Dynamic Statistical Model Research Articles

Empirical Effects of Dynamic Human-Body Blockage in 60 GHz Communications

The millimeter-wave (mmWave) bands and other high frequencies above 6 GHz have emerged as a central component of fifth generation cellular standards to deliver high data rates and ultra-low latency. A key challenge in these bands is blockage from obstacles, including the human body. In addition to the reduced coverage, blockage can result in highly intermittent links where the signal quality varies significantly with motion of obstacles in the environment. The blockages have widespread consequences throughout the protocol stack including beam tracking, link adaptation, cell selection, handover, and congestion control. Accurately modeling these blockage dynamics is therefore critical for the development and evaluation of potential mmWave systems. In this work, we present a novel spatial dynamic channel sounding system based on phased array transmitters and receivers operating at 60 GHz. Importantly, the sounder can measure multiple directions rapidly at high speed to provide detailed spatial dynamic measurements of complex scenarios. The system is demonstrated in an indoor home entertainment type setting with multiple moving blockers. Preliminary results are presented on analyzing this data with a discussion of the open issues toward developing statistical dynamic models.

Impact of aerosols on reservoir inflow: A case study for Big Creek Hydroelectric System in California

Accurate and reliable reservoir inflow forecast is instrumental to the efficient operation of the hydroelectric power systems. It has been discovered that natural and anthropogenic aerosols have a great influence on meteorological variables such as temperature, snow water equivalent, and precipitation, which in turn impact the reservoir inflow. Therefore, it is imperative for us to quantify the impact of aerosols on reservoir inflow and to incorporate the aerosol models into future reservoir inflow forecasting models. In this paper, a comprehensive framework was developed to quantify the impact of aerosols on reservoir inflow by integrating the Weather Research and Forecasting model with Chemistry (WRF-Chem) and a dynamic regression model. The statistical dynamic regression model produces forecasts for reservoir inflow based on the meteorological output variables from the WRF-Chem model. The case study was performed on the Florence Lake and Lake Thomas Alva Edison of the Big Creek Hydroelectric Project in the San Joaquin Region. The simulation results show that the presence of aerosols results in a significant reduction of annual reservoir inflow by 4-14%. In the summer, aerosols reduce precipitation, snow water equivalent, and snowmelt that leads to a reduction in inflow by 11-26%. In the spring, aerosols increase temperature and snowmelt which leads to an increase in inflow by 0.6-2%. Aerosols significantly reduce the amount of inflow in the summer when the marginal value of water is extremely high and slightly increase the inflow in the spring when the run-off risk is high. In summary, the presence of aerosols is detrimental to the optimal utilization of hydroelectric power systems.

Extreme Climate Event Changes in China in the 1.5 and 2 °C Warmer Climates: Results From Statistical and Dynamical Downscaling

AbstractThis study compared statistical downscaling model (SD) and dynamical downscaling model (DD) for changes in extreme temperature and precipitation indices, driven by the same global climate model output, in the 1.5 and 2 °C warmer climates in China. Simple bias correction (BC) methods were used to correct the climatology of temperature and precipitation in both models. After BC, both models show comparable performance in reproducing the spatial distributions of the extreme temperature and precipitation indices. Corrected model data were used to analyze future changes. Changing patterns of the extreme temperature indices are similar in the two models. Compared with the 2 °C warmer climate, warming 0.5 °C less can help reduce about 6% of summer day (SU) and 11% of tropical night (TR) increases (relative to 1986–2005) in China. Specifically, the reduced values of TR in northwest and northeast China are larger than 30% and 70%, respectively, in both models. Extreme wet indices will increase in most parts of China in the warmer climates. In DD, 5‐day maximum precipitation (RX5day) will increase by approximately 10% and 14% in the 1.5 and 2 °C warmer climates, respectively, with maximal values (17% and 28%, respectively) occurring in north China. In SD, differences in extreme wet indices between the two warmer climates are small, and RX5day will increase more than 20% in northeast China. In DD, specific humidity will increase, and the East Asian summer monsoon will be enhanced in the warmer climates, favoring a larger increase in wet extremes in north China compared to other parts of east China.

An Improved Prediction of Indian Summer Monsoon Onset From State‐of‐the‐Art Dynamic Model Using Physics‐Guided Data‐Driven Approach

AbstractPrediction of Indian Summer Monsoon onset, which marks the beginning of the South Asian monsoon, has significant implications on monsoon characteristics and subsequent nationwide agricultural planning. Here we make the most skillful prediction of monsoon onset with a physics‐guided data‐driven model integrated with the operational monsoon prediction by Coupled Forecast System version 2. Drivers of the variability of onset, which are well simulated by Coupled Forecast System version 2, viz., tropical Pacific sea surface temperature, reversal of tropospheric temperature gradient and strengthening of westerlies over AS during the early summer are identified as potential predictors. Data‐driven method, Least Absolute Shrinkage and Selection Operator, makes the final selection of predictors for the linear regression model of onset. We obtain an improved correlation of 0.6 between simulated and observed onset dates with the proposed integrated statistical dynamical model as compared to 0.44 obtained with the operational dynamic monsoon prediction model.

Bioinformatics Analysis of Transcriptomic Data Reveals Refined Functional Networks for the Self-Renewal of Mouse Spermatogonial Stem Cells.

Spermatogonial stem cells (SSCs) are exquisitely regulated to reach a balance between proliferation and differentiation in the niche of seminiferous epithelium. Several extrinsic factors such as GDNF are reported to switch the transition, activating various intrinsic signaling pathways. Transcriptomics analysis could provide a comprehensive landscape of gene expression and regulation. Here, we reanalyzed a previously published transcriptome of two cell types (standing for self-renewing and differentiating SSCs correspondingly). First, we proposed a new parameter, the expression index, to sort the genes considering both absolute and relative expression levels. Using a dynamic statistical model, we identified a list of 1119 candidate genes for SSC self-renewal with the best enrichment of canonical markers. Finally, based on interaction relations, we further optimized the list and constructed a refined network containing integrated information of interactions, expression alternations, biological functions, and disease associations. Further annotation of the 521 refined genes involved in the network revealed an enrichment of well-studied signaling pathways. We believe that the refined network could help us better understand the regulation of SSCs' fates, as well as find novel regulators or targets for SSC self-renewal or preservation of male fertility.

Heterogeneity and Unemployment Dynamics

This paper develops new estimates of flows into and out of unemployment that allow for unobserved heterogeneity across workers as well as direct effects of unemployment duration on unemployment-exit probabilities. Unlike any previous paper in this literature, we develop a complete dynamic statistical model that allows us to measure the contribution of different shocks to the short-run, medium-run, and long-run variance of unemployment as well as to specific historical episodes. We find that changes in the inflows of newly unemployed are the key driver of economic recessions and identify an increase in permanent job loss as the most important factor.

A Statistical Dynamic Model for the Turbulent Transport of Kinetic Energy in Shear Layers

Dynamic statistical equations for the turbulent fluxes of scalars and momentum in incompressible flows are derived after time wise integration of the equation for the oscillating transported property, decomposing the turbulent fluxes in terms representing distinct features of the main and fluctuating flow that influence the respective turbulent transport. These expressions provide a means for discussing the gradient diffusion hypothesis for the turbulent transport, for reconsidering the mixed length model in entirely continuous terms, and for seeking possible alternatives or corrections. Applying this methodology to the turbulent transport flux of kinetic energy, two dominating terms are found: one identified with a kinetic energy gradient model for shear layers; the other related to the main velocity gradient. Accordingly, a composed, Statistical Dynamic model is proposed for the turbulent transport of kinetic energy in shear layers, adding a velocity derivative term to Daly and Harlow’s generalized gradient model. This velocity derivative term is calibrated in a nearly homogeneous turbulent shear flow, and the resulting Statistical Dynamic model is proved superior to Daly and Harlow’s and other gradient models in channel and boundary layer flows.

Dynamic Statistical Models for Pyroclastic Density Current Generation at Soufrière Hills Volcano

To mitigate volcanic hazards from pyroclastic density currents, volcanologists generate hazard maps that provide long-term forecasts of areas of potential impact. Several recent efforts in the field develop new statistical methods for application of flow models to generate fully probabilistic hazard maps that both account for, and quantify, uncertainty. However a limitation to the use of most statistical hazard models, and a key source of uncertainty within them, is the time-averaged nature of the datasets by which the volcanic activity is statistically characterized. Where the level, or directionality, of volcanic activity frequently changes, e.g. during protracted eruptive episodes, or at volcanoes that are classified as persistently active, it is not appropriate to make short term forecasts based on longer time-averaged metrics of the activity. Thus, here we build, fit and explore dynamic statistical models for the generation of pyroclastic density current from Soufriere Hills Volcano (SHV) on Montserrat including their respective collapse direction and flow volumes based on 1996-2008 flow datasets. The development of this approach allows for short-term behavioral changes to be taken into account in probabilistic volcanic hazard assessments. We show that collapses from the SHV lava dome follow a clear pattern, and that a series of smaller flows in a given direction often culminate in a larger collapse and thereafter directionality of the flows change. Such models enable short term forecasting (weeks to months) that can reflect evolving conditions such as dome and crater morphology changes and non-stationary eruptive behavior such as extrusion rate variations. For example, the probability of inundation of the Belham Valley in the first 180 days of a forecast period is about twice as high for lava domes facing Northwest toward that valley as it is for domes pointing East toward the Tar River Valley. As rich multi-parametric volcano monitoring dataset become increasingly available, eruption forecasting is becoming an increasingly viable and important research field. We demonstrate an approach to utilize such data in order to appropriately ‘tune’ probabilistic hazard assessments for pyroclastic flows. Our broader objective with development of this method is to help advance time-dependent volcanic hazard assessment, by bridging the

On One Estimate of the Boundary of the Rossby Regime Zone in the Atmosphere

A two-zone model of the atmospheric circulation over the hemisphere is considered. The geographic latitude φ of the boundary between the Rossby circulation regime zone at middle and high latitudes and the Hadley circulation regime zone at low latitudes serves as a model variable. The closeness between the actual and reference (exponential) air-mass distribution over the hemisphere, with respect to Ertel’s modified potential vorticity (MPV), is accounted for. The informational entropy of the statistical MPV distribution in the hemispheric atmosphere and the informational entropy of the eddy regime in the basic storm-track zone are used to determine a statistically (climatically) equilibrium value of φ. The question of atmospheric blocking over the hemisphere is considered using the proposed statistical–dynamical model.

Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical–statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

Land Cover Change Simulations in Yangon Under Several Scenarios of Flood and Earthquake Vulnerabilities with Master Plan

Yangon is the largest city and major economic area in Myanmar. However, it is considered to have a high risk of floods and earthquakes. In order to mitigate future flood and earthquake damage in Yangon, land cover change simulations considering flood and earthquake vulnerabilities are needed to support urban planning and management. This paper proposes land cover change simulations in Yangon from 2020 to 2040 under various scenarios of flood and earthquake vulnerabilities with a master plan. In our methodology, we used a dynamic statistical model to predict urban expansion in Yangon from 2020 to 2040. We employed a master plan as the future dataset to enhance the prediction of urban expansion. We applied flood and earthquake vulnerabilities based on multi-criteria analysis as the areas vulnerable to disaster. We simulated land cover changes from 2020 to 2040 considering the vulnerable areas with a master plan for multiple scenarios. The experiments indicated that by using a master plan, some of the predicted urban areas are still located in areas highly vulnerable to floods and earthquakes. By integrating the prediction of urban expansion with flood and earthquake vulnerabilities, the predicted urban areas can effectively avoid areas highly vulnerable to floods and earthquakes.

Statistical–Dynamical Typhoon Intensity Predictions in the Western North Pacific Using Track Pattern Clustering and Ocean Coupling Predictors

Abstract A statistical–dynamical model for predicting tropical cyclone (TC) intensity has been developed using a track-pattern clustering (TPC) method and ocean-coupled potential predictors. Based on the fuzzy c-means clustering method, TC tracks during 2004–12 in the western North Pacific were categorized into five clusters, and their unique characteristics were investigated. The predictive model uses multiple linear regressions, where the predictand or the dependent variable is the change in maximum wind speed relative to the initial time. To consider TC-ocean coupling effects due to TC-induced vertical mixing and resultant surface cooling, new potential predictors were also developed for maximum potential intensity (MPI) and intensification potential (POT) using depth-averaged temperature (DAT) instead of sea surface temperature (SST). Altogether, 6 static, 11 synoptic, and 3 DAT-based potential predictors were used. Results from a series of experiments for the training period of 2004–12 using TPC and DAT-based predictors showed remarkably improved TC intensity predictions. The model was tested on predictions of TC intensity for 2013 and 2014, which are not used in the training samples. Relative to the nonclustering approach, the TPC and DAT-based predictors reduced prediction errors about 12%–25% between 24- and 96-h lead time. The present model is also compared with four operational dynamical forecast models. At short leads (up to 24 h) the present model has the smallest mean absolute errors. After a 24-h lead time, the present model still shows skill that is comparable with the best operational models.

Cyclone-track based seasonal prediction for South Pacific tropical cyclone activity using APCC multi-model ensemble prediction

This study aims to predict the seasonal TC track density over the South Pacific by combining the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) dynamical prediction system with a statistical model. The hybrid dynamical-statistical model is developed for each of the three clusters that represent major groups of TC best tracks in the South Pacific. The cross validation result from the MME hybrid model demonstrates moderate but statistically significant skills to predict TC numbers across all TC clusters, with correlation coefficients of 0.4 to 0.6 between the hindcasts and observations for 1982/1983 to 2008/2009. The prediction skill in the area east of about 170°E is significantly influenced by strong El Nino, whereas the skill in the southwest Pacific region mainly comes from the linear trend of TC number. The prediction skill of TC track density is particularly high in the region where there is climatological high TC track density around the area 160°E–180° and 20°S. Since this area has a mixed response with respect to ENSO, the prediction skill of TC track density is higher in non-ENSO years compared to that in ENSO years. Even though the cross-validation prediction skill is higher in the area east of about 170°E compared to other areas, this region shows less skill for track density based on the categorical verification due to huge influences by strong El Nino years. While prediction skill of the developed methodology varies across the region, it is important that the model demonstrates skill in the area where TC activity is high. Such a result has an important practical implication—improving the accuracy of seasonal forecast and providing communities at risk with advanced information which could assist with preparedness and disaster risk reduction.

Accurate prediction of shape and size of polyvinyl alcohol beads produced by extrusion dripping

Beads are one of the particulate delivery systems used to achieve protection and/or controlled delivery of different active ingredients or microorganisms. Polyvinyl alcohol is a non-toxic and biodegradable polymer and possesses extensive applications as a biomaterial. In the present work, two different strategies were applied for the prediction of shape and size of polyvinyl alcohol beads. These beads were obtained by extrusion dripping of a boric acid–polyvinyl alcohol aqueous solution into a basic aqueous gelling bath. The shapes and sizes of immature, mature and dry beads were determined using optical microscopy. Two different strategies included statistical and fluid dynamical (mechanistic) models to fit the experimental data. The shape of immature and mature beads was found to be dependent on the viscosity of the dripping solution for the former and the maturation time for the latter. The shape of dry beads was found to be mainly dependent on the particle contraction in the drying process. The size of mature and dried beads was correctly predicted from the operating conditions by means of a statistically developed model and from the dripping solution properties by means of a fluid dynamical approach. The optimal conditions for minimal dried bead size were calculated. The obtained mathematical models allow reduction in the amount of resources and time taken in the initial stages of the development of a novel encapsulated formulation. The mechanistic model may be applied to other polymeric systems once the corresponding parameters have been determined during proof-of-concept experiments.

Dynamic stress-strain relationship of concrete subjected to chloride and sulfate attack

The corrosion of sulfate and chloride salt on concrete has always been an important issue which the multitudinous scholars pay attention, although many of them have carried out fruitful researches on this area, the mechanical responses of concrete corroded by chloride ion and sulfate ions under dynamic loading should be further studied. This article is proposes to explore the relationship between stress and strain of concrete which have been immersed in sodium chloride and sodium sulfate solution, and the dynamic tests of the specimens were carried out at the same time. The dynamic stress-strain curves obtained from tests, theory of statistical damage and Weibull distribution were employed to establish a dynamic statistical damage stress-strain relational model of corroded concrete. The results show that, the damage that defined by the micro-unit inside the concrete can be effectively used to establish this model, and the theoretical curves were in good agreement with experimental curves, which indicates that the established relational model can provide a more accurate description of the dynamic mechanical responses of salt-corroded concrete under dynamic loading, and give some suggestions to durability inspection, service status evaluation, anti-dynamic loading design, seismic design and numerical simulation, etc.

A comparison of large-scale climate signals and the North American Multi-Model Ensemble (NMME) for drought prediction in China

Drought is an extreme natural disaster that can lead to huge socioeconomic losses. Drought prediction ahead of months is helpful for early drought warning and preparations. In this study, we developed a statistical model, two weighted dynamic models and a statistical-dynamic (hybrid) model for 1–6 month lead drought prediction in China. Specifically, statistical component refers to climate signals weighting by support vector regression (SVR), dynamic components consist of the ensemble mean (EM) and Bayesian model averaging (BMA) of the North American Multi-Model Ensemble (NMME) climatic models, and the hybrid part denotes a combination of statistical and dynamic components by assigning weights based on their historical performances. The results indicate that the statistical and hybrid models show better rainfall predictions than NMME-EM and NMME-BMA models, which have good predictability only in southern China. In the 2011 China winter-spring drought event, the statistical model well predicted the spatial extent and severity of drought nationwide, although the severity was underestimated in the mid-lower reaches of Yangtze River (MLRYR) region. The NMME-EM and NMME-BMA models largely overestimated rainfall in northern and western China in 2011 drought. In the 2013 China summer drought, the NMME-EM model forecasted the drought extent and severity in eastern China well, while the statistical and hybrid models falsely detected negative precipitation anomaly (NPA) in some areas. Model ensembles such as multiple statistical approaches, multiple dynamic models or multiple hybrid models for drought predictions were highlighted. These conclusions may be helpful for drought prediction and early drought warnings in China.

Comparison of the performance of three types of multiple regression for phenology in Bavaria in a dynamical-statistical model approach

Comparison of the performance of three types of multiple regression for phenology in Bavaria in a dynamical-statistical model approach

NMME-based hybrid prediction of Atlantic hurricane season activity

A hybrid dynamical–statistical model is pursued for prediction of Atlantic seasonal hurricane activity driven by output of the North American Multimodel Ensemble (NMME). This is an updated version of a proven multiple linear regression method conditioned on forecast vertical wind shear from the Climate Forecast System and observed sea surface temperatures (SSTs). The method pursued for prediction utilizes August–October (ASO) Main Development Region (MDR; 10–20°N, 20–80°W) vertical wind shear and observed North Atlantic (NATL; 55–65°N, 30–60°W) SST averaged over the 3 months preceding the forecast in conjunction with the full hurricane climatology. NMME forecasts improve upon representations relative to individual members. The NMME multi-model mean better reproduces vertical wind shear distributions over the MDR and captures the observed relationships between SST and vertical wind shear with hurricane trend and interannual variability despite occasionally poor reproductions by individual members. Cross-validation reveals the multi-model average of the hybrid model outputs from the individual NMME members yields forecast errors 10–30% less than the individual members, while correlations with observed hurricane-related activity typically improve. The NMME methodology is shown to be competitive with official outlooks from Colorado State University and the National Oceanic and Atmospheric Administration over recent years.

Statistical state dynamics-based analysis of the physical mechanisms sustaining and regulating turbulence in Couette flow

This paper describes a study of the self-sustaining process in wall-turbulence based on a second order statistical state dynamics (SSD) model of Couette flow. SSD models with this form are referred to as S3T models and self-sustain turbulence with a mean flow and second order perturbation structure similar to that obtained by DNS. The use of a SSD model to study the physical mechanisms underlying turbulence has advantages over the traditional approach of studying the dynamics of individual realizations of turbulence. One advantage is that the analytical structure of SSD isolates and directly expresses the interaction between the coherent mean flow and the incoherent perturbation components of the turbulence. Isolation of the interaction between these components reveals how this interaction underlies both the maintenance of the turbulence variance by transfer of energy from the externally driven flow to the perturbation components as well as the enforcement of the observed statistical mean turbulent state by feedback regulation between the mean and perturbation fields. Another advantage of studying turbulence using SSD models is that the analytical structure of S3T turbulence can be completely characterized. For example, turbulence in the S3T system is maintained by a parametric growth mechanism. Furthermore, the equilibrium statistical state of the turbulence can be demonstrated to be enforced by feedback regulation in which transient growth of the incoherent perturbations episodically suppresses coherent streak growth preventing runaway parametric growth of the incoherent turbulent component. Using S3T to isolate these parametric growth and feedback regulation mechanisms allows a detailed characterization of the dynamics of the self-sustaining process in S3T turbulence with compelling implications for understanding the mechanism of wall-turbulence.

Statistical state dynamics based theory for the formation and equilibration of Saturn's north polar jet

Coherent jets with most of the kinetic energy of the flow are common in atmospheric turbulence. In the gaseous planets these jets are maintained by incoherent turbulence excited by small-scale convection. Large-scale coherent waves are sometimes observed to coexist with the jets; a prominent example is Saturn's hexagonal North polar jet (NPJ). The mechanism responsible for forming and maintaining such a turbulent state remains elusive. The coherent planetary-scale component of the turbulence arises and is maintained by interaction with the incoherent small-scale turbulence component. Theoretical understanding of the dynamics of the jet/wave/turbulence coexistence regime is gained by employing a statistical state dynamics (SSD) model. Here, a second-order closure implementation of a two-layer beta-plane SSD is used to develop a theory that accounts for the structure and dynamics of the NPJ. Asymptotic analysis of the SSD equilibrium in the weak jet damping limit predicts a universal jet structure in agreement with NPJ observations. This asymptotic theory also predicts the wavenumber (six) of the prominent jet perturbation. Analysis with this model of the jet/wave/turbulence regime dynamics reveals that jet formation is controlled by the effective value of $\beta$; the required value of this parameter for correspondence with observation is obtained. As this is a robust prediction it is taken as an indirect observation of a deep poleward sloping stable layer beneath the NPJ. The slope required is obtained from observations of NPJ structure as is the small-scale turbulence excitation required to maintain the jet. The observed jet structure is then predicted by the theory as is the wave-six disturbance. This wave, which is identified with the least stable mode of the equilibrated jet, is shown to be primarily responsible for equilibrating the jet with the observed structure and amplitude.