Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses

Abstract

Ensemble Kalman filter (EnKF) techniques have been proposed for obtaining atmospheric state estimates on the scale of individual convective storms from radar and other observations, but tests of these methods with observations of real convective storms are still very limited. In the current study, radar observations of the 8 May 2003 Oklahoma City tornadic supercell thunderstorm were assimilated into the National Severe Storms Laboratory (NSSL) Collaborative Model for Multiscale Atmospheric Simulation (NCOMMAS) with an EnKF method. The cloud model employed 1-km horizontal grid spacing, a single-moment bulk precipitationmicrophysics scheme, and a base state initialized with sounding data. A 50-member ensemble was produced by randomly perturbing base-state wind profiles and by regularly adding random local perturbations to the horizontal wind, temperature, and water vapor fields in and near observed precipitation.In a reference experiment, only Doppler-velocity observations were assimilated into the NCOMMAS ensemble. Then, radar-reflectivity observations were assimilated together with Doppler-velocity observations in subsequent experiments. Influences that reflectivity observations have on storm-scale analyses were revealed through parameter-space experiments by varying observation availability, observation errors, ensemble spread, and choices for what model variables were updated when a reflectivity observation was assimilated. All experiments produced realistic storm-scale analyses that compared favorably with independent radar observations. Convective storms in the NCOMMAS ensemble developed more quickly when reflectivity observations and velocity observations were both assimilated rather than only velocity, presumably because the EnKF utilized covariances between reflectivity and unobserved model fields such as cloud water and vertical velocity in efficiently developing realistic storm features.Recurring spatial patterns in the differences between predicted and observed reflectivity were noted particularly at low levels, downshear of the supercell's updraft, in the anvil of moderate-to-light precipitation, where reflectivity in the model was typically lower than observed. Bias errors in the predicted rain mixing ratios and/or the size distributions that the bulk scheme associates with these mixing ratios are likely responsible for this reflectivity underprediction. When a reflectivity observation is assimilated, bias errors in the model fields associated with reflectivity (rain, snow, and hail-graupel) can be projected into other model variables through the ensemble covariances. In the current study, temperature analyses in the downshear anvil at low levels, where reflectivity was underpredicted, were very sensitive both to details of the assimilation algorithm and to ensemble spread in temperature. This strong sensitivity suggests low confidence in analyses of low-level cold pools obtained through reflectivity-data assimilation.

10th European Conference on Radar in Meteorology and Hydrology (ERAD 2018) : 1-6 July 2018, Ede-Wageningen, The Netherlands

“…To date, there is a limited worldwide experience in the assimilation of reflectivity volumes in a ENKF scheme (Bick et al, 2016;Gastaldo et al, 2018;Dong and Xue, 2008;Dowell et al, 2011;Aksoy et al, 2009), with results attested mainly on single case studies. For this reason, the first goal to be achieved is to, at least, match the results from the benchmark run, conv.…”

Assimilation of radial velocity and reflectivity data from coastal WSR‐88D radars using an ensemble Kalman filter for the analysis and forecast of landfalling hurricaneIke(2008)

“…We note here that even for continental convection where most of the EnKF radar data assimilation research has occurred, the assimilation of reflectivity data using EnKF is also still an active topic for research (e.g. Dowell et al, 2011). There are more uncertainties with the reflectivity observation operators due to the involvement of microphysics size distributions, etc., and the nonlinearity of the operators poses additional challenges (Jung et al, 2008(Jung et al, , 2010.…”

Assimilation of Lightning Data Using a Nudging Method Involving Low-Level Warming

“…These include three-dimensional variational data assimilation (3DVAR; e.g., Hu et al 2006;Xiao and Sun 2007), fourdimensional variational data assimilation (4DVAR; e.g., Mahfouf et al 2005;Lopez 2011), ensemble Kalman filter (EnKF; e.g., Snyder and Zhang 2003;Dowell et al 2011), and four-dimensional data assimilation (FDDA) nudging (e.g., Krishnamurti et al 1991;Manobianco et al 1994). Each of these methods utilizes an observation operator to translate model prognostic variables or control variables on the model grid to the observed data, or vice versa in the case of nudging.…”

“…Consequently, at these locations the ensemble spread is zero and the EnKF and ensemble square root filter methods are prevented from increasing graupel volume where lightning is observed but none is simulated (Evensen 2003;Anderson 2001;Whitaker and Hamill 2002). However, this issue could be alleviated by perturbing the graupel mixing ratio and other model fields of the ensemble members near areas of observed lightning (e.g., Snyder and Zhang 2003;Dowell et al 2004;Dowell and Wicker 2009). If the same observation operator from is used with a variational method, then a similar complication occurs.…”