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Review of NCEP GFS Forecast Skills and Major Upgrades Fanglin Yang IMSG - Environmental Modeling Center National Centers for Environmental Prediction Camp.

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Presentation on theme: "Review of NCEP GFS Forecast Skills and Major Upgrades Fanglin Yang IMSG - Environmental Modeling Center National Centers for Environmental Prediction Camp."— Presentation transcript:

1 Review of NCEP GFS Forecast Skills and Major Upgrades Fanglin Yang IMSG - Environmental Modeling Center National Centers for Environmental Prediction Camp Springs, Maryland, USA 24 th Conference on Weather and Forecasting & 20 th Conference on Numerical Weather Prediction 91th AMS Annual Meeting, 23–27 January 2011, Seattle, WA

2 2 GFS 500hPa Height Anomaly Correlation, 00Z Cycle Day-5 Forecast GFS forecast skill has been steadily improving in both the Northern and Southern Hemisphere. GFS performs better in NH than in SH, especially in early years. NH score was improved significantly in 2010.

3 3 Day-5 Northern-Hemisphere 500-hPa Height Anomaly Correlation GFS: NCEP Global Forecast System; FNO: Navy Fleet Numerical Meteorology and Oceanography Center; ECM: European Center for Medium-Range Weather Forecasts; UKM: The United Kingdom Met Office; CMC: The Canadian Meteorological Center; CDAS: T version of GFS used for the NCEP/NCAR Reanalysis. The forecast skills of all NWP models have been steadily improving. GFS lags behind ECMWF, and is comparable to UKM. The difference between GFS and CDAS is an indicator of improvement in GFS physics, dynamics and data assimilation system.

4 4 Day-5 Southern-Hemisphere 500-hPa Height Anomaly Correlation GFS lags behind ECMWF; UKM surpassed GFS since 2005; CMC improved in the past couple of years. GFS: NCEP Global Forecast System; FNO: Navy Fleet Numerical Meteorology and Oceanography Center; ECM: European Center for Medium-Range Weather Forecasts; UKM: The United Kingdom Met Office; CMC: The Canadian Meteorological Center; CDAS: T version of GFS used for the NCEP/NCAR Reanalysis.

5 5 Tropical Wind RMSE 850 hPa200 hPa GFS 850hPa wind has been significantly improved over the years, and is closing to ECMWF forecast. Improvement of 200hPa wind is modest. It is worth noting that RMSE can be misleading if the forecast model is heavily damped.

6 6 NH SH 500-hPa HGT RMSE, Dec hPa HGT, Fcst v.s. Analysis ECMWF develops cold bias in the stratosphere Each NWP model has its own strength and weakness. For instance, GFS has smaller forecast error than ECMWF in the stratosphere. Forecast Hour 0-240

7 7 GFS Annual Mean Day-5 NH 500-hPa Height Anomaly Correlation Forecasts with AC >= 0.6 is usually regarded as useful. GFS useful forecasts have improved from 6.4 days in 2001 to 8 days in ECMWF is still about 0.5 day ahead of GFS. Day-5 AC has improved by about 0.1 in the past 10 years

8 8 GFS Annual Mean Day-5 SH 500-hPa Height Anomaly Correlation Forecasts with AC >= 0.6 is usually regarded as useful. GFS SH useful forecasts have improved from 6.1 days in 2001 to 7.4 days in ECMWF is about 0.8 day ahead of GFS. SH Day-5 AC has improved by about 0.12 in the past 10 years

9 9 GFS Precip Skill Scores Over CONUS, fh60-84 (day-3) ETS increased and BIAS decreased in recent years

10 10 Precip Skill Scores contingency table: –Hits (a): occasions/counts where both forecast and observation are greater than or equal to a threshold over, say, CONUS; –False alarms (b): occasions where forecast is above a threshold whereas observation is under the same threshold; –Misses (c): occasions where the observation is above a threshold and forecast is under the same threshold; –No forecasts (d): occasions where both forecast and observation are under the threshold. Bias Score: BS=(a + b)/(a + c) measures over-forecasts (BS>1) or under-forecasts (BS<1) precipitation frequency over an area for a selected threshold. Equitable Threat Score: EQ_TS=(a - ar)/(a + b + c - ar) where ar is the expected number of correct forecasts above the threshold in a random forecast where forecast occurrence/non-occurrence is independent from observation/non-observation, ar=(a + b)*(a + c)/(a + b + c + d). EQ_TS=1 means a perfect forecast. EQ_TS <=0 means the forecast is useless. Obs YesObs NO Fcst YESab Fcst NOcd

11 11 Reduce unrealistic excessive heavy precipitation (so called grid-scale storm or bull’s eye precipitation) New (SAS, PBL and Shallow Convection) 24 h accumulated precipitation ending at 12 UTC, July 24, 2008 from (a) observation and h forecasts with (b) control GFS and (c) revised model OBS CTL Han & Pan, 2010

12 12 GFS Hurricane Track and Intensity Forecasts, Atlantic Basin Intensity forecast has been significantly improved, mainly due to increases in model resolution. The current GFS intensity forecast is catching up with HWRF. Forecasters have started to take GFS into account for intensity forecast. Track forecast within 3 days has been steadily improving, although the pace is slow. Beyond day 3, the forecast still varies from year to year.

13 13 GFS Hurricane Track and Intensity Forecasts, Eastern Pacific Intensity forecast not improved in the Eastern Pacific Basin. Why ?? Track forecast within 3 days has been steadily improving, although the pace is slow.

14 14 What kind of model changes have contributed to the improvement of GFS forecast skills? Does very model upgrade always lead to better forecast skill?

15 15 GFS Changes 3/1999 –AMSU-A and HIRS-3 data 2/2000 –Resolution change: T126L28  T170L42 (100 km  70 km) –Next changes 7/2000 (hurricane relocation) 8/2000 (data cutoff for 06 and 18 UTC) 10/2000 – package of minor changes 2/2001 – radiance and moisture analysis changes 5/2001 –Major physics upgrade (prognostic cloud water, cumulus momentum transport) –Improved QC for AMSU radiances –Next changes 6/2001 – vegetation fraction 7/2001 – SST satellite data 8/200 – sea ice mask, gravity wave drag adjustment, random cloud tops, land surface evaporation, cloud microphysics…) 10/ 2001 – snow depth from model background 1/2002 – Quikscat included The “GFS Changes” slides were first scripted by Stephen Lord

16 16 GFS Changes (cont) 11/2002 –Resolution change: T170L42  T254L64 (70 km  55 km) –Recomputed background error –Divergence tendency constraint in tropics turned off –Next changes 3/2003 – NOAA-17 radiances, NOAA-16 AMSU restored, Quikscat 0.5 degree data 8/2003 – RRTM longwave and trace gases 10/2003 – NOAA-17 AMSU-A turned off 11/2003 – Minor analysis changes 2/2004 – mountain blocking added 5/2004 – NOAA-16 HIRS turned off 5/2005 –Resolution change: T254L64  T382L64 ( 55 km  38 km ) –2-L OSU LSM  4-L NOHA LSM –Reduce background vertical diffusion –Retune mountain blocking –Next changes 6/2005 – Increase vegetation canopy resistance 7/2005 – Correct temperature error near top of model

17 17 GFS Changes (cont) 8/2006 –Revised orography and land-sea mask –NRL ozone physics –Upgrade snow analysis 5/2007 –SSI (Spectral Statistical Interpolation)  GSI ( Gridpoint Statistical Interpolation). –Vertical coordinate changed from sigma to hybrid sigma-pressure –New observations (COSMIC, full resolution AIRS, METOP HIRS, AMSU-A and MHS) 12/2007 –JMA high resolution winds and SBUV-8 ozone observations added 2/2009 –Flow-dependent weighting of background error variances –Variational Quality Control –METOP IASI observations added –Updated Community Radiative Transfer Model coefficients 7/2010 –Resolution Change: T382L64  T574L64 ( 38 km  23 km ) –Major radiation package upgrade (RRTM2, aerosol, surface albedo etc) –New mass flux shallow convection scheme; revised deep convection and PBL scheme –Positive-definite tracer transport scheme to remove negative water vapor

18 hPa Height AC Frequency distribution, GFS 00Z Cycle Day-5 Forecast Twenty bins were used to count for the frequency distribution, with the 1st bin centered at and the last been centered at The width of each bin is Look at the history of extremes in the distribution –Poor Forecasts (AC < 0.7 ) –Excellent forecasts ( AC > 0.9 )

19 19 Resolution: 1. 3/1991: T80L18  T126L28 (100km) 2. 2/2000: T126L28  T170L42 (70km) 3. 11/2002: T170L42  T254L64 (55km) 4. 6/2005: T254L64  T382L64 (38km) 5. 7/2010: T382L64  T574L64 (23km) Percent of Poor Forecasts (AC <0.7) v.s. Model Changes Physics and Data Assimilation: A. 3/1999: AMSU-A & HIRS-3 data B. 5/2001: prognostic cloud water, cumulus momentum transport C. 6/2005: OSU 2-L LSM to 4-L NOHA LSM D. 5/2007: SSI to GSI; Hybrid sigma-p; New observations E. 2/2009: flow- dependent error covariance; Variational QC F. 7/2010: New shallow convection; updated SAS and PBL; positive- definite tracer transport. A 2 B 3 4, C 5, F year NH

20 20 Resolution: 1. 3/1991: T80L18  T126L28 (100km) 2. 2/2000: T126L28  T170L42 (70km) 3. 11/2002: T170L42  T254L64 (55km) 4. 6/2005: T254L64  T382L64 (38km) 5. 7/2010: T382L64  T574L64 (23km) Percent of Poor Forecasts (AC <0.7) v.s. Model Changes A 2 B 3 4, C 5, F year SH D Physics and Data Assimilation: A. 3/1999: AMSU-A & HIRS-3 data B. 5/2001: prognostic cloud water, cumulus momentum transport C. 6/2005: OSU 2-L LSM to 4-L NOHA LSM D. 5/2007: SSI to GSI; Hybrid sigma-p; New observations E. 2/2009: flow- dependent error covariance; Variational QC F. 7/2010: New shallow convection; updated SAS and PBL; positive- definite tracer transport. E

21 21 Resolution: 1. 3/1991: T80L18  T126L28 (100km) 2. 2/2000: T126L28  T170L42 (70km) 3. 11/2002: T170L42  T254L64 (55km) 4. 6/2005: T254L64  T382L64 (38km) 5. 7/2010: T382L64  T574L64 (23km) Percent of Excellent Forecasts (AC >0.9) v.s. Model Changes 4, C 5, F year NH Physics and Data Assimilation: A. 3/1999: AMSU-A & HIRS-3 data B. 5/2001: prognostic cloud water, cumulus momentum transport C. 6/2005: OSU 2-L LSM to 4-L NOHA LSM D. 5/2007: SSI to GSI; Hybrid sigma-p; New observations E. 2/2009: flow- dependent error covariance; Variational QC F. 7/2010: New shallow convection; updated SAS and PBL; positive- definite tracer transport. E 3

22 22 Resolution: 1. 3/1991: T80L18  T126L28 (100km) 2. 2/2000: T126L28  T170L42 (70km) 3. 11/2002: T170L42  T254L64 (55km) 4. 6/2005: T254L64  T382L64 (38km) 5. 7/2010: T382L64  T574L64 (23km) Percent of Excellent Forecasts (AC >0.9) v.s. Model Changes 3 D year SH Physics and Data Assimilation: A. 3/1999: AMSU-A & HIRS-3 data B. 5/2001: prognostic cloud water, cumulus momentum transport C. 6/2005: OSU 2-L LSM to 4-L NOHA LSM D. 5/2007: SSI to GSI; Hybrid sigma-p; New observations E. 2/2009: flow- dependent error covariance; Variational QC F. 7/2010: New shallow convection; updated SAS and PBL; positive- definite tracer transport. E ?

23 23 Comments Most implementations include both major and minor changes. They all contribute to improving the system. Accumulated impact of many small changes is significant but not measurable. Predictability may change from year to year.

24 24 Most Recent GFS Upgrade 28-July-2010 Implementation T382L64 (38 km)  T574L64 (23 km) & Major physics upgrade Major changes Testing and evaluation Benefits and remaining issues

25 25 Major Changes Resolution and ESMF –T382L64 to T574L64 ( ~38 km -> ~27 km) for fcst1 (0-192hr) & T190L64 for fcst2 ( hr). –fcst2 step with digital filter turned on –ESMF 3.1.0rp2 Radiation and cloud –Changing SW routine from ncep0 to RRTM2 –Changing longwave computation frequency from three hours to one hour –Adding stratospheric aerosol SW and LW and tropospheric aerosol LW –Changing aerosol SW single scattering albedo from 0.90 in the operation to 0.99 –Changing SW aerosol asymmetry factor. Using new aerosol climatology. –Changing SW cloud overlap from random to maximum-random overlap –Using time varying global mean CO2 instead of constant CO2 in the operation –Using the Yang et al. (2008) scheme to treat the dependence of direct-beam surface albedo on solar zenith angle over snow-free land surface

26 26 Example: Improving GFS Surface Albedo Using ARM-SURFRAD Observations Dependencies of direct-beam albedo, normalized by the diffuse albedo, on SZA. The ten colored long-dashed lines represent the empirical fits derived from observations at the three ARM and seven SURFRAD stations for the entire-day cases. The blue line with filled circles is based on the observations at all stations except the Desert Rock station (the line with crosses). The black lines with open circles and squares are governed by the NCEP GFS parameterization with the constant being set to 0.4 and 0.1, respectively. Fits using data at ARM and SURFRAD stations Fanglin Yang, Kenneth Mitchell, Yu-Tai Hou, Yongjiu Dai, Xubin Zeng, Zhou Wang, and Xin-Zhong Liang, 2008: Dependence of land surface albedo on solar zenith angle: observations and model parameterizations. Journal of Applied Meteorology and Climatology. No.11, Vol 47,

27 27 Gravity-Wave Drag Parameterization –Using a modified GWD routine to automatically scale mountain block and GWD stress with resolution. –Compared to the T382L64 GFS, the T574L64 GFS uses four times stronger mountain block and one half the strength of GWD. Removal of negative water vapor –Using a positive-definite tracer transport scheme in the vertical to replace the operational central-differencing scheme to eliminate computationally-induced negative tracers. –Changing GSI factqmin and factqmax parameters to reduce negative water vapor and supersaturation points from analysis step. –Modifying cloud physics to limit the borrowing of water vapor that is used to fill negative cloud water to the maximum amount of available water vapor so as to prevent the model from producing negative water vapor. –Changing the minimum value of water vapor mass mixing ratio in radiation from 1.0e-5 in the operation to 1.0e-20. Otherwise, the model artificially injects water vapor in the upper atmosphere where water vapor mixing ratio is often below 1.0e-5. Major Changes

28 28 Vertical Advection of Tracers: Current GFS Scheme Flux form conserves mass Current GFS uses central differencing in space and leap-frog in time. The scheme is not positive definite and may produce negative tracers.

29 29 Example: Removal of Negative Water Vapor Fanglin Yang et al., 2009: On the Negative Water Vapor in the NCEP GFS: Sources and Solution. 23rd Conference on Weather Analysis and Forecasting/19th Conference on Numerical Weather Prediction, 1-5 June 2009, Omaha, NE Sources of Negative Water Vapor Vertical advection Data assimilation Spectral transform Borrowing by cloud water SAS Convection Ops GFS _ Positive-definite Data Assimilation A: vertical advection, computed in finite-difference form with flux-limited positive-definite scheme in space Flux-Limited Vertically-Filtered Scheme, central in time New B: horizontal advection, computed in spectral form with central differencing in space Data Assimilation

30 30 Vertical Advection of Tracers: Flux-Limited Scheme Thuburn (1993) Van Leer (1974) Limiter, anti- diffusive term Special boundary conditions

31 31 Vertical Advection of Tracers: Flux-Limited Scheme Thuburn (1993) Van Leer (1974) Limiter, anti- diffusive term Special boundary condition

32 32 Vertical Advection of Tracers: Idealized Case Study wind Upwind (diffusive) Flux-Limited GFS Central-in-Space Initial condition

33 33 Summary: Negative Water Vapor in the GFS CausesImportanceSolutions Vertical Advection1. Semi-Lagrangian 2. Flux-Limited Positive- Definite Scheme for current Eulerian GFS GSI AnalysisTuning factqmin and factqmax Spectral Transform1. Semi-Lagrangian GFS: running tracers on grid, no spectral transform 2. Eulerian GFS: no solution yet. Cloud Water BorrowingLimiting the borrowing to available amount of water vapor SAS Mass-FluxRemains to be resolved

34 34 New mass flux shallow convection scheme (Han & Pan 2010) –Use a bulk mass-flux parameterization same as deep convection scheme –Separation of deep and shallow convection is determined by cloud depth (currently 150 mb) –Entrainment rate is given to be inversely proportional to height (which is based on the LES studies) and much smaller than that in the deep convection scheme –Mass flux at cloud base is given as a function of the surface buoyancy flux (Grant, 2001), which contrasts to the deep convection scheme using a quasi-equilibrium closure of Arakawa and Shubert (1974) where the destabilization of an air column by the large-scale atmosphere is nearly balanced by the stabilization due to the cumulus Revised deep convection scheme (Han & Pan 2010) –Random cloud top selection in the current operational scheme is replaced by an entrainment rate parameterization with the rate dependent upon environmental moisture –Include the effect of convection-induced pressure gradient force to reduce convective momentum transport (reduced about half) –Trigger condition is modified to produce more convection in large-scale convergent regions but less convection in large-scale subsidence regions –A convective overshooting is parameterized in terms of the convective available potential energy (CAPE) Major Changes

35 35 Revised Boundary Layer Scheme (Han & Pan 2010) –Include stratocumulus-top driven turbulence mixing based on Lock et al.’s (2000) study –Enhance stratocumulus top driven diffusion when the condition for cloud top entrainment instability is met –Use local diffusion for the nighttime stable PBL rather than a surface layer stability based diffusion profile –Background diffusivity for momentum has been substantially increased to 3.0 m2s-1 everywhere, which helped reduce the wind forecast errors significantly Hurricane relocation –Running hurricane relocation at the 1760x880 forecast grid instead of the 1152x576 analysis grid –Posting GDAS pgb files first on Guassian grid (1760x880), then convert to 0.5-deg for hurricane relocation. Major Changes

36 36 Operational shallow convection scheme (Diffusion scheme, Tiedke, 1983 ) New shallow convection scheme (Mass flux scheme) Mass flux analogy (de Roode et al., 2000) : A u (updraft area)=0.5 A d (downdraft area)=0.5 A u ~0.0; A d ~1.0 Environment is dominated by subsidence resulting in environmental warming and drying. Example: New Mass-Flux Based Shallow Convection By Jongil Han and Hua-lu Pan

37 37 Ops GFS New shallow convection scheme Heating by Shallow Convection

38 38 ISCCP Ops GFSNew Shallow Low cloud cover (%) Marine Stratus

39 39 No stratocumulus top driven diffusion With stratocumulus top driven diffusion Low cloud cover (%)

40 40 Reduce unrealistic excessive heavy precipitation (so called grid-scale storm or bull’s eye precipitation) New 24 h accumulated precipitation ending at 12 UTC, July 24, 2008 from (a) observation and h forecasts with (b) control GFS and (c) revised model OBS CTL

41 41 Parallel Test & Evaluation –2008 Hurricane Season (June 20 – November 10) –2009 Hurricane Season (June 20 – November 10) –2009/2010 Winter and 2010 Spring (December 1 – present)

42 42 500hPa Height AC NH 2008NH 2009 SH 2009 SH 2008 Significant improvement in Anomaly correlations for week-one Fcst

43 43 Tropical Wind RMSE hPa Significant reduction in tropical wind RMSE

44 44 Precipitation Skill Scores over CONUS Significantly improved EQ scores, reduced biases for heavy precip events

45 45 Hurricane Track and Intensity: 2008 Atlantic Track Reduced track errors in both basins, significantly improved intensity forecast Atlantic Intensity East Pacific Track East Pacific Intensity T574 T382

46 46 Hurricane Track and Intensity: 2009 Atlantic Track Reduced track error in East Pacific, significantly improved intensity forecast in both basins. Atlantic Intensity East Pacific Track East Pacific Intensity

47 47 Hurricane Intensity Tendency Forecast: 2008 Better tendency forecast Atlantic East Pacific T382 ControlT574 Parallel

48 48 Summary The upcoming T574L64 implementation in July 2010 was a major improvement upon the last operational T382L64 GFS in terms of height AC, wind RMSE, precipitation skill score, and hurricane track and intensity. However, there are still a few remaining issues.

49 T382 GFS is closer to ECMWF than the T574 GFS does. T574 GFS has weaker easterly than T382 GFS in 2009 and This is caused by overly too strong vertical diffusion of momentum in the stratosphere. The spring 2011 GFS implementation will has this problem corrected. T574 ECMWF Ops T382 QBO transition from westerly phase to easterly phase

50 50 T878 L64 or L91 Semi- Lagrangian GFS NEMS (a unified national environment modeling system) Planed Upgrades

51 51 Annual Mean AC GFS ECMWF

52 52 GFS v.s. ECMWF (AC < 0.7) GFS ECMWF GFS lags ECMWF more in the SH than in the NH, especially in recent years.

53 53 GFS v.s. ECMWF (AC >0.9) GFSECMWF

54 54 GFS 4-Cycle Comparison, 500-hPa Height day-5 AC

55 55 GFS 4-Cycle Comparison, Tropical Wind RMSE

56 Larger height RMS in the lower stratosphere likely caused by to small a minimum value of water vapor mixing ratio (1.0E-20)


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