Roanu台风中突然天气变化的自动气象站资料评估

以斯里兰卡南部5.936 108°N、80.574 900°E处的自动气象站（AWS）的气象时间序列观测数据为依据，对2015年12月至2016年10月大气边界层的变化进行了定量分析.结果表明，印度洋北部的季风、气温、气压、相对湿度、降水和向下短波辐射的扰动随着季风的逆转而变化.2016年5月台风Roanu经过时，气压降低、相对湿度增大、降水增强和向下短波辐射减小，其特征是温度、相对湿度、降水和风速均迅速增加，之后气温和降水下降，而气压、向下短波辐射在急剧减小之后又急剧增大.自动气象站记录了台风到达前的气象条件，并自2016年5月13日起各个参数开始响应台风变化.从2016年5月28日开始，自动气象站记录台风通过后的气象条件，此时降水和向下辐射均减少.这些信号说明应用自动气象站可以持续观测台风条件.这项研究表明，斯里兰卡南部地区的气象数据可以用来进行天气评估，并可以对南部沿海地区的海气关系现象进行分析.此外，自动气象站的现场数据可以用作模型验证和参数化.

Quality assessment of Automated Weather Station (AWS) data for abrupt weather changing during cyclone Roanu

Meteorological time-series observations from the Automated Weather Station (AWS) located at 5.936 108°N,80.574 900°E south of Sri Lanka were used to quantify the variability in boundary layer from Dec 2015 to Oct 2016.The observations show the fluctuation of wind,temperature,pressure,relative humidity,precipitation,and downward shortwave radiation,accompanied by monsoon reversing over the northern Indian Ocean during this period of time.The episodic event has been noted by the low air pressure,high relative humidity,high precipitation and reduction of downward radiation during the passage of cyclone Roanu in May 2016.The event is characterized by the rapid increase in temperature,relative humidity,precipitation and wind speed followed by the decline of temperature and precipitation,and the abrupt decrease then dramatic increase in pressure and downward shortwave radiation.The reason behind these fluctuations is investigated using the dataset from AWS during May 2016.The analysis shows that AWS recorded pre-cyclone conditions which start to respond since 13th May,2016.The AWS also recorded post-cyclone conditions which are illustrated by reduction of downward radiation and precipitations after 28th May,2016.These signals are evident for the sustainability of AWS to the cyclone conditions.This study suggests that AWS dataset can be used for weather assessment in southern Sri Lanka region and analysis of air-sea relation phenomena in the southern coastal region.Furthermore,AWS can be used as in situ data source for model validation and parameterization.