Convection-permitting Ensemble Data Assimilation of Inner-core - - PowerPoint PPT Presentation

Convection-permitting Ensemble Data Assimilation of Inner-core Observations for Hurricane Prediction Fuqing Zhang and Yonghui Weng Penn State University

Tropical cyclone intensity is strongly dependent on internal dynamics and moist convection which are smaller in scales, more chaotic, under-observed, under-resolved, and/or intrinsically less predictable?

National Hurricane Center Official Intensity Errors

• Model: Weather Research and Forecast Model (WRF) with 4 domains two-way

nested and grid sizes of 40.5, 13.5, 4.5, and 1.5km

• Data: Doppler winds from three coastal weather surveillance radars [available

routinely for more than 20 years but never used in any NOAA operational models]

• Data assimilation method: Ensemble Kalman Filter (Meng and Zhang 2008a,b)

Assimilate W88D Doppler Winds with WRF-EnKF

D1

KCRP KHGX KLCH

(Zhang et al. 2009 MWR)

Super-Obs: QC and thinning of WSR-88D Vr Obs

• Define SO position depended on the radial distance
• Average10 nearest data points in the raw polar scan to create a SO
• Averaging bin is 5km max radial range and 5° max azimuthally resolution
• There are at least 4 valid velocity data within an averaging bin.
• The standard deviation checking of the velocities.

0.5degree RAW data 0.5degree SO

(Zhang et al. 2009 MWR; Weng et al 2011 CiSE)

Min SLP Max wind

The WRF/3DVAR (as a surrogate of operational algorithm) assimilates the same radar data but without flow-dependent background error covariance, its forecast failed to develop the storm despite fit to the best-track observation better initially

WRF/EnKF Forecast vs. Observations vs. 3DVAR

(Zhang et al. 2009 MWR)

Assimilate W88D Doppler Vr for Humberto’05

Analysis Forecast Analysis Forecast

Successive Covariance Localization (SCL)

D1

• Dense observations contain information of the state

at different scales, e.g., hurricanes.

• Rationale: larger-scale errors have larger correlation

length scales thus need fewer observations, large radii of influence; more observations with smaller radius of influence are needed to constrain small- scale errors (Zhang et al. 2006).

• SCL has some similarity to successive correction

method (SCM) used in some earlier empirical

• bjective analysis schemes (e.g., Barnes 1964),

though subgrouping of observations is used in the EnKF so the same observation not used twice. (Zhang et al. 2009 MWR)

Covariance Relaxation: Inflation through Relaxation to Prior

D1

• α is the relaxation or mixing coefficient
• Treats sampling issues with respect to

both model error and ensemble size

• More inflation in the area of denser
• bservations while no inflation if no obs
• The method is adopted from the

commonly used relaxation method in interactive numerical solver

• It is the 1st known adaptive covariance

inflation method (Zhang, Snyder and Sun 2004 MWR)

(x’a)new = α x’f + (1-α) x’a

(Poterjoy, Zhang & Weng, 2013)

Assimilate Airborne Doppler Winds with WRF-EnKF Available for 20+ years but never used in operational models due to the lack of

resolution and/or the lack of efficient data assimilation methods Superobservations: 1. Separate forward and backward scans; 2. treat every 3 adjacent full scans as one fixed-space radar (translation<5km); 3. thinning ---one bin for 2 km in radial distance and 3 degree in scanning angle; 4. use medium as SO after additional QC checking These SOs are generated on flight of NOAA P3’s; transmitted to ground in real-time

(Weng and Zhang 2012 MWR)

WRF-EnKF: 3 domains (40.5, 13.5&4.5km), 60-member ensemble

WRF-EnKF Performance Assimilating Airborne Vr

Mean absolute track (km) & intensity (kts) error for all 2008-2010 P3 missions

Interpolated WSP(t) = WSP(t) - 36h - t 36h ´ Bias(6h) æ è ç ö ø ÷

(Zhang et al. 2011 GRL)

WRF-EnKF: 3 domains (40.5, 13.5&4.5km), 30-member ensemble

Position error (km) Intensity error (knots)

Earl 2010

Tail Doppler

Updates: Performance Assimilating Airborne Vr

all 100+ P3 TDR missions during 2008-2012 Quasi-operational evaluation by NOAA/NHC since 2011 as stream 1.5 run

Interpolated WSP(t) = WSP(t) - 36h - t 36h ´ Bias(6h) æ è ç ö ø ÷

WRF-EnKF: 3 domains (27, 9 , 3km), 60-member ensemble, PSU TC flux scheme

(Zhang and Weng, 2013)

Position error (km) Intensity error (knots)

Realtime EnKF assimilation of airborne Doppler winds for Hurricane Forecasts

PSU WRF-EnKF 4-day Rainfall Forecast from 00Z/26 Oct

NWS 4km 96-h rainfall APSU 96-h deterministic rainfall forecast

Further Updates: Cycling WRF-EnKF Retrospective Runs Assimilating Airborne Dropsonde, Flight-level and/or TDR Vr Observations at NHC’s Request

NOAA/HFIP Tiger Team RECON tests and evaluation for 2013 stream 1.5 run

Interpolated WSP(t) = WSP(t) - 36h - t 36h ´ Bias(6h) æ è ç ö ø ÷

Cycling WRF-EnKF: 3 domains (27, 9 , 3km), 60-member ensemble, PSU TC flux

Position error (km) Intensity error (knots)

PSU WRF-EnKF 2013 Realtime Stream-1.5 Run

Tropical Storm Gabriel from 12Z/Aug29 to 12Z/Sep13 including 3 HS3 GH missions Hurricane Ingrid from 12Z/Sep8 to 00Z/Sep17 including 1 HS3 GH missions

PSU WRF-EnKF 2013 Realtime Stream-1.5 Run

3 sample Forecasts for Tropical Storm Karen (12Z of 2, 3, 4 Oct)

E4DVAR: 2-way Full Coupling of EnKF with 4DVar

Necessary Variable Changes: EnKF provides ensemble-based background error covariance (Pf) for 4DVar EnKF provides the prior ensemble mean ( ) as the first guess for 4DVar 4DVar provides deterministic analysis ( ) to replace the posterior ensemble mean for the next ensemble forecast

x

f

a

x

1st proof-of-concept in Zhang, Zhang and Hansen (2009 AAS) 1st real-data experiments in Zhang and Zhang (2012 MWR)

Total RMSE of U, V, T and Q with 0~72 lead time (Zhang and Zhang 2012; Zhang et al. 2013 MWR)

Inter-comparison of E4DVar, E3DVar vs. EnKF, 3DVar, 4DVar u v T RH

Concluding Remarks

• Hurricane intensity prediction can be improved by advanced

assimilation of core observations into convection-permitting models

• The Super-observations (SOs), Successive Covariance Localization

(SCL), Covariance Inflation through Relaxation to Prior methods we developed could be easily adapted to treat other dense and/or inhomogeneous observations that contains multi-scale information

• Further forecast improvement may come from two-way full coupling
• f EnKF and 4DVar, as will be shown in Jon Poterjoy’s talk

Vertical velocity at 5km (colored) and surface cold pool (black lines, every 2K)

Observations: radial velocity Vr only, available every 5 minutes where reflectivity dBZ>12 (Snyder and Zhang 2003 MWR; Zhang, et al. 2004 MWR; Dowell et al. 2004MWR)

First Test of EnKF for Limited-area Models: Assimilation of Radar Observations of Supercells

Truth EnKF

Rainfall Forecasts with PSU WRF-EnKF

Assimilate WSR88D Vr Obs: Number of SOs

D1

Super-Ob of KCRP and KHGX at 09Z/12 Number of Assimilated SOs

• WRF/EnKF starts assimilating hourly Vr obs of CRP, HGX and LCH WSR88D

radars from 09Z/12 to 21Z/12 after a 9-h ensemble forecast from GFS/FNL analysis

• Successive covariance localization with different ROIs for different subset of SOs

500 1000 1500 2000 2500 3000 09Z12 12Z12 15Z12 18Z12 21Z12 00Z13 03Z13 06Z13 num

KLCH KCRP KHGX

WRF/EnKF Analysis vs. Observations vs. NoDA

KHGX base Vr EnKF Analysis Mean Pure EF Mean w/o EnKF 09Z/12 18Z/12 03Z/13

Tropical cyclone track is mostly determined by larger-scale environment whose forecast improves with better observations, better models, higher resolution and more than 100,000 times faster computers

National Hurricane Center Official Track Errors