陈敏,范水勇,仲跻芹,楚艳丽,郑祚芳,张朝林,王迎春. 2010. 全球定位系统的可降水量资料在北京地区快速更新循环系统中的同化试验[J]. 气象学报, 68(4):450-463, doi:10.11676/qxxb2010.044
全球定位系统的可降水量资料在北京地区快速更新循环系统中的同化试验
An experimental study of assimilating the Global Position System precipitable water vapor observations into the rapid updated cycle system for the Beijing area.
投稿时间:2008-03-22  修订日期:2008-08-01
DOI:10.11676/qxxb2010.044
中文关键词:  资料同化, GPS可降水量, 三维变分, 观测系统试验
英文关键词:Data assimilation, Global Position System precipitable water vapor, 3DVAR, Observation systent experiment
基金项目:北京科技新星计划(2006A02),国家自然科学基金面上项目(40505020 )和公益性行业(气象)科研专项经费项目(GYHY200706004,GYHY200706042)
作者单位
陈敏 中国气象局北京城市气象研究所北京100089 
范水勇 中国气象局北京城市气象研究所北京100089 
仲跻芹 中国气象局北京城市气象研究所北京100089 
楚艳丽 中国气象局北京城市气象研究所北京100089 
郑祚芳 中国气象局北京城市气象研究所北京100089 
张朝林 中国气象局北京城市气象研究所北京100089 
王迎春 中国气象局北京城市气象研究所北京100089 
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中文摘要:
      针对发生于2006年8月和2007年6—8月的28次降水过程,在3 h快速更新循环同化预报系统平台上分别进行了有无同化局地GPS可降水量资料的试验。结果表明,局地GPS可降水量的同化可以使模式对大阈值降水的时段、强度和落区的预报性能均获得全面的提高,此现象在模式积分最初的0—6小时表现得更为明显。由于同化所用的GPS可降水量观测资料主要集中在极为局地的京津冀地区,而且GPS PWV资料的同化仅能对模式中的水汽含量发生影响,因此该资料的同化与否并不能为模式总体预报性能带来明显差异。但从以54511测站为代表的北京地区局地常规要素预报效果来看,快速更新循环的同化方式能使高时空分辨率的GPS可降水量在北京局地的同化效果得以累积,并为气象要素在局地的预报效果带来显著的正面影响。通过对一次强对流个例进行分析可以发现,以快速更新循环的方式可以同化较高时间频率的GPS可降水量观测,从而对模式大气的初始湿度条件起到持续累积的影响。在一定的动力和热力条件下,由GPS可降水量同化带来的累积的模式大气增湿也有望在模式中形成利于局地对流形成和发展的环境,提高模式对局地对流的预报水平。
英文摘要:
      A rapid updated cycling assimilation and forecast system which is capab le of assimilating various kinds of the observations including the data from the local automatic weather stations (AWS) and the Global Position System (GPS) precipitable water vapor (PWV) data in the Beijing area with 3 hour updated time interval was built in the Institute of Urban Meteorology (IUM), CMA. To evaluate the impact of assimilating the local GPS PWV data, two parallel experiments with and without assimilating the GPS PWV observations into the WRF based rapid updated cycling assimilation and forecast system for the urban area were performed for 28 precipitation incidents occurring in August 2006 and in summer of 2007. From the verification scores of precipitation forecast, it's revealed that the ETS scores for almost all precipitation thresholds were improved with the local GPS PWV data assimilated, especially during the first 0-6 hours, i.e. the impact caused by assimilating the GPS PWV data was more evident during the initial stage of integration. It is found that the forecasted precipitation area was enlarged with the assimilation of GPS PWV. The underprediction of precipitation for the larger thresholds such as 10 mm/6 hours and 25 mm/12 hours was ameliorated as the consequence of assimilation of GPS PWV data. However, the assimilation of the local GPS/PWV observations in the Beijing area does not produce significant improvement in the forecast skills for both the surface and upper air prognostic variables. This is to be expected, since the distribution of the ground based GPS sites is very narrow, and moisture is the only affected variable among these variables above. But it can be found that the forecast performance for the conventional elements in the Beijing local area is significantly improved during the first 6 hours forecast owing to the accumulation of the assimilation effects by the rapid updated assimilating. In other words, the rapid local change information of moisture unable to be captured by conventional observations can be assimilated into the model's initial conditions via the rapid updated cycling assimilation of the very local GPS/PWV data in the interval of 3 hours, which is also consistent to the improved precipitation forecast skill during the first 6 hours forecast. Therefore, the assimilation of local GPS PWV data would not only improve the forecast quality in the current cycle, but also help to prepare better background for the next cycle assimilation. It is due to this kind of accumulation of local data assimilation effects leading to the better forecast performance. In additi on, according to the results of a convective case study, it is shown that how muc h or even whether the assimilation of GPS PWV observations may bring to better precipitation forecast, is still up to the general effects of the assimilation with the other different kinds of observations incorporated. Moreover, the accumulated assimilation effects of local GPS PWV observations would be able to create a circumstance more favorable to the formation and sustaining of local convection in the model, thus causing improved forecast skills for convective precipitation.
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