Prediction of the Development of an MCS over the Continental US - - PowerPoint PPT Presentation

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Prediction of the Development of an MCS over the Continental US - - PowerPoint PPT Presentation

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A Comparison of GSI-based EnKF and 3DVar for Multi-scale Data Assimilation and Prediction of the Development of an MCS over the Continental US Aaron Johnson and Xuguang Wang University of Oklahoma, Norman, OK, USA Acknowledgement: Jacob Carley

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A Comparison of GSI-based EnKF and 3DVar for Multi-scale Data Assimilation and Prediction of the Development of an MCS

  • ver the Continental US

Aaron Johnson and Xuguang Wang University of Oklahoma, Norman, OK, USA

Acknowledgement: Jacob Carley (NCEP) Lou Wicker, Chris Karstens, Nai Kang, Jian Zhang (NSSL)

6th WMO Data Assimilation Symposium College Park, MD 7-11 October 2013

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Background

GSI-based 3DVar/EnKF/Hybrid GFS

Hurricane- WRF (HWRF) Rapid Refresh (WRF ARW) NAM (WRF-NMM)

 The GSI-based EnKF-Var hybrid DA system showed significant improvement compared to GSI 3DVar and became operational on May 22, 2012 for GFS.  It has also been extended to a 4DEnsVar hybrid and showed further improvements.  Efforts are being conducted to integrate, further develop and research GSI based EnKF-Var hybrid DA for operational regional forecast systems, e.g.,

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Sandy 2012

  • Model: operational HWRF
  • Observations: radial velocity

from Tail Doppler Radar (TDR)

  • nboard NOAA P3 aircraft
  • Initial and LBC ensemble: GFS

global hybrid DA system

  • Ensemble size: 40

Xu Lu Poster

GSI hybrid for HWRF: Retrospective and real time tests

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Track and MSLP forecasts for 2012-2013 cases with NOAA P3 missions

Track MSLP

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Background and Motivation

System development/enhancement for radar DA

  • ver CONUS
  • This study aims to extend the GSI-based 3DVar/EnKF/Hybrid

system for WRF ARW to add capability to directly assimilate ground based radar data (e.g., add hydrometeors control/state variables for reflectivity DA).

  • The development takes advantages of existing capabilities in

GSI and therefore directly integrates with the operational DA system.

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Background and Motivation

  • This study assimilates both synoptic/regional scale observations

and storm-scale radar observations to compare the performance and understand the differences of GSI based 3DVar/EnKF/hybrid at multiple scales (e.g., GSI-EnKF vs. GSI-3DVar).

  • Prediction of a complex MCS with multiple storm modes and

interactions, and a largely heterogeneous environment remains a challenge.

  • Successful prediction of such complex system with multi-scale

interactions requires accurate estimate at multiple scales.

  • Comparison of Var with EnKF or EnsVar hybrid for such complex

cases with convection permitting/resolving resolution is still limited.

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20 May 2010 case overview

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www.mmm.locust.ucar.edu

  • Complex evolution of multiple interacting storms with upscale growth

into an MCS.

  • Johnson et al. (2013) using wavelet analysis showed particularly strong

sensitivity of precipitation forecast to both small scale and large scale perturbations.

  • A broad, deep layer cyclone; well defined cold

front, warm front and dry line on surface.

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Experiment Design

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  • WRF ARW
  • Outer Domain

– Assimilate operational conventional surface and mesonet observations, RAOB, wind profiler, ACARS, and satellite derived winds to define synoptic/mesoscale environment

  • Convection-permitting Inner Domain

– Assimilate velocity and reflectivity from WSR88D radar network

WSR88D

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Mesoscale analysis and forecasts

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  • GSI-EnKF shows better forecast except low level temperature.
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Need for storm scale analysis

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  • Mesoscale environment defined in outer domain EnKF analysis was able to

support the development of a realistic-looking MCS.

  • However, spin-up time and resulting westward displacement throughout

storm period suggests need for storm scale data assimilation. Inner domain PQPF (>12.7mm/h) initialized with outer domain EnKF

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GSI-based EnKF vs. 3DVAR for storm scale analysis

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GSI-3DVar GSI-EnKF KTLX Observation

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GSI-3DVar after radar DA GSI-EnKF after radar DA

Probabilistic precipitation forecasts

  • Prob. (> 12.7mm/h)
  • Phase lag is largely

improved after radar DA.

  • 3DVar worse than

EnKF

The storm was

  • pushed out to the east

by the cold pools faster than observed;

  • out to the south too

much than observed;

  • dissipated too quickly

than observed

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BS for probabilistic precipitation forecasts

  • Prob. (> 12.7mm/h)

BS: the lower the better

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Impact of mesoscale analysis on storm scale forecast

  • The better evolution of MCS

at 4-8 h suggests the environment was better analyzed by EnKF than 3DVar

  • Mesoscale warm front in

eastern OK was analyzed too far south for GSI 3DVar, explaining the forecast differences after the first few hours.

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EnKF 3DVar

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Vr vs. Vr+dBZ for GSI 3DVAR

  • The forecast with reflectivity is subjectively worse than velocity-only
  • Including reflectivity caused much more expansive cold pools (not shown)
  • This may be a result of the deficiency of the 3DVar static background error covariance -- added

hydrometeors largely evaporating and falling out since without adjusting temp/wind fields consistently.

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Vr with dBZ Vr only

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Vr vs. Vr+dBZ for GSI EnKF

  • Improved

forecast at 2-3 h due to better forecast of central OK storm

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Vr with dBZ Vr only

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Summary and Conclusions

  • GSI-based 3DVar/EnKF/Hybrid system is further developed/extended to directly

assimilate radar data for WRF ARW.

  • Implemented for a real data, MCS case study with complex and heterogeneous

environment assimilating data at multiple scales. Performance was evaluated at both mesoscale and storm scale.

  • Comparison between GSI-3DVar and GSI-EnKF suggests
  • a. using the flow-dependent ensemble can improve the analysis at both meso and

convective scales.

  • b. Forecast quality is sensitive to both the quality of the storm scale analysis and the

mesoscale environment analysis.

  • Improvement of static covariance for storm scale assimilation is needed.
  • Further compare to understand GSI-based Var, EnKF and hybrid (3DEnsVar, 4DEnsVar)

for convective scale DA.

  • Systematic tests with more cases.

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Posters

  • Xu Lu: Assimilation of Airborne Doppler Radar observations using the GSI- based hybrid

ensemble-variational data assimilation system to improve high resolution hurricane forecast by HWRF

  • Nick Gasperoni: Improving Ensemble-based Observation Impact Estimate using a Group

Filter Technique

  • Terra Thompson: Multi-Scale Data Assimilation of the June 13, 2010 VORTEX2 Tornadic

Supercell

  • Ting Lei: GSI-based Hybrid Data Assimilation for NCEP GFS: How Is the Dual Resolution

Hybrid Compared to the Single Resolution Hybrid?

  • Kutty Govindan: A Comparison of Impacts of Radiosonde and AMSU Radiance

Observations In GSI-based Hybrid and 3DVar Data Assimilation Systems for NCEP GFS

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Acknowledgement: Local organizing committee

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20 May 2010 case overview

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www.mmm.locust.ucar.edu

Johnson et al. (2013) using wavelet analysis showed particularly strong sensitivity of precipitation forecast to both small scale and large scale perturbations.

Johnson et al., 2013: Multiscale characteristics and evolution of perturbations for warm season convection-allowing precipitation forecasts: Dependence on background flow and method of

  • perturbation. Mon. Wea. Rev., in review.