Ensemble Of Parameters Research Articles

A Novel Black Widow Optimized Controller Approach for Automatic Generation Control in Modern Hybrid Power Systems

This research paper demonstrates an application of the Black Widow Optimization (BWO) approach to address the issue of load-frequency control (LFC) in networked power systems. BWO is an innovative metaheuristic method that quickly suggests technique is initially evaluated on a non-reheat thermal-thermal (NRTT) power system spanning two areas of interconnection, and then it is applied to two different actual power systems: (a) a two-area thermal-thermal considering Generation Rate Constraint (GRC); and (b) a two-area having thermal, hydro, wind, solar, and gas systems. The BWO method uses two fitness functions based on integral time multiplied absolute error (ITAE) and integral square error (ISE) to optimize controller gains. The suggested BWO algorithm's performance has been compared to that of existing meta-heuristic optimization methods, such as grey wolf optimization (GWO), comprehensive learning particle swarm optimization (CLPSO), and an ensemble of parameters in differential evolution (EPSDE). The simulation results show that BWO's tuning skills are better than other population-based planning methods like CLPSO, EPSDE, and GWO. The ITAE value is enhanced by 33.28% (GWO), 40.28% (EPSDE), and 43.27% (CLPSO) when the BWO algorithm is used in conjunction with the PID Controller for thermal system.

PREDICTING SHORELINE EVOLUTION IN A CHANGING WAVE CLIMATE

Reliable predictions of shoreline evolution at a range of time scales both now and by end of the century are required for assessing coastal vulnerability in a changing climate. This is particularly important given the possible changes in regional wave climates and/or ocean water levels due to climate variability. To this end, much work has gone into the development of simple and efficient semi-empirical shoreline models that can be used to predict shoreline evolution over time scales ranging from seasonal to multi-decadal. An alternative is to use time-varying model parameters to improve model predictability at interannual timescales. Kalman filter techniques offer a framework to detect time-varying (or non-stationary) model parameters by adjusting them as shoreline observations become available. Ibaceta et al. (2020) implemented a dual state parameter Ensemble Kalman Filter (EnKF) within the shoreline evolution model ShoreFor (Davidson et al., 2013), and showed that this methodology is suitable to detect parameter changes that best hindcasted observed shoreline evolution. Additionally, they demonstrated that this observed parameter non-stationarity could be linked to the changing characteristics of the underlying wave forcing. The application of this methodology over long-term datasets now enables the parametrization and physical interpretation of the model parameters as a function of the multi-year variability in wave forcing, allowing for enhanced shoreline predictions out of the selected training period.

Uncertainty quantification for high explosive reactant and product equations of state

Equations of state (EOSs) are typically represented as physics-informed models with tunable parameters that are adjusted to replicate calibration data as closely as possible. Uncertainty quantification (UQ) allows for the development of an ensemble of EOS parameters that are consistent with the calibration data instead of a single EOS. In this work, we perform UQ for the reactant and product EOSs for a variety of high explosives (HEs). In doing so, we demonstrate a strategy for dealing with heterogeneous (both experimental and calculated) data. We also use a statistical distance metric to quantify the differences between the various HEs using the UQ results.

Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing

Abstract. Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill in terms of making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample the uncertainty in 37 model parameters related to aerosols, clouds, and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian process emulators) against satellite-derived observations over several cloudy regions. Our analysis of a very large set of model variants exposes model internal inconsistencies that would not be apparent in a small set of model simulations, of an order that may be evaluated during model-tuning efforts. Incorporating observations associated with these inconsistencies weakens any forcing constraint because they require a wider range of parameter values to accommodate conflicting information. We show that, by neglecting variables associated with these inconsistencies, it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range (around −1.3 to −0.1 W m−2) in close agreement with energy balance constraints. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then very likely narrow the uncertainty further and possibly alter the central estimate. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach.

Sensitivities of the Asian Summer Monsoon Simulations to Physical Parameters for the Perturbed Parameter Ensemble of HadGEM3‐GC3.05

AbstractThe simulation skill of the perturbed parameter ensemble (PPE) of HadGEM3‐GC3.05 on the mean climate pattern of the Asian summer monsoon (ASM) is evaluated in this study. The sensitivities of the model bias to the perturbed parameters are investigated based on metrics. The results show that the PPE mean (PPE‐20M) could effectively capture the general ASM precipitation and wind patterns, with a correlation coefficient of 0.78. PPE‐20M mainly shows positive precipitation biases over the tropical western Pacific, southern slope of the Tibetan Plateau, Indochina Peninsula, and South China and negative precipitation biases over the Indian continent and Bay of Bengal. The magnitude of the precipitation biases is more sensitive than its pattern to the variation of the perturbed parameters. Four parameters (ent_fac_dp, qlmin, ps_cloud‐ph, and psm) are found to be crucial for simulating the ASM precipitation intensity, and their combined effects are related to the simulated precipitation biases.

Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics

AbstractIn a quest to represent the Earth system, climate models have become more and more complex. This generates problems, for example, hindering model interpretability. This study contributes to a regain of model understanding and proposes simplifications to decrease scheme complexity. We reflect on the reasons for model complexity and the problems it generates or deepens, connecting perspectives from atmospheric science and the philosophy of climate science. Using an emulated perturbed parameter ensemble of the cloud microphysics (CMP) process efficiencies, we investigate the sensitivity of the model to process perturbations. The sensitivity analysis characterizes the scheme and model behavior, contrasting it to physical process understanding as well as an alternative CMP formulation (comparing the 2M (Lohmann et al., 2007, https://doi.org/10.5194/acp-7-3425-2007) to the P3 scheme (Morrison & Milbrandt, 2015, https://doi.org/10.1175/JAS-D-14-0065.1; Dietlicher et al., 2018, https://doi.org/10.5194/gmd-11-1557-2018)). For the 2M scheme, ice crystal autoconversion dominates the model sensitivity in the ice phase. The P3 scheme removes this artificial process and thus shows more balanced sensitivities. Model behavior sometimes aligns with process understanding, but many process sensitivities are masked by other more dominant processes or the model finally responds differently due to adjustments. We identify processes that the model is not sensitive to and test their simplification. For example, heterogeneous freezing or secondary ice production are drastically simplifiable. Depending on one's modeling vision one may interpret this study's findings as pointing to simplification potential in the CMP scheme or the need for process representation improvements where the model behavior does not tally with our physical understanding.

Parameter sensitivity analysis for a biochemically-based photosynthesis model

A challenge for the development of Land Surface Models (LSMs) is improving transpiration of water exchange and photosynthesis of carbon exchange between terrestrial plants and the atmosphere, both of which are governed by stoma in leaves. In the photosynthesis module of these LSMs, variations of parameters arising from diversity in plant functional types (PFTs) and climate remain unclear. Identifying sensitive parameters among all photosynthetic parameters before parameter estimation can not only reduce operation cost, but also improve the usability of photosynthesis models worldwide. Here, we analyzed 13 parameters of a biochemically-based photosynthesis model (FvCB), implemented in many LSMs, using two sensitivity analysis (SA) methods (i.e., the Sobol’ method and the Morris method) for setting up the parameter ensemble. Three different model performance metrics, i.e., Root Mean Squared Error (RMSE), Nash Sutcliffe efficiency (NSE), and Standard Deviation (STDEV) were introduced for model assessment and sensitive parameters identification. The results showed that among all photosynthetic parameters only a small portion of parameters were sensitive, and the sensitive parameters were different across plant functional types: maximum rate of Rubisco activity (Vcmax25), maximum electron transport rate (Jmax25), triose phosphate use rate (TPU) and dark respiration in light (Rd) were sensitive in broad leaf-evergreen trees (BET), broad leaf-deciduous trees (BDT) and needle leaf-evergreen trees (NET), while only Vcmax25 and TPU are sensitive in short vegetation (SV), dwarf trees and shrubs (DTS), and agriculture and grassland (AG). The two sensitivity analysis methods suggested a strong SA coherence; in contrast, different model performance metrics led to different SA results. This misfit suggests that more accurate values of sensitive parameters, specifically, species specific and seasonal variable parameters, are required to improve the performance of the FvCB model.

Flow Regime-Dependent, Discharge Uncertainty Envelope for Uncertainty Analysis with Ensemble Methods

A discharge uncertainty envelope is presented that provides an observation error model for data assimilation (DA) using discharge observations derived from measurement of stage using a rating curve. It uniquely represents the rating curve representation error, which is due to scale and process incompatibility between the rating curve hydrodynamic model and “true” discharge, within the observation error model. Ensemble methods, specifically, the iterative ensemble smoother (IES) algorithms in PEST++, provide the DA framework for this observation error model. The purpose of the uncertainty envelope is to describe prior observation uncertainty for ensemble methods of DA. Envelope implementation goals are (1) limiting the spread of the envelope to avoid conditioning to extreme parameter values and producing posterior parameter distributions with increased variance, and (2) incorporating a representative degree of observation uncertainty to avoid overfitting, which will introduce bias into posterior parameter estimates and predicted model outcomes. The expected uncertainty envelope is flow regime dependent and is delineated using stochastic, statistical methods before undertaking history matching with IES. Analysis of the goodness-of-fit between stochastically estimated “true” discharge and observed discharge provides criteria for the selection of best-fit parameter ensembles from IES results.

First detailed study of two eccentric eclipsing binaries: TYC 5378-1590-1 and TYC 8378-252-1

Aims. The analysis of combined photometry and spectroscopy of eccentric eclipsing binary systems facilitates the derivation of very precise values for a large ensemble of physical parameters of the component stars and their orbits, thereby providing stringent tests of theories of stellar structure and evolution. In this paper two eccentric eclipsing binary systems, TYC 5378-1590-1 and TYC 8378-252-1, are studied in detail for the first time. Methods. Radial velocities were obtained using cross-correlation methods applied to mid-resolution spectra covering almost the entire orbital phase domains of these two systems. TESS photometry was used for the analysis of TYC 5378-1590-1, whereas ASAS-SN photometry was used for the analysis of TYC 8378-252-1. Results. We obtained the first precise derivation of the physical parameters of these systems. Both systems display moderately eccentric orbits (e ∼ 0.3 and 0.2) with periods of 3.73235 and 2.87769 days, respectively. The apsidal motion is very slow, with a duration of several centuries for both systems. We present two models for the apsidal motion of TYC 5378-1590-1. The internal structure constant derived from observations for TYC 8378-252-1 is approximately 11% lower than theoretical predictions. We discuss possible reasons for this discrepancy. Our analysis indicates that the components of both systems are on the main sequence. The components of TYC 5378-1590-1 are relatively young stars (age ∼600 Myr) close to the ZAMS, whereas the components of TYC 8378-252-1 are relatively old stars (age ∼4 Gyr) close to the TAMS. Our finding that the circularization timescale for TYC 5378-1590-1 is ∼200 times longer than its evolutionary age is compatible with circularization theory; however, our finding that the evolutionary age of TYC 8378-252-1 is approximately ten times longer than the circulation age, while its orbital eccentricity is quite high (e ∼ 0.2), challenges the present theories of circularization.

Bayesian Inversion of Finite‐Fault Earthquake Slip Model Using Geodetic Data, Solving for Non‐Planar Fault Geometry, Variable Slip, and Data Weighting

AbstractA precise finite‐fault model including the fault geometry and slip distribution is essential to understand the physics of an earthquake. However, the conventional linear inversion of geodetic data for a finite‐fault model cannot fully resolve the fault geometry. In this study, we developed a Bayesian inversion framework that can comprehensively solve a non‐planar fault geometry, the corresponding fault slip distribution with spatially variable directions, and objective weighting for multiple data types. In the proposed framework, the probability distributions of all the model parameters are sampled using the Monte Carlo method. The developed methodology removes the requirement for manual intervention for the fault geometry and data weighting and can provide an ensemble of plausible model parameters. The performance of the developed method is tested and demonstrated through inversions for synthetic oblique‐slip faulting models. The results show that the constant rake assumption can significantly bias the estimates of fault geometry and data weighting, whereas additional consideration of the variability of slip orientations can allow plausible estimates of a non‐planar fault geometry with objective data weighting. We applied the method to the 2013 Mw 6.5 Lushan earthquake in Sichuan province, China. The result reveals dominant thrust slips with left‐lateral components and a curved fault geometry, with the confidence interval of the dip angles being between 20°–25° and 56°–58°. The proposed method provides useful insights into the scope of imaging a non‐planar fault geometry, and could help to interpret more complex earthquake sources in the future.

Statistical constraints on climate model parameters using a scalable cloud-based inference framework

Abstract Atmospheric aerosols influence the Earth’s climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these processes could help improve model-based climate predictions. We propose a scalable statistical framework for constraining the parameters of expensive climate models by comparing model outputs with observations. Using the C3.AI Suite, a cloud computing platform, we use a perturbed parameter ensemble of the UKESM1 climate model to efficiently train a surrogate model. A method for estimating a data-driven model discrepancy term is described. The strict bounds method is applied to quantify parametric uncertainty in a principled way. We demonstrate the scalability of this framework with 2 weeks’ worth of simulated aerosol optical depth data over the South Atlantic and Central African region, written from the model every 3 hr and matched in time to twice-daily MODIS satellite observations. When constraining the model using real satellite observations, we establish constraints on combinations of two model parameters using much higher time-resolution outputs from the climate model than previous studies. This result suggests that within the limits imposed by an imperfect climate model, potentially very powerful constraints may be achieved when our framework is scaled to the analysis of more observations and for longer time periods.

Climate Change, Grape Phenology, and Frost Risk in Southeast England

Background and Aims. The cultivation of grapevines in England is expected to benefit under climate change. Yet assessments of future wine climates remain undeveloped. Accordingly, this study assesses how climate change might modify frost risk for Chardonnay in the Southeast England viticulture region. Methods and Results. Cold-bias-corrected climate projections from the UKCP18 Regional (12 km) perturbed parameter ensemble (PPE) climate model under RCP8.5 are applied with phenological models to determine how frost risk and the timing of key grapevine phenophases might alter under climate change. Notwithstanding the uncertainties associated with projections of key viticulture-related bioclimate variables, the last spring frost was found to advance at a greater rate than budburst, indicating a general decrease in frost risk. Conclusions. Although projections point to an improving climate for viticulture across Southeast England, frost will remain a risk for viticulture, albeit at a reduced level compared to the present. Furthermore, the strong cold-bias found for temperature simulations used in this study needs to be given careful consideration when using the UKCP18 projections for viticulture impact assessments of climate change. Significance of the Study. This study highlights the present sensitivity of viticulture to climate variability and the inherent uncertainty associated with making future projections of wine climate under climate change.

Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning

Differential evolution (DE) has recently attracted a lot of attention as a simple and powerful numerical optimization approach for solving various real-world applications. However, the performance of DE is significantly influenced by the configuration of control parameters and mutation strategy. To address this issue, we proposed reinforcement learning-based hybrid parameters and mutation strategies differential evolution (RL-HPSDE) in this paper. The RL-HPSDE is based on the Q-learning framework, the population individual is regarded as an agent. The optimization problem's dynamic fitness landscape analysis results are utilized to represent the environmental states. The ensemble of parameters and mutation strategy is employed as optional actions for the agent. Furthermore, a reward function is designed to guide the agent to perform the optimal action strategy. Based on its reinforcement learning experience stored by the corresponding Q table, the agent could adaptively select an optimal combination of mutation strategy and parameters to generate offspring individual during each generation. The proposed algorithm is evaluated using the CEC2017 single objective test function set. Several well-known DE variants are also compared with the proposed algorithm. Empirical studies suggest that the proposed RL-HPSDE algorithm is competitive with all other competitors.

EAKF-Based Parameter Optimization Using a Hybrid Adaptive Method

Abstract To effectively reduce model bias and improve assimilation quality, we adopt a hybrid adaptive approach of ensemble adjustment Kalman filter (EAKF) and multigrid analysis (MGA), called EAKF-MGA, to implement parameter optimization as follows. For each assimilation cycle, observations are used to adjust the prior ensembles of both state variables and parameters using the EAKF without inflation. Then, the MGA is adaptively triggered to extract multiscale information from the observational residual to innovate the ensemble mean of the state once again. Results of biased twin experiments consisting of a barotropic spectral model and idealized observation systems show that the proposed EAKF-MGA is insensitive to state variance inflation and localization during the parameter optimization process, compared with the EAKF with adaptive inflation. We also find that computational efficiency is another important advantage of the EAKF-MGA for both state estimation and parameter estimation since extremely small ensemble size is allowed, while the EAKF with adaptive inflation does not work anymore. In essence, the EAKF-MGA is designed to estimate and correct systematic errors jointly with model’s state variables. Through alleviating biases, including the model bias caused by the biased parameter and the analysis bias resulting from the sampling noise given the limited ensemble size, it can be guaranteed that the analysis in the EAKF-MGA will be proceeded onward with the standard assumption of the unbiased model background field in modern data assimilation theory to be met.

Identification of hydraulic conductivity field of a karst aquifer by using transition probability geostatistics and discrete cosine transform with an ensemble method

AbstractKnowledge of the spatial distribution characteristics of hydraulic parameters is essential for the management and protection of karst groundwater resources. In this study, we propose a workflow integrating the transition probability geostatistics (T‐PROGS) and the discrete cosine transform (DCT) with the ensemble smoother with multiple data assimilation (ES‐MDA) method to map the hydraulic conductivity of karst aquifers that usually follow a multimodal distribution. The priori parameter ensemble is constructed from the T‐PROGS and transformed into an approximately Gaussian distributed coefficient field containing critical spatial structural features about the aquifer using DCT. The ES‐MDA method is employed to update the DCT coefficients by assimilating the measurements. In addition, a postprocessing process based on cumulative distribution function (CDF) mapping is used to address the problem of parameters gradually tending to a Gaussian distribution after updating using the ES‐MDA and the inappropriate selection of initial parameter values. In practice, the limited amount of available data makes it difficult to fully capture the spatial distribution of the parameters in the initial ensemble of a single realization. Therefore, we suggest a new strategy for structuring the initial ensemble by mixing samples from multiple realizations. We then apply the proposed approach to four single realization models and a combined multiple realizations model in a field hydraulic tomography investigation of a real karst aquifer. The computed results show that this method can effectively identify the characteristics of the spatial distribution of hydraulic conductivity in karst aquifers. Compared with an individual realization model, the uncertainty of the hydraulic conductivity estimated by the combined multiple realizations model is significantly reduced. Therefore, including more uncertainty of the aquifer in the initial ensemble is beneficial to improve the accuracy of the parameter estimation.

A new bootstrap technique to quantify uncertainty in estimates of ground surface temperature and ground heat flux histories from geothermal data

Abstract. Estimates of the past thermal state of the land surface are crucial to assess the magnitude of current anthropogenic climate change as well as to assess the ability of Earth System Models (ESMs) to forecast the evolution of the climate near the ground, which is not included in standard meteorological records. Subsurface temperature reacts to long-term changes in surface energy balance – from decadal to millennial time scales – thus constituting an important record of the dynamics of the climate system that contributes, with low-frequency information, to proxy-based paleoclimatic reconstructions. Broadly used techniques to retrieve past temperature and heat flux histories from subsurface temperature profiles based on a singular value decomposition (SVD) algorithm were able to provide robust global estimates for the last millennium, but the approaches used to derive the corresponding 95 % confidence interval were wrong from a statistical point of view in addition to being difficult to interpret. To alleviate the lack of a meaningful framework for estimating uncertainties in past temperature and heat flux histories at regional and global scales, we combine a new bootstrapping sampling strategy with the broadly used SVD algorithm and assess its performance against the original SVD technique and another technique based on generating perturbed parameter ensembles of inversions. The new bootstrap approach is able to reproduce the prescribed surface temperature series used to derive an artificial profile. Bootstrap results are also in agreement with the global mean surface temperature history and the global mean heat flux history retrieved in previous studies. Furthermore, the new bootstrap technique provides a meaningful uncertainty range for the inversion of large sets of subsurface temperature profiles. We suggest the use of this new approach particularly for aggregating results from a number of individual profiles, and to this end, we release the programs used to derive all inversions in this study as a suite of codes labeled CIBOR v1: Codes for Inverting BORholes, version 1.

Single-spacecraft techniques for shock parameters estimation: A systematic approach

Spacecraft missions provide the unique opportunity to study the properties of collisionless shocks utilising in situ measurements. In the past years, several diagnostics have been developed to address key shock parameters using time series of magnetic field (and plasma) data collected by a single spacecraft crossing a shock front. A critical aspect of such diagnostics is the averaging process involved in the evaluation of upstream/downstream quantities. In this work, we discuss several of these techniques, with a particular focus on the shock obliquity (defined as the angle between the upstream magnetic field and the shock normal vector) estimation. We introduce a systematic variation of the upstream/downstream averaging windows, yielding to an ensemble of shock parameters, which is a useful tool to address the robustness of their estimation. This approach is first tested with a synthetic shock dataset compliant with the Rankine-Hugoniot jump conditions for a shock, including the presence of noise and disturbances. We then employ self-consistent, hybrid kinetic shock simulations to apply the diagnostics to virtual spacecraft crossing the shock front at various stages of its evolution, highlighting the role of shock-induced fluctuations in the parameters’ estimation. This approach has the strong advantage of retaining some important properties of collisionless shock (such as, for example, the shock front microstructure) while being able to set a known, nominal set of shock parameters. Finally, two recent observations of interplanetary shocks from the Solar Orbiter spacecraft are presented, to demonstrate the use of this systematic approach to real events of shock crossings. The approach is also tested on an interplanetary shock measured by the four spacecraft of the Magnetospheric Multiscale (MMS) mission. All the Python software developed and used for the diagnostics (SerPyShock) is made available for the public, including an example of parameter estimation for a shock wave recently observed in-situ by the Solar Orbiter spacecraft.

Evaluating uncertainty in aerosol forcing of tropical precipitation shifts

Abstract. An observed southward shift in tropical rainfall over land between 1950 and 1985, followed by a weaker recovery post-1985, has been attributed to anthropogenic aerosol radiative forcing and cooling of the Northern Hemisphere relative to the Southern Hemisphere. We might therefore expect models that have a strong historic hemispheric contrast in aerosol forcing to simulate a further northward tropical rainfall shift in the near-term future when anthropogenic aerosol emission reductions will predominantly warm the Northern Hemisphere. We investigate this paradigm using a perturbed parameter ensemble (PPE) of transient coupled ocean–atmosphere climate simulations that span a range of aerosol radiative forcing comparable to multi-model studies. In the 20th century, in our single-model ensemble, we find no relationship between the magnitude of pre-industrial to 1975 inter-hemispheric anthropogenic aerosol radiative forcing and tropical precipitation shifts. Instead, tropical precipitation shifts are associated with major volcanic eruptions and are strongly affected by internal variability. However, we do find a relationship between the magnitude of pre-industrial to 2005 inter-hemispheric anthropogenic aerosol radiative forcing and future tropical precipitation shifts over 2006 to 2060 under scenario RCP8.5. Our results suggest that projections of tropical precipitation shifts will be improved by reducing aerosol radiative forcing uncertainty, but predictive gains may be offset by temporary shifts in tropical precipitation caused by future major volcanic eruptions.

Sensitivity Analysis of Wind and Turbulence Predictions With Mesoscale‐Coupled Large Eddy Simulations Using Ensemble Machine Learning

AbstractCoupling between mesoscale models and large‐eddy simulation (LES) models is increasingly used to more realistically represent the wide range of scales of atmospheric motions affecting boundary layer winds and turbulence that need to be simulated accurately for applications such as wind energy. However, such mesoscale‐to‐microscale coupled modeling frameworks are potentially affected by a large number of uncertain closure parameters. Here, we investigate the sensitivity associated with six closure parameters related to a 1.5‐order subgrid‐scale turbulence closure for an ensemble of mesoscale‐coupled LES. The simulations are performed using the Weather Research and Forecasting model nested from horizontal resolutions of greater than a kilometer down to tens of meters. Closure parameters are varied to generate perturbed parameter ensembles for two case studies of highly sheared, convective boundary layers observed in the Columbia Basin of Oregon and Washington during the Second Wind Forecast Improvement Project. Machine learning algorithms are used to explore the sensitivity of LES predictions, considering the effects of the perturbed physical parameters alongside categorical factors such as the case study identity, measurement location, and LES resolution. For the conditions we examine, a single parameter, the eddy viscosity coefficient, is the dominant source of parametric sensitivity and its importance is comparable to the categorical factors for several of the simulation response variables we examine.

Coupling a Parametric Wave Solver into a Hydrodynamic Circulation Model to Improve Efficiency of Nested Estuarine Storm Surge Predictions

Efficiency in storm surge modeling is crucial for forecasting coastal hazards in real-time. While computation cost may not be the main concern for organizations with ample resources, the robustness of forecasts generated by most parties are restricted by wall-clock time. The Parametric Wave Solver (PARAM) was developed by Boyd and Weaver (2021) as an alternative to computationally expensive wind–wave models when modeling restricted estuarine environments. For this study, PARAM has been tightly coupled with the ADCIRC hydrodynamic model to create ADCparam, then integrated into the Multistage mini-ensemble modeling system (MMEMS), a one-way nesting framework for modeling waves and circulation in coastal estuaries developed by Taeb and Weaver (2019). In the MMEMS framework, ADCIRC + SWAN is used to simulate the coarser ocean domain and ADCparam is applied to the nested high resolution estuarine mesh. ADCparam has greatly reduced computation time for the high resolution nested sub-model compared to the third-generation wave model originally used. While the PARAM wave solution shows dissimilarities with the SWAN solution, significant wave height and wave period results are consistent and warrant further pursuit of the parametric wave ensemble method as a substitute to SWAN within MMEMS. ADCparam models demonstrated run times up to 51% faster than ADCIRC coupled with SWAN, an established iterative wave model tightly coupled to ADCIRC and packaged with the MMEMS repository. ADCparam wall time is comparable to running ADCIRC without wave forcing, nearly eliminating the computational cost of including the wave forcing in the high-resolution estuarine domain of MMEMS. Computational efficiency is greatly increased while maintaining solution integrity. Though ADCparam, and its application to MMEMS, are still being refined and validated, the coupled model system has proven to be an efficient, viable path for implementing waves in any estuarine circulation model.