CAJ | 학술논문

美国国家环境预报中心( NCEP)开发的气候预报系统(CFS)预报数据资料有1981-2008年共 28年历史预报数据，有实时的预报产品（含有未来9个月的预报值）。与NCEP资料相比，CFS能较好模拟季风环流的季节变化，能超前几个月模拟出ENSO发展和衰减时期的海温异常发展，可以用于广东季节降水预测。采用CFS预报产品开发基于最优子集回归和多元均生函数的广东季节降水的两种统计降尺度预报方法。经过分析检验，分别选取海平面气压场、风场和位势高度场显著影响区域作为同期预报因子，从多年( 2001-2008 年)的历史回报检验来看，虽然两种预测模型对于个别季节存在年内预报效果不稳定性，但综合而言，大部分季节降水的气候预测评分总体平均在64分以上。2009/2010年的实时预报检验表明，两种预测模型均达到较好的预测水平，降水预测结果与实况较接近。与基于NCEP观测资料的传统统计方法比较，CFS预报产品具有实时性、更新快等优点。基于CFS预报产品的降尺度统计方法可以超前三个季预报广东降水，丰富了传统统计方法，但预报的稳定性还需进一步改进。
미국국가배경예보중심( NCEP)개발적기후예보계통(CFS)예보수거자료유1981-2008년공 28년역사예보수거，유실시적예보산품（함유미래9개월적예보치）。여NCEP자료상비，CFS능교호모의계풍배류적계절변화，능초전궤개월모의출ENSO발전화쇠감시기적해온이상발전，가이용우엄동계절강수예측。채용CFS예보산품개발기우최우자집회귀화다원균생함수적엄동계절강수적량충통계강척도예보방법。경과분석검험，분별선취해평면기압장、풍장화위세고도장현저영향구역작위동기예보인자，종다년( 2001-2008 년)적역사회보검험래간，수연량충예측모형대우개별계절존재년내예보효과불은정성，단종합이언，대부분계절강수적기후예측평분총체평균재64분이상。2009/2010년적실시예보검험표명，량충예측모형균체도교호적예측수평，강수예측결과여실황교접근。여기우NCEP관측자료적전통통계방법비교，CFS예보산품구유실시성、경신쾌등우점。기우CFS예보산품적강척도통계방법가이초전삼개계예보엄동강수，봉부료전통통계방법，단예보적은정성환수진일보개진。
The Climate Forecast Systems (CFS) provided by National Centers for Environmental Prediction (NCEP ), which cover 1981 to 2008, can forecast atmospheric circulation nine months ahead. Compared with the NCEP datasets, CFS datasets successfully simulate many major features of the Asian monsoon circulation systems and exhibit reasonably high skill in simulating and predicting ENSO events. Based on the CFS forecasting results, a downscaling method of Optimal Subset Regression and mean generational function model of multiple variables are used to forecast seasonal precipitation in Guangdong. After statistical analysis tests, sea level pressure, wind and geopotential height field are made predictors. Although there are unstable results in some individual annual seasons, both Optimal Subset Regression and mean generational function model of multiple variables are methods that can provide good forecasting after operational tests that score more than sixty points. CFS datasets are available and updated real time as compared with the NCEP dataset. The downscaling forecast method based on the CFS datasets can predict Guangdong’s seasonal precipitation three seasons in advance, enriching traditional statistical methods while its forecasting stability needs to be improved.