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Tropical cyclone rapid intensification (RI) prediction still remains a big international challenge in numerical weather prediction. Hurricane Matthew (2016) underwent extreme and non-classic RI, intensifying from a Category 1 storm to a Category 5 hurricane within 24 h under a strong vertical shear environment. However, most models failed to capture this RI, and limited or no inner core, and outflow observations were assimilated in the NWS operational HWRF Model before the onset of RI for Matthew (2016). The goals of the study are to (1) explore the best way to assimilate the High-Density Observations (HDOB, including FL and SFMR) and AMV data; (2) study the impact of assimilating these observations on the analysis of both the inner-core and outflow structures; and (3) examine the impact of assimilating these data on the prediction of RI for Matthew. The main results are as follows: (1) With proper pre-processing of the HDOB observations and by using a 4DEnVar method, the inner-core structure analysis was improved. And the RI prediction is more consistent with the best track without spin-down for the first 24 h. Assimilating CIMMS AMV observations on top of the HDOB observations further improves both the track and intensity forecasts. Specifically, both the magnitude and timing of the peak intensity are further improved. (2) Diagnostics are conducted to understand how the assimilation of these different types of observations impacts RI prediction. Without assimilating HODB and AMV data, baseline experimentover-predict the intensification rate during the first 18 h, but under-predict RI after 18 h. However, the assimilation of FL and SFMR and CIMMS AMV correctly weakens the upper-level outflow and improves the shear-relative structure of the inner-core vortex, such as reducing the low-level moisture in the downshear left quadrant. The deep convection on the downshear side is weaker than baseline for the first 18 h but keeps enhancing, later moving cyclonically to the USL quadrant, and then causes more subsidence warming, maximizing in the USL quadrant and the maximum wind increases faster. Moreover, the rapid intensification rate is much more consistent with the best track and the forecast skill of RI is improved. Therefore, 4DEnVar assimilation with proper pre-processing of the high-density observations can indeed correct the shear-relative moisture and structural distributions of both the inner core and environment for TCs imbedded in the stronger shear, which is important for shear-TC RI prediction. [ABSTRACT FROM AUTHOR]

The impact of assimilating ground‐based radar reflectivity on the rainband structure and secondary eyewall formation (SEF) of Hurricane Matthew (2016) is investigated within the framework of the Hurricane Weather Research and Forecasting model and its hybrid three‐dimensional ensemble‐variational data assimilation (DA) system. Compared to the control experiment (no radar reflectivity DA), the radar reflectivity DA experiment shows a clear signal of concentric eyewall and eyewall replacement cycle. Results demonstrate that radar reflectivity DA improves the stratiform rainband analysis, resulting in the mid‐level cooling associated with mesoscale descending inflow (MDI). The MDI further contributes to the low‐level acceleration maximum with boundary layer dynamics and triggers new convective updrafts in the SEF region. Momentum budget analysis also suggests that the mean vertical advection of absolute angular momentum plays an important role in the local momentum tendency in the SEF region in Hurricane Matthew (2016). Plain Language Summary: The impact of assimilating radar reflectivity on the secondary eyewall formation (SEF) of Hurricane Matthew (2016) is investigated within the framework of the Hurricane Weather Research and Forecasting model and its hybrid three‐dimensional ensemble‐variational data assimilation (DA) system. Compared to the control experiment (no radar reflectivity DA), the radar reflectivity DA experiment shows a clear signal of secondary eyewall and eyewall replacement cycle. Results demonstrate that radar reflectivity DA improves the stratiform rainband analysis, resulting in mesoscale descending inflow (MDI). The MDI further contributes to the low‐level tangential wind increase in boundary layer and triggers new convective updrafts in the SEF region. Results also suggests that the mean vertical advection of absolute angular momentum plays an important role in the local momentum tendency in the SEF region in Hurricane Matthew (2016). Key Points: Utilizing radar reflectivity data assimilation enhances the analysis of stratiform rainbands associated with mesoscale descending inflow (MDI)Momentum budget analysis indicates that the MDI brings high angular momentum to the boundary layer through the mean vertical advection termThe high angular momentum by the MDI contributes to the low‐level acceleration maximum in the secondary eyewall formation region [ABSTRACT FROM AUTHOR]

Forecast sensitivity to observation (FSO) methods have become increasingly popular over the past two decades, providing the ability to quantify the impacts of various observing systems on forecasts without having to conduct costly data denial experiments. While adjoint- and ensemble-based FSO are employed in many global operational systems, their use for regional convection-allowing data assimilation (DA) and forecast systems have not been fully examined. In this study, ensemble FSO (EFSO) is explored for high-frequency convective-scale DA for a severe weather case study over the Dallas–Fort Worth testbed. This testbed, originally established by the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, aims to improve high-resolution DA systems by assimilating a variety of existing state and regional mesoscale observing systems to fill gaps of conventional observing networks. This study utilizes EFSO to estimate relative impacts of nonconventional surface observations against conventional observations, and further incorporates assimilated radar observations into EFSO. Results show that, when applying advected localization and a neighborhood upscale averaging technique, EFSO estimates remain correlated and skillful with the actual error reduction of all assimilated observations for the duration of 2-h forecasts. The ability for EFSO to verify against other metrics (surface T, u, υ, q) beside energy norms is also demonstrated, emphasizing that EFSO can be used to evaluate impacts of specific parts of the forecast system rather than integrated quantities. Partitioned EFSO revealed that while conventional and radar observations contributed to most of the total energy, nonconventional observations contributed a significant percentage (up to 25%) of the total impact to surface thermodynamic fields. [ABSTRACT FROM AUTHOR]

The Gridpoint Statistical Interpolation (GSI)-based four- and three-dimensional ensemble–variational (4DEnVar and 3DEnVar) methods are compared as a smoother and a filter, respectively, for rapidly changing storms using the convective-scale direct radar reflectivity data assimilation (DA) framework. Two sets of experiments with varying DA window lengths (WLs; 20, 40, 100, and 160 min) and radar observation intervals (RIs; 20 and 5 min) are conducted for the 5–6 May 2019 case. The RI determines the temporal resolution of ensemble perturbations for the smoother and the DA interval for the filter spanning the WL. For experiments with a 20-min RI, evaluations suggest that the filter and the smoother have comparable performance with a 20-min WL; however, extending the WL results in the outperformance of the filter over the smoother. Diagnostics reveal that the degradation of the smoother is attributed to the increased degree of nonlinearity and the issue of time-independent localization as the WL extends. Evaluations for experiments with different RIs under the same WL indicate that the outperformance of the filter over the smoother diminishes for most forecast hours at thresholds of 30 dBZ and above when shortening the RI. Diagnostics show that more frequent interruptions of the model introduce model imbalance for the filter, and the increased temporal resolution of ensemble perturbations enhances the degree of nonlinearity for the smoother. The impact of model imbalance on the filter overwhelms the enhanced nonlinearity on the smoother as the RI reduces. Significance Statement: The background uncertainties of rapidly changing storms suffer from fast error growth and high degrees of nonlinearities during the data assimilation (DA) period. Two variants of the ensemble-based DA method can account for such temporal evolution. The smoother uses background ensemble from multiple observation times over an assimilation period to estimate the propagation of statistics. The filter frequently calculates the statistics at multiple observation times over the same period. Current comparisons of the smoother and the filter were mostly performed using simple models; however, unknowns remain for convection-allowing forecasts with additional complexities. This study compares the filter and the smoother for the convective-scale analysis and prediction using a real-data study and finds that the comparison varies with the assimilation period and the observation interval. [ABSTRACT FROM AUTHOR]

Aeroelastic effects, which affect the dynamic responses of low-rise roof structures to extreme wind conditions, are often neglected or oversimplified in current wind engineering design standards and applications. However, it is crucial to understand the details of those aeroelastic effects for performance-based wind engineering. This paper presents an isogeometric fluid–structure interaction (FSI) tool to investigate the aeroelastic effects of wind pressure distributions on roof structures under different turbulent wind conditions. A representative low-rise roof structure is simulated with an FSI model using an Arbitrary Lagrange-Eulerian-based variational multi-scale formulation coupled with isogeometric Kirchhoff-Love shells. The simulation results are compared to the quasi-steady approach and wind load provisions from ASCE 7-22. It shows that the quasi-steady approach and the design standard underestimate the pressure fluctuations, indicating the necessity of using FSI simulations to capture the aeroelastic effect for the roof of low-rise structures. This paper also studies the impacts of different roof configurations, e.g., the number of roof panels and inflow turbulent intensity, on the distribution of pressure coefficients and roof deflections. For the given mean wind speed, the mean pressure coefficient remains almost the same regardless of the turbulent intensity and roof configuration. However, the pressure fluctuation (standard deviation) varies significantly with the turbulence intensity and roof configuration. The aeroelastic effect also leads to complicated roof deflections at the crucial location having the maximum pressure coefficient. The paper first describes the mathematical details of the FSI model and simulation setup. Then, the pressure coefficients by the FSI simulation and design code are compared. Finally, the roof deflection with different inlet turbulence intensities and roof configurations are presented and discussed. [ABSTRACT FROM AUTHOR]

Multiscale valid time shifting (VTS) was explored for a real-time convection-allowing ensemble (CAE) data assimilation (DA) system featuring hourly assimilation of conventional in situ and radar reflectivity observations, developed by the Multiscale Data Assimilation and Predictability Laboratory. VTS triples the base ensemble size using two subensembles containing member forecast output before and after the analysis time. Three configurations were tested with 108-member VTS-expanded ensembles: VTS for individual mesoscale conventional DA (ConVTS) or storm-scale radar DA (RadVTS) and VTS integrated to both DA components (BothVTS). Systematic verification demonstrated that BothVTS matched the DA spread and accuracy of the best-performing individual component VTS. The 10-member forecasts showed BothVTS performs similarly to ConVTS, with RadVTS having better skill in 1-h precipitation at forecast hours 1–6, while Both/ConVTS had better skill at later hours 7–15. An objective splitting of cases by 2-m temperature cold bias revealed RadVTS was more skillful than Both/ConVTS out to hour 10 for cold-biased cases, while BothVTS performed best at most hours for less-biased cases. A sensitivity experiment demonstrated improved performance of BothVTS when reducing the underlying model cold bias. Diagnostics revealed enhanced spurious convection of BothVTS for cold-biased cases was tied to larger analysis increments in temperature than moisture, resulting in erroneously high convective instability. This study is the first to examine the benefits of a multiscale VTS implementation, showing that BothVTS can be utilized to improve the overall performance of a multiscale CAE system. Further, these results underscore the need to limit biases within a DA and forecast system to best take advantage of VTS analysis benefits. [ABSTRACT FROM AUTHOR]

The Advanced Baseline Imager (ABI) aboard the GOES-16 and GOES-17 satellites provides high-resolution observations of cloud structures that could be highly beneficial for convective-scale DA. However, only clear-air radiance observations are typically assimilated at operational centers due to a variety of problems associated with cloudy radiance data. As such, many questions remain about how to best assimilate all-sky radiance data, especially when using hybrid DA systems such as EnVar wherein a nonlinear observation operator can lead to cost function gradient imbalance and slow minimization. Here, we develop new methods for assimilating all-sky radiance observations in EnVar using the novel Rapid Refresh Forecasting System (RRFS) that utilizes the Finite-Volume Cubed-Sphere (FV3) model. We first modify the EnVar solver by directly including brightness temperature (Tb) as a state variable. This modification improves the balance of the cost function gradient and speeds up minimization. Including Tb as a state variable also improves the model fit to observations and increases forecast skill compared to utilizing a standard state vector configuration. We also evaluate the impact of assimilating ABI all-sky radiances in RRFS for a severe convective event in the central Great Plains. Assimilating the radiance observations results in better spinup of a tornadic supercell. These data also aid in suppressing spurious convection by reducing the snow hydrometeor content near the tropopause and weakening spurious anvil clouds. The all-sky radiance observations pair well with reflectivity observations that remove primarily liquid hydrometeors (i.e., rain) closer to the surface. Additionally, the benefits of assimilating the ABI observations continue into the forecast period, especially for localized convective events. [ABSTRACT FROM AUTHOR]

This study evaluates simulated radiance forecasts from a series of controlled experiments consisting of FV3‐LAM forecasts with different configurations of model physics and vertical resolution. The forecasts were produced during the 2020 Hazardous Weather Testbed Spring Forecasting Experiments on the same forecast cases. The evaluation includes grid‐point, neighborhood‐based and object‐based verification. The experiments include forecasts that were identical except for the physics (EMC‐LAM vs. EMC‐LAMx), vertical resolution (EMC‐LAMx vs. NSSL‐LAM), or combined initial conditions, physics and vertical resolution (GSL‐LAM). It is found that the EMC‐LAM generally provided better simulated radiance forecasts than the other three configurations at most forecast lead times, due to its unique physics configuration. All configurations generally over‐forecasted high level clouds. EMC‐LAM reduced the over‐forecasting of high clouds, but also under‐forecasted the coverage of mid‐level clouds. In contrast, at early lead times the EMC‐LAM had relatively poor performance relative to the other forecasts. Furthermore, EMC‐LAM was an outlier in terms of the vertical structure of clouds. It is also found that the NSSL‐LAM consistently improved upon the EMC‐LAMx, which had fewer vertical levels than NSSL‐LAM. Compared to EMC‐LAMx, NSSL‐LAM had less cloud over‐forecasting bias, especially with small cloud objects, and less overall error. The differences between EMC‐LAMx and GSL‐LAM were generally much smaller than the differences between EMC‐LAMx and EMC‐LAM/NSSL‐LAM. Finally, it is found that a non‐linear bias correction conditioned on symmetric brightness temperature reduced the overall root‐mean‐square error by about a factor of 2 while improving the unrealistic vertical structure of clouds in the EMC‐LAM. Plain Language Summary: While forecasts from different convection‐allowing model (CAM) configurations are often evaluated in terms of simulated reflectivity or precipitation, many users are also interested in simulated brightness temperature and associated cloud features. This study evaluates the forecast performance of several configurations of the FV3‐LAM CAM in terms of simulated infrared brightness temperature. The different configurations highlight the impacts of vertical resolution and physical parameterizations, in particular. It is found that the older physics parameterization, with Geophysical Fluid Dynamics Laboratory microphysics and Eddy‐Diffusivity Mass‐Flux boundary layer, performed generally better than a newer configuration, with Thompson microphysics and Mellor‐Yamada‐Nakanishi‐Niino boundary layer, that had previously been found to improve precipitation and simulated reflectivity forecasts. The difference is related to both the presence of an additional cumulus parameterization in the older physics, as well as the tendency of the newer Thompson microphysics scheme to produce large regions of high cloud near deep convection. However, at early lead times the older physics configuration took longer to spin up ongoing convection. It is also found that enhanced vertical resolution consistently improved the forecasts. A caveat to these conclusions is that the FV3‐LAM and its physics configuration are undergoing continuous development which may change these results in the newest model versions. Key Points: FV3‐LAM physics configuration strongly impacts the performance of simulated brightness temperature forecastsVertical resolution has a consistent impact, related to a reduction in over‐forecasting of small clouds with enhanced vertical resolutionBias correction greatly reduces the brightness temperature forecast errors as well as reducing errors in cloud vertical structure [ABSTRACT FROM AUTHOR]

This study introduces a simultaneous multiscale data assimilation method by implementing model space spatial scale‐dependent localization (SDL) and variable‐dependent localization (VDL) within an ensemble variational system. This method updates all resolved scales by assimilating all observations at once. The impact of such an approach is examined by a series of radar data assimilation experiments. Single‐observation experiments show that SDL concurrently and more properly updates the storm and its ambient environments compared to a traditional single scale localization (SSL) for radar data assimilation. Including VDL on top of SDL (SDLVDL) realistically decreases the spatial coverage and intensity of moisture increments compared to SDL. Comparisons are then performed on the analyses and forecasts of the 8 May 2003 Oklahoma City supercell storm. Results show that SDL improves the analyses and forecasts during the data assimilation cycling by producing more realistic enhanced low‐level convergences than SSL. SDLVDL obtains more accurate analyses and subsequent forecasts for moisture than SDL. SDLVDL yields the best performance in reflectivity forecasts and storm maintenance. Compared to SSL, SDL has higher forecast skills before 2230 UTC and produces degraded forecasts in the later lead time. Plain Language Summary: Convection‐allowing numerical weather prediction models resolve atmospheric flows at a wide range of scales. Therefore an effective data assimilation method is required to properly update all resolved scales. This study introduces and describes an ensemble based simultaneous multiscale data assimilation method by utilizing a scale‐dependent and variable‐dependent localization (VDL) method in the model state space. This approach corrects storms and corresponding larger scale environments concurrently. Results and diagnostics from a tornadic supercell case study assimilating radar observations show that the proposed multiscale approach improves the analyses and the earlier forecasts compared to the traditional single‐scale method. Further applying a VDL method in the multiscale approach yields the best forecast performance during the entire forecast period. Key Points: This study introduces a simultaneous multiscale data assimilation method using spatial scale‐dependent and variable‐dependent localization (VDL)The scale‐dependent method concurrently updates storm and its ambient environments compared to a traditional single scale methodFurther applying VDL is beneficial in the analysis and forecast of a tornadic supercell [ABSTRACT FROM AUTHOR]

The current global operational four-dimensional ensemble-variational (4DEnVar) data assimilation (DA) system at NCEP adopts a background ensemble at a reduced resolution, which restricts the range of spatial scales that the ensemble background error covariance can resolve. A prior study developed a multiresolution ensemble 4DEnVar method and determined that this approach can provide a comparable forecast to an approach using solely high-resolution members, while substantially reducing the computational cost. This study further develops the multiresolution ensemble 4DEnVar approach to allow for a flexible number of low- and high-resolution ensemble members as well as varying localization length scales between the high- and low-resolution ensembles. Three 4DEnVar experiments with the same computational costs are compared. The first experiment has an 80-member high-resolution background ensemble with single-scale optimally tuned localization (SR-High). The second and third experiments utilize the multiresolution background ensembles. One has 130 low-resolution and 40 high-resolution members (MR170) while the other has 180 low-resolution members and 24 high-resolution members (MR204). Both multiresolution ensemble experiments utilize differing localization radii with ensemble resolution. Despite having the same costs, both MR170 and MR204 improves global forecasts and decreases tropical cyclone track errors for up to 5 days' lead time compared to SR-High. Improvements are most apparent in larger-scale features, such as jet streams and the environmental steering flow of tropical cyclones. Additionally, MR170 outperforms MR204 in terms of global and tropical cyclone track forecasts, demonstrating the value of both increasing sampling at large scales and retaining substantial information at small scales. [ABSTRACT FROM AUTHOR]

This study describes a real-time implementation of valid time shifting (VTS) within the Gridpoint Statistical Interpolation–based ensemble-variational (EnVar) data assimilation system, developed at the Multi-Scale Data Assimilation and Predictability Laboratory. This system, featuring data assimilation of mesoscale conventional observations and storm-scale radar reflectivity observations and interfaced with the next-generation Finite Volume Cubed Sphere Dynamical Core limited-area model (FV3-LAM), was run in real-time during the 2021 Hazardous Weather Testbed Spring Forecast Experiment. The VTS method efficiently increases ensemble size by incorporating ensemble forecast output before and after the central analysis. Two configurations were examined to systematically evaluate VTS: a baseline 36-member system with hourly data assimilation (NOVTS), and an experiment testing VTS for the radar analysis step. Verification across 22 cases shows statistically significant benefits of VTS to increase ensemble spread and better fit first guesses to observations. Control member forecasts launched at 0000 UTC have consistently higher skill, lower bias, and higher reliability in VTS than in NOVTS throughout the 18-h forecast evaluation period, especially from severe cases often featuring upscale growth into mesoscale convective systems. Verification of updraft helicity-based ensemble surrogate severe probabilistic forecasts against observed storm reports shows higher skill of VTS when verifying on finer scales, with benefits to constraining higher probabilities over report locations and reducing probabilities over no-report locations. This study is a first step toward the next-generation Rapid Refresh Forecast System (RRFS), demonstrating the feasibility of such a real-time system and the potential benefits of VTS implementation. [ABSTRACT FROM AUTHOR]

Using the behavioural theory of the firm as a theoretical lens, an integrated framework for this study was developed to examine how negative performance relative to aspiration as well as organisational slack shapes a firm's openness in its innovation search behaviour. Furthermore, the study investigates whether the extent to which slack is committed to specific organisational procedures determines its moderating effect on the relationship between negative performance feedback and open innovation search behaviour. Patent archives and financial data from 1688 manufacturing firms publicly listed in the US were collected to construct a panel dataset of 13,685 observations. By examining the open innovation search behaviours of these manufacturers from 1986 to 2006, it was found that negative performance feedback encourages firms to be more open to external knowledge sources. The findings also revealed that the absorption degree of slack indeed determines its moderating role. Specifically, underperforming firms with more unabsorbed slack exhibited more openness in their innovation search behaviours, whereas absorbed slack failed to show significance. Possible explanations for the insignificance of absorbed slack are further discussed. [ABSTRACT FROM AUTHOR]

The sensitivity of convection-allowing forecasts over the continental United States to radar reflectivity data assimilation (DA) frequency is explored within the Gridpoint Statistical Interpolation (GSI)-based ensemble–variational (EnVar) system. Experiments with reflectivity DA intervals of 60, 20, and 5 min (RAIN60, RAIN20, and RAIN5, respectively) are conducted using 10 diverse cases. Quantitative verification indicates that the degree of sensitivity depends on storm features during the radar DA period. Five developing storms show high sensitivity, whereas five mature or decaying storms do not. The 20-min interval is the most reliable given its best overall performance compared to the 5- and 60-min intervals. Diagnostics suggest that the differences in analyzed cold pools (ACPs) among RAIN60, RAIN20, and RAIN5 vary by storm features during the radar DA period. Such ACP differences result in different forecast skills. In the case where RAIN20 outperforms RAIN60 and the case where RAIN5 outperforms RAIN20, assimilation of reflectivity with a higher frequency commonly produces enhanced and widespread ACPs, promoting broader storms that match better with reality than a lower frequency. In the case where RAIN5 performs worse than RAIN20, the model imbalance of RAIN5 overwhelms information gain associated with frequent assimilation, producing overestimated and spuriously fast-moving ACPs. In the cases where little sensitivity to the reflectivity DA frequency is found, similar ACPs are produced. [ABSTRACT FROM AUTHOR]

Ensemble‐based convective‐scale radar data assimilation commonly suffers from an underdispersive background ensemble. This study introduces a multivariate additive‐inflation method to address such deficiency. The multivariate additive inflation (AI) approach generates coherent random perturbations drawn from a newly constructed convective‐scale static background error covariance matrix for all state variables including hydrometeors and vertical velocity. This method is compared with a previously proposed univariate AI approach, which perturbs each variable individually without cross‐variable coherency. Comparisons are performed on the analyses and forecasts of the 8 May 2003 Oklahoma City tornadic supercell. Within assimilation cycles, the multivariate approach is more efficient in increasing reflectivity spread and thus has a reduced spinup time than the univariate approach; the additional inclusion of hydrometeors and vertical velocity results in more background spread for both reflectivity and radial velocity. Significant differences among AI experiments also exist in the subsequent forecasts and are more pronounced for the forecasts initialized from the earlier assimilation cycles. The multivariate approach yields better forecasts of low‐level rotation, reflectivity distributions, and storm maintenance for most lead times. The additional inclusion of hydrometeor and vertical velocity in the multivariate method is beneficial in forecasts. Conversely, the additional inclusion of hydrometeor and vertical velocity in the univariate method poses negative impacts for the majority of forecast lead times. Plain Language Summary: Data assimilation (DA) requires an accurate estimation of the background uncertainty. In ensemble‐based DA, such uncertainty is represented by the statistics of a background ensemble. However, it is difficult to construct ideal background ensembles that truly represent forecast errors. The deficient background ensemble becomes more problematic for convective‐scale radar DA when all ensemble members miss the observed storms. This study proposes a multivariate additive‐inflation approach to address such deficiency. The mitigation of ensemble deficiency is achieved by drawing spatially and physically coherent random perturbations from a recently constructed convective‐scale static background error covariance matrix and adding them to each ensemble member. This study assesses the new approach by comparing it with a previously proposed univariate approach, which perturbs each variable individually without cross‐variable coherency. Results and diagnostics from a tornadic supercell study show that the multivariate approach improves the analysis and forecast compared to the univariate approach. The multivariate approach by additionally perturbing hydrometeors and vertical velocity further improves the forecast. In contrast, the univariate approach including the hydrometeors and vertical velocity perturbations degrades the forecast. Key Points: A multivariate additive‐inflation approach is introduced to address the deficient ensemble for ensemble‐based radar data assimilationThe multivariate approach improves the analysis and forecast of a tornadic supercell relative to a previous univariate approachThe multivariate approach by additionally perturbing hydrometeors and vertical velocity is beneficial in analysis and forecast [ABSTRACT FROM AUTHOR]

This work demonstrated the effect of surfactant on the rheology and stability properties of emulsion explosive matrices (EEM), combined with rheology test, interfacial tension measurement, and the crystallization test of emulsions. It was shown that the interfacial tension of EEM were dependent on the different types of surfactants. The low-molecular weight surfactant show a great ability of reducing the interfacial tension of emulsion, comparing with the polymeric surfactants. However, the emulsions exhibited the greater rheological and stability properties when the polymeric surfactants were used. It was also demonstrated that the rheological and stability properties of emulsions varied when the combinations of polymeric and low-molecular-weight surfactants were used. The interfacial tension of EEM decreased first and then increased with the increase of composite ratio. The viscosity, yield stress, and storage modulus of emulsion increase with the decrease of interfacial tension. In addition, there exists an optimal composite ratio to maximize stability of EEM. [ABSTRACT FROM AUTHOR]

[Display omitted] • The CFD simulation determined that 15 min/h was the best stirring time. • Explore the combined effects of stirring and net energy output. • Hydrogen nutrition is the main methanogenic pathway. • Metabolic analysis showed that the methane production pathway was enhanced. This study optimized the stirring time of cattle manure (CM)-corn stover (CS) co-digestion to improve Anaerobic digestion (AD) performance. The results showed that the experimental (T2) with the optimum stirring time (15 min/h) determined by computational fluid dynamics (CFD) simulation achieved the highest cumulative methane yield of 224.17 mL/g. Compared to continuous stirring (T1, 60 min/h) and intermittent stirring (T3, 5 min/h), T2 increased the cumulative methane yield by 18.65 % and 7.11 %, respectively. Additionally, microbial community analysis indicated that the relative abundance of the predominant methanogen Methanosarcina in T2 was increased by 9.56 % and 26.14 % compared to T1 and T3, respectively. Functional prediction by PICRUSt2 revealed that T2 enhanced the hydrogenotrophic methanogenesis pathway, which played a major role in the methanogenesis pathway. In summary, this study provides insights for optimizing the stirring time of AD and plays an important role in improving the methane yield efficiency and reducing energy consumption of biogas projects. [ABSTRACT FROM AUTHOR]

Different development stages of porcine embryos have different tolerance to low temperature. Therefore, we took the porcine embryos after parthenogenetic activation (PA) as the model, to explore the optimal development stage for vitrification during morula (D4), early blastocyst (D5), and expanded blastocyst (D6) after PA (D0). Embryos were observed with microscope and analyzed by different staining after cryo-recovery for 24 hours. The quality of embryos was damaged after vitrification, including embryonic nuclei, DNA, cytoskeleton, and organelles. The re-expansion rate at 24 hours of D5 embryos was significantly higher than those of D4 and D6 embryos (D5 vs. D4 vs. D6, 27.620 ± 0.041 vs. 7.809 ± 0.027 vs. 13.970 ± 0.032, p < 0.05). Therefore, D5 embryos were selected as research objects to explore the effect of vitrification on lipid in vitrified embryos. The results showed that the expression levels of perilipin PLIN3 messenger RNA (mRNA) and triacylglycerol synthesis-related genes AGPAT1 and DGAT mRNA are significantly reduced (p < 0.05). Vitrification affected lipid synthesis, which might have an irreversible impact on embryonic development. In conclusion, our data demonstrated that the optimal stage of vitrification was D5 for early blastocysts. [ABSTRACT FROM AUTHOR]

There is a growing interest in the use of ground-based remote sensors for numerical weather prediction, which is sparked by their potential to address the currently existing observation gap within the planetary boundary layer. Nevertheless, open questions still exist regarding the relative importance of and synergy among various instruments. To shed light on these important questions, the present study examines the forecast benefits associated with several different ground-based profiling networks using 10 diverse cases from the Plains Elevated Convection at Night (PECAN) field campaign. Aggregated verification statistics reveal that a combination of in situ and remote sensing profilers leads to the largest increase in forecast skill, in terms of both the parent mesoscale convective system and the explicitly resolved bore. These statistics also indicate that it is often advantageous to collocate thermodynamic and kinematic remote sensors. By contrast, the impacts of networks consisting of single profilers appear to be flow-dependent, with thermodynamic (kinematic) remote sensors being most useful in cases with relatively low (high) convective predictability. Deficiencies in the data assimilation method as well as inherent complexities in the governing moisture dynamics are two factors that can further limit the forecast value extracted from such networks. [ABSTRACT FROM AUTHOR]

This article attempts to reveal the detection ability of microstrip slot resonator for water and electrical tree defects in insulating samples. The characteristics of microstrip gap resonator are analyzed in high-frequency electromagnetic simulation software. A gap capacitance extraction method is proposed based on two-port transmission theory, and the gap capacitances under different dielectric constants are analyzed based on this method. The resonant frequency of sensor is found to be negatively correlated with the dielectric constant of the materials. In the experimental part, the sensor made of double-sided copper clad plate with medium FR4 was used to detect the water tree and electrical tree in low-density polyethylene (LDPE) samples, and the experiment results showed good feasibility. [ABSTRACT FROM AUTHOR]

The Micro Unmanned Robot Observation Network (MURON) is a planned in-situ observation network over the surface of West Pacific Ocean, and it is designed to sample high spatial and temporal resolution observations of wind and mass fields over the ocean surface. The impacts of MURON observations for Tropical Cyclone (TC) intensity forecast are investigated using Observation System Simulation Experiments. The regional Ensemble Kalman Filter (EnKF) system of Gridpoint Statistical Interpolation is used with the Advanced Research version of the Weather Research and Forecasting model to conduct OSSEs for typhoon Haiyan (2013) while Haiyan goes through rapid intensification. Assimilating MURON observations improves the TC structure and intensity analysis and forecast. The intensity forecast is improved largely due to the correction of initial vorticity and vertical transport of mass flux. The improvement of intensity forecast is attributed largely to the assimilated MURON wind observations when Haiyan is at the tropical disturbance stage, and then by the MURON mass observations when Haiyan enters the tropical storm stage. In addition, our results show that the quality of moisture analysis is sensitive to the choice of the moisture control variable (CV) in the EnKF system. Using the default pseudo relative humidity (PRH) as the moisture CV degrades the accuracy of the moisture analysis. This is likely due to the neglect of updated temperature field during the nonlinear conversion from the PRH CV to the mixing ratio variable and due to the larger deviation of the PRH from Gaussian distribution. The use of mixing ratio moisture CV mitigates these problems. [ABSTRACT FROM AUTHOR]

In this study, hourly data assimilation (DA) cycling is performed during a 24-h time period for Hurricane Matthew (2016), assimilating ground-based (GBR) and tail-Doppler radar (TDR) observations together, as well as separately using HWRF and its Hybrid 3DEnVar DA system. The objective is to examine the impacts of assimilating such data on the analysis and prediction of the weakening and re-intensification stages of the eyewall replacement cycle (ERC) of Matthew. Experiments assimilating GBR observations make quicker corrections to the initially inconsistent storm structure than does the TDR experiment, resulting in the primary and secondary eyewalls being realistically represented during the DA cycling period. The TDR experiment analyses show less-realistic concentric eyewall structure before, during, and after TDR observations become available. The forecasts from experiments assimilating GBR observations show more-realistic structural and point intensity changes for the ERC consistently throughout the cycling period when compared with the experiments assimilating TDR observations. Combined assimilation of GBR and TDR observations show similar ERC forecasts, on average, to the GBR experiment. The superior performance of the GBR experiments is shown to be tied to its earlier and longer availability despite its limited low-level coverage especially at the early stage of the cycling. The inferior performance of the TDR experiments even during the availability of TDR is hypothesized to be a result of rapidly changing 3D observational coverage during the high-frequency cycling. Brief mechanism diagnostics additionally suggest the need of properly initializing the TC concentric eyewalls to capture the ERC during the forecasts. [ABSTRACT FROM AUTHOR]

The present study introduces the online non‐linear bias correction for the assimilation of all‐sky GOES‐16 Advanced Baseline Imager (ABI) channel 9 (6.9 μm) radiances in a rapidly cycled EnKF for convective scale data assimilation (DA). This study is the first to explore the use of the radar reflectivity as the anchoring observation for ABI all sky radiance assimilation. The online and offline nonlinear bias correction methods are compared and evaluated for a case of rapidly developing supercells over Oklahoma and Texas. The analysis and background of the online bias correction perform better than the offline approach during the suppression of spurious clouds and the establishment of non‐precipitating and precipitating regions when the supercell storms are observed to develop. The online approach not only improves the analysis and background over the radar anchored region but also the unanchored non‐precipitating regions compared to the offline approach. Both quantitative and subjective verification of the deterministic forecasts showed consistent superior performance from the online bias correction over the offline approach. Diagnostics reveal that the online bias correction retains useful information in the innovation, which in turn improves subsequent analysis, background and background ensemble spread for both the thermodynamic and dynamic fields. The effect is accumulated during the DA cycling that is responsible for the superior analysis and forecast of the supercells. Plain Language Summary: The effective assimilation of satellite radiance observations requires removal of observation biases. Past studies have shown the advantages of the online bias correction approach for the clear‐sky radiance data assimilation relative to the offline bias correction approach. The advantage is achieved by simultaneously updating the model states and the bias correction terms and through the assimilation of unbiased observations together with the radiance observations. The anchoring unbiased observation in the online bias correction allows a more effective separation of the observation biases and the model bias. In this study, we propose and implement an online nonlinear bias correction method for the rapid EnKF assimilation of the all‐sky ABI radiances. The radar reflectivity observations are explored as the anchoring observations. Our results and diagnostics from a supercell case study show the online nonlinear bias correction approach improve the analysis and forecast compared to the offline nonlinear bias correction approach. Key Points: An online nonlinear bias correction for Advanced Baseline Imager (ABI) all‐sky radiance assimilation is proposed and implemented in a rapidly updated EnKFRadar reflectivity is shown to be effective in anchoring the state variable update by distinguishing the model and ABI radiance biasesThe online bias correction improves the analysis and forecast for rapidly developing supercells relative to the offline approach [ABSTRACT FROM AUTHOR]

A valid time shifting (VTS) method is explored for the GSI-based ensemble variational (EnVar) system modified to directly assimilate radar reflectivity at convective scales. VTS is a cost-efficient method to increase ensemble size by including subensembles before and after the central analysis time. Additionally, VTS addresses common time and phase model error uncertainties within the ensemble. VTS is examined here for assimilating radar reflectivity in a continuous hourly analysis system for a case study of 1–2 May 2019. The VTS implementation is compared against a 36-member control experiment (ENS-36), to increase ensemble size (3 × 36 VTS), and as a cost-savings method (3 × 12 VTS), with time-shifting intervals τ between 15 and 120 min. The 3 × 36 VTS experiments increased the ensemble spread, with largest subjective benefits in early cycle analyses during convective development. The 3 × 12 VTS experiments captured analysis with similar accuracy as ENS-36 by the third hourly analysis. Control forecasts launched from hourly EnVar analyses show significant skill increases in 1-h precipitation over ENS-36 out to hour 12 for 3 × 36 VTS experiments, subjectively attributable to more accurate placement of the convective line. For 3 × 12 VTS, experiments with τ ≥ 60 min met and exceeded the skill of ENS-36 out to forecast hour 15, with VTS-3 × 12τ90 maximizing skill. Sensitivity results demonstrate preference to τ = 30–60 min for 3 × 36 VTS and 60–120 min for 3 × 12 VTS. The best 3 × 36 VTS experiments add a computational cost of 45%–67%, compared to the near tripling of costs when directly increasing ensemble size, while best 3 × 12 VTS experiments save about 24%–41% costs over ENS-36. Significance Statement: The purpose of this work is to study a valid time shifting method to improve the prediction of severe convective storm systems over the continental United States. This method improves ensemble-based radar reflectivity analyses by including ensemble member information at times before and after the analysis time, thereby increasing the ensemble size at just a fractional added computational cost. The results show the method can boost the accuracy of high-resolution convection prediction out to at least 12 h. This case study motivates future systematic testing in a real-time setting and potential implementation to enhance the U.S. operational ensemble-based convection-allowing forecast model and data assimilation system. [ABSTRACT FROM AUTHOR]

Supercapacitors have immense promise in the field of energy storage, and the choice of electrode materials is of utmost importance. In this study, we have developed a ternary composite electrode material consisting of poly(3,4-ethylenedioxythiophene)/ Ti 3 C 2 T x /Co 3 S 2 (PEDOT/Ti 3 C 2 T x /Co 3 S 2) using a solid-state heating method, without the need for an oxidant. Interestingly, we observed that the exclusion of an oxidant such as ferric chloride has proven advantageous in preventing the oxidation of Ti 3 C 2 T x nanosheets. Moreover, the synergistic effect between Co 3 S 2 and Ti 3 C 2 T x nanosheets enhances the structural stability of the composite material during the electrochemical process. The presence of PEDOT on the surface of Ti 3 C 2 T x /Co 3 S 2 acts as a reinforcing agent, effectively preventing the self-stacking of Ti 3 C 2 T x and thereby increasing the specific surface area and conductivity of the resulting composite. Furthermore, the synergistic effects between different phases in the composite material can accelerate the interface charge transfer in the electrochemical redox process. Electrochemical results show that the gravimetric capacitance of the PEDOT/Ti 3 C 2 T x / Co 3 S 2 is 1135.6 F g-1. The quasi-solid supercapacitor assembled by combining the PEDOT/Ti 3 C 2 T x /Co 3 S 2 with alkaline hydrogel electrolyte (PVA/KOH) exhibits an energy density of 35.94 Wh kg-1 and a power density of 750.05 W kg-1 at 4 A g-1. After 20,000 cycles, the capacitance retention rate is 110.89%, and the Coulomb efficiency is 95.37%. [Display omitted] • PEDOT/Ti 3 C 2 T x /Co 3 S 2 composite is prepared by solid-phase heating method. • The interaction between Co 3 S 2 and Ti 3 C 2 T x enhances the structural stability of composite in electrochemical process. • The PEDOT can be benefit for the increasing of specific surface area and conductivity of composite. • The composite has a high specific capacitance of 1135.6 F g-1 and a cycle stability of up to 110.89%. [ABSTRACT FROM AUTHOR]

• A strategy for chemicalome and associated metabolome profiling of Chinese patent drug was performed. • Chemical profiling of Huanghou antidiarrhea dropping pills was firstly analyzed by UHPLC-QTOF-MS/MS. • A prototypes-metabolites network of Huanghou antidiarrhea dropping pills was established by biotransformation pathways. • Tissue distributions of active ingredients were explored in colon, liver, heart, spleen, lung and kidney. Huanghou antidiarrhea dropping pills (HADP) is an efficient Chinese patent drug that is clinically used to treat diarrhea. However, its functional materials remain unclear due to the characteristics of traditional Chinese medicine, which is a multi-component and multi-target complex system. In this study, we investigated the intrinsic chemical components and combined with in vivo metabolism to reveal the functional material basis of HADP. Spectral behavior (accurate molecular weight and secondary fragmentation) and chromatographic behavior (retention time) were key criterions that throughout the whole research of components identification, prototypes screening, and tissue distribution. Mass defect filter (MDF), characteristic product ion filter (PIF), and neutral loss filter (NLF) were other three criterions for metabolites searching. Consequently, a total of 102 components in HADP, including alkaloids, lignans, lactones, gingerols, and alkaloid complexes were identified or tentatively characterized. About 39 metabolites that related to 37 prototypes were calculated and matched in bio-samples. Among them, 14 prototypes and 18 metabolites were detected distribution in colon, liver, heart, spleen, lung or kidney. This study provides a systematic investigation into the metabolism of HADP and offers effective analytical strategies for the characterization of compounds and metabolites in Chinese patent drugs. [ABSTRACT FROM AUTHOR]

Short-term spinup for strong storms is a known difficulty for the operational Hurricane Weather Research and Forecasting (HWRF) Model after assimilating high-resolution inner-core observations. Our previous study associated this short-term intensity prediction issue with the incompatibility between the HWRF Model and the data assimilation (DA) analysis. While improving physics and resolution of the model was found to be helpful, this study focuses on further improving the intensity predictions through the four-dimensional incremental analysis update (4DIAU). In the traditional 4DIAU, increments are predetermined by subtracting background forecasts from analyses. Such predetermined increments implicitly require linear evolution assumption during the update, which are hardly valid for rapidly evolving hurricanes. To confirm the hypothesis, a corresponding 4D analysis nudging (4DAN) method, which uses online increments is first compared with the 4DIAU in an oscillation model. Then, variants of 4DIAU are proposed to improve its application for nonlinear systems. Next, 4DIAU, 4DAN and their proposed improvements are implemented into the HWRF 4DEnVar DA system and are investigated with Hurricane Patricia (2015). Results from both the oscillation model and HWRF Model show that 1) the predetermined increments in 4DIAU can be detrimental when there are discrepancies between the updated and background forecasts during a nonlinear evolution; 2) 4DAN can improve the performance of incremental update upon 4DIAU, but its improvements are limited by the overfiltering; 3) relocating initial background before the incremental update can improve the corresponding traditional methods; and 4) the feature-relative 4DIAU method improves the incremental update the most and produces the best track and intensity predictions for Patricia among all experiments. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is heading to its mature development and continuously evolving itself as an essential component of the fifth-generation (5G) internet. IoT infrastructure requires networks for which 5G network is developed. Indeed global interest and the advancement in 5G networks has transformed the vision of smart cities into reality in terms of smart homes, smart consumer items, driverless cars, and similar daily appliances. Congestion in traffic affected the areas around the world and causing different issues like fuel wastage, anxiety, and delayed conveyances problems. To locate the exact location of congestion is one of the significant problem in the intelligent transportation system. It perceives the variation from the norm of traffic with the assistance of various sensors utilized in the progression of traffic. So far the development of intelligent transportation system given the capacity to researchers to investigate new procedures for finding the congested zones. This paper proposes the novel model for the prediction of traffic congestion in internet of vehicle using information fusion with artificial neural network and support vector machine. The proposed model obtained 98.4% accuracy, which is greater than previously mentioned approaches in the literature. [ABSTRACT FROM AUTHOR]

A convective-scale static background-error covariance (BEC) matrix is further developed to include the capability of direct reflectivity assimilation and evaluated within the GSI-based three-dimensional variational (3DVar) and hybrid ensemble–variational (EnVar) methods. Specific developments are summarized as follows: 1) Control variables (CVs) are extended to include reflectivity, vertical velocity, and all hydrometeor types. Various horizontal momentum and moisture CV options are included. 2) Cross correlations between all CVs are established. 3) A storm intensity-dependent binning method is adopted to separately calculate static error matrices for clear-air and storms with varying intensities. The resultant static BEC matrices are simultaneously applied at proper locations guided by the observed reflectivity. 4) The EnVar is extended to adaptively incorporate static BECs based on the quality of ensemble covariances. Evaluation and examination of the new static BECs are first performed on the 8 May 2003 Oklahoma City supercell. Detailed diagnostics and 3DVar examinations suggest zonal/meridional winds and pseudo–relative humidity are selected as horizontal momentum and moisture CVs for direct reflectivity assimilation, respectively; inclusion of cross correlations favors spin up and maintains the analyzed storms; application of binning improves characteristics and persistence of the simulated storm. Relative to an experiment using the full ensemble BECs (Exp-PureEnVar), incorporating static BECs in hybrid EnVar reduces spinup time and better analyzes reflectivity distributions while the background ensemble is deficient in sampling errors. Compared to both pure 3DVar and Exp-PureEnVar, hybrid EnVar better predicts reflectivity distributions and better maintains a strong mesocyclone. Further examination through the 20 May 2013 Oklahoma supercells confirms these results and additionally demonstrates the effectiveness of adaptive hybridization. [ABSTRACT FROM AUTHOR]

In the present study, antioxidant activities and functional properties of cowhide collagen antioxidant peptides (CCAPs) with different molecular weight (MW) were investigated. The optimum preparation conditions of CCAPs were hydrolysis time of 1.53 hr, temperature of 54.9 °C, pH 7.38, and neutral enzyme to trypsin ratio of 0.048 g: 0.016 g according to single factor test and response surface methodology (RSM). Three fractions (CCAP‐I, CCAP‐II, and CCAP‐III) were obtained by ultrafiltration and lyophilization. Antioxidant activities revealed that CCAP‐III had high reducing power activity (0.323 ± 0.035) and scavenging effect on 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) radicals (64.30 ± 5.99%), 2,2‐azino‐bis‐(3‐ethylbenzothiazoline)‐6‐sulfonic acid (ABTS) radicals (75.25 ± 3.14%), and hydroxyl radicals (68.26 ± 6.74%) compared to the other fractions. In addition, LC‐MS/MS analysis showed that Ala‐Gly‐Glu‐Arg, Gly‐Ile‐Ala‐Gly‐Glu‐Arg, Gly‐Pro‐Ala‐Gly‐Pro‐Ala‐Gly‐Pro‐Arg, Gly‐Val‐Val‐Gly‐Pro‐Glu‐Gly‐Ala‐Arg and Gly‐Phe‐Ser‐Gly‐Leu‐Asp‐Gly‐Ala‐Lys were the major peptides of CCAP‐III. CCAP‐III showed good hygroscopicity (HYG), water holding capacity (WHC), and oil holding capacity (OHC) when compared with CCAP‐I and CCAP‐II. However, CCAP‐II has great emulsifying properties, and CCAP‐I has excellent foaming properties. Therefore, CCAPs can be used as a promising source of functional peptides with antioxidant properties. Practical Application: This study demonstrated the peptides of cowhide collagen has superior antioxidant and functional properties. This study provided a scientific basis for the preparation of antioxidant peptides from cowhide collagen. [ABSTRACT FROM AUTHOR]

The axial capacity and force demand of a steel column are expected to change continuously during a fire incident. In assessing the structural performance in the event of a fire, it is important to monitor both the capacity and force demand at the entire temperature range. Although the axial capacity of steel columns at elevated temperatures can be calculated with closed form-equations in the design codes, limited information is available regarding the changes in force demand due to the time-dependent temperature load. This paper proposes a set of demand-capacity curves for steel columns at elevated temperatures with various initial force and constraint conditions. A series of full-scale steel columns are tested using the hybrid fire simulation (HFS) method to validate the developed demand-capacity curves. The hybrid fire simulation results are also replicated numerically using a finite element model. The test results match the developed curve with minimal calibration. The developed curves can be used as a quick tool to evaluate the axial capacity and force demand of a steel column at elevated temperatures. [ABSTRACT FROM AUTHOR]

Although numerous studies have demonstrated that increasing model spatial resolution in free forecasts can potentially improve tropical cyclone (TC) intensity forecasts, studies on the impact of model resolution during data assimilation (DA) on TC prediction are lacking. In this study, using the ensemble-variational DA system for the Hurricane Weather Research and Forecasting (HWRF) Model, we investigated the individual impact of increasing the model resolution of first guess (FG) and background ensemble (BE) forecasts during DA on initial analyses and subsequent forecasts of Hurricane Patricia (2015). The impacts were compared between horizontal and vertical resolutions and also between the tropical storm (TS) and hurricane assimilation during Patricia. The results show that increasing the horizontal or vertical resolution in FG has a larger impact than increasing the resolution in BE on improving the analyzed TC intensity and structure for the hurricane stage. The result is reversed for the TS stage. These results are attributed to the effectiveness of increasing the FG resolution in intensifying the background vortex for the hurricane stage relative to the TS stage. Increasing the BE resolution contributes to improving the analyzed intensity through the better-resolved background correlation structure for both the hurricane and TS stages. Increasing horizontal resolution has an overall larger effect than increasing vertical resolution in improving the analysis at the hurricane stage and their effects are close for the analysis at the TS stage. Additionally, the more accurately analyzed primary circulation, secondary circulation, and warm-core structures via the increased resolution in DA lead to improved TC intensity forecasts. [ABSTRACT FROM AUTHOR]

A scale-dependent localization (SDL) method was formulated and implemented in the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (4DEnVar) system for NCEP FV3-based Global Forecast System (GFS). SDL applies different localization to different scales of ensemble covariances, while performing a single-step simultaneous assimilation of all available observations. Two SDL variants with (SDL-Cross) and without (SDL-NoCross) considering cross-wave-band covariances were examined. The performance of two- and three-wave-band SDL experiments (W2 and W3, respectively) was evaluated through 1-month cycled data assimilation experiments. SDL improves global forecasts to 5 days over scale-invariant localization including the operationally tuned level-dependent scale-invariant localization (W1-Ope). The W3 SDL-Cross experiment shows more accurate tropical storm–track forecasts at shorter lead times than W1-Ope. Compared to the W2 SDL experiments, the W3 SDL counterparts applying tighter horizontal localization at medium-scale wave band generally show improved global forecasts below 100 hPa, but degraded global forecasts above 50 hPa. While the outperformance of the W3 SDL-NoCross experiment versus the W2 SDL-NoCross experiment below 100 hPa lasts for 5 days, that of the W3 SDL-Cross experiment versus the W2 SDL-Cross experiment lasts for 3 days. Due to local spatial averaging of ensemble covariances that may alleviate sampling error, the SDL-NoCross experiments show slightly better forecasts than the SDL-Cross experiments at shorter lead times. However, the SDL-Cross experiments outperform the SDL-NoCross experiments at longer lead times, likely from retention of more heterogeneity of ensemble covariances and resultant analyses with improved balance. Relative performance of tropical storm–track forecasts in the W2 and W3 SDL experiments are generally consistent with that of global forecasts. [ABSTRACT FROM AUTHOR]

Supercapacitors play a crucial role in energy storage applications due to their high energy and power densities. In this context, synthesizing composites such as: poly(3,4propylenedioxythiophene)/CoNi 2 S 4 @hollow carbon sphere (PProDOT/CoNi 2 S 4 @HCS) through solid-state thermal polymerization and hydrothermal ion exchange has gained significant attention. The PProDOT/CoNi 2 S 4 @HCS composite exhibits exceptional electrochemical performance, including a high specific capacitance of 1054 C g−1 and an initial capacity retention rate of 83.3% at 20 A g−1. This signifies that the composite can effectively store and deliver electrical energy. The composite possesses a three-dimensional core-shell structure, wherein HCS acts as a carbon-based substrate that prevents the aggregation of CoNi 2 S 4 nanoparticles. Simultaneously, the PProDOT film ensures the structural integrity of CoNi 2 S 4 and facilitates efficient electron/ion transfer within the composite. Furthermore, researchers have assembled a quasi-solid-state supercapacitor using the PProDOT/CoNi 2 S 4 @HCS composite and a gel electrolyte composed of polyvinyl alcohol/sulfonated cellulose/gelatin (PVA/sCMC/GEL/KOH). This supercapacitor demonstrates outstanding characteristics, such as a high energy density of 85.3 Wh kg−1, a high power density of 7957 W kg−1, and a capacity retention rate of 87.56% after 15,000 cycles at 4 A g−1. These results indicate that the composite-based supercapacitor can provide long-term stability and reliable performance under demanding operating conditions. [Display omitted] • A ternary core-shell material with ultra-high capacity retention at high current density has been realized. • The conductive polymer coating was prepared by in-situ solid-state thermal polymerization. • Designed a gel electrolyte with high ionic conductivity. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new variable intermittent stirring strategy was proposed. • Compare different stirring strategies in anaerobic digestion. • An energy output index optimization stirring strategy was proposed. • The optimal stirring strategy was determined. The stirring strategy has a significant effect on the efficiency of anaerobic digestion (AD). In this study, the suspension velocity of multi-layer blade particles was characterized based on CFD simulation, and a variable frequency intermittent stirring strategy was proposed to study the stirring effect and mixing energy consumption. Firstly, the fermentation cycle was divided into three parts, and three stirring speeds and mixing times were set for each part to carry out an orthogonal combination test. Taking methane production and mixing energy consumption as optimization indexes, a net energy output equation was established to optimize the new stirring strategy. The results showed that the new variable frequency intermittent stirring strategy could effectively improve digestion efficiency. This provides a theoretical basis for setting a stirring strategy in a biogas project. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) is the cornerstone of the design and analysis process in many engineering applications. Not only has it been applied in the design phase, but it has also been employed for analyzing the fluid flow phenomena during the operation phase for many in-use structures, such as vehicles, buildings, and landscapes. However, creating a 3D mesh-based model of in-use structures that can be used by conventional boundary-fitted CFD methods is labor-intensive, time-consuming, and sometimes impossible. Due to the challenges introduced by geometry complexity and lack of design information, it is often difficult to perform an accurate and efficient CFD analysis of these objects. This paper aims to overcome such challenges by proposing a novel photogrammetry-based CFD framework for simulating in-use structures whose design models and analysis meshes are hard to obtain. The proposed framework integrates machine learning-based 3D point cloud reconstruction of structures from 2D images obtained from portable devices (e.g., cell phones and drones) and an immersogeometric approach that can carry out flow analysis directly on reconstructed point clouds. We first present the technical details of point cloud reconstruction techniques and the immersogeometric analysis method. We then simulate the flow past a standard 12 oz soda can reconstructed using photogrammetry and compare the results with reference solutions to assess the accuracy of the approach. Finally, the proposed photogrammetry-based CFD is applied to simulations of a bell tower and the Kavita and Lalit Bahl Smart Bridge on the University of Illinois Urbana-Champaign (UIUC) campus to demonstrate the robustness of the framework and its applicability to real-world in-use civil structures. [ABSTRACT FROM AUTHOR]

Nocturnal convection is often initiated by mechanisms that cannot be easily observed within the large gaps between rawinsondes or by conventional surface networks. To improve forecasts of such events, we evaluate the systematic impact of assimilating a collocated network of high-frequency, ground-based thermodynamic and kinematic profilers collected as part of the 2015 Plains Elevated Convection At Night (PECAN) experiment. For 13 nocturnal convection initiation (CI) events, we find small but consistent improvements when assimilating thermodynamic observations collected by Atmospheric Emitted Radiance Interferometers (AERIs). Through midlevel cooling and moistening, assimilating the AERIs increases the fractions skill score (FSS) for both nocturnal CI and precipitation forecasts. The AERIs also improve various contingency metrics for CI forecasts. Assimilating composite kinematic datasets collected by Doppler lidars and radar wind profilers (RWPs) results in slight degradations to the forecast quality, including decreases in the FSS and traditional contingency metrics. The impacts from assimilating thermodynamic and kinematic profilers often counteract each other, such that we find little impact on the detection of CI when both are assimilated. However, assimilating both datasets improves various properties of the CI events that are successfully detected (timing, distance, shape, etc.). We also find large variability in the impact of assimilating these remote sensing profilers, likely due to the number of observing sites and the strength of the synoptic forcing for each case. We hypothesize that the lack of flow-dependent methods to diagnose observation errors likely contributes to degradations in forecast skill for many cases, especially when assimilating kinematic profilers. [ABSTRACT FROM AUTHOR]

The hydrogenation of two-dimensional (2D) systems can efficiently modify the physical and chemical properties of materials. Here we report a systematic study on the hydrogenation of 2D semiconductor Sn2Bi on Si(111) by scanning tunneling microscopy experiments and first principle calculations. The unique butterfly-like and trench-like features were observed for single H adsorption sites and hydrogen-saturated surfaces respectively, from which the bridge-site adsorption geometry can be unambiguously determined. The structural model was further confirmed by the theoretical calculations, which is in good agreement with the experimental observation. In addition, the hydrogenation is found to vanish the flat band of Sn2Bi and increase the band gap obviously. [ABSTRACT FROM AUTHOR]

Diverse observations, such as the High Definition Sounding System (HDSS) dropsonde observations from the Tropical Cyclone Intensity (TCI) program, the Tail Doppler Radar (TDR), Stepped Frequency Microwave Radiometer (SFMR), and flight-level observations from the Intensity Forecasting Experiment (IFEX) program, and the atmospheric motion vectors (AMVs) from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) simultaneously depicted the three-dimensional (3D) structure of Hurricane Patricia (2015). Experiments are conducted to understand the relative impacts of each of these observation types on Patricia's analysis and prediction using the Gridpoint Statistical Interpolation (GSI)-based ensemble-variational data assimilation system for the Hurricane Weather Research and Forecasting (HWRF) Model. In comparing the impacts of assimilating each dataset individually, results suggest that 1) the assimilation of 3D observations produces better TC structure analysis than the assimilation of two-dimensional (2D) observations; 2) the analysis from assimilating observations collected from platforms that only sample momentum fields produces a less improved forecast with either short-lived impacts or slower intensity spinup as compared to the forecast produced after assimilating observations collected from platforms that sample both momentum and thermal fields; and 3) the structure forecast tends to benefit more from the assimilation of inner-core observations than the corresponding intensity forecast, which implies better verification metrics are needed for future TC forecast evaluation. [ABSTRACT FROM AUTHOR]

The potential future installation of a multifunction phased-array radar (MPAR) network will provide capabilities of case-specific adaptive scanning. Knowing the impacts adaptive scanning may have on short-term forecasts will influence scanning strategy decision-making in hopes to produce the most optimal ensemble forecast while also benefiting human severe weather warning decision-making. An ensemble-based targeted observation algorithm is applied to an observing system simulation experiment (OSSE) where the impacts of synthetic idealized supercell radial velocity observations are estimated before the observations are "collected" and assimilated. The forecast metric of interest is the low-level rotation forecast metric (0–1-km updraft helicity), a surrogate for tornado prediction. It is found that the ensemble-based targeted observation approach can reasonably estimate the true error variance reduction when an effective method that treats sampling error is applied, the period of model forecast is associated with less degrees of nonlinearity, and the observation information content relative to the background forecast is larger. In some scenarios, a subset of a full-volume scan assimilation produces better forecasts than all observations within the full volume. Assimilating the full-volume scan increases the number of potential spurious correlations arising between the forecast metric and radial velocity observation induced state perturbations, which may degrade the forecast metric accuracy. [ABSTRACT FROM AUTHOR]

Using data from the 6 July 2015 PECAN case study, this paper provides the first objective assessment of how the assimilation of ground-based remote sensing profilers affects the forecasts of bore-driven convection. To account for the multiscale nature of the phenomenon, data impacts are examined separately with respect to (i) the bore environment, (ii) the explicitly resolved bore, and (iii) the bore-initiated convection. The findings from this work suggest that remote sensing profiling instruments provide considerable advantages over conventional in situ observations, especially when the retrieved data are assimilated at a high temporal frequency. The clearest forecast improvements are seen in terms of the predicted bore environment where the assimilation of kinematic profilers reduces a preexisting bias in the structure of the low-level jet. Data impacts with respect to the other two forecast components are mixed in nature. While the assimilation of thermodynamic retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) results in the best convective forecast, it also creates a positive bias in the height of the convectively generated bore. Conversely, the assimilation of wind profiler data improves the characteristics of the explicitly resolved bore, but tends to further exacerbate the lack of convection in the control forecasts. Various dynamical diagnostics utilized throughout this study provide a physical insight into the data impact results and demonstrate that a successful prediction of bore-driven convection requires an accurate depiction of the internal bore structure as well as the ambient environment ahead of it. [ABSTRACT FROM AUTHOR]

A gridpoint statistical interpolation (GSI)-based hybrid ensemble–variational (EnVar) scheme was extended for convective scales—including radar reflectivity assimilation—and implemented in real-time spring forecasting experiments. This study compares methods to address model error during the forecast under the context of multiscale initial condition error sampling provided by the EnVar system. A total of 10 retrospective cases were used to explore the optimal design of convection-allowing ensemble forecasts. In addition to single-model single-physics (SMSP) configurations, ensemble forecast experiments compared multimodel (MM) and multiphysics (MP) approaches. Stochastic physics was also applied to MP for further comparison. Neighborhood-based verification of precipitation and composite reflectivity showed each of these model error techniques to be superior to SMSP configurations. Comparisons of MM and MP approaches had mixed findings. The MM approach had better overall skill in heavy-precipitation forecasts; however, MP ensembles had better skill for light (2.54 mm) precipitation and reduced ensemble mean error of other diagnostic fields, particularly near the surface. The MM experiment had the largest spread in precipitation, and for most hours in other fields; however, rank histograms and spaghetti contours showed significant clustering of the ensemble distribution. MP plus stochastic physics was able to significantly increase spread with time to be competitive with MM by the end of the forecast. The results generally suggest that an MM approach is best for early forecast lead times up to 6–12 h, while a combination of MP and stochastic physics approaches is preferred for forecasts beyond 6–12 h. [ABSTRACT FROM AUTHOR]

An object-based probabilistic (OBPROB) forecasting framework is developed and applied, together with a more traditional neighborhood-based framework, to convection-permitting ensemble forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability (MAP) laboratory during the 2017 and 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. Case studies from 2017 are used for parameter tuning and demonstration of methodology, while the 2018 ensemble forecasts are systematically verified. The 2017 case study demonstrates that the OBPROB forecast product can provide a unique tool to operational forecasters that includes convective-scale details such as storm mode and morphology, which are typically lost in neighborhood-based methods, while also providing quantitative ensemble probabilistic guidance about those details in a more easily interpretable format than the more commonly used paintball plots. The case study also demonstrates that objective verification metrics reveal different relative performance of the ensemble at different forecast lead times depending on the verification framework (i.e., object versus neighborhood) because of the different features emphasized by object- and neighborhood-based evaluations. Both frameworks are then used for a systematic evaluation of 26 forecasts from the spring of 2018. The OBPROB forecast verification as configured in this study shows less sensitivity to forecast lead time than the neighborhood forecasts. Both frameworks indicate a need for probabilistic calibration to improve ensemble reliability. However, lower ensemble discrimination for OBPROB than the neighborhood-based forecasts is also noted. [ABSTRACT FROM AUTHOR]

A multiresolution ensemble (MR-ENS) method is developed to resolve a wider range of scales of the background error covariance (BEC) in the hybrid four-dimensional ensemble–variational (4DEnVar) while saving computational costs. MR-ENS is implemented in the NCEP Global Forecast System (GFS) gridpoint statistical interpolation (GSI) hybrid 4DEnVar. MR-ENS generates analysis increment by incorporating high-resolution static BEC and flow-dependent ensemble BECs from both high and low resolutions. MR-ENS is compared with three 4DEnVar update approaches: 1) the single-resolution (SR)-Low approach where the analysis increments are generated from the ensemble BEC and the static BEC at the same low resolution; 2) the dual-resolution (DR) approach where the analysis increment is generated using the high-resolution static BEC and low-resolution ensemble BEC; and 3) the SR-High approach, which is the same as 1) except that all covariances are at high-resolution. Experiments show that MR-ENS improves global and tropical cyclone track forecasts compared to SR-Low and DR. Inclusion of the high-resolution ensemble leads to increased background ensemble spread, better fitting of the background to observations, increased effective ranks, more accurate ensemble error correlation, and increased power of analysis increment at small scales. The majority of the improvement of MR-ENS relative to SR-Low is due to the partial use of high-resolution background ensemble. Compared to SR-High, MR-ENS decreases the overall cost by about 40% and shows comparable global and tropical cyclone track forecast performances. Diagnostics show that particularly in the tropics, MR-ENS improves the analysis increment over a wide range of scales and increases the effective rank of the ensemble BEC to the degree comparable to SR-High. [ABSTRACT FROM AUTHOR]

Numerical weather prediction models often fail to correctly forecast convection initiation (CI) at night. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This study evaluates the impacts made to a nocturnal CI forecast on 26 June 2015 by assimilating a network of atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radio wind profilers, high-frequency rawinsondes, and mobile surface observations using an advanced, ensemble-based data assimilation system. Relative to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI event. Specifically, radio wind profilers and rawinsondes are shown to be the most impactful instrument by enhancing the moisture advection into the region of CI in the forecast. Assimilating thermodynamic profiles collected by the AERIs increases midlevel moisture and improves the ensemble probability of CI in the forecast. The impacts of assimilating the radio wind profilers, AERI retrievals, and rawinsondes remain large throughout forecasting the growth of the CI event into a mesoscale convective system. Assimilating Doppler lidar and surface data only slightly improves the CI forecast by enhancing the convergence along an outflow boundary that partially forces the nocturnal CI event. Our findings suggest that a mesoscale network of profiling and surface instruments has the potential to greatly improve short-term forecasts of nocturnal convection. [ABSTRACT FROM AUTHOR]

The dropsondes released during the Tropical Cyclone Intensity (TCI) field campaign provide high-resolution kinematic and thermodynamic measurements of tropical cyclones within the upper-level outflow and inner core. This study investigates the impact of these upper-level TCI dropsondes on analyses and prediction of Hurricane Patricia (2015) during its rapid intensification (RI) phase using an ensemble–variational data assimilation system. In the baseline experiment (BASE), both kinematic and thermodynamic observations of TCI dropsondes at all levels except the upper levels are assimilated. The upper-level wind and thermodynamic observations are assimilated in additional experiments to investigate their respective impacts. Compared to BASE, assimilating TCI upper-level wind observations improves the accuracy of outflow analyses verified against independent atmospheric motion vector (AMV) observations. It also strengthens the tangential and radial wind near the upper-level eyewall. The inertial stability within the upper-level eyewall is enhanced, and the maximum outflow is more aligned toward the inner core. Additionally, the analyses including the upper-level thermodynamic observations produce a warmer and drier core at high levels. Assimilating both upper-level kinematic and thermodynamic observations also improves the RI forecast. Compared to BASE, assimilating the upper-level wind induces more upright and inward-located eyewall convection, resulting in more latent heat release closer to the warm core. This process leads to stronger inner-core warming. Additionally, the initial warmer upper-level inner core produced by assimilating TCI thermodynamic observations also intensifies the convection and latent heat release within the eyewall, thus further contributing to the improved intensity forecasts. [ABSTRACT FROM AUTHOR]

The ensemble Kalman filter is typically implemented either by applying the localization on the background error covariance matrix (B-localization) or by inflating the observation error variances (R-localization). A mathematical demonstration suggests that for the same effective localization function, the background error covariance matrix from the B-localization method shows a higher rank than the R-localization method. The B-localization method is realized in the ensemble transform Kalman filter (ETKF) by extending the background ensemble perturbations through modulation (MP-localization). Specifically, the modulation functions are constructed from the leading eigenvalues and eigenvectors of the original B-localization matrix. Because of its higher rank than the classic R-localized ETKF, the B-/MP-localized ETKF is termed as the high-rank ETKF (HETKF). The performances of the HETKF and R-localized ETKF were compared through cycled data assimilation experiments using the Lorenz model II. The results show that the HETKF outperforms the R-localized ETKF especially for a small ensemble. The improved analysis in the HETKF is likely associated with the higher rank from the B-/MP-localization method, since its higher rank is expected to contribute more positively to alleviating the rank deficiency issue and thus improve the analysis for a small ensemble. The HETKF is less sensitive to the localization length scales and inflation factors. Furthermore, the experiments suggest that the above conclusion comparing the HETKF and R-localized ETKF does not depend on how the analyzed ensemble perturbations are subselected in the HETKF. [ABSTRACT FROM AUTHOR]

Four case studies from the Plains Elevated Convection at Night (PECAN) field experiment are used to investigate the impacts of horizontal and vertical resolution, and vertical mixing parameterization, on predictions of bore structure and upscale impacts of bores on their mesoscale environment. The reduction of environmental convective inhibition (CIN) created by the bore is particularly emphasized. Simulations are run with horizontal grid spacings ranging from 250 to 1000 m, as well as 50 m for one case study, different vertical level configurations, and different closure models for the vertical turbulent mixing at 250-m horizontal resolution. The 11 July case study was evaluated in greatest detail because it was the best observed case and has been the focus of a previous study. For this case, it is found that 250-m grid spacing improves upon 1-km grid spacing, LES configuration provides further improvement, and enhanced low-level vertical resolution also provides further improvement in terms of qualitative agreement between simulated and observed bore structure. Reducing LES grid spacing further to 50 m provided very little additional advantage. Only the LES experiments properly resolved the upscale influence of reduced low-level CIN. Expanding on the 11 July case study, three other cases from PECAN with diverse observed bore structures were also evaluated. Similar to the 11 July case, enhancing the horizontal and vertical grid spacings, and using the LES closure model for vertical turbulent mixing, all contributed to improved simulations of both the bores themselves and the larger-scale modification of CIN to varying degrees on different cases. [ABSTRACT FROM AUTHOR]

Two methods for assimilating radar reflectivity into deterministic convection-allowing forecasts were compared: an operationally used, computationally less expensive cloud analysis (CA) scheme and a relatively more expensive, but rigorous, ensemble Kalman filter–variational hybrid method (EnVar). These methods were implemented in the Nonhydrostatic Multiscale Model on the B-grid and were tested on 10 cases featuring high-impact deep convective storms and heavy precipitation. A variety of traditional, neighborhood-based, and features-based verification metrics support that the EnVar produced superior free forecasts compared to the CA procedure, with statistically significant differences extending up to 9 h into the forecast. Despite being inferior, the CA scheme was able to provide benefit compared to not assimilating radar reflectivity at all, but limited to the first few forecast hours. While the EnVar is able to partially suppress spurious convection by assimilating 0-dBZ reflectivity observations directly, the CA is not designed to reduce or remove hydrometeors. As a result, the CA struggles more with suppression of spurious convection in the first-guess field, which resulted in high-frequency biases and poor forecast evolution, as illustrated in a few case studies. Additionally, while the EnVar uses flow-dependent ensemble covariances to update hydrometers, thermodynamic, and dynamic variables simultaneously when the reflectivity is assimilated, the CA relies on a radar reflectivity-derived latent heating rate that is applied during a separate digital filter initialization (DFI) procedure to introduce deep convective storms into the model, and the results of CA are shown to be sensitive to the window length used in the DFI. [ABSTRACT FROM AUTHOR]

Assimilating inner-core observations collected from recent field campaign programs such as Tropical Cyclone Intensity (TCI) and Intensity Forecasting Experiment (IFEX) together with the enhanced atmospheric motion vectors (AMVs) produce realistic three-dimensional (3D) analyses using the newly developed GSI-based, continuously cycled, dual-resolution hybrid ensemble–variational data assimilation (DA) system for the Hurricane Weather Research and Forecasting (HWRF) Model for Hurricane Patricia (2015). However, more persistent surface wind maximum spindown is found in the intensity forecast initialized from the realistic analyses produced by the DA system but not from the unrealistic initial conditions produced through vortex modification. Diagnostics in this study reveal that the spindown issue is likely attributed to the deficient HWRF Model physics that are unable to maintain the realistic 3D structures from the DA analysis. The horizontal diffusion is too strong to maintain the realistically observed vertical oscillation of radial wind near the eyewall region. The vertical diffusion profile cannot produce a sufficiently strong secondary circulation connecting the realistically elevated upper-level outflow produced in the DA analysis. Further investigations with different model physics parameterizations demonstrate that spindown can be alleviated by modifying model physics parameterizations. In particular, a modified turbulent mixing parameterization scheme together with a reduced horizontal diffusion is found to significantly alleviate the spindown issue and to improve the intensity forecast. Additional experiments show that the peak-simulated intensity and rapid intensification rate can be further improved by increasing the model resolution. But the model resolution is not as important as model physics in the spindown alleviation. [ABSTRACT FROM AUTHOR]

A novel object-based algorithm capable of identifying and tracking convective outflow boundaries in convection-allowing numerical models is presented in this study. The most distinct feature of the proposed algorithm is its ability to seamlessly analyze numerically simulated density currents and bores, both of which play an important role in the dynamics of nocturnal convective systems. The unified identification and classification of these morphologically different phenomena is achieved through a multivariate approach combined with appropriate image processing techniques. The tracking component of the algorithm utilizes two dynamical constraints, which improve the object association results in comparison to methods based on statistical assumptions alone. Special attention is placed on some of the outstanding challenges regarding the formulation of the algorithm and possible ways to address those in future research. Apart from describing the technical details behind the algorithm, this study also introduces specific algorithm applications relevant to the analysis and prediction of bores. These applications are illustrated for a retrospective case study simulated with a convection-allowing ensemble prediction system. The paper highlights how the newly developed algorithm tools naturally form a foundation for understanding the initiation, structure, and evolution of bores and convective systems in the nocturnal environment. [ABSTRACT FROM AUTHOR]