Accuracy Comparison of Coastal Wind Speeds between WRF Simulations Using Different Input Datasets in Japan

Abstract

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.

Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana

“…It was hoped that by selecting the periods simulated in this manner (as has been done in other resource assessment sensitivity studies [22,32]), the effect of major seasonal changes on annual winds would be captured by the options being tested. The grid nudging option of the WRF Four-Dimensional Data Assimilation (FDDA) system is a technique that has been used in several studies on wind downscaling for resource assessment purposes [20,52,53]. The technique bridges the gap between the model simulations and time-interpolated values from input data.…”

“…The technique bridges the gap between the model simulations and time-interpolated values from input data. All three simulation domains were nudged during all the simulations, following practices of previous studies [20,52,53]. Nudging options and simulation lengths were chosen based on recommendations from a previous study in coastal Ghana [49].…”

“…The empirical method was chosen for its simplicity and often relatively better or on-par relative test rank, which was in terms of total normalized results. The distributions for observed data and the configurations were compared via the maximum absolute error of the cumulative distribution function (max CDF error), which was determined as the maximum difference between the cumulative distributions of observed and predicted data, (evaluated in 0.5 m/s bins as recommended by [57]) [53]:…”

Application of the WRF model to the coastal area at Ise Bay, Japan: evaluation of model output sensitivity to input data

“…Previous studies reported that accuracy of WRF output varies with input data, which can be roughly classified into three types: (a) atmospheric data (e.g. Akimoto and Kusaka 2010;Carvalho, Rocha, and Gómez-Gesteira 2012;Misaki et al 2019), (b) topography and land use (e.g. Cheng et al 2013;Jee and Kim 2016;Jiménez-Esteve et al 2018;Mallard, Spero, and Taylor 2018;Kikuchi, Fukushima, and Ishihara 2020), and (c) sea surface temperature (SST) as a lower boundary condition (e.g.…”

“…They concluded that local winds were simulated accurately and realistically when atmospheric input data that are representative of local conditions were used. Misaki et al (2019) conducted WRF simulations using three atmospheric datasets to evaluate accuracy of WRF simulated wind speeds above the sea surface in Niigata and Ibaraki, Japan. Kikuchi, Fukushima, and Ishihara (2020) conducted WRF simulations for the area around the offshore observation station (meteorological mast) at Choshi, Japan to evaluate the effect of replacing the land use dataset from the US Geological Survey (USGS) by the high resolution dataset provided by the National Land Information Division.…”