Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008)

The impact of assimilating radar radial velocity and reflectivity on the analyses and forecast of Hurricane IKE is investigated within the framework of the WRF (Weather Research and Forecasting) model and its three-dimensional variational (3DVar) data assimilation system, including the hydrometeor control variables. Hurricane IKE in the year 2008 was chosen as the study case. It was found that assimilating radar data is able to effectively improve the small-scale information of the hurricane vortex area in the model background. Radar data assimilation experiments yield significant cyclonic wind increments in the inner-core area of the hurricane, enhancing the intensity of the hurricane in the model background. On the other hand, by extending the traditional control variables to include the hydrometeor control variables, the assimilation of radar reflectivity can effectively adjust the water vapor and hydrometeors of the background, further improving the track and intensity forecast of the hurricane. The precipitation forecast skill is also enhanced to some extent with the radar data assimilation, especially with the extended hydrometeor control variables.

Adjoint-Derived Impact of Assimilated Observations on Tropical Cyclone Intensity Forecasts of Hurricane Joaquin (2015) and Hurricane Matthew (2016)

The adjoint-derived observation impact method is used as a diagnostic to derive the impact of assimilated observations on a metric representing the forecast intensity of a tropical cyclone (TC). Storm-centered composites of observation impact and the model background state are computed across 6-hourly analysis/forecast cycles to compute the composite observation impact throughout the life cycle of Hurricane Joaquin (2015) to evaluate the impact of in situ wind and temperature observations in the upper and lower troposphere, as well as the impact of brightness temperature and precipitable water observations, on intensity forecasts with forecast lengths from 12 to 48 h. The compositing across analysis/forecast cycles allows for the exploration of consistent relationships between the synoptic-scale state of the initial conditions and the impact of observations that are interpreted as flow-dependent interactions between model background bias and correction by assimilated observations on the TC intensity forecast. The track of Hurricane Matthew (2016), with an extended period of time near the coasts of Florida, Georgia, and the Carolinas, allows for a comparison of the impact of aircraft reconnaissance observations with the impact of nearby overland rawinsonde observations available within the same radius of the TC.

Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System

Ensemble data assimilation experiments were performed to assess the ability of satellite all-sky infrared brightness temperatures and different bias correction (BC) predictors to improve the accuracy of short-range forecasts used as the model background during each assimilation cycle. Satellite observations sensitive to clouds and water vapor in the upper troposphere were assimilated at hourly intervals during a 3-day period. Linear and nonlinear conditional biases were removed from the infrared observations using a Taylor series polynomial expansion of the observation-minus-background departures and BC predictors sensitive to clouds and water vapor or to variations in the satellite zenith angle. Assimilating the all-sky infrared brightness temperatures without BC degraded the forecast accuracy based on comparisons to radiosonde observations. Removal of the linear and nonlinear conditional biases from the satellite observations substantially improved the results, with predictors sensitive to the location of the cloud top having the largest impact, especially when higher-order nonlinear BC terms were used. Overall, experiments employing the observed cloud-top height or observed brightness temperature as the bias predictor had the smallest water vapor, cloud, and wind speed errors, while also having less degradation to temperatures than occurred when using other predictors. The forecast errors were smaller during these experiments because the cloud-height-sensitive BC predictors were able to more effectively remove the large conditional biases for lower brightness temperatures associated with a deficiency in upper-level clouds in the model background.

Elastic model low- to intermediate-wavenumber inversion using reflection traveltime and waveform of multicomponent seismic data

Low-, intermediate-, and high-wavenumber components of P- and S-wave velocities jointly influence the elastic wave propagation and scattering in an isotropic medium. By taking advantage of all information in the data, elastic full-waveform inversion (E-FWI) has the potential to recover these model components. However, if the transmitted wave data are insufficient to illuminate the deeper part of the subsurface, we should rely on the solutions using reflection data. To reduce the nonlinearity of waveform inversion, we choose to decouple the effects of the model background and perturbation on the reflected waves within a linearized inversion framework. This resorts to three stages aiming to gradually fit the traveltimes and waveforms of the reflected PP and PS waves based on data or gradient preconditioning through P/S mode decomposition. For the first two stages, once the multicomponent seismograms have been separated into PP and PS reflection recordings, reflection traveltime inversion using an acoustic wave propagator (A-RTI) can successively recover the low-wavenumber components of P- and S-wave velocities. In the last stage, starting from the models having reliable low-wavenumber components, elastic reflection waveform inversion (E-RWI) can easily get out of the local minima and continue to retrieve the increasing wavenumber features sensitive to the waveform and amplitude variations. This is supported by gradient preconditioning through P/S mode decomposition of the extrapolated normal and adjoint wavefields, and alternately updating model background and high-wavenumber components in terms of linearized least-squares inversion. Numerical examples have demonstrated the performance of our E-RWI approach and the validity of the three-stage inversion workflow.

X-events and their interpretation

We point out that when doubly-charged bileptons are pair produced at the LHC, kinematics dictate that they are both almost at rest in the lab frame and therefore their decays lead to final state muons in a characteristic X-shape with only very tiny track curvature because of the high muon energies. Such X-events have essentially no Standard Model background and provide a smoking gun for the 331 model.