Long‐term simulations using version 5.1 of the National Center for Atmospheric Research’ Community Atmosphere Model at low (T42), medium (T106), and high (T266) resolutions were carried out to study the impact of horizontal resolution on the model's performance in reproducing the climatological features of precipitation over East Asia. The simulated spatial pattern of annual mean precipitation amount improves significantly with increased resolution. The low‐resolution model is inadequate to reproduce the precipitation closely associated with fine‐scale orographic forcing, such as the narrow large‐rainfall belt along the southern edge of the Tibetan Plateau. The distribution of rainfall over and around the elevation of the Tibetan Plateau and high‐altitude mountains becomes more realistic at higher resolutions. The proportion of the large‐bias (small‐bias) area continuously reduces (increases) when moving from T42 to T266 resolution. Simulations at all three resolutions can capture the key features of the major seasonal variation of rainfall arising from the onset and advancement of the Asian monsoon. A novel method is used to evaluate the sensitivity of the simulated intensity‐frequency structure to the horizontal resolution. The proportion of light rain, which demonstrates large positive bias in climate models, decreases dramatically at higher resolution. The intensity‐frequency structures averaged over steep‐terrain regions and plain areas become more distinctive and realistic as the resolution increases.

This paper describes the development and evaluation of a new nonhydrostatic dynamical framework for global and regional atmospheric modeling, with an emphasis on the numerical performance of dry dynamics. The model is formulated in a layer‐averaged manner using a generalized hybrid sigma‐mass vertical coordinate and an unstructured mesh. The mass‐based equations allow a flexible and effective switch between the hydrostatic and nonhydrostatic solvers. The unstructured mesh treats the conventional icosahedral grid and the more general Voronoi polygon in a consistent manner, allowing a flexible switch between quasi‐uniform and variable‐resolution modeling. The horizontal discretization and vertical discretization are formulated in an explicit Eulerian approach, while those terms describing the vertically propagating fast waves are solved implicitly. The model is equipped with physically based Smagorinsky diffusion as a tuning tool. A suite of multiscale test cases from hydrostatic to nonhydrostatic regimes is used to assess the model performance. The general strategies for evaluation focus on two aspects: (i) the nonhydrostatic solver should behave similarly to its hydrostatic counterpart under the hydrostatic regime and (ii) the nonhydrostatic solver should produce unique nonhydrostatic responses under the nonhydrostatic regime. In the context of model evaluation, model sensitivity to numerical configurations is further explored to understand the impact of isolated components, helping to identify appropriate configurations for realistic modeling applications. The present framework is a prototype toward a Global‐Regional Integrated forecast SysTem (GRIST).

Engineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer technology continues to improve, machine learning has proven to be a useful solution to some of these problems. The purpose of this paper is to further promote the development of turbulence modeling using data-driven machine learning; it begins by reviewing the development of turbulence modeling techniques, as well as the development of turbulence modeling for machine learning applications using a time-tracking approach. Afterwards, it examines the application of different algorithms to turbulent flows. In addition, this paper discusses some methods for the assimilation of data. As a result of the review, analysis, and discussion presented in this paper, some limitations in the development process are identified, and related developments are suggested. There are some limitations identified and recommendations made in this paper, as well as development goals, which are useful for the development of this field to some extent. In some respects, this paper may serve as a guide for development.

Numerical experiments are conducted to investigate the differences between summer precipitation over continental East Asia simulated by the Community Atmosphere Model, version 5 (CAM5), and superparameterized CAM5 (SPCAM5, a multiscale modeling framework). The results show that SPCAM5 effectively alleviates several original biases. Overestimates of precipitation on the eastern periphery of the Tibetan Plateau are reduced from CAM5 to SPCAM5 as a result of decreases in both the average hourly precipitation frequency and mean hourly intensity. Underestimates along the coastal regions in southern China are improved following a corresponding increase in mean hourly intensity and a decrease in average hourly precipitation frequency. The frequency–intesnsity relationship is also more realistic in SPCAM5. For western China, overestimated frequency values (in CAM5) of both weak-to-moderate (0–20 mm day−1) and heavy (20–50 mm day−1) intensity ranges are reduced in SPCAM5. For southern China, overestimates of frequency values (in CAM5) in the weak-to-moderate range are also reduced, whereas underestimates in the intense ranges are enhanced. In terms of diurnal variability, SPCAM5 generally exhibits a delay in the afternoon peak time and greater diurnal amplitude. The possible physical reasons for the variations in the precipitation between the models are further investigated. It is found that the change in deep convection intensity is a primary factor governing the shift in the precipitation simulations. SPCAM5 better simulates an intermediate transition stage from shallow to deep convection, which helps the deep convection to grow more fully to a greater magnitude, thus delaying the peak time and increasing the precipitation maxima.