Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective SystemMon. Wea. Rev.


Nathan Snook, Ming Xue, Youngsun Jung
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The DOI for this manuscript is doi: 10.1175/MWR-D-13-00262.1

The final published version of this manuscript will replace the preliminary version at the above DOI once it is available.

If you would like to cite this EOR in a separate work, please use the following full citation:

Snook, N., M. Xue, and Y. Jung, 2014: Multi-Scale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic

Mesoscale Convective System. Mon. Wea. Rev. doi:10.1175/MWR-D-1300262.1, in press.



SOCIETY © 2014 American Meteorological Society 1

Multi-Scale EnKF Assimilation of Radar and Conventional Observations and

Ensemble Forecasting for a Tornadic Mesoscale Convective System

Nathan Snook 1 , Ming Xue 1,2 , and Youngsun Jung 1

Center for Analysis and Prediction of Storms 1 and School of Meteorology 2

University of Oklahoma, Norman OK 73072

Submitted to Monthly Weather Review

Originally submitted 18 Aug. 2013

Revised 25 Nov. 2014

Corresponding author address:

Nathan Snook

Center for Analysis and Prediction of Storms

University of Oklahoma, 120 David Boren Blvd, Room 5425, Norman OK 73072

Manuscript (non-LaTeX)

Click here to download Manuscript (non-LaTeX): SXJ_datasources_rev3_v6.docx 2


In recent studies, the authors have successfully demonstrated the ability of an ensemble

Kalman filter (EnKF), assimilating real radar observations, to produce skillful analyses and subsequent ensemble-based probabilistic forecasts for a tornadic mesoscale convective system (MCS) that occurred over Oklahoma and Texas on 9 May 2007. The current study expands upon this prior work, performing experiments for this case on a larger domain using a nested-grid

EnKF which accounts for mesoscale uncertainties through the initial ensemble and lateral boundary condition perturbations. In these new experiments, conventional observations (including surface, wind profiler, and upper-air observations) are assimilated in addition to the

WSR-88D and CASA radar data used in the previous studies, better representing meso- and convective-scale features. The relative impacts of conventional and radar data on analyses and forecasts are examined, and biases within the ensemble are investigated.

The new experiments produce a substantially-improved forecast, including better representation of the convective lines of the MCS. Assimilation of radar data substantially improves the ensemble precipitation forecast. Assimilation of conventional data together with radar observations substantially improves the forecast of near-surface mesovortices within the

MCS, improves forecasts of surface temperature and dewpoint, and imparts a slight but noticeable improvement to short-term precipitation forecasts. Furthermore, ensemble analyses and forecasts are found to be sensitive to the localization radius applied to conventional data within the EnKF. 1 1. Introduction 1

The ensemble Kalman filter (EnKF), first developed by Evensen (1994, 2003), has been 2 successfully applied to atmospheric data assimilation (DA) using both simulated and real data from 3 a variety of observation platforms, for models ranging from global to convective storm scales 4 (Houtekamer and Mitchell 1998; Hamill and Snyder 2000; Anderson 2001; Whitaker and Hamill 5 2002; Snyder and Zhang 2003; Dowell et al. 2004; Zhang et al. 2004; Dirren et al. 2007; Tong and 6

Xue 2008a; Xue et al. 2010; Dawson et al. 2011; Snook et al. 2011; Jung et al. 2012; Yussouf and 7

Stensrud 2012; Yussouf et al. 2013). Though EnKF is rather expensive in terms of computation, 8 requiring an ensemble of forecasts (typically using several dozen members), it provides flow-9 dependent multivariate background error covariances that less computationally-intensive 3-10 dimensional variational (3DVAR) methods cannot. Cross-covariances produced by the EnKF 11 system are very valuable, especially for convective-scale DA, because state variables that are not 12 directly observed can be retrieved (Tong and Xue 2005, 2008a). Further discussion of DA 13 techniques commonly used for assimilation of weather observations, including 3DVAR, 4-14 dimensional variational methods (4DVAR), and EnKF can be found in Tong and Xue (2005). 15

Analysis ensembles generated using EnKF are generally well-suited as initial conditions for 16 convective-scale ensemble forecasts. EnKF assimilation of Doppler radar data has proven to be 17 effective in retrieving wind, temperature, and microphysical fields at the convective scale (e.g., 18

Dowell et al. 2004; Tong 2006; Snook et al. 2011; Jung et al. 2012; Putnam et al. 2013). 19

Furthermore, EnKF analyses, in principle, also characterize the analysis uncertainty; this is a 20 particularly desirable quality in the ensemble forecast initial conditions. Forecast ensembles 21 initialized from EnKF analyses have been shown to produce superior probabilistic predictions 22 2 compared to ensembles initialized using traditional perturbation methods (Houtekamer et al. 2005; 23

Hamill and Whitaker 2010). EnKF analyses have been successfully applied to ensemble forecasts 24 of convective systems, including supercell thunderstorms (e.g. Aksoy et al. 2009; Aksoy et al. 2010; 25

Dawson et al. 2011) and mesoscale convective systems (e.g. Snook et al. 2012; Putnam et al. 2013), 26 as well as tropical cyclones (e.g. Wu et al. 2010; Aksoy et al. 2012; Aksoy et al. 2013). As available 27 computational power increases, it will become increasingly feasible to run a real-time convective-28 scale ensemble analysis system (e.g., Xue et al. 2008) incorporating EnKF DA (e.g. Snook et al. 29 2012), as envisioned in the “warn-on-forecast” paradigm being developed by the National Weather 30