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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Designing one seat for multi-sitters and multi-activities is challenging especially in a very restrained aircraft economy class cabin. In this paper, the effects of seat parameters and sitters' anthropometric dimensions on seat profile and optimal compressed seat pan surface were studied using a newly built multi-adjustable experimental seat. The 'optimal' seat pan contact surface was obtained by controlling the height of 52 cylinders so that the normal contact force was distributed to all cylinders as evenly as possible. With 13 other motorized adjustments controllable by a computer, individual seat profile in the symmetry plane such as seat height, seat pan length, seat pan angle, lumbar protrusion and headrest position were also studied. Data were collected from 36 men and women of varying body size testing 40 seat configurations. Parametric models were obtained for predicting seat profile and optimal compressed seat pan seat surface in function of seat pan and back rest angles for two sitting postures. It is expected that the proposed parametric models provide necessary reference values in seat development for a better fit of a target population of sitters with large varying body size. [ABSTRACT FROM AUTHOR]

The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dallas–Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system. Many nonconventional observations, including Earth Networks and Citizen Weather Observer Program surface stations, are combined with conventional operational data to form the test bed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. In this study, the GSI-based EnKF system was used together with the WRF-ARW Model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case. Data denial experiments tested the impact of high-frequency (5 min) assimilation of nonconventional data on the timing and location of CI and subsequent storm evolution. Results showed nonconventional observations were necessary to capture details in the dryline structure causing localized enhanced convergence and leading to CI. Diagnosis of denial-minus-control fields showed the cumulative influence each observing network had on the resulting CI forecast. It was found that most of this impact came from the assimilation of thermodynamic observations in sensitive areas along the dryline gradient. Accurate metadata were found to be crucial toward the future application of nonconventional observations in high-resolution assimilation and forecast systems. [ABSTRACT FROM AUTHOR]

This paper presents a case study from an intensive observing period (IOP) during the Plains Elevated Convection at Night (PECAN) field experiment that was focused on a bore generated by nocturnal convection. Observations from PECAN IOP 25 on 11 July 2015 are used to evaluate the performance of high-resolution Weather Research and Forecasting Model forecasts, initialized using the Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter. The focus is on understanding model errors and sensitivities in order to guide forecast improvements for bores associated with nocturnal convection. Model simulations of the bore amplitude are compared against eight retrieved vertical cross sections through the bore during the IOP. Sensitivities of forecasts to microphysics and planetary boundary layer (PBL) parameterizations are also investigated. Forecasts initialized before the bore pulls away from the convection show a more realistic bore than forecasts initialized later from analyses of the bore itself, in part due to the smoothing of the existing bore in the ensemble mean. Experiments show that the different microphysics schemes impact the quality of the simulations with unrealistically weak cold pools and bores with the Thompson and Morrison microphysics schemes, cold pools too strong with the WDM6 and more accurate with the WSM6 schemes. Most PBL schemes produced a realistic bore response to the cold pool, with the exception of the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme, which creates too much turbulent mixing atop the bore. A new method of objectively estimating the depth of the near-surface stable layer corresponding to a simple two-layer model is also introduced, and the impacts of turbulent mixing on this estimate are discussed. [ABSTRACT FROM AUTHOR]

A prototype convection-allowing system using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model and employing an ensemble Kalman filter (EnKF) data assimilation technique has been developed and used during the spring 2016 and 2017 Hazardous Weather Testbeds. This system assimilates WSR-88D reflectivity and radial velocity, geostationary satellite cloud water path (CWP) retrievals, and available surface observations over a regional domain with a 3-km horizontal resolution at 15-min intervals, with 3-km initial conditions provided by an experimental High-Resolution Rapid Refresh ensemble (HRRR-e). However, no information on upper-level thermodynamic conditions in cloud-free regions is currently assimilated, as few timely observations exist. One potential solution is to also assimilate clear-sky satellite radiances, which provide information on mid- and upper-tropospheric temperature and moisture conditions. This research assimilates GOES-13 imager water vapor band (6.5 μm) radiances using the GSI-EnKF system to take advantage of the Community Radiative Transfer Model (CRTM) integration. Results using four cases from May 2016 showed that assimilating radiances generally had a neutral-to-positive impact on the model analysis, reducing humidity bias and/or errors at the appropriate model levels where verification observations were present. The effects on high-impact weather forecasts, as verified against forecast reflectivity and updraft helicity, were mixed. Three cases (9, 22, and 24 May) showed some improvement in skill, while the other (25 May) performed worse, despite the improved environment. This research represents the first step in designing a high-resolution ensemble data assimilation system to use GOES-16 Advanced Baseline Imager data, which provides additional water vapor bands and increased spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Objective The effects of seat height and anthropometric dimensions on drivers' preferred postures were investigated using a multiadjustable vehicle mock-up with a large number of adjustments and extended ranges. Background Many studies have been conducted on preferred driving posture under different test conditions showing mixed and even contradictory findings. No studies thus far have considered the clutch and compared Chinese and European drivers. Method Four seat height conditions were tested: free and three imposed heights (250, 300, and 350 mm). Sixty-one subjects (40 French-born and 21 Chinese-born) participated in the experiment, covering a large range of stature and sitting height-to-stature ratio. The RAMSIS kinematic model was used to reconstruct postures, and main intersegmental angles were extracted for characterizing posture. Results Under the free seat height condition, no significant differences in preferred intersegmental angles were observed between different participant groups. Seat height mainly affected trunk-thigh angle, whereas it had almost no effect on trunk orientation and other intersegmental angles. Chinese participants sat more forward in the seat, leading to a more opened trunk-thigh angle and a more reclined trunk. Conclusions Results suggest that intersegmental angles of preferred posture are not dependent on anthropometric dimensions, although shorter drivers prefer a slightly less reclined trunk. Self-selected driving posture results from a compromise between maintaining the intersegmental angles in one's preferred range and a preferred trunk orientation in space. Applications The findings contribute to a better understanding of preferred driving postures and would be helpful for improving vehicle interior design. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for rapid seismic risk assessment of bridges using aerial photogrammetric surveys conducted by Unmanned Aerial Vehicles (UAVs). First, the data acquisition process for the photogrammetric 3D reconstruction of an asset and the subsequent procedure for computer-vision-based automatic extraction of visible geometric features are presented. Then, the extracted features are combined in the structural models to perform a seismic risk assessment in terms of capacity-to-demand ratios. Uncertainties are related to the material and geometric hidden variables. Monte Carlo simulation is performed, assuming uniform (uninformed) distributions for the input variables affected by epistemic uncertainty. The ranges of such distributions are estimated based on engineering judgment and knowledge of construction practices. The analysis provides an estimate of the epistemic uncertainties of the capacity-to-demand ratios, with limited available information obtained from UAV aerial photogrammetry. The feasibility and applicability of the proposed framework are demonstrated through a case study of a simply supported bridge in the Italian highway network. The 3D reconstruction process is validated by comparing the aerial survey with a laser scanner survey. The seismic risk of the selected bridge is assessed using the geometric information obtained from the UAV aerial photogrammetric survey. Finally, the paper discusses the applicability of the proposed methodology for bridge management purposes in an automated framework and at network scale. • Framework for rapid seismic risk assessment of bridges using UVA surveys. • Workflow of UAV survey-based 3D reconstruction process and validation. • Accounting Epistemic uncertainty from the input variables by Monte Carlo simulations. • Estimation of the pier capacity-to-demand ratios with the epistemic uncertainties. [ABSTRACT FROM AUTHOR]

Multiscale ensemble-based data assimilation and forecasts were performed in real time during the Plains Elevated Convection At Night (PECAN) field experiment. A 20-member ensemble of forecasts at 4-km grid spacing was initialized daily at both 1300 and 1900 UTC, together with a deterministic forecast at 1-km grid spacing initialized at 1300 UTC. The configuration of the GSI-based data assimilation and forecast system was guided by results presented in Part I of this two-part study. The present paper describes the implementation of the real-time system and the extensive forecast products that were generated to support the unique interests of PECAN researchers. Subjective and objective verification of the real-time forecasts from 1 June through 15 July 2015 is conducted, with an emphasis on nocturnal mesoscale convective systems (MCSs), nocturnal convective initiation (CI), nocturnal low-level jets (LLJs), and bores on the nocturnal stable layer. Verification of nocturnal precipitation during overnight hours, a proxy for MCSs, shows both greater skill and spread for the 1300 UTC forecasts than the 1900 UTC forecasts. Verification against observed soundings reveals that the forecast LLJs systematically peak, veer, and dissipate several hours before the observations. Comparisons with bores that passed over an Atmospheric Emitted Radiance Interferometer reveal an ability to predict borelike features that is greatly improved at 1-km, compared with 4-km, grid spacing. Objective verification of forecast CI timing reveals strong sensitivity to the PBL scheme but an overall unbiased ensemble. [ABSTRACT FROM AUTHOR]

Few investigations have been performed on how the ranges of preferred angles should be used for vehicle interior discomfort evaluation. This study investigated the ranges of the least uncomfortable joint angles considering both inter-individual and intra-individual variability. The driving postures of sixty-one subjects were collected using two multi-adjustable vehicle mock-ups under four test conditions by gradually adding the number of control parameters (constraints), from the "least-constrained" driving condition to the configurations close to currently existing vehicles. With help of subjective discomfort evaluation, the intra-and inter-individual variation ranges of least uncomfortable postural angles were quantified. Results show that intra-individual variation ranges of postural angles were much smaller than those of inter-individual variation as expected. An individual may not feel comfortable throughout the whole range of comfortable angles from all participants. Possible relationships between perceived discomfort and ranges of inter and inter individual variations in least uncomfortable angles were explored, suggesting that the inter ranges could be used to detect potential problems of postural discomfort and the intra ranges could be considered as optimum ranges. A three color model, based on the intra-and inter-individual variability ranges of comfortable driving postures, was proposed for ergonomics assessment of a vehicle configuration. [ABSTRACT FROM AUTHOR]

In this exploratory study, a series of perturbations to the land surface model (LSM) component of the Weather Research and Forecasting (WRF) Model was developed to investigate the sensitivity of forecasts of severe thunderstorms and heavy precipitation at 4-km grid spacing and whether such perturbations could improve ensemble forecasts at this scale. The perturbations (generated using a combination of perturbing fixed parameters and using separate schemes, one of which-Noah-MP-is new among the WRF modeling community) were applied to a 10-member ensemble including other mixed physics parameterizations and compared against an identically configured ensemble that did not include the LSM perturbations to determine their impact on probabilistic forecasts. A third ensemble using only the LSM perturbations was also configured. The results from 14 (in total) 36-h ensemble forecasts suggested the LSM perturbations resulted in systematic improvement in ensemble dispersion and error characteristics. Lower-tropospheric temperature, moisture, and wind fields were all improved, as were probabilistic precipitation forecasts. Biases were not systematically altered, although some outlier members are present. Examination of near-surface temperature and mixing ratio fields, surface energy fluxes, and soil fields revealed tendencies caused by certain perturbations. A case study featuring tornadic supercells illustrated the physical causes of some of these tendencies. The results of this study suggest LSM perturbations can sample a dimension of model error not yet sampled systematically in most ensembles and should be included in convection-allowing ensembles. [ABSTRACT FROM AUTHOR]

The impacts of multiscale flow-dependent initial condition (IC) perturbations for storm-scale ensemble forecasts of midlatitude convection are investigated using perfect-model observing system simulation experiments. Several diverse cases are used to quantitatively and qualitatively understand the impacts of different IC perturbations on ensemble forecast skill. Scale dependence of the results is assessed by evaluating 2-h storm-scale reflectivity forecasts separately from hourly accumulated mesoscale precipitation forecasts. Forecasts are initialized with different IC ensembles, including an ensemble of multiscale perturbations produced by a multiscale data assimilation system, mesoscale perturbations produced at a coarser resolution, and filtered multiscale perturbations. Mesoscale precipitation forecasts initialized with the multiscale perturbations are more skillful than the forecasts initialized with the mesoscale perturbations at several lead times. This multiscale advantage is due to greater consistency between the IC perturbations and IC uncertainty. This advantage also affects the short-term, smaller-scale forecasts. Reflectivity forecasts on very small scales and very short lead times are more skillful with the multiscale perturbations as a direct result of the smaller-scale IC perturbation energy. The small-scale IC perturbations also contribute to some improvements to the mesoscale precipitation forecasts after the ~5-h lead time. Altogether, these results suggest that the multiscale IC perturbations provided by ensemble data assimilation on the convection-permitting grid can improve storm-scale ensemble forecasts by improving the sampling of IC uncertainty, compared to downscaling of IC perturbations from a coarser-resolution ensemble. [ABSTRACT FROM AUTHOR]

The efficacy of a stochastic kinetic energy backscatter (SKEB) scheme to improve convection-allowing probabilistic forecasts was studied. While SKEB has been explored for coarse, convection-parameterizing models, studies of SKEB for convective scales are limited. Three ensembles were compared. The SKMP ensemble used mixed physics with the SKEB scheme, whereas the MP ensemble was configured identically but without using the SKEB scheme. The SK ensemble used the SKEB scheme with no physics diversity. The experiment covered May 2013 over the central United States on a 4-km Weather Research and Forecasting (WRF) Model domain. The SKEB scheme was successful in increasing the spread in all fields verified, especially mid- and upper-tropospheric fields. Additionally, the rmse of the ensemble mean was maintained or reduced, in some cases significantly. Rank histograms in the SKMP ensemble were flatter than those in the MP ensemble, indicating the SKEB scheme produces a less underdispersive forecast distribution. Some improvement was seen in probabilistic precipitation forecasts, particularly when examining Brier scores. Verification against surface observations agree with verification against Rapid Refresh (RAP) model analyses, showing that probabilistic forecasts for 2-m temperature, 2-m dewpoint, and 10-m winds were also improved using the SKEB scheme. The SK ensemble gave competitive forecasts for some fields. The SK ensemble had reduced spread compared to the MP ensemble at the surface due to the lack of physics diversity. These results suggest the potential utility of mixed physics plus the SKEB scheme in the design of convection-allowing ensemble forecasts. [ABSTRACT FROM AUTHOR]

A GSI-based data assimilation (DA) system, including three-dimensional variational assimilation (3DVar) and ensemble Kalman filter (EnKF), is extended to the multiscale assimilation of both meso- and synoptic-scale observation networks and convective-scale radar reflectivity and velocity observations. EnKF and 3DVar are systematically compared in this multiscale context to better understand the impacts of differences between the DA techniques on the analyses at multiple scales and the subsequent convective-scale precipitation forecasts. Averaged over 10 diverse cases, 8-h precipitation forecasts initialized using GSI-based EnKF are more skillful than those using GSI-based 3DVar, both with and without storm-scale radar DA. The advantage from radar DA persists for ~5 h using EnKF, but only ~1 h using 3DVar. A case study of an upscale growing MCS is also examined. The better EnKF-initialized forecast is attributed to more accurate analyses of both the mesoscale environment and the storm-scale features. The mesoscale location and structure of a warm front is more accurately analyzed using EnKF than 3DVar. Furthermore, storms in the EnKF multiscale analysis are maintained during the subsequent forecast period. However, storms in the 3DVar multiscale analysis are not maintained and generate excessive cold pools. Therefore, while the EnKF forecast with radar DA remains better than the forecast without radar DA throughout the forecast period, the 3DVar forecast quality is degraded by radar DA after the first hour. Diagnostics revealed that the inferior analysis at mesoscales and storm scales for the 3DVar is primarily attributed to the lack of flow dependence and cross-variable correlation, respectively, in the 3DVar static background error covariance. [ABSTRACT FROM AUTHOR]

The goal of this study is to improve an ensemble-based estimation for forecast sensitivity to observations that is straightforward to apply using existing products of any ensemble data assimilation system. Because of limited ensemble sizes compared to the large degrees of freedom in typical models, it is necessary to apply localization techniques to obtain accurate estimates. Fixed localization techniques do not guarantee accurate impact estimates, because as forecast time increases the error correlation structures evolve with the flow. Here a dynamical localization method is applied to improve the observation impact estimate. The authors employ a Monte Carlo 'group filter' technique to limit the effects of sampling error via regression confidence factor (RCF). Experiments make use of the local ensemble transform Kalman filter (LETKF) with a simple two-layer primitive equation model and simulated observations. Results show that the shape, location, time dependency, and variable dependency of RCF localization functions are consistent with underlying dynamical processes of the model. Application of RCF localization to ensemble-estimated impact showed marked improvement especially for longer forecasts and at midlatitudes, when systematically verified against actual impact in RMSE and skill scores. The impact estimates near the equator were not as effective because of large discrepancies between the RCF function and the localization used at assimilation time. These latter results indicate that there exists an inherent relationship between the localization applied during the assimilation time and the proper localization choice for observation impact estimates. Application of RCF for automatically tuned localization is introduced and tested for a single observation experiment. [ABSTRACT FROM AUTHOR]

This paper presents a novel quadruple-mode coaxial cavity resonator and its application to bandpass filters with compact size and improved quality factor (Q-factor). Unlike the conventional coaxial cavity resonator, the proposed resonator is made up of four inner conductive posts within a single cavity, which provides new quadruple resonant modes to realize bandpass filter. No metallic walls inside the cavity are required, and thus the utilization efficiency of the cavity space is improved. As a result, the unloaded Q can be approximately 15% higher in comparison to the conventional coaxial resonator, or 30%–35% volume saving can be achieved while maintaining the similar Q-factor value with the conventional designs. In addition, due to the multiple cross-coupling occurring within the cavity, including the source-to-load direct coupling, four flexible transmission zeros can be created to realize different filtering functions. The complete design methodology of filters based on the proposed resonator is discussed, starting with the resonator characteristics and the coupling scheme. Simulations as well as experimental results of four- and eight-pole filters are presented to validate this attractive design concept. Good agreement between measured and computed results is obtained. [ABSTRACT FROM PUBLISHER]

A four-dimensional (4D) ensemble-variational data assimilation (DA) system (4DEnsVar) was developed, building upon the infrastructure of the gridpoint statistical interpolation (GSI)-based hybrid DA system. 4DEnsVar used ensemble perturbations valid at multiple time periods throughout the DA window to estimate 4D error covariances during the variational minimization, avoiding the tangent linear and adjoint of the forecast model. The formulation of its implementation in GSI was described. The performance of the system was investigated by evaluating the global forecasts and hurricane track forecasts produced by the NCEP Global Forecast System (GFS) during the 5-week summer period assimilating operational conventional and satellite data. The newly developed system was used to address a few questions regarding 4DEnsVar. 4DEnsVar in general improved upon its 3D counterpart, 3DEnsVar. At short lead times, the improvement over the Northern Hemisphere extratropics was similar to that over the Southern Hemisphere extratropics. At longer lead times, 4DEnsVar showed more improvement in the Southern Hemisphere than in the Northern Hemisphere. The 4DEnsVar showed less impact over the tropics. The track forecasts of 16 tropical cyclones initialized by 4DEnsVar were more accurate than 3DEnsVar after 1-day forecast lead times. The analysis generated by 4DEnsVar was more balanced than 3DEnsVar. Case studies showed that increments from 4DEnsVar using more frequent ensemble perturbations approximated the increments from direct, nonlinear model propagation better than using less frequent ensemble perturbations. Consistently, the performance of 4DEnsVar including both the forecast accuracy and the balances of analyses was in general degraded when less frequent ensemble perturbations were used. The tangent linear normal mode constraint had positive impact for global forecast but negative impact for TC track forecasts. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the effects of the free pedal position adjustment on clutching movements of the left lower limb as well as on the perceived discomfort. Six automotive clutch pedal configurations were tested by 20 subjects (5 young females, 5 young males, 5 older females, 5 older males) using a multi-adjustable experimental mock-up. Results showed that the pedal position was adjusted to ensure a good starting pedal position allowing a less flexed ankle and avoiding unnecessary leg displacement from the foot rest to the position at start depression. Pedal position adjustment seemed not motivated by reducing joint torque though discomfort ratings were found significantly correlated with knee and ankle torques at the end of depression. The present work also illustrates that the less-constrained motion concept is helpful for a better understanding of people preference and useful for identifying motion-related biomechanical parameters to be considered for defining assessment criteria. [Copyright &y& Elsevier]

Two approaches for accounting for errors in quantitative precipitation forecasts (QPFs) due to uncertainty in the microphysics (MP) parameterization in a convection-allowing ensemble are examined. They include mixed MP (MMP) composed mostly of double-moment schemes and perturbing parameters within the Weather Research and Forecasting single-moment 6-class microphysics scheme (WSM6) MP scheme (PPMP). Thirty-five cases of real-time storm-scale ensemble forecasts produced by the Center for Analysis and Prediction of Storms during the NOAA Hazardous Weather Testbed 2011 Spring Experiment were examined. The MMP ensemble had better fractions Brier scores (FBSs) for most lead times and thresholds, but the PPMP ensemble had better relative operating characteristic (ROC) scores for higher precipitation thresholds. The pooled ensemble formed by randomly drawing five members from the MMP and PPMP ensembles was no more skillful than the more accurate of the MMP and PPMP ensembles. Significant positive impact was found when the two were combined to form a larger ensemble. The QPF and the systematic behaviors of derived microphysical variables were also examined. The skill of the QPF among different members depended on the thresholds, verification metrics, and forecast lead times. The profiles of microphysics variables from the double-moment schemes contained more variation in the vertical than those from the single-moment members. Among the double-moment schemes, WDM6 produced the smallest raindrops and very large number concentrations. Among the PPMP members, the behaviors were found to be consistent with the prescribed intercept parameters. The perturbed intercept parameters used in the PPMP ensemble fell within the range of values retrieved from the double-moment schemes. [ABSTRACT FROM AUTHOR]

Multiscale convection-allowing precipitation forecast perturbations are examined for two forecasts and systematically over 34 forecasts out to 30-h lead time using Haar Wavelet decomposition. Two small-scale initial condition (IC) perturbation methods are compared to the larger-scale IC and physics perturbations in an experimental convection-allowing ensemble. For a precipitation forecast driven primarily by a synoptic-scale baroclinic disturbance, small-scale IC perturbations resulted in little precipitation forecast perturbation energy on medium and large scales, compared to larger-scale IC and physics (LGPH) perturbations after the first few forecast hours. However, for a case where forecast convection at the initial time grew upscale into a mesoscale convective system (MCS), small-scale IC and LGPH perturbations resulted in similar forecast perturbation energy on all scales after about 12 h. Small-scale IC perturbations added to LGPH increased total forecast perturbation energy for this case. Averaged over 34 forecasts, the small-scale IC perturbations had little impact on large forecast scales while LGPH accounted for about half of the error energy on such scales. The impact of small-scale IC perturbations was also less than, but comparable to, the impact of LGPH perturbations on medium scales. On small scales, the impact of small-scale IC perturbations was at least as large as the LGPH perturbations. The spatial structure of small-scale IC perturbations affected the evolution of forecast perturbations, especially at medium scales. There was little systematic impact of the small-scale IC perturbations when added to LGPH. These results motivate further studies on properly sampling multiscale IC errors. [ABSTRACT FROM AUTHOR]

An ensemble Kalman filter-variational hybrid data assimilation system based on the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) system was developed. The performance of the system was investigated using the National Centers for Environmental Prediction (NCEP) Global Forecast System model. Experiments covered a 6-week Northern Hemisphere winter period. Both the control and ensemble forecasts were run at the same, reduced resolution. Operational conventional and satellite observations along with an 80-member ensemble were used. Various configurations of the system including one- or two-way couplings, with zero or nonzero weights on the static covariance, were intercompared and compared with the GSI 3DVar system. It was found that the hybrid system produced more skillful forecasts than the GSI 3DVar system. The inclusion of a static component in the background-error covariance and recentering the analysis ensemble around the variational analysis did not improve the forecast skill beyond the one-way coupled system with zero weights on the static covariance. The one-way coupled system with zero static covariances produced more skillful wind forecasts averaged over the globe than the EnKF at the 1-5-day lead times and more skillful temperature forecasts than the EnKF at the 5-day lead time. Sensitivity tests indicated that the difference may be due to the use of the tangent linear normal mode constraint in the variational system. For the first outer loop, the hybrid system showed a slightly slower (faster) convergence rate at early (later) iterations than the GSI 3DVar system. For the second outer loop, the hybrid system showed a faster convergence. [ABSTRACT FROM AUTHOR]

Forecasts generated by the Center for Analysis and Prediction of Storms with 1- and 4-km grid spacing using the Advanced Research Weather Research and Forecasting Model (ARW-WRF; ARW1 and ARW4, respectively) for the 2009-11 NOAA Hazardous Weather Testbed Spring Experiments are compared and verified. Object-based measures, including average values of object attributes, the object-based threat score (OTS), and the median of maximum interest (MMI) are used for the verification. Verification was first performed against observations at scales resolvable by each forecast model and then performed at scales resolvable by both models by remapping ARW1 to the ARW4 grid (ARW1to4). Thirty-hour forecasts of 1-h accumulated precipitation initialized at 0000 UTC on 22, 36, and 33 days during the spring of 2009, 2010, and 2011, respectively, are evaluated over a domain covering most of the central and eastern United States. ARW1, ARW1to4, and ARW4 all significantly overforecasted the number of objects during diurnal convection maxima. The overforecasts by ARW1 and ARW1to4 were more pronounced than ARW4 during the first convection maximum at 1-h lead time. The average object area and aspect ratio were closer to observations for ARW1 and ARW1to4 than for ARW4. None of the models showed a significant advantage over the others for average orientation angle and centroid location. Increased accuracy for ARW1, compared to ARW4, was statistically significant for the MMI but not the OTS. However, ARW1to4 had similar MMI and OTS as ARW4 at most lead times. These results are consistent with subjective evaluations that the greatest impact of grid spacing is on the smallest resolvable objects. [ABSTRACT FROM AUTHOR]

The steady-state assumption commonly used in object-based tracking algorithms may be insufficient to determine the right track when a convective storm goes through a complicated evolution. Such an issue is exacerbated by the relatively coarse output frequency of current convection allowing model (CAM) forecasts (e.g., hourly), giving rise to many spatially well resolved but temporally not well resolved storms that steady-state assumption could not account for. To reliably track simulated storms in CAM outputs, this study proposed an object-based method with two new features. First, the method explicitly estimated the probability of each probable track based on either its immediate past and future motion or a reliable "first-guess motion" derived from storm climatology or near-storm environmental variables. Second, object size was incorporated into the method to help identify temporally not well resolved storms and minimize false tracks derived for them. Parameters of the new features were independently derived from a storm evolution analysis using 2-min Multi-Radar Multi-Sensor (MRMS) data and hourly CAM forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability Laboratory (MAP) from May 2019. The performance of the new method was demonstrated with hourly MRMS and CAM forecast examples from May 2018. A systematic evaluation of four severe weather events indicated 99% accuracy achieved for over 600 hourly MRMS tracks derived with the proposed tracking method. [ABSTRACT FROM AUTHOR]