利用850 hPa的纬向风异常建立一个逐候东亚-太平洋(East Asian Pacific,EAP)型指数,研究其季节内演变特征,发现东亚-太平洋型经向波列是东亚夏季风季节内变化的主要模态。其演变过程为:扰动首先出现在北太平洋中部,并通过正压不稳定过程从基本气流中获得能量而发展,在高层罗斯贝波能量向南频散,激发热带对流异常和赤道罗斯贝波,并相互锁相,因赤道罗斯贝波受β效应影响而共同向西移动。热带对流和环流异常在菲律宾附近达到最强,此时在东亚沿岸出现经向三极型波列,此后中低纬度异常继续向西北方向移动,使降水异常在长江流域能维持较长时间。东亚-太平洋型在东亚发展和维持有以下原因:首先,菲律宾暖水上空的对流和低层环流之间存在正反馈;其次,由于海陆热力差异导致暖大陆和冷海洋之间存在特殊的纬向温度梯度和北风垂直切变,东亚-太平洋型在经向上有向北倾斜的斜压结构,能通过斜压能量转换从平均有效位能中获得能量,同时,也能从经向温度梯度的平均有效位能中获得能量。
To better assimilate Advanced TIROS Operational Vertical Sounder(ATOVS) radiance data and provide more accurate initial fields for a numerical model,two bias correction schemes are employed to correct the ATOVS radiance data.The difference in the two schemes lies in the predictors use in air-mass bias correction.The predictors used in SCHEME 1 are all obtained from model first-guess,while those in SCHEME 2 are from model first-guess and radiance observations.The results from the two schemes show that after bias correction,the observation residual became smaller and closer to a Gaussian distribution.For both land and ocean data sets,the results obtained from SCHEME 1 are similar to those from SCHEME 2,which indicates that the predictors could be used in bias correction of ATOVS data.
In this study, the observational data acquired in the South China Heavy Rainfall Experiment （SCHeREX） from May to July 2008 and 2009 were integrated and assimilated with the US National Oceanic and Atmospheric Administration＇s （NOAA） Local Analysis and Prediction System （LAPS; information available online at http：//laps.fsl.noaa.gov）. A high-resolution mesoscale analysis dataset was then generated at a spatial resolution of 5 km and a temporal resolution of 3 h in four observational areas： South China, Central China, Jianghuai area, and Yangtze River Delta area. The quality of this dataset was evaluated as follows. First, the dataset was qualitatively compared with radar reflectivity and TBB image for specific heavy rainfall events so as to examine its capability in reproduction of mesoscale systems. The results show that the SCHeREX analysis dataset has a strong capability in capturing severe mesoscale convective systems. Second, the mean deviation and root mean square error of the SCHeREX mesoscale analysis fields were analyzed and compared with radiosonde data. The results reveal that the errors of geopotential height, temperature, relative humidity, and wind of the SCHeREX analysis were within the acceptable range of observation errors. In particular, the average error was 45 m for geopotential height between 700 and 925 hPa, 1.0-1.1°C for temperature, less than 20% for relative humidity, 1.5-2.0 m s-1 for wind speed, and 20-25° for wind direction. The above results clearly indicate that the SCHeREX mesoscale analysis dataset is of high quality and sufficient reliability, and it is applicable to refined mesoscale weather studies.
