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In this study, the ensemble of three-dimensional variational data assimilation (En3DVar) method for convective-scale weather is adopted and evaluated using an idealized supercell storm simulated by the Weather Research and Forecasting (WRF) Model. Synthetic radar radial velocity, reflectivity, satellitederived cloud water path (CWP), and total precipitable water (TPW) data are produced from the simulated supercell storm and then these data are assimilated into another WRF Model run that starts with no convection. Two types of experiments are performed. The first assimilates radar and satellite CWP data using a perfect storm environment. The second assimilates additional TPW data using a storm environment with dry bias. The first set of experiments indicates that incorporating CWP and radar data into the assimilation leads to a much faster initiation of supercell storms than found using radar data alone. Assimilating CWP data primarily improves the analyses of nonprecipitating hydrometeor variables. The results from the second set of experiments demonstrate the critical importance of the storm environment. When using the biased storm environment, assimilation of CWP and radar data enhances the analyses, but the forecast skill rapidly decreases over the subsequent 1-h forecast. Further experiments show that assimilating theTPWdata has a large impact on storm environment that is essential to the accuracy of the storm forecasts. In general, the combination of radar data and satellite data within the En3DVar results in better analyses and forecasts than when only radar data are used, especially for an imperfect storm environment. [ABSTRACT FROM AUTHOR]

This study was designed to investigate the role of growth arrest and DNA damage-inducible β (GADD45B) in modulating fear memory acquisition and elucidate its underlying mechanisms. Adeno-associated virus (AAV) that knockdown or overexpression GADD45B were injected into ventral hippocampal CA1 (vCA1) by stereotactic, and verified by fluorescence and Western blot. The contextual fear conditioning paradigm was employed to examine the involvement of GADD45B in modulating aversive memory acquisition. The Y-maze and novel location recognition (NLR) tests were used to examine non-aversive cognition. The synaptic plasticity and electrophysiological properties of neurons were measured by slice patch clamp. Knockdown of GADD45B in the vCA1 significantly enhanced fear memory acquisition, accompanied by an upregulation of long-term potentiation (LTP) expression and intrinsic excitability of vCA1 pyramidal neurons (PNs). Conversely, overexpression of GADD45B produced the opposite effects. Notably, silencing the activity of vCA1 neurons abolished the impact of GADD45B knockdown on fear memory development. Moreover, mice with vCA1 GADD45B overexpression exhibited impaired spatial cognition, whereas mice with GADD45B knockdown did not display such impairment. These results provided compelling evidence for the crucial involvement of GADD45B in the formation of aversive memory and spatial cognition. [Display omitted] [ABSTRACT FROM AUTHOR]

To improve severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize NWP forecasts at convection-resolving scales. The first step of the algorithm identifies areas of deep moist convection by utilizing the vertically integrated liquid water (VIL) derived from three-dimensional radar reflectivity fields. Once VIL is obtained, pseudo–water vapor observations are derived based on reflectivity thresholds within columns characterized by deep moist convection. Areas of spurious convection also are identified by the algorithm to help reduce their detrimental impact on the forecast. The third step is to assimilate the derived pseudo–water vapor observations into a convection-resolving-scale NWP model along with radar radial velocity and reflectivity fields in a 3DVAR framework during 4-h data assimilation cycles. Finally, 3-h forecasts are launched every hour during that period. The performance of this method is examined for two selected high-impact severe thunderstorm events: namely, the 24 May 2011 Oklahoma and 16 May 2017 Texas and Oklahoma tornado outbreaks. Relative to a control simulation that only assimilated radar data, the analyses and forecasts of these supercells (reflectivity patterns, tracks, and updraft helicity tracks) are qualitatively and quantitatively improved in both cases when the water vapor information is added into the analysis. [ABSTRACT FROM AUTHOR]