Model Initial Conditions Research Articles

Assimilation of Passive Microwave Streamflow Signals for Improving Flood Forecasting: A First Study in Cubango River Basin, Africa

Floods are among the most frequently occurring and disastrous natural hazards in the world. The overarching goal of this study is to investigate the utility of passive microwave AMSR-E signal and TRMM based precipitation estimates in improving flood prediction at the sparsely gauged Cubango River Basin, Africa. This is accomplished by coupling a widely used conceptual rainfall-runoff hydrological model with Ensemble Square Root Filter (EnSRF) to account for uncertainty in both forcing data and model initial conditions. Three experiments were designed to quantify the contributions of the AMSR-E signal to the flood prediction accuracy, in comparison to the benchmark assimilation of in-situ streamflow observations, for both “Open Loop” and “Assimilation” modules. In general, the EnSRF assimilation of both in-situ observations and AMSR-E signal-converted-streamflow effectively improved streamflow modeling performance in terms of three statistical measures. In order to further investigate AMSR-E signals' contribution to extreme events prediction skill, the upper 10th percentile daily streamflow was taken as the threshold. Results show significantly improved skill and detectability of floods as well as reduced false alarm rates. Given the global availability of satellite-based precipitation from current TRMM and future GPM, together with soil moisture information from the current AMSR-E and future SMAP mission at near real-time, this “first attempt” study at a sparsely gauged African basin shows that opportunities exist for an integrated application of a suite of satellite data in improving flood forecasting worldwide by careful fusion of remote sensing and in-situ observations.

Influence of porosity structures on mixing-induced reactivity at chemical equilibrium in mobile/immobile Multi-Rate Mass Transfer (MRMT) and Multiple INteracting Continua (MINC) models

Trapping mechanisms and slow diffusion in poorly connected porosities are well modeled by several anomalous transport models including the Multi-Rate Mass Transfer framework (MRMT). In MRMT, solutes in fast mobile advective zones are slowed down by first-order exchanges with immobile zones. While MRMT models have been used essentially for conservative transport, we investigate their relevance to reactive transport. To this end, we analyze the influence of the structure of the diffusive porosity zone on the distribution of concentrations within the immobile zone and on the reactivity of simple precipitation/dissolution bimolecular reactions at equilibrium. We build Multi-Rate Mass Transfer (MRMT) and Multiple INteracting Continua (MINC) models with equivalent transport characteristics. Both models have the same mobile zone concentrations at any time. They however differ by the connectivity structure of their immobile zones. MRMT has a star-shape connectivity structure with the mobile zone linked to all immobile zones and acting as the sole exchanger. MINC has a chained-type connectivity where immobile zones are mutually connected on a line. We show that both connectivity structures give the same concentration variance whatever the model parameters, dimensionality and initial conditions. Reaction rates of bimolecular reaction at chemical equilibrium are also highly similar but not equal as long as concentration gradients within the diffusive zone remain low like in the uniform injection case, or at large times when high initial concentration gradients have been reduced. For high initial immobile concentration gradients in the diffusive zone, however, reaction rates are much lower in the star-shape connectivity structure (MRMT), and consequently depend on the organization of the immobile porosity structure. Negative concentrations also occur in some of the immobile zones of the equivalent MRMT as a result of the direct connection of the mobile and immobile zones. While acceptable for conservative components, negative concentrations limit the relevance of MRMT to model reactivity at high immobile concentration gradients. The concept of immobile zone concentration should thus be taken with great care and systematically be assessed.

Averaging Level Control to Reduce Off-Spec Material in a Continuous Pharmaceutical Pilot Plant

The judicious use of buffering capacity is important in the development of future continuous pharmaceutical manufacturing processes. The potential benefits are investigated of using optimal-averaging level control for tanks that have buffering capacity for a section of a continuous pharmaceutical pilot plant involving two crystallizers, a combined filtration and washing stage and a buffer tank. A closed-loop dynamic model is utilized to represent the experimental operation, with the relevant model parameters and initial conditions estimated from experimental data that contained a significant disturbance and a change in setpoint of a concentration control loop. The performance of conventional proportional-integral (PI) level controllers is compared with optimal-averaging level controllers. The aim is to reduce the production of off-spec material in a tubular reactor by minimizing the variations in the outlet flow rate of its upstream buffer tank. The results show a distinct difference in behavior, with the optimal-averaging level controllers strongly outperforming the PI controllers. In general, the results stress the importance of dynamic process modeling for the design of future continuous pharmaceutical processes.

Rainfall anomaly prediction using statistical downscaling in a multimodel superensemble over tropical South America

This study addresses the predictability of rainfall variations over South America and the Amazon basin. A primary factor leading to model inaccuracy in precipitation forecasts is the coarse resolution data utilized by coupled models during the training phase. By using MERRA reanalysis and statistical downscaling along with the superensemble methodology, it is possible to obtain more precise forecast of rainfall anomalies over tropical South America during austral fall. Selective inclusion (and exclusion) of member models also allows for increased accuracy of superensemble forecasts. The use of coupled atmospheric–ocean numerical models to predict the rainfall anomalies has had mixed results. Improvement in individual member models is also possible on smaller spatial scales and in regions where substantial topographical changes were not handled well under original model initial conditions. The combination of downscaling and superensemble methodologies with other research methods presents the potential opportunity for increased accuracy not only in seasonal forecasts but on shorter temporal scales as well.

Multi-component Reynolds-averaged Navier–Stokes simulations of Richtmyer–Meshkov instability and mixing induced by reshock at different times

Turbulent mixing generated by shock-driven acceleration of a perturbed interface is simulated using a new multi-component Reynolds-averaged Navier–Stokes (RANS) model closed with a two-equation \(K\)–\(\epsilon \) model. The model is implemented in a hydrodynamics code using a third-order weighted essentially non-oscillatory finite-difference method for the advection terms and a second-order central difference method for the gradients in the source and diffusion terms. In the present reshocked Richtmyer–Meshkov instability and mixing study, an incident shock with Mach number \(M\!a_{\mathrm{s}}=1.20\) is generated in air and progresses into a sulfur hexafluoride test section. The time evolution of the predicted mixing layer widths corresponding to six shock tube test section lengths are compared with experimental measurements and three-dimensional multi-mode numerical simulations. The mixing layer widths are also compared with the analytical self-similar power-law solution of the simplified model equations prior to reshock. A set of model coefficients and initial conditions specific to these six experiments is established, for which the widths before and after reshock agree very well with experimental and numerical simulation data. A second set of general coefficients that accommodates a broader range of incident shock Mach numbers, Atwood numbers, and test section lengths is also established by incorporating additional experimental data for \(M\!a_{\mathrm{s}}=1.24\), \(1.50\), and \(1.98\) with \(At=0.67\) and \(M\!a_{\mathrm{s}}=1.45\) with \(At=-0.67\) and previous RANS modeling. Terms in the budgets of the turbulent kinetic energy and dissipation rate equations are examined to evaluate the relative importance of turbulence production, dissipation and diffusion mechanisms during mixing. Convergence results for the mixing layer widths, mean fields, and turbulent fields under grid refinement are presented for each of the \(M\!a_{\mathrm{s}}=1.20\) cases.

A modeling analysis of rainfall and water cycle by the cloud-resolving WRF model over the western North Pacific

Simulated regional precipitation, especially extreme precipitation events, and the regional hydrologic budgets over the western North Pacific region during the period from May to June 2008 were investigated with the high-resolution (4-km grid spacing) Weather Research and Forecast (WRF v3.2.1) model with explicit cloud microphysics. The model initial and boundary conditions were derived from the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Reanalysis 2 data.

Heavy quark pair production in high energy pA collisions: Open heavy flavors

We study open heavy flavor meson production in proton-nucleus (pA) collisions at RHIC and LHC energies within the Color Glass Condensate framework. We use the unintegrated gluon distribution at small Bjorken's x in the proton obtained by solving the Balitsky-Kovchegov equation with running coupling correction and constrained by global fitting of HERA data. We change the initial saturation scale of the gluon distribution for the heavy nucleus. The gluon distribution with McLerran-Venugopalan model initial condition is also used for comparison. We present transverse momentum spectra of single D and B productions in pA collisions, and the so-called nuclear modification factor. The azimuthal angle correlation of open heavy flavor meson pair is also computed to study the modification due to the gluon saturation in the heavy nucleus at the LHC.

Computation of bang-bang and singular controls in collision avoidance

We study optimal cooperative collision avoidance strategies for twoparticipants in a planar close proximity encounter. Previousresearch focused on special cases of this problem and showed thatbang-bang strategies without switching are optimal in mostsituations, while singular controls only appear for the case ofparticipants with unequal linear speeds under certain conditions.This paper extends the earlier analyses to a general case of acoplanar close proximity encounter, for which both parameters of theproblem may take arbitrary admissible values. For such a case, wepresent a theoretical and numerical study of the structure ofoptimal controls. We prove that both controls can not be singularsimultaneously and that the only possible singular control is a zerocontrol. We derive formulas for the singular surfaces and verifythat sufficient conditions hold for the computed extremal solutions.We identify different types of structural changes of the controlstrategies and show how the control structure changes with thechange in the model parameters and initial conditions.

Robustness of complex feedback systems: application to oncological biochemical networks

Biochemical transduction networks can be modelled through a proper set of differential equations, and at the same time they can be experimentally analysed only by measuring a few signals at the end of the cascades. The study of those networks is of special importance in oncology.The approach proposed in this paper is aimed at characterising the network robustness with respect to both parameters and initial condition perturbations. The key idea is to introduce a robustness index, the proliferation index, and to study its behaviour over the parameter space. The index relates the measurable signals, and the shape of their time behaviour, to the model parameters and initial conditions. In addition, the paper will also provide specific results for a dynamic model of the EGFR–IGFR receptor pathway, which turns out to be relevant to the study of some specific pathologies, such as lung cancer. The connection between the proposed robustness index and the pathology will also be addressed.

Effect of different uncertainty sources on the skill of 10 day ensemble low flow forecasts for two hydrological models

This paper aims to investigate the effect of uncertainty originating from model inputs, parameters and initial conditions on 10 day ensemble low flow forecasts. Two hydrological models, GR4J and HBV, are applied to the Moselle River and performance in the calibration, validation and forecast periods, and the effect of different uncertainty sources on the quality of low flow forecasts are compared. The forecasts are generated by using meteorological ensemble forecasts as input to GR4J and HBV. The ensembles provided the uncertainty range for the model inputs. The Generalized Likelihood Uncertainty Estimation (GLUE) approach is used to estimate parameter uncertainty. The quality of the probabilistic low flow forecasts has been assessed by the relative confidence interval, reliability and hit/false alarm rates. The daily observed low flows are mostly captured by the 90% confidence interval for both models. However, GR4J usually overestimates low flows whereas HBV is prone to underestimate them, particularly when the parameter uncertainty is included in the forecasts. The total uncertainty in GR4J outputs is higher than in HBV. The forecasts issued by HBV incorporating input uncertainty resulted in the most reliable forecast distribution. The parameter uncertainty was the main reason reducing the number of hits. The number of false alarms in GR4J is twice the number of false alarms in HBV when considering all uncertainty sources. The results of this study showed that the parameter uncertainty has the largest effect whereas the input uncertainty had the smallest effect on the medium range low flow forecasts.

The Effect of Topographic Variability on Initial Condition Sensitivity of Low-Level Wind Forecasts. Part I: Experiments Using Idealized Terrain

Abstract The concept of improving the accuracy of numerical weather forecasts by targeting additional meteorological observations in areas where the initial condition error is suspected to grow rapidly has been the topic of numerous studies and field programs. The challenge faced by this approach is that it typically requires a costly observation system that can be quickly adapted to place instrumentation where needed. The present study examines whether the underlying terrain in a mesoscale model influences model initial condition sensitivity and if knowledge of the terrain and corresponding predominant flow patterns for a region can be used to direct the placement of instrumentation. This follows the same concept on which earlier targeted observation approaches were based, but eliminates the need for an observation system that needs to be continually reconfigured. Simulations from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its adjoint are used to characterize the locations, variables, and magnitudes of initial condition perturbations that have the most significant impact on the surface wind forecast. This study examines a relatively simple case where an idealized mountain surrounded by a flat plain is located upwind of the forecast verification region. The results suggest that, when elevated terrain is present upstream of the target forecast area, the largest forecast impact (defined as the difference between the simulation with perturbed initial conditions and a control simulation where the initial condition was not perturbed) occurs when the initial analysis perturbations are made in regions with complex terrain.

An examination of two pathways to tropical cyclogenesis occurring in idealized simulations with a cloud-resolving numerical model

Abstract. Simulations are conducted with a cloud-resolving numerical model to examine the transformation of a weak incipient mid-level cyclonic vortex into a tropical cyclone. Results demonstrate that two distinct pathways are possible and that development along a particular pathway is sensitive to model physics and initial conditions. One pathway involves a steady increase of the surface winds to tropical cyclone strength as the radius of maximum winds gradually decreases. A notable feature of this evolution is the creation of small-scale lower tropospheric cyclonic vorticity anomalies by deep convective towers and subsequent merger and convergence by the low-level secondary circulation. The second pathway also begins with a strengthening low-level circulation, but eventually a significantly stronger mid-level circulation develops. Cyclogenesis occurs subsequently when a small-scale surface concentrated vortex forms abruptly near the center of the larger-scale circulation. The small-scale vortex is warm core throughout the troposphere and results in a fall in local surface pressure of a few millibars. It usually develops rapidly, undergoing a modest growth to form a small tropical cyclone. Many of the simulated systems approach or reach tropical cyclone strength prior to development of a prominent mid-level vortex so that the subsequent formation of a strong small-scale surface concentrated vortex in these cases could be considered intensification rather than genesis. Experiments are performed to investigate the dependence on the inclusion of the ice phase, radiation, the size and strength of the incipient mid-level vortex, the amount of moisture present in the initial vortex, and the sea surface temperature. Notably, as the sea surface temperature is raised, the likelihood of development along the second pathway is increased. This appears to be related to an increased production of ice. The sensitivity of the pathway taken to model physics and initial conditions revealed by these experiments raise the possibility that the solution to this initial value problem is near a bifurcation point. Future improvements to model parameterizations and more accurate observations of the transformation of disturbances to tropical cyclones should clarify the conditions that favor a particular pathway when starting from a mid-level vortex.

Viscous Flow in Heavy-Ion Collisions from RHIC to LHC

We present a systematic hydrodynamic study of the evolution of hadron spectra and their azimuthal anisotropy from the lowest collision energy studied at the Relativistic Heavy Ion Collider (RHIC), sqrt(s) = 7.7 A GeV, to the highest energy reachable at the Large Hadron Collider (LHC), sqrt(s) = 5500 A GeV. The energy dependence of the flow observables are quantitatively studied for both the Monte-Carlo Glauber and Monte-Carlo Kharzeev-Levin-Nardi (MC-KLN) models. For MC-Glauber model initial conditions with {\eta}/s = 0.08, the differential charged hadron elliptic flow v_2^{ch}(p_T, sqrt(s)) is found to exhibit a very broad maximum in the region 39 < sqrt(s) < 2760 A GeV. For MC-KLN initial conditions with {\eta}/s = 0.2, a similar "saturation" is not observed up to LHC energies. We emphasize that this "saturation" of elliptic flow arises from the interplay between radial flow and elliptic flow which shifts with sqrt(s) depending on the fluid's viscosity. By generalizing the definition of spatial eccentricity to isothermal hyper-surface, we also calculate {\epsilon}_x on the kinetic freeze-out surface at different collision energies.

Satellite observations and modeling of oil spill trajectories in the Bohai Sea

On June 4 and 17, 2011, separate oil spill accidents occurred at two oil platforms in the Bohai Sea, China. The oil spills were subsequently observed on different types of satellite images including SAR (Synthetic Aperture Radar), Chinese HJ-1-B CCD and NASA MODIS. To illustrate the fate of the oil spills, we performed two numerical simulations to simulate the trajectories of the oil spills with the GNOME (General NOAA Operational Modeling Environment) model. For the first time, we drive the GNOME with currents obtained from an operational ocean model (NCOM, Navy Coastal Ocean Model) and surface winds from operational scatterometer measurements (ASCAT, the Advanced Scatterometer). Both data sets are freely and openly available. The initial oil spill location inputs to the model are based on the detected oil spill locations from the SAR images acquired on June 11 and 14. Three oil slicks are tracked simultaneously and our results show good agreement between model simulations and subsequent satellite observations in the semi-enclosed shallow sea. Moreover, GNOME simulation shows that the number of ‘splots’, which denotes the extent of spilled oil, is a vital factor for GNOME running stability when the number is less than 500. Therefore, oil spill area information obtained from satellite sensors, especially SAR, is an important factor for setting up the initial model conditions.

Effect of charged particle multiplicity fluctuations on flow harmonics in event-by-event hydrodynamics

In nucleon-nucleon collisions, a charged particle's multiplicity fluctuates. We have studied the effect of multiplicity fluctuation on flow harmonics in nucleus-nucleus collisions in event-by-event hydrodynamics. Assuming that the charged particle's multiplicity fluctuations are governed by the negative binomial distribution, the Monte Carlo Glauber model of initial condition is generalized to include the fluctuations. Explicit simulations with the generalized Monte Carlo Glauber model initial conditions indicate that the multiplicity fluctuations do not have a large effect on the flow harmonics.

GloFAS – global ensemble streamflow forecasting and flood early warning

Abstract. Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of populations affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System (GloFAS), which has been set up to provide an overview on upcoming floods in large world river basins. GloFAS is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the earth.

Operational hydrological data assimilation with the recursive ensemble Kalman filter

Abstract. This paper describes the design and use of a recursive ensemble Kalman filter (REnKF) to assimilate streamflow data in an operational flow forecasting system of seven catchments in New Zealand. The REnKF iteratively updates past and present model states (soil water, aquifer and surface storages), with lags up to the concentration time of the catchment, to improve model initial conditions and hence flow forecasts. We found the REnKF overcame instabilities in the standard EnKF, which were associated with the natural lag time between upstream catchment wetness and flow at the gauging locations. The forecast system performance was correspondingly improved in terms of Nash–Sutcliffe score, persistence index and bounding of the measured flow by the model ensemble. We present descriptions of filter design parameters and explanations and examples of filter behaviour, as an information source for other groups wishing to assimilate discharge observations for operational forecasting.

Implicit Estimation of Ecological Model Parameters

We introduce an implicit method for state and parameter estimation and apply it to a stochastic ecological model. The method uses an ensemble of particles to approximate the distribution of model solutions and parameters conditioned on noisy observations of the state. For each particle, it first determines likely values based on the observations, then samples around those values. This approach has a strong theoretical foundation, applies to nonlinear models and non-Gaussian distributions, and can estimate any number of model parameters, initial conditions, and model error covariances. The method is called implicit because it updates the particles without forming a predictive distribution of forward model integrations. As a point of comparison for different assimilation techniques, we consider examples in which one or more bifurcations separate the true parameter from its initial approximation. The implicit estimator is asymptotically unbiased, has a root-mean-squared error comparable to or less than the other methods, and is accurate even with small ensemble sizes.

Vehicle path following control in the presence of driver inputs

The main contribution of this article is the development of a controller for tracking of the driver intended path of an integrated driver/vehicle system. The controller receives heading and lateral deviation errors as well as the driver input and determines a corrective steering angle and a direct yaw moment to be applied to the vehicle so that a desired path is achieved. A genetic algorithm procedure is utilized in order to adapt a set of optimized controller parameters suitable for various driving styles, road conditions and the initial errors of vehicle position and orientation. The sensitivity of driver/vehicle system response to the driver model, road and vehicle initial conditions is investigated. Computer simulations are performed to study the effectiveness of the proposed controller in different driving conditions, namely single- and double-lane changes, J-turn and other desired tracks. Simulation results demonstrated that the proposed controller was able to effectively keep the vehicle path very close to the desired path even in the presence of the driver commands.

Modeling the Impacts of the Large-Scale Atmospheric Environment on Inland Flooding during the Landfall of Hurricane Floyd (1999)

The contribution of the large-scale atmospheric environment to precipitation and flooding during Hurricane Floyd was investigated in this study. Through the vortex removal technique in the Weather Research and Forecasting (WRF) model, the vortex associated with Hurricane Floyd (1999) was mostly removed in the model initial conditions and subsequent integration. Results show that the environment-induced precipitation can account for as much as 22% of total precipitation in the innermost model domain covering North Carolina coastal area and 7% in the focused hydrological study area. The high-resolution precipitation data from the WRF model was then used for input in a hydrological model to simulate river runoff. Hydrological simulation results demonstrate that without the tropical systems and their interactions with the large-scale synoptic environment the synoptic environment would only contribute 10% to the total discharge at the Tarboro gauge station. This suggests that Hurricane Floyd and Hurricane Dennis preceding it, along with the interactions between these tropical systems and the large-scale environment, have contributed to the bulk (90%) of the record amount of flood water in the Tar-Pamlico River Basin.