Convection-permitting forecasts initialized with continuously-cycling

limited-area 3DVAR, EnKF and “hybrid”data assimilation systems

Craig Schwartz and Zhiquan LiuNCAR/NESL/MMM

schwartz@ucar.edu

NCAR is sponsored by the National Science Foundation

Introduction• Convection-permitting forecasts have

commonly been initialized from operational analyses (e.g., GFS, NAM)– Example: Interpolate GFS analysis onto WRF

domain

• Continuously cycling mesoscale data assimilation systems can produce initial conditions for convection-permitting forecasts– Dynamically consistent analysis/forecast

system

A few data assimilation approaches• Three-dimensional variational (3DVAR)

– Background error covariances (BECs) typically fixed/time-invariant

– May yield poor results when actual flow differs from that encapsulated within the fixed “climatology”

• Ensemble Kalman filter (EnKF)– Time-evolving, “flow-dependent” BECs

estimated from a background ensemble

• “Hybrid” variational/ensemble– Incorporates ensemble background errors

within a variational framework – Combination of fixed and

time-evolving background errors

A few data assimilation approaches

75% squirrel25% cat

Experimental design•Full-cycling (6-hr period) between May 6 – June 21, 2011

•Data assimilation/cycling on a 20-km domain

•Three experiments assimilating identical observations:

•Pure 3DVAR•Pure EnKF•Hybrid

•0000 UTC analyses initialized 36-hr 4-km forecasts

•EnKF: 4-km forecasts initialized from mean analyses

•Control: Interpolate 0000 UTC GFS analyses directly onto the domain and run forecasts

•GFS initialized from 3DVAR analyses in 2011

Cycling data assimilation: Hybrid/EnKF flowchart

Computational domain

WRF settings and physics•Forecast model: WRF-ARW (version 3.3.1)

•57 vertical levels, 10 hPa top

•Physics:

•Morrison double-moment microphysics

•RRTMG longwave and shortwave radiation

•MYJ PBL

•Tiedtke cumulus parameterization (20-km domain

only)

•NOAH land surface model

•Aerosol, ozone climatologies for RRTMG

Selected data assimilation settings•NCEP’s Gridpoint Statistical Interpolation (GSI) data assimilation system:

-GSI-3DVAR -GSI-hybrid -Ensemble square root Kalman filter (EnSRF)

•50 ensemble members

•Hybrid: 75% of the background errors from the ensemble, 25% from the static contribution

•Used posterior inflation for EnSRF and localization in both EnSRF and hybrid

Observation snapshot (0000 UTC 25 May)

Precipitation verification

•Focus on 4-km precipitation forecasts

•NCEP Stage IV observations as “truth”

•Verified hourly precipitation forecasts

•All precipitation statistics shown are aggregated over 44 4-km forecasts

•Fractions skill score (FSS) quantifies displacement errors

Precipitation BiasAggregated hourly over the

first 12 forecast hrsAggregated hourly

over18-36-hr forecasts

FSS: The first 12-hrs

0.25 mm/hr 1.0 mm/hr

5.0 mm/hr 10.0 mm/hr

FSS: Forecast hours 18-36

0.25 mm/hr 1.0 mm/hr

5.0 mm/hr 10.0 mm/hr

For more information…

• All of the previous material was summarized in this publication:

Schwartz, C. S., and Z. Liu, 2014: Convection-permitting forecasts initialized with continuously-cycling limited-area 3DVAR, ensemble Kalman filter, and “hybrid” variational-ensemble data assimilation systems. Mon. Wea. Rev., 142, 716–738, doi: 10.1175/MWR-D-13-00100.1.

Preview of new work• Recently, the exact same experiments

were performed but over a new period:– May 4 – June 30, 2013– 55 4-km forecasts

• Near identical configuration as before, except used Thompson microphysics

• Also performed dual-resolution hybrid analyses with a 4-km deterministic background and 20-km ensemble

Cycling data assimilation: Hybrid/EnKF flowchart

4-km

20-km

FSS: The first 12-hrs2013 experiments: FSS aggregated over

55 forecasts

0.25 mm/hr 1.0 mm/hr

5.0 mm/hr 10.0 mm/hr

FSS: The first 12-hrs2013 experiments: FSS aggregated over

55 forecasts

0.25 mm/hr 1.0 mm/hr

5.0 mm/hr 10.0 mm/hr

Dual-resolution hybrid: 4-km analyses and subsequent forecasts

FSS: Forecast hours 18-362013 experiments: FSS aggregated over

55 forecasts

0.25 mm/hr 1.0 mm/hr

5.0 mm/hr 10.0 mm/hr

Summary• Precipitation bias characteristics similar

in the cycling experiments• Differences in precipitation placement

evident– Hybrid and EnSRF performed best– Shows the benefit of flow-dependent

background errors

• Further improvement possible with high-resolution analyses

Example forecast

6-hr forecast initialized 0000 UTC 24 May 2011

Example forecast

30-hr forecast initialized 0000 UTC 24 May 2011