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The West African monsoon (WAM) system is a critical climatic phenomenon with significant socio-economic impacts on millions of people. Despite many advancements in numerical weather and climate models, accurately representing the WAM remains a challenge due to its intricate dynamics and inherent uncertainties. Building upon our previous work utilizing the ICON (icosahedral nonhydrostatic) numerical model to construct statistical surrogate models for quantities of interest (QoIs) characterizing the WAM, this paper focuses on the optimization of the three uncertain model parameters of entrainment rate, fall speed of ice, and soil moisture evaporation fraction through innovative multi-objective optimization (MOO) techniques. The problem is approached in two distinct ways: (1) optimization of 15 designated QoIs, such as the latitude and magnitude of the African rain belt or African easterly jet, using existing surrogate models and (2) optimization of twelve 2D meteorological output fields, such as precipitation, cloud cover, and pressure, using new surrogate models that employ principal component analysis. The objectives subject to minimization in the MOO process are defined as the difference between the surrogate model and reference data for each QoI or output field. As reference data, Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) mission are used for precipitation, and the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts are used for all other quantities. The multi-objective optimization problems are tackled through two strategies: (1) assignment of weights with uncertainties to the objectives based on expert opinion and (2) variation in these weights in order to assess their influence on the optimal values of the uncertain model parameters. Results show that the ICON model is already generally well-tuned for the WAM system. However, a lower entrainment parameter would lead to a more accurate simulation of accumulated precipitation, averaged 2 m dew point temperature, and mean sea level pressure over the considered domain (15° W to 15° E, 0 to 25° N). An improvement in 2D output fields instead of QoIs is barely possible with the considered parameters, which confirms meaningful default values of the model parameters for the region. Nevertheless, optimal model parameters strongly depend on the assigned weights for the objectives. To further enhance the accuracy of climate simulations and potentially improve weather predictions, it is crucial to prioritize the refinement of the overall physical models, including the reduction in inherent structural errors, rather than solely adjusting the uncertain parameters in existing model parametrizations. Nevertheless, our methodology demonstrates the potential of integrating statistical and expert-driven approaches to assess and improve the simulation accuracy of the WAM. The findings underscore the importance of considering uncertainties in MOO and the need for a holistic understanding of the WAM's dynamics to enhance prediction skills. [ABSTRACT FROM AUTHOR]

Very recently, an innovative mesh-free method, the so-called line element-less method (LEM), has been proposed to determine the response of plates under uniformly distributed edge moments. However, the previous formulations do not solve the problem related to plates with reentrant angles. In this paper, an innovative formulation is introduced to evaluate the deflection function of a simply supported plate possessing reentrant angles at the boundary and loaded by uniformly distributed edge moments. Framed in the LEM, this formulation consents to obtain a plate deflection function, implementing a simple algorithm within Mathematica software. Compelling savings in terms of time and computational costs are achieved, since there is no need to discretize the domain or the contour. Finally, the accuracy of the proposed results is highlighted by a good match with those achieved by classical methods and the finite-element method. [ABSTRACT FROM AUTHOR]

Simulating the West African monsoon (WAM) system using numerical weather and climate models suffers from large uncertainties, which are difficult to assess due to nonlinear interactions between different components of the WAM. Here we present a fundamentally new approach to the problem by approximating the behavior of a numerical model – here the Icosahedral Nonhydrostatic (ICON) model – through a statistical surrogate model based on universal kriging, a general form of Gaussian process regression, which allows for a comprehensive global sensitivity analysis. The main steps of our analysis are as follows: (i) identify the most important uncertain model parameters and their probability density functions, for which we employ a new strategy dealing with non-uniformity in the kriging process. (ii) Define quantities of interest (QoIs) that represent general meteorological fields, such as temperature, pressure, cloud cover and precipitation, as well as the prominent WAM features, namely the tropical easterly jet, African easterly jet, Saharan heat low (SHL) and intertropical discontinuity. (iii) Apply a sampling strategy with regard to the kriging method to identify model parameter combinations which are used for numerical modeling experiments. (iv) Conduct ICON model runs for identified model parameter combinations over a nested limited-area domain from 28° W to 34° E and from 10° S to 34° N. The simulations are run for August in 4 different years (2016 to 2019) to capture the peak northward penetration of rainfall into West Africa, and QoIs are computed based on the mean response over the whole month in all years. (v) Quantify sensitivity of QoIs to uncertain model parameters in an integrated and a local analysis. The results show that simple isolated relationships between single model parameters and WAM QoIs rarely exist. Changing individual parameters affects multiple QoIs simultaneously, reflecting the physical links between them and the complexity of the WAM system. The entrainment rate in the convection scheme and the terminal fall velocity of ice particles show the greatest effects on the QoIs. Larger values of these two parameters reduce cloud cover and precipitation and intensify the SHL. The entrainment rate primarily affects 2 m temperature and 2 m dew point temperature and causes latitudinal shifts, whereas the terminal fall velocity of ice mostly affects cloud cover. Furthermore, the parameter that controls the evaporative soil surface has a major effect on 2 m temperature, 2 m dew point temperature and cloud cover. The results highlight the usefulness of surrogate models for the analysis of model uncertainty and open up new opportunities to better constrain model parameters through a comparison of the model output with selected observations. [ABSTRACT FROM AUTHOR]