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Impact of 3D Var GSI-ENKF hybrid data assimilation system

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Abstract

The hybrid two-way coupled 3DEnsVar assimilation system was tested with the NCMRWF global data assimilation forecasting system. At present, this system consists of T574L64 deterministic model and the grid-point statistical interpolation analysis scheme. In this experiment, the analysis system is modified with a two-way coupling with an 80 member Ensemble Kalman Filter of T254L64 resolution and runs are carried out in parallel to the operational system for the Indian summer monsoon season (June–September) for the year 2015 to study its impact. Both the assimilation systems are based on NCEP GFS system. It is found that hybrid assimilation marginally improved the quality of the forecasts of all variables over the deterministic 3D Var system, in terms of statistical skill scores and also in terms of circulation features. The impact of the hybrid system in prediction of extreme rainfall and cyclone track is discussed.

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References

  • Barker D M 1998 Var scientific development paper 25: The use of synoptic-dependent error structure in 3D Var; UK MetOffice Tech. Rep. (Available from the Met Office, Saughton House, Broomhouse Dr., Edinburgh EH11 3XQ, United Kingdom).

  • Buehner M 2010 Error Statistics in Data Assimilation: Estimation and Modelling; Springer, Berlin, Heidelberg, pp. 93–112.

  • Courtier P et al. 1998 The ECMWF implementation of three-dimensional variational assimilation (3DVAR) I: Formulation; Quart. J. Roy. Meteor. Soc. 124 1783– 1807.

    Google Scholar 

  • Ferranti L, Klinker E, Hollingsworth A and Hoskins B J 2002 Diagnosis of systematic forecast errors dependent on flow pattern; Quart. J. Roy. Meteorol. Soc. 128 1623–1640.

    Article  Google Scholar 

  • Hamill T M and Snyder C 2000 A hybrid ensemble Kalman filter-3D variational analysis scheme; Mon. Wea. Rev. 128 2905–2919.

    Article  Google Scholar 

  • Hamill T M, Whitaker J S, Kleist D T, Fiorino M and Benjamin S 2011 Prediction of 2010’s tropical cyclones using the GFS and ensemble based data assimilation methods; Mon. Wea. Rev. 139 3243–3247.

    Article  Google Scholar 

  • Hamrud M, Bonavita M and Isaksen L 2015 EnKF and hybrid gain ensemble data assimilation. Part I: EnKF implementation; Mon. Wea. Rev. 143 4847–4864.

    Article  Google Scholar 

  • Hu M, Shao H, Stark D and Newman K 2014 Gridpoint Statistical Interpolation (GSI; Version 3.3 User’s Guide), Developmental Testbed Centre, NCAR, NOAA, USA, http://www.dtcenter.org/com-GSI/users/index.php.

  • Kleist D T 2012 An evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS; PhD Thesis, Dept. of Atmospheric and Oceanic Science, University of Maryland–College Park, 149p, http://drum.lib.umd.edu/handle/1903/13135.

  • Kleist D T and Ide K 2015a An OSSE-based evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS. Part I: System description and 3D-hybrid results; Mon. Wea. Rev. 143 443–451.

  • Kleist D T and Ide K 2015b An OSSE-based evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants; Mon. Wea. Rev. 143 452–469.

  • Liu J, Fertig E J, Li H, Kalnay E, Hunt B R, Kostelich E J, Szunyogh I and Todling R 2008 Comparison between local ensemble transform Kalman filter and PSAS in the NASA finite volume GCM perfect model experiments; Nonlin. Process. Geophys. 15 645–659.

    Article  Google Scholar 

  • Lorenc A C 2003 The potential of the ensemble Kalman filter for NWP – a comparison with 4D-VAR; Quart. J. Roy. Meteor. Soc. 129 3183–3203.

    Article  Google Scholar 

  • Marchok T 2002 How the NCEP Tropical Cyclone Tracker Works; Proc. 25th Conference on Hurricanes and Tropical Meteorology, San Diego, CA.

  • Mitra A K, Bohra A K, Rajeevan M and Krishnamurti T N 2009 Daily Indian precipitation analyses formed from a merge of rain-gauge with TRMM TMPA satellite derived rainfall estimates; J. Meteor. Soc. Japan 87A 265–279.

    Article  Google Scholar 

  • Pan Y, Zhu K, Xue M, Wang X, Hu M, Benjamin S G, Weygandt S S and Whitaker J S 2014 A GSI-based coupled EnSRF–En3DVar hybrid data assimilation system for the operational rapid refresh model: Tests at a reduced resolution; Mon. Wea. Rev. 142 3756– 3780.

    Article  Google Scholar 

  • Parrish D F and Derber J C 1992 The National Meteorological Center’s spectral statistical interpolation analysis system; Mon. Wea. Rev. 120 1747–1763.

    Article  Google Scholar 

  • Prasad V S and Johny C J 2016 Impact of hybrid GSI analysis using ETR ensembles; J. Earth Syst. Sci. 125 (3) 521–538.

    Article  Google Scholar 

  • Privé N C, Errico R M and Tai K -S 2013 The influence of observation errors on analysis error and forecast skill investigated with an observing system simulation experiment; J. Geophys. Res. Atmos. 118 5332– 5346.

    Article  Google Scholar 

  • Purser J R, Wu W S, Parrish D F and Roberts N M 2003 Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances; Mon. Wea. Rev. 131 1536–1548.

    Article  Google Scholar 

  • Szunyogh I, Kostelich E J, Gyarmati G, Kalnay E, Hunt B R, Ott E, Satterfield E and Yorke J A 2008 A local ensemble transform Kalman filter data assimilation system for the NCEP global model; Tellus 60A 113– 130.

    Google Scholar 

  • Thomas C, Kleist D and Mahajan R 2014 Improving balance in the NCEP Hybrid Ensemble-Var data assimilation system; World Weather Open Science Conference 2014, Montreal, Canada.

  • Wang X 2010 Incorporating ensemble covariance in the gridpoint statistical interpolation variational minimization: A mathematical framework; Mon. Wea. Rev. 138 2990–2995.

    Article  Google Scholar 

  • Wang X and Lei T 2014 GSI-based four-dimensional ensemble–variational (4DEnsVar) data assimilation: Formulation and single-resolution experiments with real data for NCEP global forecast system; Mon. Wea. Rev. 142 3303–3325.

    Article  Google Scholar 

  • Wang X, Snyder C and Hamill T M 2007 On theoretical equivalence of differently proposed ensemble/3D-Var hybrid analysis schemes; Mon. Wea. Rev. 135 222–227.

    Article  Google Scholar 

  • Wang X, Barker D M, Snyder C and Hamill T M 2008 A hybrid ETKF-3DVar data assimilation scheme for WRF model. Part 2: Real observation experiments; Mon. Wea. Rev. 136 5132–5147.

    Article  Google Scholar 

  • Wang X, David P, Daryl K and Whitaker J S 2013 GSI 3DVar-based ensemble–variational hybrid data assimilation for NCEP global forecast system: Single-resolution experiments; Mon. Wea. Rev. 141 4098– 4117.

    Article  Google Scholar 

  • Whitaker J S, Hamill T M and Wei X 2008 Ensemble data assimilation with NCEP global forecast system; Mon. Wea. Rev. 136 463–482.

    Article  Google Scholar 

  • Wu W S, Purser R J and Parrish D F 2002 Three-dimensional variational analysis with spatially inhomogenous covariances; Mon. Wea. Rev. 130 2905–2916.

    Article  Google Scholar 

  • Zhang M and Zhang F 2012 E4DVar: Coupling an ensemble Kalman filter with four-dimensional variational data assimilation in a limited-area weather prediction model; Mon. Wea. Rev. 140 587–600.

    Article  Google Scholar 

  • Zhang M, Zhang F, Huang X Y and Zhang X 2011 Intercomparison of an ensemble Kalman filter with three- and four-dimensional variational data assimilation methods in a limited-area model over the month of June 2003; Mon. Wea. Rev. 139 566–572.

    Article  Google Scholar 

  • Zhang F, Zhang M and Poterjoy J 2013 E3DVar: Coupling an ensemble Kalman filter with three dimensional variational data assimilation in a limited area weather prediction model and comparison to 4DVar; Mon. Wea. Rev. 141 900–917.

    Article  Google Scholar 

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Acknowledgements

Authors are thankful to the Head, NCMRWF for the support and encouragement. We thank NOAA Center for Weather and Climate Prediction, USA and Daryl Kleist, University of Maryland, USA for their scientific support and discussion in designing the experiment. We also thank the Monsoon Desk at NCEP, NOAA, for providing codes and technical support.

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Authors and Affiliations

  1. National Centre for Medium Range Weather Forecasting, A-50, Sector-62, Noida, India

    V S Prasad & C J Johny

  2. Andhra University, Visakhapatanam, Andhra Pradesh, India

    Jagdeep Singh Sodhi

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  1. V S Prasad
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  2. C J Johny
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  3. Jagdeep Singh Sodhi
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Correspondence to V S Prasad.

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Corresponding editor: Ashok Karumuri

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Prasad, V.S., Johny, C.J. & Sodhi, J.S. Impact of 3D Var GSI-ENKF hybrid data assimilation system. J Earth Syst Sci 125, 1509–1521 (2016). https://doi.org/10.1007/s12040-016-0761-3

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  • DOI: https://doi.org/10.1007/s12040-016-0761-3

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