Search for: All records

Abstract Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions nonparametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low-dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 and 2020, comparing results from the PF with those from an ensemble Kalman filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within preexisting data assimilation frameworks. Significance StatementThe accurate prediction of severe storms using numerical weather models depends on effective parameterization schemes for small-scale processes and the assimilation of incomplete observational data in a manner that faithfully represents the probabilistic state of the atmosphere. Current generation methods for data assimilation typically assume a standard form for the error distributions of relevant quantities, which can introduce bias that not only hinders numerical prediction, but that can also confound the characterization of errors from the model itself. The current study performs data assimilation using a novel method that does not make such assumptions and explores characteristics of resulting model fields and forecasts that might make such a method useful for improving model parameterization schemes. 

Abstract On 24 May 2016, a supercell that produced 13 tornadoes near Dodge City, Kansas, was documented by a rapid-scanning, X-band, polarimetric, Doppler radar (RaXPol). The anomalous nature of this storm, particularly the significant deviations in storm motion from the mean flow and number of tornadoes produced, is examined and discussed. RaXPol observed nine tornadoes with peak radar-derived intensities (Δ V max ) and durations ranging from weak (~60 m s −1 ) and short lived (<30 s) to intense (>150 m s −1 ) and long lived (>25 min). This case builds on previous studies of tornado debris signature (TDS) evolution with continuous near-surface sampling of multiple strong tornadoes. The TDS sizes increased as the tornadoes intensified but lacked direct correspondence to tornado intensity otherwise. The most significant growth of the TDS in both cases was linked to two substantial rear-flank-downdraft surges and subsequent debris ejections, resulting in growth of the TDSs to more than 3 times their original sizes. The TDS was also observed to continue its growth as the tornadoes decayed and lofted debris fell back to the surface. The TDS size and polarimetric composition were also found to correspond closely to the underlying surface cover, which resulted in reductions in Z DR in wheat fields and growth of the TDS in terraced dirt fields as a result of ground scouring. TDS growth with respect to tornado vortex tilt is also discussed.