Research Of The Marine Monitoring Technique And The Spatial And Temporal Variations

As the most uncertain factor in the climate simulation, aerosol plays an importantrole in the global and regional climate change. The deposition of aerosols over seashas significant impact on the seawater which is also the main source of natural aerosol,salt aerosol and moisture. In order to comprehensively understand the role of theaerosol in the biogeochemical cycle and climate change, the aerosol characteristicshould be investigated. But now the characteristic and distribution of the aerosol overChina Sea are not clear because of the lack of monitoring data. Therefore, it is urgentto study the monitoring technique of the marine aerosols, and put the observed data ofaerosol over China Sea into network.The key techniques in marine aerosol monitoring which contains inversionalgorithm of the observation instrument, data acquisition and post-processing andcalibration, are investigated in this study. The observation methods of monitoringmarine aerosol by using instruments have been established. Computer program whichcalculates aerosol optical depth (AOD) from digital signals is developed based onAOD measuring principle of sun photometer; the errors of root-mean-square are lessthan0.05, compared with output results of sun photometer. When the sun photometeris used to measure AOD on ships, the default settings should be changed and the datashould be post-processed based on COV methods in order to decrease the influence ofthe poor platform stability. An improved method for measuring AOD using fieldspectrometer is proposed, which could remove the error produced by the baffle-boardused in the observation.In this paper,654groups of data obtained using Microtops sunphotometer onship and more than10,000groups of data obtained using CE318were taken toanalyze the temporal and spatial distributions and types of the marine aerosols overChina Sea. Air over the South China Sea is cleanest due to the small influence of landand abundant precipitation. The industrial pollution of Liaoning and North Koreawhich are nearby the North Yellow Sea is lighter, so air over the North Yellow Sea isrelatively clean too. Air over Bohai Sea is the most turbid due to nearby industrialpollution, and the clean air just distributes over Beidaihe to Caofeidian in the westerncoast and the center area of Bohai Sea. Air over the South Yellow sea is relativelyturbid due to the industrial pollution of Shandong, Jiangsu and the Yangtze RiverDelta nearby and frequent sea fog. The variation of Angstrom exponent is larger overthe Laizhou Bay, Liaodong Bay, Bohai Strait, Liaohe Estuary, northern area of Weihaicity, and eastern of Dalian city while it is smaller over the areas far from land. When βis smaller than0.2, air is relatively clean, and the variation of aerosol composition isthe main reason of the variation of AOD, so there is a positive correlation between α and AOD. When β is larger than0.2, air is relatively turbid and the aerosol particlesare from sandstorm or air pollution. There are several factors which can influence thevariation of AOD, so there is not a definite correlation between α and AOD, eventhere is a negative correlation between α and AOD in some sections. In Bohai Sea andYellow sea, AOD shows minimum value in the cold and dry winter while AOD showsmaximum value in spring and summer because of the influence of sandstorm and seafog. AOD monitored at Yuandao Station located in Yellow Sea could present this trendtoo. But AOD monitored in Dalian shows a maximum in winter because of theinfluence of fuel burning for winter heating. In the southern seas, AOD shows amaximum in winter and a minimum in summer because of the the washing out oflarge amounts of precipitation and salt aerosols brought by southeast monsoon. AODmonitored over Sansha Station and Dawanshan Station shows this trend.According to the difference of Angstrom exponent and turbidity coefficient, themarine aerosol (mainly in the northern seas) could be divided into three types: cleanair aerosol (background aerosol), dust aerosol, mixed-mode aerosol. The clean airaerosols account for55%of the total monitoring station which could be considered asthe background aerosol, dust aerosols account for12%and the mixed-mode aerosolsaccount for33%.The temporal variations in different time window are analyzed from a long timeAOD serial, which provides a basis for selecting the optimal time window in erroranalysis of inversion algorithm for satellite data validation. When AOD is small, itsvariation is small; and when AOD is large, its variation is large. As the time windowbecomes larger, both the average relative error and absolute error of the AOD serialincrease. Under the same time window, the average absolute error increases as theAOD increases, but the average relative error changes little. At the station with littleAOD change, a large window could be selected; and at the station with large AODchange, a small window should be selected. The optimal time window could not belarge than±0.5h in the satellite validation over China Sea.