Fine-Scale Structures in the Mid-Level Eyewall of Super Typhoon Rammasun (2014) Simulated With the WRF-LES Framework

It has been numerically demonstrated that the turbulence above the boundary is important to tropical cyclone intensification and rapid intensification, but the three-dimensional structures of the sub-grid-scale (SGS) eddy have not been revealed due to the lack of observational data. In this study, two numerical simulations of Super Typhoon Rammasun (2014) were conducted with the Advanced Weather Research and Forecast (WRF) model by incorporating the large-eddy simulation (LES) technique, in which the enhanced eyewall convection and the process of rapid intensification are captured. Consistent with previous observational studies, the strong turbulent kinetic energy (TKE) is found throughout the whole eyewall inside of the radius of maximum wind in both experiments. The simulations indicate that the strong TKE is associated with horizontal rolls with the horizontal extent of 2–4 km, which are aligned azimuthally in the intense eyewall convection. It is indicated that the three-dimensional structures of the SGS eddy can be simulated with the vertical grid spacing of ∼100 m when the horizontal grid spacing is 74 m. It is suggested that there is considerable turbulence associated with azimuthally-aligned horizontal rolls in the mid-level eyewall of tropical cyclone.

Removing the stability limit of the time–space domain explicit finite difference schemes for acoustic modeling with stability condition-based spatial operators

The time step and grid spacing in explicit finite-difference (FD) modeling are constrained by the Courant-Friedrichs-Lewy (CFL) condition. Recently, it has been found that spatial FD coefficients may be designed through simultaneously minimizing the spatial dispersion error and maximizing the CFL number. This allows one to stably use a larger time step or a smaller grid spacing than usually possible. However, when using such a method, only second-order temporal accuracy is achieved. To address this issue, I propose a method to determine the spatial FD coefficients, which simultaneously satisfy the stability condition of the whole wavenumber range and the time–space domain dispersion relation of a given wavenumber range. Therefore, stable modeling can be performed with high-order spatial and temporal accuracy. The coefficients can adapt to the variation of velocity in heterogeneous models. Additionally, based on the hybrid absorbing boundary condition, I develop a strategy to stably and effectively suppress artificial reflections from the model boundaries for large CFL numbers. Stability analysis, accuracy analysis and numerical modeling demonstrate the accuracy and effectiveness of the proposed method.

Horizontal Grid Spacing Comparison Among Random Forest Algorithms to Nowcast Cloud-to-Ground Lightning Occurrence

This paper discusses the use of Random Forest (RF), a popular Machine Learning (ML) algorithm, to perform spatially explicit nowcasting of cloud-to-ground lightning occurrence. An application to the Italian territory and the surrounding seas is presented. Specifically, 1-hour ahead lightning occurrences over the months of August, September and October from 2017 to 2019 have been modelled using a dataset including geo-environmental features. Results obtained with three different spatial resolutions have been compared, for nowcasting both positive and negative strokes. The features’ importance resulting from the best RF models showed how datadriven models are able to identify the relationships between meteorological variables, in agreement with previous physically based knowledge of the phenomenon. The encouraging results obtained in terms of forecasting accuracy support the idea to use ML-based algorithms in early warning procedures for disaster risk management.

Forming of the products of non-hydrostatic modelling systems for short-range weather prediction COSMO-RuBy (Hydrometcentre of Russia) и WRF-ARW (Belhydromet)

The paper considers the results of activities on the development of output products for the non-hydrostatic short-range numerical weather prediction systems: COSMO-RuBy with a grid spacing of 2.2 km at the Hydrometcentre of Russia and WRF-ARW with a grid spacing of 3 km in Belhydromet. The important results of the activities are the organization of the exchange of unified products between the countries and the development at the Hydrometcentre of Russia of two technologies for obtaining the unified products: the multi-model lagged ensemble system and the system for the complex correction based on machine learning of model results. A specialized web-site providing convenient work of forecasters with the COSMO-RuBy results and unified products was created at the Hydrometcentre of Russia based on the feedback from forecasters. The systems of common visualization and verification of COSMO-RuBy and WRF-ARW results are implemented in Belhydromet. Keywords: numerical weather prediction, ensemble forecasting, visualization, machine learning

Lattice Phase Field Model for Nanomaterials

The lattice phase field model is developed to simulate microstructures of nanoscale materials. The grid spacing in simulation is rescaled and restricted to the lattice parameter of real materials. Two possible approaches are used to solve the phase field equations at the length scale of lattice parameter. Examples for lattice phase field modeling of complex nanostructures are presented to demonstrate the potential and capability of this model, including ferroelectric superlattice structure, ferromagnetic composites, and the grain growth process under stress. Advantages, disadvantages, and future directions with this phase field model are discussed briefly.

Assessing the WRF-Solar model performance using satellite-derived irradiance from the National Solar Radiation Database

WRF-Solar is a numerical weather prediction (NWP) model specifically designed to meet the increasing demand for accurate solar irradiance forecasting. The model provides flexibility in the representation of the aerosol-cloud-radiation processes. This flexibility can be argued to make more difficult to improve the model’s performance due to the necessity of inspecting different configurations. To alleviate this, WRF-Solar has a reference configuration to use it as a benchmark in sensitivity experiments. However, the scarcity of high-quality ground observations is a handicap to accurately quantify the model performance. An alternative to ground observations are satellite irradiance retrievals. Herein we analyze the adequacy of the National Solar Radiation Database (NSRDB) to validate the WRF-Solar performance using high-quality global horizontal irradiance (GHI) observations across the CONUS. Based on the sufficient performance of NSRDB, we further analyze the WRF-Solar forecast errors across the CONUS, the growth of the forecasting errors as a function of the lead time, sensitivities to the grid spacing, and to the representation of the radiative effects of unresolved clouds. Our results based on WRF-Solar forecasts spanning the year of 2018 reveal a 7% median degradation of the mean absolute error (MAE) from the first to the second daytime period. Reducing the grid spacing from 9 km to 3 km leads to a 4% improvement in the MAE, whereas activating the radiative effects of unresolved clouds is desirable over most of the CONUS even at 3 km of grid spacing. A systematic overestimation of the GHI is found. These results illustrate the potential of GHI retrievals to contribute increasing the WRF-Solar performance.

On the application and grid-size sensitivity of the urban dispersion model CAIRDIO for the simulation of a real city and realistic meteorology

There is a gap between the need for city-wide air-quality simulations considering the intra-urban variability and mircoscale dispersion features and the computational capacities that conventional urban microscale models require. This gap can be bridged by targeting model applications on the gray zone situated between the mesoscale and large-eddy scale. The urban dispersion model CAIRDIO is a new contribution to the class of computational-fluid dynamics models operating in this scale range. It uses a diffuse-obstacle boundary method to represent buildings as physical obstacles at gray-zone resolutions in the order of tens of meters. The main objective of this approach is to find an acceptable compromise between computationally inexpensive grid sizes for spatially comprehensive applications and the required accuracy in the description of building and boundary-layer effects. For this purpose, CAIRDIO is applied in dispersion simulation of black carbon and particulate matter for an entire mid-size city using an uniform horizontal resolution of 40 m in this paper. For evaluation, the simulation results are compared with measurements from 5 operational air monitoring stations, which are representative for the urban background and high-traffic roads, respectively. Moreover, the comparison includes the mesoscale host simulation, which provides the boundary conditions. The temporal variability of the concentration measurements at the background sites was largely influenced only by the characteristics of the mixing layer. As a consequence, the model results were not significantly dependent on spatial resolution, so that the mesoscale simulation also performed reasonably well. At the traffic sites, however, concentrations were in addition markedly influenced by the proximity to road-traffic sources and the surrounding building environment. Here, the mesoscale simulation indiscriminately reproduced almost the same urban-background profiles, which resulted in a large positive model bias. On the other hand, the CAIRDIO simulation was able to respond to the significantly amplified diurnal variability with its pronounced rush-hour peaks. This resulted in a consistent improvement of the model deviation to mea- surements compared to the mesoscale simulation. Nevertheless, discrepancies to measurements remain in the 40 m-CAIRDIO simulation, e.g., an underestimation of peak concentrations at two traffic sites inside narrow street canyons. To further research resolution sensitivity, the horizontal grid spacing of locally nested CAIRDIO domains is refined down to 5 m. While for the street canyons the representation of peak concentrations can be improved using horizontal grid spacings of up to 10 m, no further improvements beyond this resolution can be observed. This suggests that the too low peak concentrations with the default grid spacing of 40 m result from an inadequate representation of the traffic emissions inside narrow street canyons. If the total gain in accuracy due to the grid refinements is put in relation to the remaining model error, the improvements are only modest. In conclusion, the proposed gray-scale modeling is a promising downscaling approach for urban air-quality applications. Nevertheless, the results also show that aspects other than the actual resolution of flow patterns and numerical effects can determine the simulations at the urban microscale.

Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain

We evaluate the influence of a forest parametrization on the simulation of the boundary layer flow over moderate complex terrain in the context of the Perdigão 2017 field campaign. The numerical simulations are performed using the Weather research and forecasting model using its large eddy simulation mode (WRF-LES). The short-term high resolution (40 m horizontal grid spacing) and long-term (200 m horizontal grid spacing) WRF-LES are evaluated for an integration time of 12 hours and 1.5 months, respectively, with and without forest parameterization. The short-term simulations focus on low-level jet events over the valley, while the long-term simulations cover the whole intensive observation period (IOP) of the field campaign. The results are validated using lidar and meteorological tower observations. The mean diurnal cycle during the IOP shows a significant improvement of the along-valley wind speed and the wind direction when using the forest parametrization. However, the drag imposed by the parametrization results in an underestimation of the cross-valley wind speed, which can be attributed to a poor representation of the land surface characteristics. The evaluation of the high-resolution WRF-LES shows a positive influence of the forest parametrization on the simulated winds in the first 500 m above the surface.

Evaluating Quantitative Precipitation Forecasts Using the 2.5 km CReSS Model for Typhoons in Taiwan: An Update through the 2015 Season

In this study, 24 h quantitative precipitation forecasts (QPFs) by a cloud-resolving model (with a grid spacing of 2.5 km) on days 1–3 for 29 typhoons in six seasons of 2010–2015 in Taiwan were examined using categorical scores and rain gauge data. The study represents an update from a previous study for 2010–2012, in order to produce more stable and robust statistics toward the high thresholds (typically with fewer sample points), which is our main focus of interest. This is important to better understand the model’s ability to predict such high-impact typhoon rainfall events. The overall threat scores (TS, defined as the fraction among all verification points that are correctly predicted to reach a given threshold to all points that are either observed or predicted to reach that threshold, or both) were 0.28 and 0.18 on day 1 (0–24 h) QPFs, 0.25 and 0.16 on day 2 (24–48 h) QPFs, and 0.15 and 0.08 on day 3 (48–72 h) QPFs at 350 mm and 500 mm, respectively, showing improvements over 5 km models. Moreover, as found previously, a strong dependence of higher TSs for larger rainfall events also existed, and the corresponding TSs at 350 and 500 mm for the top 5% of events were 0.39 and 0.25 on day 1, 0.38 and 0.21 on day 2, and 0.25 and 0.12 on day 3. Thus, for the top typhoon rainfall events that have the highest potential for hazards, the model exhibits an even higher ability for QPFs based on categorical scores. Furthermore, it is shown that the model has little tendency to overpredict or underpredict rainfall for all groups of events with different rainfall magnitude across all thresholds, except for some tendency to under-forecast for the largest event group on day 3. Some issues associated with categorical statistics to be aware of are also demonstrated and discussed.