Comparison between Dual-Doppler and EnKF Storm-Scale Wind Analyses: Observing System Simulation Experiments with a Supercell Thunderstorm

Abstract

Kinematical analyses of mobile radar observations are critical to advancing the understanding of supercell thunderstorms. Maximizing the accuracy of these and subsequent dynamical analyses, and appropriately characterizing the uncertainty in ensuing conclusions about storm structure and processes, requires thorough knowledge of the typical errors obtained using different retrieval techniques. This study adopts an observing system simulation experiment (OSSE) framework to explore the errors obtained from ensemble Kalman filter (EnKF) assimilation versus dual-Doppler analysis (DDA) of storm-scale mobile radar data. The radar characteristics and EnKF model errors are varied to explore a range of plausible scenarios.When dual-radar data are assimilated, the EnKF produces substantially better wind retrievals at higher altitudes, where DDAs are more sensitive to unaccounted flow evolution, and in data-sparse regions such as the storm inflow sector. Near the ground, however, the EnKF analyses are comparable to the DDAs when the radar cross-beam angles (CBAs) are poor, and slightly worse than the DDAs when the CBAs are optimal. In the single-radar case, the wind analyses benefit substantially from using finer grid spacing than in the dualradar case for the objective analysis of radar observations. The analyses generally degrade when only singleradar data are assimilated, particularly when microphysical parameterization or low-level environmental wind errors are introduced. In some instances, this leads to large errors in low-level vorticity stretching and Lagrangian circulation calculations. Nevertheless, the results show that while multiradar observations of supercells are always preferable, judicious use of single-radar EnKF assimilation can yield useful analyses.

Correcting Fast-Mode Pressure Errors in Storm-Scale Ensemble Kalman Filter Analyses

“…Having verified the robustness of the pressure retrieval scheme, we then applied the technique to mean posteriors from an EnKF OSSE conducted by Potvin and Wicker [11] (pictured in Figure 1). In that experiment (labeled "2-LFO" in [11]), mobile dual-Doppler velocity pseudoobservations were generated from the "truth" simulation of Potvin et al [23], perturbed with random errors, and assimilated at two-minute intervals.…”

“…The EnKF [1] has become a popular and valuable tool for storm-scale research [2][3][4][5][6][7][8][9][10][11][12]. Particularly when dual-Doppler radar data are available, EnKF data assimilation can provide reliable analyses of wind and, to a lesser degree, temperature and microphysical variables in convective storms.…”

Impact from a Volumetric Radar-Sampling Operator for Radial Velocity Observations within EnKF Supercell Assimilation

“…where i, j, and k are indices for range, azimuth, and elevation, respectively; Z ijk is the (model computed) reflectivity factor (linear units); V ijk is the radial component of the three-dimensional wind vector; and the illuminator Aksoy et al (2009) x x x x Caya et al (2005) x x x Dowell et al (2004) x x x x Dowell and Wicker (2009) x x x x Dowell et al (2011) x x x x Gao and Xue (2008) x x Jung et al (2008) x x x Lu and Xu (2009) x x x Potvin and Wicker (2012) x x x x Snyder and Zhang (2003) x x Crook (1997, 1998) x x x Sun and Crook (2001) x x x x Tong and Xue (2005) x x Tong and Xue (2008) x x x x Xiao et al (2005) x x x Xu et al (2008) x x x x x Xue et al (2006) x x x Yussouf and Stensrud (2010) x x x Zhang et al (2004) x x I is a composite weighting function of the antenna beam pattern based on the Weather Surveillance Radar-1988 Doppler (WSR-88D). It includes a weighting function in range for the transmitted power and a weighting function in azimuth and elevation to represent a two-way antenna beam pattern (Wood et al 2001).…”

Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E

“…Several studies have investigated multi-Doppler wind retrieval uncertainties by identifying or utilizing (i) the importance of Doppler radar measurement errors and beam geometry (e.g., Doviak et al, 1976;Nelson and Brown, 1987;Matejka and Bartels, 1998;Bousquet et al, 2008), (ii) the influence of radar data objective analysis (e.g., Clark et al, 1980;Gal-Chen, 1982;Testud and Chong, 1983;Given and Ray, 1994;Majcen et al, 2008;Shapiro et al, 2010;Collis et al, 2010), and (iii) OSSEs (e.g., Fanyou and Jietai, 1994;Gao et al, 1999;Liou and Chang, 2009;Potvin et al, 2012b;Potvin and Wicker, 2012). However, few studies have compared practical retrieval performance to other independent air motion estimates from aircraft or ground-based profiling radars (e.g., Collis et al, 2013;Newsom et al, 2014).…”