCEP/CMC combination process on 1 degree Combine bias corrected NCEP/CMC DPT to generate probabilistic forecasts Compare DPT and T2m probabilistic forecasts, DPT are smaller than T2m Combine NCEP/CMC RH2m to generate probabilistic forecasts Adjust RH2m probabilistic forecasts, values not larger than 100% or smaller than 0% Downscaling process Get downscaling vectors (DV) for DPT, T2m and RH2m at each 6-hr cycle Apply DV to produce down-scaled DPT,T2m and RH2m for each 6-hr lead-time period Generate the mean, spread, mode, 10%, 50% and 90% based on above step 23

24 Schemes to develop T2m, Td2m, RH probabilistic products Products 1x1 degree resolution NDGD (5km) resolution Raw ensemble members T2m Td2m RH T2m Td2m RH Derived from t2m and RH No No No Bias corrected ensemble members One end is bounded (T2m) Two ends are bounded (0,100) No No No Ensemble mean, mode, 10%, 50% and 90% One end is bounded (T2m) Two ends are bounded (0,100) Apply DV RTMA - Apply DV RTMA - Apply DV RTMA - One end is bounded Two ends are bounded Ensemble spread Interpolated Interpolated Interpolated bounded Probabilistic products and spread of T2m and Td2m are not compatible to RH 24

25 Tmax Tmin CRPS CRPS Latest evaluation for CONUS by apply : Temperature CRPS 1. Bias correction at 1*1 degree for NCEP GFS and GEFS, CMC/GEFS 2. Hybrid bias corrected NCEP/GFS and NCEP/GEFS 3. Combined all forecasts at 1*1 degree with adjustment 4. Apply statistical downscaling for joint forecast at 5*5 km (NDGD) grid - NAEFS 25

26 U 10m V 10m CRPS CRPS Wind speed Wind direction CRPS CRPS 26

27 Dew point T RH2m RMS & Spread RMS & Spread Dew Point T RH2m CRPS CRPS 27

28 Development Plan of Statistical Post-Processing for NAEFS Opportunities for improving the post-processor Utilization of additional input information More ensembles - high resolution control forecasts, SREF, GEFS Using reforecast information to improve week-2 and precipitation Improving analysis fields (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 plan (overlook beyond 2-3 years) 28 Bias correct all model output variables (>200 which include precipitation)

29 Background 29

30 58 levels Vertical Staggering In the current Version (GEM3), the information on winds, temperature and humidity are on the same levels. In GEM 4, Levels with winds (red) are inserted between the levels with temperature and Peter Houtekamer (ARMA) humidity (blue) New discretization is

31 New version of the Canadian N Deep Convection Land Surface Scheme GEPS Mixing length vertical mixing parameter GWD Backscattering stochastic Physics 0 Kain & Fritsch ISBA Bougeault 1,0 std No No Kain & Fritsch Oldkuo Kain & Fritsch Oldkuo Kain & Fritsch Force-restore Force-restore ISBA ISBA ISBA Blackadar Blackadar Bougeault Bougeault Blackadar 1,0 1,0 0,85 0,85 1,0 Weak Strong Weak Strong Weak Oldkuo Kain & Fritsch Oldkuo Kain & Fritsch Oldkuo ISBA ISBA ISBA Force-restore Force-restore Blackadar Bougeault Bougeault Bougeault Bougeault 1,0 1,0 1,0 1,0 1,0 Strong Weak Strong Weak Strong Kain & Fritsch Oldkuo Kain & Fritsch Oldkuo Kain & Fritsch Force-restore Force-restore ISBA ISBA Force-restore Bougeault Bougeault Blackadar Blackadar Blackadar 0,85 0,85 0,85 0,85 0,85 Weak Strong Weak Strong Weak Oldkuo Kain & Fritsch Oldkuo Kain & Fritsch Oldkuo Force-restore Force-restore Force-restore ISBA ISBA Blackadar Blackadar Blackadar Bougeault Bougeault 0,85 1,0 1,0 0,85 0,85 Strong Weak Strong Weak Strong

32 FNMOC A FNMOC B 5 K NCEP Good - after adjustment NCEP Bad after adjustment FNMOC C D Still not good after adjustment FNMOC NCEP Perfect don t need adjustment NCEP 32

33 Downscaling CONUS Expansion Current status for downscaling - CONUS and Alaska Statistical downscaling methods implemented to NAEFS for CONUS, 2007 Statistical downscaling methods implemented to NAEFS for Alaska, Dec.7, 2010 Downscaling advantage and benefits Statistical down-scaling adds value Bias correction with downscaling adds significant value to the forecasts T2m, Tmax and Tmin are significant improved when compared to winds Downscaling CONUS expansion Tmax & Tmin: similar to process for Alaska choosing proper daily Tmax & Tmin period definition Wind speed and direction: similar to process for Alaska T2d, or RH: new method design 33

34 NAEFS status Multi-model, multi-center ensemble NCEP GFS high resolution deterministic NCEP GFS based ensembles (GEFS) CMC global ensembles Current status NCEP 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 hurricane tracks Plan for future development Expand variables: T2d, RH, total cloud cover, PWAT? Expand domain??? Blending ECMWF ensemble in??? Improve downscale methods (smartini + downscaled vector)? 34

35 Outlines NAEFS status Current NCEP/EMC statistical post-processing system NAEFS downscaling parameters and products Downscaled products verification for CONUS and Alaska PQPF products Support Hanson Dam flood threat Special guidance, 4 times per days, out to 16 days Support general public Precipitation types, 4 times per days, out to 16 days Downscaling CONUS expansion Process to downscale Tmax & Tmin for CONUS Process to downscale wind speed & direction for CONUS Process to downscale dew point temperature Future plan and questions for discussion 35

36 Tmax and Tmin calculations for CONUS "Daytime is defined as Local Standard Time and overnight is defined as Local Standard Time." OUTPUT: (00UTC) Tmax: f06(no), f12(no), f18(no), f24(no), f30(yes:6-30hrs),f36(no), f42(no),f48(no), f52(yes:30-52hrs), Tmin: f06(no), f12(no), f18(yes:0-18hrs), f24(no), f30(no), f36(no), f42(yes:18-42hrs), f48(no), Initial 00UTC Tmax 00UTC 06UTC 12UTC 18UTC 00UTC 06UTC 12UTC 18UTC 00UTC 00UTC temperature forecast at valid time Tmax Tmin Tmin Tmin CONUS: Tmax period: 11/12UTC (7am-local) 23/00UTC (7pm-local) - EAST Tmin period: 23/00UTC(7pm-local) 12/13UTC (8am-local) - EAST OUTPUT: (06UTC) Tmax: f06(no), f12(no), f18(no), f24(yes:0-24hrs), f30(no), f36(no), f42(no), f48(yes:24-48hrs), f52(no), Tmin: f06(no), f12(no), f18(no), f24(no), f30(n0), f36(yes:12-36hrs), f42(no), f48(no), f52(no) Initial 06UTC Tmax Tmin Tmax Tmin 06UTC 12UTC 18UTC 00UTC 06UTC 12UTC 18UTC 00UTC 06UTC 06UTC temperature forecast at valid time 36

37 Proposed Dew Point Temperature & Relative Humidity Calculation Compute Dew Point Temperature (Bolton 1980): es = * exp((17.67 * T)/(T )); e = es * (RH/100.0); Td = log(e/6.112)*243.5/(17.67-log(e/6.112)); where: T = temperature in deg C; es = saturation vapor pressure in mb; e = vapor pressure in mb; RH = Relative Humidity in percent; Td = dew point in deg C Compute Relative Humidity (Bolton 1980): es = 6.112*exp((17.67*T)/(T )); e = 6.112*exp((17.67*Td)/(Td )); RH = * (e/es); where: es = saturation vapor pressure in mb; e = vapor pressure in mb; RH = Relative Humidity in percent 37

38 Comparison between Operational and Proposed Methods Operational GEFS/GFS dew point temperature Only computes dew point temp with respect to water (Hui-Ya Chuang ) Operational dew point temperature calculation process Derive saturation vapor pressure from pressure & specific humidity Construct a table of dew point temp as a function of vapor pressure that range from rvp1(0.0001e0 ) and rvp2 (10.E0) centibar New operational post for GEFS dew point temperature Dew point temp is computed wrt ice when T<-20C Plan to be implemented in Sep (Hui-Ya Chuang) Proposed strategy for dew point temperature Derive dew point temp from temperature and relative humidity following paper of Bolton (1980, MWR, p.1047) Consistent with method used in RTMA analysis and GEMPAK (Manuel Pondeca) Exchanged variables from CMC are temperature and relative humidity Two method comparison Two ways yield similar answers unless temperature very cold 38

39 Diff. between Operational Product and Proposed Method (Valid Time: ) New DPT warm than operational product 39

40 Hanson Dam flood threat special guidance 4 times per days (initial forecast) 24 hours PQPF for every 6-hours out to 16 days Covers most of CONUS, and east pacific Special thresholds for 1, 2, 4 and 6 inches 40

41 Map of PQPF and Precipitation Types: every 6 hours, 4 different thresholds 41

42 Alaska Down-Scaling Statistical Down-Scaling Standard variables Surface pressure, 2-m temperature, 10-meter wind component Work well using current operational technique for CONUS Derived variables Daily maximum and minimum temperature (Tmax, Tmin) Choose proper period definition Wind speed and wind direction There are many ways to interpolate winds Solution to avoid interpolation of wind direction Not bad, but difficult for wind direction improvement Alaska Down-Scaling evaluations Verification for all 8 variables for Alaska domain Preliminary experiments since early 2009 Real time parallel in early 2010 Implemented by December

43 Probabilities (percent) 2-m temp 10/90 probability forecast verification Northern Hem, period of Dec Feb P10-expect P10-ncepraw P10-naefs P90-expect P90-ncepraw P90-naefs % month verifications % Lead time (hours) 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

44 Short-term Plan and Questions for Discussion Plan Working on variables Tmax/Tmin, wind speed and direction for CONUS Working on DPT and RH2m? Generating new climatological mean and standard deviation for about 20 variables Half-degree for global and 3 hourly Bo Yang Generating climatological mean and standard deviation for precipitation which based on CCPA Half-degree for CONUS and 3 hourly (?) Yan Luo Questions for discussion Products resolution: 5km, or 2.5km, or both? 5x5 km RTMA for CONUS and 6x6 km RTMA for Alaska 9 variables: surface pressure, T2m, 10m U & V, 10m wind speed & direction, dew point temp (DPT), specific humidity ( SPFH), surface height 2.5x2.5 km RTMA for CONUS only 9 variables same as the above Tmax and Tmin problem on NAEFS 1*1 degree, Tmax smaller than Tmin Bias corrected Tmax/Tmin of NAEFS probabilistic products are from model 6hr output Different process for NCEP and CMC ens. due to different available analysis Same bias for CMC ens. and different bias for NCEP Possible happen (not sure) if Tmax and Tmin are very close to each other, Bias of Tmax and Tmin are in opposite direction Downscaled Tmax/Tmin, no problem anymore due to different processing Other variables - PWAT (Precipitable Water), POP (Probability of Precipitation) 44