Plans for NOAA s regional ensemble systems: NARRE, HRRRE, and a regional hybrid assimilation

Transcription

1 NOAA Earth System Research Laboratory Plans for NOAA s regional ensemble systems: NARRE, HRRRE, and a regional hybrid assimilation Tom Hamill (substituting for Stan Benjamin and team) NOAA Earth System Research Lab Boulder, Colorado USA tom.hamill@noaa.gov, stan.benjamin@noaa.gov 1

2 Hourly Updated NWP Models 13-km Rapid Refresh (RAP) (mesoscale) Replaced RUC at NCEP 05/01/12 WRF, GSI, RUC features 13-km RUC (mesoscale) 3-km HRRR (stormscale) High-Resolution Rapid Refresh Experimental 3-km nest inside RAP, hourly 15-h forecast

3 Rapid Refresh Hourly Update Cycle Partial cycle atmospheric fields introduce GFS information 2x/day Fully cycle all land-surface fields Hourly Observations RAP 2012 N. Amer Rawinsonde (T,V,RH) 120 Profiler NOAA Network (V) 21 Profiler 915 MHz (V, Tv) 25 Radar VAD (V) 125 Radar reflectivity - CONUS 2km Lightning (proxy reflectivity) NLDN, GLD360 1-hr fcst 1-hr fcst 1-hr fcst Aircraft (V,T) 2-15K Aircraft - WVSS (RH) Background Fields Analysis Fields Surface/METAR (T,Td,V,ps,cloud, vis, wx) Buoys/ships (V, ps) D-Var 3D-Var Mesonet (T, Td, V, ps) flagged GOES AMVs (V) Obs Obs AMSU/HIRS/MHS radiances Used GOES cloud-top pressure/temp 13km Time (UTC) GPS Precipitable water ~250 WindSat scatterometer 2-10K

4 Current HRRR Initialized from RAP (with radar-lh-dfi in WRF) Testing underway: 3-km cycling of cloud, hydrometeors, land-surface fields Full 3-km cycling with GSI and radar/cloud assimilation RAP HRRR

5 The 29 June 2012 mid-atlantic derecho Start HRRR run A fast moving damaging wind event 11 AM 2 PM 4 PM 6 PM 8 PM 10 PM MID 700 mile long swath of damage, 5 million without power, 22 fatalities

6 NOAA Next-Generation RAP / HRRR system forecast of mid-atlantic derecho Radar observed HRRR 12-h forecast Composite Reflectivity (dbz) Valid 11PM EDT 29 June 2012

7 Many users want enhanced, higher-resolution regional ensemble guidance, with more rapid refresh than with SREF Severe storm prediction Aviation weather Wind and renewable energy 7

8 NOAA s current operational ensemble portfolio, 2012 SREF: 32-km regional ensemble; 4x daily, 21 members GEFS: T254 week 1, T190 week 2. L28, 4x daily, 21 members CRSR: T126, intended for climate. 4 members / day run to 45 days. Large ensemble if include lagged forecasts. [of course, joint products with Canada also through NAEFS also] 8

9 Challenge for rapid refresh ensemble : computational expense System cost will scale as: Third power of (inverse of) horizontal grid spacing. Proportional to frequency of refresh (i.e., 1- hourly update 6x more expensive than 6- hourly) Proportional to ensemble size. And this leaves off extra expense for possibly ensemble data assimilation, reforecasts to support post-processing. 9

10 Tiding us over for now time-lagged ensembles Enabled by 1h update cycles (RAP, HRRR) Examples: HRRR Probabilistic Convective Forecast for thunderstorm, tornado forecasts Curtis Alexander, ESRL/GSD (+Steve Weygandt, Eric James, others) Available from ESRL/GSD NARRE-TL (time-lagged) Primarily aviation forecasts, available in real-time from NCEP (Binbin Zhou, NCEP) 10

11 Model Init Time 18z 17z 16z 15z 14z 13z 12z 11z Model runs used Time-lagged ensemble Example: 13z + 2, 4, 6 hour HTPF model has 2hr latency 13z+2 12z+3 11z+4 13z+4 12z+5 11z z+6 12z+7 11z+8 HTPF 11z 12z 13z 14z 15z 16z 17z 18z 19z 20z 21z 22z 23z Curtis Alexander, GSD Forecast Valid Time (UTC)

12 Radar Observed HRRR 18z + 9h fcst 03z 30 June HRRR 19z + 8h fcst HRRR 20z + 7h fcst Run-to-run consistency in HRRR runs, suitable for TL ensemble

13 Example: 27 April z + 09hr fcst Valid 22z 27 April z SPC Tornado Probability Valid UTC 28 Apr 27 April 2011 Storm Reports Valid UTC 28 Apr HRRR-based Tornadic Storm Probability (%) Curtis Alexander, GSD

14 13z + 09hr fcst Valid 22z 27 April 2011 Example: 27 April 2011 Observed Reflectivity 22z 27 April 27 April 2011 Storm Reports Valid UTC 28 Apr Tornadic Storm Probability (%) Reflectivity (dbz) Tornado = Red Dots Curtis Alexander, GSD

15 Future plans for advanced NWP and DA for hourly assimilation May 2012 Rapid Refresh operational at NCEP 2015 High Resolution Rapid Refresh operational at NCEP for CONUS 2015 Ensemble Rapid Refresh NARRE CONUS Ensemble HRRR : HRRRE Global Rapid Refresh (GRR) Rapid Refresh Timeline contingent upon computational resources being available

16 NARRE, the North American Rapid Refresh Ensemble - NEMS-based NMMB, ARW cores - Hourly updating with GSI, possibly EnKF/hybrid var assimilation - Common NAM parent domain - Forecasts to 24 h - Initially 6 members, 3 each core - NMM to 84-h 4x per day - NMMB & ARW control data assimilation cycles with 3 hour pre-forecast period (catch-up) with hourly updating - NAM & SREF 84 hr forecasts are extensions of the 00z, 06z, 12z, & 18z runs for continuity sake. - SREF will be at same km resolution as NARRE by then - SREF will have 21 members plus 6 from NARRE for total of 27 - Inline chemistry, chem data assimilation - Storm-scale radar data assimilation - Sub-hourly data assimilation NARRE requires an increase in current HPCC funding 16

17 Modernized ensemble design in future NARRE / HRRRE? Initial condition perturbations: Ideally, consistent with GEFS control and initial perturbations (likely hybrid EnKF). Lateral boundary condition perturbations from GEFS. Model uncertainty sampled Possibly consistent with global ensembles. ESRL and EMC working on testing many options (see Phil Pegion) Possibly tailored for the different scales and different parameterizations used in regional models here. Use 3 different physics suites for ensemble members but not for data assimilation RAP physics (Thompson microphysics, Grell-3d cu, MYJ or MYNN PBL) NMM physics (Ferrier microphysics, BMJ/Janjic cu, MYJ PBL) NCAR physics (let MMM recommend - WSM,YSU,KF?) 17

18 NARRE EnKF assimilation options Separate 1-h ensemble data assimilation cycle Testing already performed by CAPS Ming Xue, Kefeng Zhu 40-km RAP EnKF assimilation GSD Ming Hu 13-km/40-km RAP EnKF assimilation Use of GFS (global) EnKF-produced background error covariance Only updated every 6h, not available at 1-h frequency Would require temporal interpolation but still might outperform fixed background error covariance

19 A preliminary experiment testing regional EnKF data assimilation in rapid refresh 40-km ensemble (1/3 Rapid Refresh resolution) Use GSI to calculate innovations Oct 2011 results EnKF, hybrid now improved over GSI Var for RR 3h RH forecast skill Results from Kefeng Zhu, Yujie Pan, Xuguang Wang, Ming Xue (OU CAPS), Jeff Whitaker (ESRL PSD)

20 Also to be considered: hybrid EnKF / variational assimilation 20

21 Standard 3-D variational data assimilation 1 1 J Var J 2 2 ' ' T 1 ' ' ' T 1 ' ' x x B Var x yo Hx R yo Hx c J : Penalty (Fit to background + Fit to observations + Constraints) x : Analysis increment (x a x b ) ; where x b is a background B Var : Background error covariance; static, estimated offline here H : Observations (forward) operator R : Observation error covariance (Instrument + representativeness) y o : Observation innovations J c : Constraints (physical quantities, balance/noise, etc.) c/o Daryl Kleist, EMC 21

22 How the EnKF may achieve its improvement relative to previous methods: better background-error covariances Output from a single-observation experiment. The EnKF is cycled for a long time. The cycle is interrupted and a single observation 1K greater than the mean prior is assimilated. Maps of the analysis minus first guess are plotted. These analysis increments are proportional to the background-error covariances between every other model grid point and the background at the observation location. 22

23 Why hybrid? Benefit from use of flow dependent ensemble covariance instead of static B VAR (3D, 4D) EnKF Hybrid References x x Hamill and Snyder 2000; Wang et al. 2007b,2008ab, 2009b, Wang 2011; Buehner et al. 2010ab Robust for small ensemble x Wang et al. 2007b, 2009b; Buehner et al. 2010b Better localization for integrated measure, e.g. satellite radiance Easy framework to add various constraints Framework to treat non- Gaussianity Use of various existing capabilities in VAR x x x x Campbell et al x x x 23

24 Hybrid variational-enkf concept Incorporate ensemble perturbations directly into variational cost function through extended control variable Lorenc (2003), Buehner (2005), Wang et. al. (2007), etc. J ' 1 ' T 1 ' 1 T 1 1 ' ' T 1 ' ' x, x B x L y Hx R y Hx f f 2 f f e 2 2 o t o t x t ' K å( ) = x f ' + a k x k e k=1 and 1 1 f e 1 f & e : weighting coefficients for fixed and ensemble covariance respectively x t : (total increment) sum of increment from fixed/static B (x f ) and ensemble B k : extended control variable; :ensemble perturbation L: correlation matrix [localization on ensemble perturbations] e x k 24

25 Regional hybrid flow chart for hurricane WRF How to configure with NARRE TBD c/o Henry Winterbottom, Jeff Whitaker, ESRL/PSD 25

26 Conclusions Users desire high-resolution, rapid refresh limited-area ensemble. Computational expense leads to challenges for how to implement this in the near term. With eventual deployment of NARRE, HRRRE, we will explore modern assimilation / ensemble initialization options such as EnKF or hybrid. 26

27 A quick refresher on the EnKF and hybrid variational system. 27

28 Canonical EnKF update equations (for time t) ( ) x a i = x b b i +K y i - Hx i K = P b H T P b = X X T ( HP b H T + R) -1 H = (possibly nonlinear) operator from model to observation space y = y + y i ' i ' yi ~ N(0, R) X = ( x b 1 - x b,, x b n - x b ) Notes: (1) An ensemble of parallel data assimilation cycles is conducted, assimilating perturbed observations. (2) Background-error covariances are estimated using the ensemble. 28

29 Propagation of state and error covariances in EnKF P a (t) = éë x a ( i t) - x a ( i t) ù û éë x a i t ( ) - x a ( i t) ù û T (P a never explicitly formed) ( ) = Mx a ( i t) if forecast model is perfect x i b t or - x b ( i t + 1) = Mx a ( i t) + h i h i h i T = Q if forecast model has model error. 29

30 The ensemble Kalman filter: a schematic (This schematic is a bit of an inappropriate simplification, for EnKF uses every member to estimate backgrounderror covariances) 30

31 Bayesian data assimilation: 2-D example Computationally expensive! Here, probabilities explicitly updated on 100x100 grid; costs multiply geometrically with the number of dimensions of model state. 31

32 How the EnKF works: 2-D example Start with a random sample from bimodal distribution used in previous Bayesian data assimilation example. Contours reflect the Gaussian distribution fitted to ensemble data. 32

33 How the EnKF may achieve its improvement relative to previous methods: better background-error covariances Output from a single-observation experiment. The EnKF is cycled for a long time. The cycle is interrupted and a single observation 1K greater than the mean prior is assimilated. Maps of the analysis minus first guess are plotted. These analysis increments are proportional to the background-error covariances between every other model grid point and the background at the observation location. 33

34 Assuming 1-h EnKF assimilation for NARRE Resolution for data assimilation ensemble km may be sufficient (and perhaps higher-res cannot be afforded) Model for data assimilation EnKF Choose single dynamic core (ARW or NMMB), do not mix cores Physics for data assimilation EnKF Apply stochastic physics to a single set Successful inflation/localization in RAP ensemble- RAP testing by CAPS

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