A model inter-comparison of secondary pollutant simulations over urban areas in Japan, the first phase of Japan’s study for reference air quality modeling (J-STREAM Phase I), was conducted using 32 model settings. Simulated hourly concentrations of nitric oxide (NO) and nitrogen dioxide (NO2), which are primary pollutant precursors of particulate matter with a diameter of 2.5 µm or less (PM2.5), showed good agreement with the observed concentrations, but most of the simulated hourly sulfur oxide (SO2) concentrations were much higher than the observations. Simulated concentrations of PM2.5 and its components were compared to daily observed concentrations by using the filter pack method at selected ambient air pollution monitoring stations (AAPMSs) for each season. In general, most models showed good agreement with the observed total PM2.5 mass concentration levels in each season and provided goal or criteria levels of model ensemble statistics in warmer seasons. The good performances of these models were associated with the simulated reproducibility of some dominant components, sulfates (SO42−) and ammonium (NH4+). The other simulated PM2.5 components, i.e., nitrates (NO3−), elemental carbon (EC), and organic carbon (OC), often show clear deviations from the observations. The considerable underestimations (approximately 30 µg/m3 for total PM2.5) of all participant models found on heavily polluted days with approximately 40–50 µg/m3 for total PM2.5 indicated some problems in the simulated local meteorology such as the atmospheric stability. This model inter-comparison suggests that these deviations may be owing to a need for further improvements both in the emission inventories and additional formation pathways in chemical transport models, and meteorological conditions also require improvement to simulate elevated atmospheric pollutants. Additional accumulated observations are likely needed to further evaluate the simulated concentrations and improve the model performance.

In order to improve the accuracy of the wind speed simulated by a mesoscale model for the wind resource assessment in coastal areas, this study evaluated the effectiveness of using the Japan Meteorological Agency (JMA)’s latest and finest (2 km × 2 km) grid point value (GPV) data, produced from the local forecast model (LFM) as input data to the mesoscale model. The evaluation was performed using wind lidar measurements at two sites located on the coasts of the Sea of Japan and Pacific Ocean. The accuracy of the LFM–GPV was first compared with that of two products from the JMA Meso Scale Model (MSM) (5 km × 5 km): MSM-GPV and mesoscale analysis (MANAL). Consequently, it was shown that LFM–GPV exhibited the most accurate wind speeds against lidar measurements. Next, dynamical downscaling simulations were performed using the weather research and forecasting model (WRF) forced by the three datasets above, and their results were compared. As compared to the GPVs, it was found that the WRF dynamical downscaling simulation using them as input can improve the accuracy of the coastal wind speeds. This was attributed to the advantage of the WRF simulation to improve the negative bias from the input data, especially for the winds blowing from the sea sectors. It was also found that even if the LFM–GPV is used as an input to the WRF simulation, it does not always reproduce more accurate wind speeds, as compared to the simulations using the other two datasets. This result is partly owing to the tendency of WRF to overestimate the wind speed over land, thus obscuring the higher accuracy of the LFM–GPV. It was also shown that the overestimation tendency cannot be improved by only changing the nudging methods or the planetary boundary layer schemes in WRF. These results indicate that it may be difficult to utilize the LFM–GPV in the WRF wind simulation, unless the overestimation tendency of WRF itself is improved first.

This study aimed to establish numerical models to replicate wind conditions for nearshore waters, sensitive to onshore topography, and to compare the characteristics of computational fluid dynamic (CFD) and mesoscale models. Vertical Doppler light detection and ranging (LiDAR) observation data were measured at an onshore site, which showed that wind conditions were affected by thermodynamic phenomena, such as land and sea breeze, and dynamical effects from neighboring onshore topography. The estimation accuracy of the CFD model depended on the height of the LiDAR data input. A height close to the target, such as the hub height of wind turbines, seemed appropriate as input data, considering that the accuracy of the wind speed shear replicated in a CFD numerical model may be uncertain. The mesoscale model replicated the wind through the thermodynamic effect and reliably estimated wind speed over nearshore waters without observation correction. Larger estimation errors were detected in the CFD model than in the mesoscale model, as the former could not account for thermodynamic effects. Wind conditions in water areas near complex coastlines may also be formed by thermodynamic factors, making analysis using a mesoscale model advantageous.

SUMMARYIn this study, the accuracy of the WRF (the Weather Research and Forecasting model)-simulated horizontal gradient of offshore wind speed near a coastline is evaluated using in-situ measurements and Envisat/ASAR (Advanced Synthetic Aperture Rader) images. The WRF simulation is conducted for the coastal waters off Shirahama in Wakayama Prefecture and the accuracy is examined in comparison with in-situ measurements from the offshore platform of Shirahama Oceanographic Observatory (referred to as Shirahama) and the Wakayama Southwest buoy (SW-buoy). It is found that WRF overestimates wind speed at Shirahama through the year, while it underestimates wind speed at SW-buoy only for northwesterly winds, which are predominant through the year. For these reasons, WRF tends to have a weaker horizontal gradient of wind speed between these two sites. Compared with wind speed fields obtained from ASAR, it is found that the underestimation at SW-buoy is partly due to the offshore shift of a strong wind zone, formed by the speed-up effect from two upstream strains, in the WRF simulation.key words: offshore wind, horizontal wind speed gradient, WRF, SAR