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Development and Testing of a Regional GSI-Based EnKF -Hybrid System for the Rapid Refresh Configuration. Yujie Pan 1 , Kefeng Zhu 1 , Ming Xue 1,2 , Xuguang Wang 1,2 , Jeffrey S. Whitaker 3 , Stanley G. Benjamin 3 and Stephen S. Weygandt 3 and Ming Hu 3

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Outline 2463075

Development and Testing of a Regional GSI-Based EnKF-Hybrid System for the Rapid Refresh Configuration

Yujie Pan1, Kefeng Zhu1, Ming Xue1,2, Xuguang Wang1,2, Jeffrey S. Whitaker3, Stanley G. Benjamin3 and Stephen S.Weygandt3 and Ming Hu3

Center for Analysis and Prediction of Storms1 and School of Meteorology2University of Oklahoma, Norman Oklahoma 73072

NOAA Earth System Research Laboratory3, Boulder, Colorado

5thEnKF Workshop

Albany, New York

May 2012


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Outline

Part 1: Introduction to the regional GSI-based EnKF-hybrid data assimilation system

Part 2: Single observation tests

Part 3: Comparison of hybrid with GSI and pure EnKF

  •  EnKF-Hybrid 1 way interactive

  •  EnKF-Hybrid 1 way with multi-physics EnKF

  •  EnKF-Hybrid 2 way interactive

  • Verification of precipitation forecasts on 13 km grid


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GSI-Hybrid: Method

Extended control variable method (Lorenc 2003) in 3D GSI hybrid (Wang 2010, MWR):

Extra term associated with extended control variable

Extra increment associated with ensemble


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Experiment Domains

EnKF Domain

207x207 grid points

~40 km, 51 levels

Precip. Forecast Domain

532x532 grid points

~13 km, 51 levels

Precip. Verification Domain

RUC Domain as indicated

Ensemble members

40

EnKF

EnKF—RR

RUC


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Sounding and profiler

Surface data from land stations and ships

Observations assimilated

Aircraft

Satellite retrieve winds


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EnKF

Hybrid

3DVAR

Single Observation Tests (Comparing GSI, Hybrid and EnKF)

Half static

Half flow-dependent

Solid line: Height at 600 hPa (background)

Shading: Temperature increment

Different weight for the static covariance in Hybrid

Weight=0

Weight=1

Weight=0.5


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EnKF analysis 2

EnKF analysis k

EnKF analysis 1

control analysis

control forecast

Hybrid GSI-EnKF DA system: 1 way coupling

observations

member 1 forecast

member 1 forecast

Wrf-DFL

0 20m 40m

GSI

Innovation

member 2 forecast

Wrf-DFL

0 20m 40m

member 2 forecast

EnKF

EnKF

……

……

……

member k forecast

member k forecast

Wrf-DFL

0 20m 40m

Ensemble covariance

control forecast

Hybrid

Hybrid

First guess forecast

data assimilation


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Hybrid And EnKF Configuration

13 KM 12 hrFcst

13 KM 12 hrFcst

Interpolation

Interpolation

EnKF & hybrid

EnKF & hybrid

EnKF & hybrid

EnKF & hybrid

…………

……

Background

Fields

Analysis Fields

Time

(UTC)

21

3hr fcst

03

00

12

3hr fcst

3hr fcst

3hr fcst

obs

obs

obs

Obs

2010-05-08 00:00

2010-05-17 21:00


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Surface Variables Verification (RMSE; 3-18 hr Forecasts)

Hybrid 1way

EnKF

3h

18h

3-18 hour forecasts verification against surface data.

GSI 3dvar


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Verifications Against Soundings (RMSE)

GSI-3dvar

Hybrid 1way

EnKF


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Verifications Against Soundings (RMSE)


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Multi-physics GSI-EnKFHybrid System Configuration


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Surface variables verification (RMSE; 3-18 hr Forecasts)

GSI 3dvar

Single-hybrid

Multi-hybrid

When Multiple-physics schemes were employed for EnKF, hybrid was also improved .


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Verifications Against Soundings (RMSE)

Multi-hybrid

GSI 3dvar

Single-hybrid


Sensitivity tests to c ovariance weight

Sensitivity Tests To Covariance Weight

Verifications Against Soundings

1100 KM horizontal localization improve the performance of hybrid at jet level

Hybrid main parameters:

Horizontal localization : ~1100 KM

Vertical localization : 1.1 ( ln(p) )


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EnKF analysis k

EnKF analysis 2

EnKF analysis 1

control analysis

control forecast

Hybrid GSI-EnKF DA system: 2 way coupling

observations

Re-center EnSR analysis ensemble

to control analysis

member 1 forecast

member 1 analysis

member 1 forecast

Wrf-DFL

0 20m 40m

GSI

Innovation

member 2 forecast

member 2 analysis

member 2 forecast

EnKF

Wrf-DFL

0 20m 40m

……

……

……

……

member k forecast

member k analysis

member k forecast

Ensemble covariance

Wrf-DFL

0 20m 40m

control forecast

Wrf-DFL

0 20m 40m

GSI-ECV

First guess forecast

data assimilation

Wang et al. 2011


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Surface Variables Verification (RMSE)

Hybrid 2way

EnKF

Single-physics EnKF was used.

GSI-3dvar


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Verifications Against Soundings (RMSE)

GSI-3dvar

Hybrid 2way

EnKF


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Verifications Against Soundings (RMSE)


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Hourly Precipitation Forecasts on 13 km Grid

Hybrid2way

OBS (NCEP Stage IV)

GSI

EnKF

2010051111

11 hr forecast started from 2010051100

2010051305

5 hr forecast started from 2010051300


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Verification of Hourly QPF on 13 km Grid

Hybrid 2way

EnKF

GSI


Conclusions

Conclusions

  • The GSI-based hybrid (run at 40 km grid spacing for RAP data set and model), with either 1-way or 2-way interaction with a single-physics EnKF and using equal weight for static and flow-dependent covariances, outperforms the GSI and pure EnKF for most verified variables (relative humidity, temperature, wind), except surface temperature. The advantage lasts up to the 18 hour forecast time.

  • The hybrid with half static covariance is better than the one without static covariance, indicating the benefit of including static covariance for the current application.

  • EnKF and hybrid predict more accurate precipitation pattern and location on a 13 km grid than GSI, which is also demonstrated by ETS score. But hybrid doesn’t improve the precipitation forecasts as much as EnKF.

  • The performance of the EnKF system is noticeably improved when multiple physics schemes are used in the ensemble forecast, especially for temperature and moisture fields.


Future plan in collaboration with gsd and emc

Future Plan (in collaboration with GSD and EMC)

  • Use height-dependent localization for flow-dependent covariance in the hybrid – found helpful within EnKF

  • Use well tuned multi-physics EnKF within 2-way hybrid.

  • Test the impact of the strong constraint available in GSI

  • Add satellite data.

  • Implement and test dual-resolution (40/13 km) hybrid

  • Test the system with hourly cycles

  • Eventual quasi-operational testing of hourly cycled, two-way interactive EnKF/hybrid system for RAP including radar data.

  • Long term: Hybrid system applied to NARRE (North America Rapid Refresh Ensemble) and HRRRE (High-Resolution Rapid Refresh Ensemble)

  • Nesting CAPS’s Storm-Scale EnKF within (see Youngsun Jung’s talk)


Thank you

Thank you!!


State dependent covariance inflation

state-dependent covariance inflation

  • Fix inflation

  • Adaptive inflation

  • Final inflation

taper(r)


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Convert to vertical grid units

Step1: vz*( log( p(k-1)/psf )-log( p(k+1)/psf) )/2

Step2: vz=vz/1.5

Pressure (hPa)

Vertical smoothing-scale (vz) in GSI

p(k): average pressure at the k-th model levelpsf: average surface pressure

loc = loc*coefficent

Vertical smoothing scales in GSI


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loc = loc*coefficent


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