资源环境科技发展态势分析平台

The seasonal and diurnal cycles of ocean vector winds in the domain of the South China Sea are characterized and compared using RapidScat and the Cross-Calibrated Multi-Platform (CCMP) data sets. Broad agreement in seasonal flow patterns exists between these data sets during the year 2015. Both observe the dramatic reversal from wintertime trade winds (November-April) to westerly flow associated with the summer monsoon (May-October). These seasonal changes have strong but not equivalent effects on mean wind divergence patterns in both data sets. Specifically near the Philippines, the data sets agree on several aspects of the seasonal mean and diurnal cycle of near-surface vector winds and divergence. In particular, RapidScat and CCMP agree that daytime onshore and nocturnal offshore flow patterns affect the diurnal cycle of winds up to similar to 200 km west of Luzon, Philippines. Observed disagreements over the diurnal cycle are explainable by measurement uncertainty, as well as shortcomings in both data sets.

Plain Language Summary It is important to know how satellite-based wind data sets compare with one another, in order to understand basic wind patterns in the South China Sea-an area that is strategically important to many countries. The seasonal and daily variability of low-altitude winds over the South China Sea was studied using two independent satellite data sets that report both wind speed and direction. One of these data sets spans a few decades, while the other is much shorter but more precisely measures the daily cycle of winds. The two data sets agree well on seasonal wind shifts during the year 2015. Both observe the dramatic reversal from winter trade winds (November-April) to the summer monsoon (May-October). Both data sets also agree on wind shifts throughout a typical day near the island of Luzon in the northern Philippines. Relative to the average wind pattern, in the morning winds blow onshore, while overnight they reverse and blow offshore as far as 200 km from the coast. The results give us confidence that we can use both data sets to understand different parts of the wind climatology near the Philippines.