The Ensemble Kalman Filter Analyses and Forecasts of the 8 May 2003 Oklahoma City Tornadic Supercell Storm Using Single- and Double-Moment Microphysics Schemes

Abstract

A combined mesoscale and storm-scale ensemble data-assimilation and prediction system is developed using the Advanced Research core of the Weather Research and Forecasting Model (WRF-ARW) and the ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed (DART) software package for a short-range ensemble forecast of an 8 May 2003 Oklahoma City, Oklahoma, tornadic supercell storm. Traditional atmospheric observations are assimilated into a 45-member mesoscale ensemble over a continental U.S. domain starting 3 days prior to the event. A one-way-nested 45-member storm-scale ensemble is initialized centered on the tornadic event at 2100 UTC on the day of the event. Three radar observation assimilation and forecast experiments are conducted at storm scale using a single-moment, a semidouble-moment, and a full double-moment bulk microphysics scheme. Results indicate that the EAKF initializes the supercell storm into the model with good accuracy after a 1-h-long radar observation assimilation window. The ensemble forecasts capture the movement of the main supercell storm that matches reasonably well with radar observations. The reflectivity structure of the supercell storm using a double-moment microphysics scheme appears to compare better to the observations than that using a single-moment scheme. In addition, the ensemble system predicts the probability of a strong low-level vorticity track of the tornadic supercell that correlates well with the observed rotation track. The rapid 3-min update cycle of the storm-scale ensemble from the radar observations seems to enhance the skill of the ensemble and the confidence of an imminent tornado threat. The encouraging results obtained from this study show promise for a short-range probabilistic storm-scale forecast of supercell thunderstorms, which is the main goal of NOAA's Warn-on-Forecast initiative.

Low-Level ZDR Signatures in Supercell Forward Flanks: The Role of Size Sorting and Melting of Hail

“…In the variable-density experiments, the term ''hail'' will be used, mainly because, as will be discussed, the density and fall speeds have already risen to the ''hail like'' part of the spectrum by the time the hydrometeors have fallen much below the melting level owing to the increase in density during melting. These configurations of the scheme will be referred to as the NSSL fixed density (NFD) and NSSL variable density (NVD) schemes [after Yussouf et al (2013)]. …”

Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble

“…In order to obtain the background for mesoscale simulation, these data such as rawinsondes, marine, mesonet, metar, satellite-derived winds and aircraft from NOAA's Meteorological Assimilation Data Ingest System were assimilated every hour from 0100 UTC 24 May to 0000 UTC 25 May 2011 with the ensemble adjustment Kalman filter (Anderson, 2001) implemented in the Data Assimilation Research Testbed software system (Anderson and Collins, 2007;Anderson et al, 2009) using a configuration similar to that employed by Yussouf et al (2013aYussouf et al ( , 2013b, Wheatley et al (2014) and Jones et al (2015). Radar observations were excluded to be assimilated in the mesoscale domain.…”

“…The promising data assimilation approaches for convectivescale forecasting are the ensemble Kalman filter (EnKF) (Snyder and Zhang, 2003;Zhang et al, 2004;Dowell et al, 2004;Tong and Xue, 2005;Aksoy et al, 2009;Yussouf et al, 2013a;Wheatley et al, 2014) and localized ensemble transfer Kalman filter method (Lange and Craig, 2014;Thompson et al, 2015). However, a limitation of the convective-scale EnKF based approach is the rapid error growth in forecasts due to the lack of balance in the model dynamics (Lange and 545 Craig, 2014).…”

The Near-Real-Time SCALE-LETKF System: A Case of the September 2015 Kanto-Tohoku Heavy Rainfall

“…A regional NWP system can be used to increase the model resolution of an area of interest at a relatively low cost. Therefore, regional EnKF systems have also been widely developed and tested (Zhang et al 2006;Meng and Zhang 2007;Torn and Hakim 2008;Anderson et al 2009;Miyoshi and Aranami 2006;Miyoshi and Kunii 2012;Kunii 2014), and they have shown promising results in analyzing and predicting a variety of mesoscale phenomena, such as tropical cyclones (e.g., Zhang et al 2009;Zhang and Weng 2015;Torn 2010) and convective rainstorms (e.g., Yussouf et al 2013;Kunii 2014;Jones et al 2015).…”