Wind resource assessment for coastal areas requires accurate wind speed simulation using a mesoscale model. Our previous study found that the annual mean wind speed simulated by the advanced research Weather Research and Forecasting (WRF-ARW) model has a remarkable positive bias in the lower part of the planetary boundary layer (PBL). This result was obtained from a comparison with wind profiler measurements at Mihama, which is an observation station of the WInd profiler Network and Data Acquisition System (WINDAS) operated by the Japan Meteorological Agency (JMA). In this study, we examine whether such a positive bias can be seen at other WINDAS stations from a comparison of the WRF-simulated wind speed profile using the Mellor-YamadaJanjic (MYJ) PBL scheme with wind profiler measurements at ten WINDAS stations. The results show that the positive bias is found at all stations, and, moreover, that the positive bias is unlikely to be caused by either error in wind profiler measurement or the objective analysis data input into WRF. In addition, we compare the wind speed profiles simulated by WRF with seven different PBL schemes for a month. The result shows that the positive bias cannot be simply reduced by using other PBL schemes.

This work discusses the accuracies of geophysical model functions (GMFs) for retrieval of sea surface wind speed from satellite-borne Synthetic Aperture Radar (SAR) images in Japanese coastal waters characterized by short fetches and variable atmospheric stability conditions. In situ observations from two validation sites, Hiratsuka and Shirahama, are used for comparison of the retrieved sea surface wind speeds using CMOD (C-band model)4, CMOD_IFR2, CMOD5 and CMOD5.N. Of all the geophysical model functions (GMFs), the latest C-band GMF, CMOD5.N, has the smallest bias and root mean square error at both sites. All of the GMFs exhibit a negative bias in the retrieved wind speed. In order to understand the reason for this bias, all SAR-retrieved wind speeds are separated into two categories: onshore wind (blowing from sea to land) and offshore wind (blowing from land to sea). Only offshore winds were found to exhibit the large negative bias, and short fetches from the coastline may be a possible reason for this. Moreover, it is clarified that in both the unstable and stable conditions, CMOD5.N has atmospheric stability effectiveness, and can keep the same accuracy with CMOD5 in the neutral condition. In short, at the moment, CMOD5.N is thought to be the most promising GMF for the SAR wind speed retrieval with the atmospheric stability correction in Japanese OPEN ACCESS Remote Sens. 2013, 5 1957 coastal waters, although there is ample room for future improvement for the effect from short fetch.

Wind direction is required as input to the geophysical model function (GMF) for the retrieval of sea surface wind speed from synthetic aperture radar (SAR) images. The present study verifies the effectiveness of using the wind direction obtained from the weather research and forecasting model (WMF) as input to the GMF to retrieve accurate wind fields in coastal waters adjacent to complex onshore terrain. The wind speeds retrieved from 42 ENVISAT ASAR images are validated based on in situ measurements at an offshore platform in Japan. Accuracies are also compared with cases using wind directions: the meso-analysis of the Japan Meteorological Agency (MANAL), the SeaWinds microwave scatterometer on QuikSCAT and the National Center for Environmental Prediction final operational global analysis data (NCEP FNL).In comparison with the errors of the SAR-retrieved wind speeds obtained using the WRF, MANAL, QuikSCAT and NCEP FNL wind directions, the magnitudes of the errors do not appear to be correlated with the errors of the wind directions themselves. In addition to wind direction, terrain factors are considered to be a main source of error other than wind direction. Focusing on onshore winds (blowing from the sea to land), the root mean square errors on wind speed are found to be 0.75 m s 1 (in situ), 0.96 m s 1 (WRF), 1.75 m s 1 (MANAL), 1.58 m s 1 (QuikSCAT) and 2.00 m s 1 (NCEP FNL), respectively, but the uncertainty is of the same order of magnitude because of the low number of cases. These results indicate that although the effectiveness of using the accurate WRF wind direction for the wind retrieval is partly confirmed, further efforts to remove the error due to factors other than wind direction are necessary for more accurate wind retrieval in coastal waters.This version of the paper [24 October 2012] was modified slightly from the first version [16 July 2012]. Mainly, the notation style of the abbreviations; WRF, MANAL and NCEP FNL, was changed for an avoidance misunderstanding. In addition, excessive detail descriptions are removed from tables. This notice is included in the online and print versions to indicate that both have been corrected.

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