Skip to main content
SpringerLink
Log in

Impact of hybrid GSI analysis using ETR ensembles

  • Published:
Journal of Earth System Science Aims and scope Submit manuscript

Abstract

Performance of a hybrid assimilation system combining 3D Var based NGFS (NCMRWF Global Forecast System) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) of resolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013 and forecast skills over different spatial domains are compared with respect to mean analysis state. Rainfall forecast is verified over Indian region against combined observations of IMD and NCMRWF. Hybrid assimilation produced marginal improvements in overall forecast skill in comparison with 3D Var. Hybrid experiment made significant improvement in wind forecasts in all the regions on verification against mean analysis. The verification of forecasts with radiosonde observations also show improvement in wind forecasts with the hybrid assimilation. On verification against observations, hybrid experiment shows more improvement in temperature and wind forecasts at upper levels. Both hybrid and operational 3D Var failed in prediction of extreme rainfall event over Uttarakhand on 17 June, 2013.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12

Similar content being viewed by others

References

  • Anumeha D N, Raghavendra Ashrit, Amit Ashish, Kuldeep Sharma, Iyengar G R, Rajagopal E N and Swati Basu 2014 Forecasting the heavy rainfall during Himalayan flooding – June 2013; Weather and Climate Extremes 4 22–34.

    Article  Google Scholar 

  • 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 modeling; Data Assimilation (Springer: Berlin Heidelberg), pp. 93–112.

  • Buehner M, Houtekamer P L, Charette C, Mitchell H L and He B 2010a Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part 1: Description and single observation experiments; Mon. Wea. Rev. 138 1550–1566.

    Article  Google Scholar 

  • Buehner M, Houtekamer P L, Charette C, Mitchell H L and He B 2010b Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part 2: One month experiment with real observations; Mon. Wea. Rev. 138 1567–1586.

    Article  Google Scholar 

  • Buehner M, Morneau J and Charette C 2013 Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction; Nonlin. Process. Geophys. 20 669–682.

    Article  Google Scholar 

  • 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 

  • Damrath U, Doms G, Fruhwald D, Heise E, Richter B and Steppeler J 2000 Operational quantitative precipitation forecasting at the German Weather Service; J. Hydrol. 239 260–285.

    Article  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 

  • Hu M, Hui Shao, Donald Stark and Kathryn Newman 2013 Gridpoint Statistical Interpolation (GSI) Version 3.2 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. Atmospheric and Oceanic Science, University of Maryland-College Park, 149p, http://drum.lib.umd.edu/handle/1903/13135.

  • Kleist D T and Ide K 2015 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.

    Google Scholar 

  • Kleist D T, Parrish D F, Derber J C, Treadon R, Wu W S and Lord S J 2009 Introduction of the GSI into the NCEP Global Data Assimilation System; Wea. Forecasting 24 1691–1705.

    Article  Google Scholar 

  • Kretschmer M, Hunt B R and Ott E 2015 Data assimilation using a climatologically augmented local ensemble transform Kalman filter; Tellus A 67 26617.

    Google Scholar 

  • Kuhl D D, Rosmond T E, Bishop C H, McClay J and Baker N L 2013 Comparison of hybrid ensemble/4DVar and 4DVar within the NAVDAS-AR data assimilation framework; Mon. Wea. Rev. 141 2740–2750.

    Article  Google Scholar 

  • 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 

  • 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 

  • Patil D, Hunt B R, Kalnay E, Yorke J A and Ott E 2001 Local low dimensionality at atmospheric dynamics; Phys. Rev. Lett. 86 5878–5881.

    Article  Google Scholar 

  • Penny S G 2014 The hybrid local ensemble transform Kalman filter; Mon. Wea. Rev. 142 2139–2149.

    Article  Google Scholar 

  • Prasad V S, Saji Mohandas, Munmun Das Gupta, Rajagopal E N and Surya Kanti Dutta 2011 Implementation of upgraded global forecasting systems (T382L64 and T574L64) at NCMRWF, NCMRWF Technical report, NCMR/TR/5/2011.

  • 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 

  • Raghavendra Ashrit, Iyengar G R, Syam Sankar, Amit Ashish, Anumeha Dube, Surya Kanti Dutta, Prasad V S, Rajagopal E N and Swati Basu 2013 Performance of Global Ensemble Forecast System (GEFS) during monsoon 2012; NCMRWF Research report, NMRF/RR/1/2013.

  • Surya K D. and Prasad V S 2011 Impact of gridpoint statistical interpolation scheme over Indian region; J. Earth Syst. Sci. 120 1095–1112.

    Article  Google Scholar 

  • Toth Z and Kalnay E 1993 Ensemble forecasting at NMC: The generation of perturbations; Bull. Am. Meteor. Soc. 74 2317–2330.

    Article  Google Scholar 

  • Toth Z and Kalnay E 1997 Ensemble forecast at NCEP and the breeding method; Mon. Wea. Rev. 125 3297–3319.

    Article  Google Scholar 

  • 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 Bishop C H 2003 A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes; J. Atmos. Sci. 60 1140–1158.

    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; 142 3303– 3325.

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

    Article  Google Scholar 

  • Wang X, Hamill T M, Whitaker J S and Bishop C H 2007b A comparison of hybrid ensemble transform Kalman filter-OI and ensemble square-root filter analysis schemes; Mon. Wea. Rev. 135 1055–1076.

    Article  Google Scholar 

  • Wang X, Barker D M, Snyder C and Hamill T M 2008a A hybrid ETKF-3DVar data assimilation scheme for WRF model part 1: Observing system simulation experiment; Mon. Wea. Rev. 136 5116–5131.

    Article  Google Scholar 

  • Wang X, Barker D M, Snyder C and Hamill T M 2008b 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, Hamill T M, Whitaker J S and Bishop C H 2009 A comparison of the hybrid and EnSRF analysis schemes in the presence of model errors due to unresolved scales; Mon. Wea. Rev. 137 3219–3232.

    Article  Google Scholar 

  • Wang X, Parrish D., Kleist D. 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 

  • Webster P J and Yang S 1992 Monsoon and ENSO: Selectively interactive systems; Quart. J. Roy. Meteor. Soc. 118 877–926.

    Article  Google Scholar 

  • Wei M, Toth Z, Wobus R and Zhu Y 2008 Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system; Tellus 60A 62–79.

    Google Scholar 

  • Wu W S, Purser R J and Parrish D F 2002 Three-dimensional variational analysis with spatially inhomogenous covariances; Mon. Weather 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 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 

Download references

Acknowledgements

Authors wish to thank Director, NCMRWF for providing constant support and encouragement. They are also thankful to Daryl Kleist, NCEP for the discussions.

Author information

Authors and Affiliations

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

    V S Prasad & C J Johny

Authors
  1. V S Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C J Johny
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V S Prasad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prasad, V.S., Johny, C.J. Impact of hybrid GSI analysis using ETR ensembles. J Earth Syst Sci 125, 521–538 (2016). https://doi.org/10.1007/s12040-016-0673-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12040-016-0673-2

Keywords

Search

Navigation