摘要
应用自组织神经网络方法对欧洲中心(ECMWF)2003年1月1日至2006年12月31日逐日数值预报产品分析场进行天气形势分型,发现11—3月影响我国的天气形势基本属于同一类型。对2004—2007年11—3月ECMWF逐日数值预报产品进行动力诊断,提取与中国近海16个测站日最大风速相关较好的预报因子,将改进后的KNN方法作为预报手段,建立11—3月近海测站日最大风速预报模型,并对2007年1—3月16个测站进行逐日检验,结果表明该方法对近海测站日最大风速有较好的预报能力。
Self-organizing neural network method is applied to classify weather patterns based on daily NWPs of ECMWF from Jan. 1,2003 to Dec. 31,2006. It shows that the weather pattern is similar over China from November to March. Dynamic diagnosis is applied to daily NWPs of ECMWF in November to March in year 2004 - 2007 to pick up predictors which have good corre-lation coefficients with daily maximum wind speed at 16 coastal weather stations. An updated KNN method is used to set up wind speed forecast models for November to March. Daily wind speed forecast for January to March of 2007 is carried out. Results show that KNN method is of good ability in daily maximum wind forecast.
出处
《气象》
CSCD
北大核心
2008年第6期67-73,共7页
Meteorological Monthly
关键词
KNN
近海测站
日最大风速预报
交叉验证
KNN method
coastal weather stations
maximum daily wind speed forecast
cross verification
