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In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia's extreme heat events. Using Gaussian process regression‐based Bayesian Optimisation (G‐BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimized parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions. Plain Language Summary This study aims to enhance the accuracy of a numerical weather model called the Weather Research and Forecasting (WRF) model, especially for simulating extreme heat events in Southeast Australia. Typically, the accuracy of such models depends on specific settings, which are often set to default values. Our research used a method known as Gaussian process regression‐based Bayesian Optimisation (G‐BO) to determine the best range of values for these settings. We found that the default settings were not optimal. By applying G‐BO, we identified more effective values that substantially improved the model's simulations of temperature and humidity during heat extremes. This improvement was consistent even when tested on an independent extreme heat event. These advancements are vital for more accurate weather forecasting, which is essential for emergency services, electricity management, and agriculture planning during extreme heat conditions. Key Points Our study optimizes WRF model parameters for Southeast Australia heat extremes, enhancing the accuracy of the model simulation G‐BO method finds optimal parameter ranges, substantially improving the simulation of temperature and humidity Results suggest updating WRF model's default settings for better extreme heat event simulations

In this paper, the design problem of Gain‐Scheduled Output‐Feedback (GSOF) controllers using inexact scheduling parameters for morphing aircraft during the wing transition process is addressed. Both the stability of the closed‐loop system and the L2 gain performance can be guaranteed under the controller based on measured (not actual) scheduling parameters. Firstly, the linear parameter‐varying (LPV) model of morphing aircraft is established by Jacobian linearization and the additive uncertainty is introduced into the scheduling parameters. By employing non‐linear transformations, the problem is formulated as the solution to a set of parameter‐dependent linear matrix inequalities (LMI) with a single‐line search parameter. Finally, the robustness of the flight control system to the wing transition process is verified under the condition of both the uncertainty of aerodynamic parameters and of scheduling parameters.

The article substantiates the degree of immersion of working bodies in the soil and soil deformation on the basis of scientific and experimental studies of the harrowing unit used. The parameters of the harrow teeth, the qualitative performance of the technological process of the accepted parameters are also theoretically justified.

Terrestrial processes influence the atmosphere by controlling land‐to‐atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally‐informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate. Plain Language Summary Land processes can affect climate by controlling the transfer of energy and water from the land to the atmosphere. Previous research has shown that uncertainty surrounding land processes (e.g., photosynthesis and the movement of water through soils) can drive uncertainty in land‐to‐atmosphere fluxes. However, it remains unclear how much that land uncertainty can impact climate. Here, we quantify how climate is sensitive to assumptions about land processes by varying 18 land model parameters to create an ensemble of 36 possible worlds in a global climate model. Land temperature ranges by 2.2°C across this ensemble, mostly due to changes in how much water is evaporated from the land surface. Changing land parameters also drives regionally variable changes in mean precipitation. This study highlights a large and underappreciated impact of land processes in determining the mean climate state, and provides insights into how climate is influenced by land process uncertainty. Key Points Land processes substantially impact the climatological mean state terrestrial temperature and precipitation Land parameters influence climate predominantly through changing evapotranspiration rather than through other mechanisms Warming driven by land processes activates different atmospheric feedbacks than radiatively‐driven warming

In this work, we develop a new unified approach to the so-called generalized Golay- Rudin-Shapiro (GRS) 2-complementary multi-parameter sequences. It is based on a new generalized iteration generating construction.

Hydrological parameters are used to tailor simulation models to the specific characteristics of a catchment so that models can accurately represent processes under different catchment conditions. In the case of the mesoscale Hydrological Model (mHM), its parameters are estimated via transfer functions using the Multiscale Parameter Regionalization (MPR) approach. In this study, the spatial and temporal variability in the sensitivity of transfer function parameters (TFP) and their relationships to corresponding simulated processes are investigated to understand how these TFP control simulated hydrological fluxes and state variables. Daily dominant model parameters are identified for 102 German catchments as a study domain with temperate climate using a temporally resolved parameter sensitivity analysis. This approach allows the comparison of spatial and temporal variability of TFP dominance. Three simulated hydrological fluxes and one state variable are used as target variables for the sensitivity analysis: runoff, actual evapotranspiration, soil moisture (SM), and groundwater recharge. The analysis leads to consistent and plausible patterns of parameter dominance in space. An evapotranspiration parameter dominates actual evapotranspiration and SM. Runoff and recharge are mainly controlled by soil texture, subsurface, and percolation parameters. The relevance of spatial versus temporal variability varies among model parameters and target variables. In some cases, parameter sensitivities are related to the magnitude of corresponding processes. Low spatial and temporal variability of dominant parameters is explained by MPR. In light of these results, a joint spatio‐temporal analysis is recommended to better understand how model parameters drive simulated states and fluxes in hydrological models to improve process accuracy. Plain Language Summary Hydrological models use parameters to represent the hydrologic system under varying climate and landscape conditions. The mesoscale hydrological model (mHM) uses a special regionalization approach (Multiscale Parameter Regionalization, MPR) to ensure spatial consistency in parameter patterns. In this study, we conducted a temporally resolved parameter sensitivity analysis that estimates daily parameter sensitivities for four hydrological variables (runoff, actual evapotranspiration, soil moisture (SM), recharge) across 102 gauges. In this way, we investigated how the parameter sensitivity varies across space and time. Our results indicate that an evapotranspiration parameter dominates not only simulated evapotranspiration but also SM. Runoff and recharge are mainly controlled by soil texture, subsurface and percolation parameters. The MPR approach ensures that the parameter sensitivity patterns are consistent across space. Additionally, our analysis reveals that in multiple cases the parameter sensitivity depends on the magnitude of the corresponding process. We see our analysis as a valuable contribution toward a better understanding of how parameters govern hydrological models. Key Points This study presents the first temporal parameter sensitivity analysis of a hydrologic model with multiscale parameter regionalization Spatio‐temporal variability in dominant parameters changes with target variables (runoff, evapotranspiration, soil moisture, recharge) Daily sensitivities of some parameters are related to magnitude of corresponding process

Parameter sensitivity analysis plays a critical role in efficiently determining main parameters, enhancing the effectiveness of the estimation of parameters and uncertainty quantification in hydrologic modeling. In this paper, we demonstrate an uncertainty and sensitivity analysis technique for the holistic Soil and Water Assessment Tool (SWAT+) model coupled with new gwflow module, spatially distributed, physically based groundwater flow modeling. The main calculated groundwater inflows and outflows include boundary exchange, pumping, saturation excess flow, groundwater–surface water exchange, recharge, groundwater–lake exchange and tile drainage outflow. We present the method for four watersheds located in different areas of the United States for 16 years (2000–2015), emphasizing regions of extensive tile drainage (Winnebago River, Minnesota, Iowa), intensive surface–groundwater interactions (Nanticoke River, Delaware, Maryland), groundwater pumping for irrigation (Cache River, Missouri, Arkansas) and mountain snowmelt (Arkansas Headwaters, Colorado). The main parameters of the coupled SWAT+gwflow model are estimated utilizing the parameter estimation software PEST. The monthly streamflow of holistic SWAT+gwflow is evaluated based on the Nash–Sutcliffe efficiency index (NSE), percentage bias (PBIAS), determination coefficient (R2) and Kling–Gupta efficiency coefficient (KGE), whereas groundwater head is evaluated using mean absolute error (MAE). The Morris method is employed to identify the key parameters influencing hydrological fluxes. Furthermore, the iterative ensemble smoother (iES) is utilized as a technique for uncertainty quantification (UQ) and parameter estimation (PE) and to decrease the computational cost owing to the large number of parameters. Depending on the watershed, key identified selected parameters include aquifer specific yield, aquifer hydraulic conductivity, recharge delay, streambed thickness, streambed hydraulic conductivity, area of groundwater inflow to tile, depth of tiles below ground surface, hydraulic conductivity of the drain perimeter, river depth (for groundwater flow processes), runoff curve number (for surface runoff processes), plant uptake compensation factor, soil evaporation compensation factor (for potential and actual evapotranspiration processes), soil available water capacity and percolation coefficient (for soil water processes). The presence of gwflow parameters permits the recognition of all key parameters in the surface and/or subsurface flow processes, with results substantially differing if the base SWAT+ models are utilized.