New 16km NCEP Short-Range Ensemble Forecast (SREF) system: what we have and what we need? (SREF.v6.0.0, implementation date: Aug. 21.

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1 New 16km NCEP Short-Range Ensemble Forecast (SREF) system: what we have and what we need? (SREF.v6.0.0, implementation date: Aug ) Jun Du, Geoff DiMego, Binbin Zhou, Dusan Jovic, Brad Ferrier, Matt Pyle, Geoff Manikin, Bo Yang, Jamie Wolff (DTC) and Brian Etherton (DTC) Acknowledgements: Ensemble Team: Yuejian Zhu, Yan Luo and Bo Cui Mesoscale Branch: Julia Zhu, Eric Rogers, Perry Shafran and Ying Lin HPC: Dave Novak and Faye Barthold AWC: David Bright and Amy Harless IBM: Jim Abeles NCO: Xiaoxue Wang, Chris Magee, Becky Cosgrove, Carissa Klemmer

2 Motivation of this talk: not simply to introduce the new SREF or show how great it is but to facilitate R2O and O2R by providing you the information what we have and what we need Research Operation Research

3 Aug. 21, 2012 SREF upgrade 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 edges w/gfs -> ARW) 2. More IC perturbation diversity (blend of regional breeding and downscaled ETR) 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) New capabilities of post-processing & product generation 1. precipitation bias correction (individual members and ensemble mean) 2. clustering and associated mean/prob/spread within a cluster 3. member performance ranking (different weights for different members) 4. downscaling to 5km using RTMA and associated ensemble products. New ensemble products 1. max/min, mode, % forecasts 2. probs of severe thunderstorm, lightning, dry lightning, fire weather (SPC) as well as LLWS, composite reflectivity, echo top, ceiling and visibility 3. addition of hourly ensemble product output from 1-39 hr. 4. ensemble mean bufr 5. a new 16km output grid covering North America (g132) 3

4 Evaluation of SLP (old SREF vs. new SREF, Oct. 23 Dec. 31, 2011) Ens mean fcst: RMSE old new Prob fcst: RPSS (12km NAM as ref) old new no skill Ens spread: Rank Histogram Outlier of old SREF = 21.6% (to miss truth) Outlier of new SREF = 15.7% (to miss truth) Prob fcst: reliability diagram old new perfect 4 old new

5 Ensemble mean forecast and ensemble spread (24h-apcp, against CCPA, Oct. 23 Dec. 31, 2011) Too much light precipitation at dryend but less chance to miss heavier precipitation events at heavier precip end old new old new " 0.1" 0.25" 0.5" 0.75" 1.0"

6 Strong cold bias in 2mT in warm season (old SREF vs. new SREF) (a) Warm season (Jun. 15-Jul. 15, 2012) (b) Cold season (Oct. 23 Dec. 31, 2011) Outlier of old SREF = 18.81% (to miss truth) Outlier of new S REF = 19.7% (to miss truth) old new Strong cold bias (due to wet GFS?) Ens spread: Rank Histogram Outlier of old SREF = 17.3% (to miss truth) Outlier of new SREF = 14.3% (to miss truth) old new

7 Dec. 1, 2011 West Coast High- Wind Event (R. Grumm) Old SREF obs New SREF 7

8 Day 1 forecast of 24h snow amount ending at 01/13/2012 Obs 4km AFWA ENS Mean HPC Old SREF Mean New SREF Mean 8

9 18h-forecast of prob of CAPE > 4000 J/kg (valid at 03z, 6/30/2012) Old SREF (underestimated) New SREF (better) 9

10 PQPF of Old SREF (upper, underestimated) vs. New SREF (lower, much improved) Slow-moving Hurricane Debby-induced heavy rain over Florida: observed 30hr Accumulation (Rich Grumm) >150mm >200mm obs Old SREF >150mm >200mm New SREF 10

11 Hurricane Issac track and rain forecasts (made landfall in the west of New Orleans around 20z Aug. 29,

12 What we have and what we need under the current framework of NCEP SREF in the following aspects? *IC perturbation including LBC and land surface initial states *Model/physics perturbation *Post processing/calibration *Ensemble products and product-generator *Other regional ensemble systems *Verification (not to be touched here) 12

13 Initial condition/lbc/land surface initial states What we have: (1) Multi-analysis: GFS, NAM and RR analyses (2) Mixed IC perturbations: *regional bred vector (7 nmmb members) *global ETR (7 wrf_arw members) *blended perturbation of smaller-scale bred vector + largerscale ETR (7 wrf_nmm members) (3) Various LBCs from global ensembles (4) Various land surface initial states from NAM, GFS, RR analyses What we need: (1) Refining IC perturbations such as 3D masking (2) Connecting to NDAS system by using EnKF perturbations (2) LBC perturbation scheme by better coupling with global model (3) Direct perturbing land surface initial states 13

14 Model and physics What we have: (1) Multi-model: NMMB, WRF_NMM, WRF_ARW (2) Multi-physics: various flavors from NMM, NCAR, GFS, RR (DTC helped to test some schemes) *see details in the table next slide (3) Stochastic parameterization (Teixeira and Reynolds 2008) in NMMB model is in place but not turned on (4) Stochastic kinetic energy backscatter (SKEB) scheme in WRF- ARW model is in place but not turned on What we need: (1) Evaluating the stochastic parameterization scheme in NMMB (2) Adding the same scheme to NMM and ARW models (3) Needing to significantly speed up the SKEB in ARW model (Judith Berner) (4) Adding SKEB to NMMB and NMM models (DTC) (5) Testing other stochastic schemes (6) Real question is to see if any stochastic physics scheme can really outperform multi-model and multi-physics approaches? 14

15 Member (Model) IC IC perturb. List of the physics physics schemes conv mp lw sw pbl Sfc layer stochastic model Land surface initial perturb. nmmb_ctl NDAS BV BMJ FER GFDL GFDL MYJ MYJ no NOAH NAM no nmmb_n1 nmmb_p1 nmmb_n2 SAS GFS GFDL GFDL GFS MYJ no NOAH nmmb_p2 nmmb_n3 BMJ WSM6 GFDL GFDL MYJ MYJ NOAH nmmb_p3 nmm_ctl GFS Blend BMJ FER (new Eta) GFDL GFDL MYJ M_Obuhov (Janjic Eta) no NOAH GFS no nmm_n1 nmm_p1 nmm_n2 SAS FER (new Eta) nmm_p2 nmm_n3 KF (new Eta) nmm_p3 arw_ctl RAP ETR KF (new Eta) FER (new Eta) FER (new Eta) arw_n1 arw_p1 arw_n2 BMJ FER (new Eta) arw_p2 arw_n3 BMJ FER (new eta) arw_p3 GFDL GFDL MYJ M_Ouhov (janjic Eta) GFDL GFDL MYJ M_obuhov (janjic Eta) GFDL GFDL MYJ M_obuhov (Janjic Eta) GFDL GFDL MYJ M_obuhov (Janjic Eta) GFDL GFDL MYJ M_Obuhov (Janjic Eta) no no NOAH NOAH no NOAH RAP no no no NOAH NOAH 15

16 Post processing and calibration What we have: (1) Decaying-average method for bias correction for basic atmospheric variables (1 st moment only) (2) Frequency-matching method for precipitation bias correction (1 st moment only) (3) Downscaling of surface variables from 16km to 5km by applying the difference between lower-res and higher-res analysis (DTC helped to test the code) What we need: (1) 2 nd moment (spread) calibration (Bruce Veenhuis/MDL, Decaying-average Bayesian Model Averaging) (2) Bias correction of model variables directly on model native grid (history file), so everything else produced by model post thereafter will be automatically bias corrected (3) Higher-moment: e.g. calibrating probability as well as estimating 16 uncertainty in probability (probability of probability)?

17 RMSE and Spread Performance of the downscaled 5km SREF (verified against RTMA, 6/18/12 7/16/12) Spread/RMSE ratio 2.5 Raw Bias corrected Downscaled Forecast hours SREF-RMSE RAW-RMSE BC-RMSE SREF-Spread RAW-Spread BC-Spread Forecast hours SREF-Spread/RMSE RAW-Spread/RMSE BC-Spread/RMSE T2m (ens mean) 17

18 Bias correction can effectively remove over-predicted light precipitation and enhance under-predicted heavier precipitation 16km SREF mean (raw) 16km SREF mean (bias corrected) 24 18

19 Ensemble products and product-generator What we have: (1)mean, spread and probability (2)max/min, mode, % forecasts (3)clustering (4)member performance ranking (different weights for different members) What we need? (1)Further testing of weighted ensemble mean (2)Probability-matching mean (DTC visitor program) (3)Neighborhood probability (DTC visitor program?) (4)Extreme weather index probability (5)Special products for wind energy and dispersion uncertainty modeling 19

20 New ensemble products for aviation weather Mean ceiling Low ceiling prob Mean visibility Low visibility prob

21 New ensemble products for convection and fire weather (SPC) Severe thunder lightning dry lightning fire weather threat

22 Ensemble Clusters 22

23 RMSE Individual member performance ranking (weights for each members): Du and Zhou 2011 MWR Ensemble mean Verified best Verified worst Individual mean Predicted best Predicted worst / / / / / / / / / /

24 Other regional ensemble systems at NCEP What we have: (1) Time-lagged North America Rapid Refresh Ensemble (NARRE- TL) for aviation: 12km, 10 members, hourly update, 12hr length (2) Dynamically downscaled Hi-Res Ensemble Forecast (HREF) system: 5km, 44 members, 12hrly cycle, 48hr length (Dualresolution hybrid ensembling method, Du 2004) (3) Time-lagged 3km-HRRR-based Storm-Scale Ensemble Forecast (SSEF TL) for convection, aviation and dispersion (under construction to replace and combine with HREF) What we need: (1) Learning from OU/NSSL/SPC SSEF and AFWA 4km-EPS experiences (2) Bigger and faster super-computer to actually run a 3km Hi-Res Rapid Refresh based SSEF system to replace HREF and HRRRE-TL as well as NARRE-TL (3) NAEFS_LAM by combining SREF with Canadian REPS 24

25 We have moved from the old to the new building! (the move-in date: 8/20/12; the SREF implementation date: 8/21/12) Next generation SREF New SREF Convection-explicit, cloudresolving, rapid-update-cycle storm-scale ensemble prediction system directly coupled with data assimilation Old SREF 25