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1.
FAN Ke 《大气和海洋科学快报》2009,2(5):251-254
The year-to-year increment prediction approach proposed by was applied to forecast the annual number of tropical cyclones (TCs) making landfall over China.The year-to-year increase or decrease in the number of land-falling TCs (LTCs) was first predicted to yield a net number of LTCs between successive years.The statistical prediction scheme for the year-to-year increment of annual LTCs was developed based on data collected from 1977 to 2007,which includes five predictors associated with high latitude circulations in both Hemispheres and the circulation over the local,tropical western North Pacific Ocean.The model shows reasonably high predictive ability,with an average root mean square error (RMSE) of 1.09,a mean absolute error (MAE) of 0.9,and a correlation coefficient between the predicted and observed annual number of LTCs of 0.86,accounting for 74% of the total variance.The cross-validation test further demonstrated the high predictive ability of the model,with an RMSE value of 1.4,an MAE value of 1.2,and a correlation coefficient of 0.74 during this period. 相似文献
2.
淮河流域夏季极端降水事件的统计预测模型研究 总被引：3，自引：1，他引：2
采用年际增量预测方法, 通过考察与淮河流域夏季极端降水事件发生频次(HRF)年际增量相关的环流, 确定了5个预测因子:冬季北太平洋涛动、12月南极涛动、春季3～4月南印度洋气压、春季3～4月白令海气压、春季3～4月印尼—澳洲附近经向风垂直切变;然后利用这5个预测因子, 通过多元线性回归方法建立HRF年际增量的预测模型, 进而预测HRF。交叉检验表明, 在1962～2005年的后报中, 这个预测模型对HRF显示了较高的预测技巧, 预测结果与实测间的相关系数为0.67, 表现出较高的预测潜力, 对淮河流域夏季极端降水事件的预测具有较大的应用价值。 相似文献
3.
利用典型相关分析(CCA)方法建立统计气候预测模型，对我国冬季气温进行了预测试验，采用历史资料独立样本检验的方法，对预报技巧给出合理的评定。结果表明，使用CCA方法对我国冬季气温进行短期气候预测，有一定的预报技巧，对于特定地区和特定时期优选的因子场组合，可以取得较为满意的预报效果。大部分地区的季平均预报时效在2个季以内时，最佳预报相关系数在0.5以上。季平均的预报水平明显高于月平均的预报。海温场是所有因子场中最好的预报因子，不仅单独海温场的预报效果较好，而且与其他因子场组合后的预报水平还可以得到进一步提高。 相似文献
4.
A Seasonal Prediction Model for the Summer Rainfall in Northeast China Using the Year-To-Year Increment Approach 总被引：2，自引：0，他引：2 
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下载免费PDF全文 ZHU Ya-Li 《大气和海洋科学快报》2011,4(3):146-150
Using the year-to-year increment approach, this study investigated the relationship of selected climatic elements with the increment time series of the summer rainfall between successive years in Northeast China, including the soil moisture content, sea surface temperature, 500 hPa geopotential height, and sea level pressure in the preceding spring for the period 1981–2008. Two spring predictors were used to construct the seasonal prediction model: the area mean soil moisture content in Northwest Eurasia and the 500 hPa geopotential height over Northeast China. Both the cross-validation and comparison with previous studies showed that the above two predictors have good predicting ability for the summer rainfall in Northeast China. 相似文献
5.
根据1979~2016年春季海表温度、土壤温度以及大尺度气候指数与中亚地区夏季温度的相关关系,确定了印度洋东南部海表温度、非洲西北部土壤温度、大西洋多年代际振荡（AMO）和东亚/西俄型（EA/WR）4个春季预测因子,进而建立了中亚地区夏季温度的预测模型。春季印度洋东南部海表温度暖异常、非洲西北部土壤温度暖异常、AMO正异常与EA/WR负异常均对应夏季中亚地区500 hPa位势高度场正异常,为该地区夏季高温发生提供有利条件。预测模型留一法交叉验证产生的1979~2016年中亚地区夏季温度无（有）趋势的时间序列与观测的无（有）趋势的时间序列的相关为0.65（0.74）,表明该预测模型具有良好的预测能力。研究结果有望帮助提高中亚地区夏季温度的预测技巧。 相似文献
6.
Long-lead precipitation forecasts for 1–4 seasons ahead are usually difficult in dynamical climate models due to the model deficiencies and the limited persistence of initial signals. But, these forecasts could be empirically improved by statistical approaches. In this study, to improve the seasonal precipitation forecast over the southern China (SC), the statistical downscaling (SD) models are built by using the predictors of atmospheric circulation and sea surface temperature (SST) simulated by the Beijing Climate Center Climate System Model version 1.1 m (BCC_CSM1.1 m). The different predictors involved in each SD model is selected based on both its close relationship with the target seasonal precipitation and its reasonable prediction skill in the BCC_CSM1.1 m. Cross and independent validations show the superior performance of the SD models, relative to the BCC_CSM1.1 m. The temporal correlation coefficient of SD models could reach > 0.4, exceeding the 95 % confidence level. The SC precipitation index can be much better forecasted by the SD models than by the BCC_CSM1.1 m in terms of the interannual variability. In addition, the errors of the precipitation forecast in all four seasons are significantly reduced over most of SC in the SD models. For the 2015/2016 strong El Niño event, the SD models outperform the dynamical BCC_CSM1.1 m model on the spatial and regional-average precipitation anomalies, mostly due to the effective SST predictor in the SD models and the weak response of the SC precipitation to El Niño-related SST anomalies in the BCC_CSM1.1 m. 相似文献
7.
中国冬季气温的集合典型相关分析和预报 总被引：2，自引：0，他引：2
以欧亚大陆地面温度、北半球500 hPa高度、热带印度洋SST(sea surface temperature)以及北太平洋SST为预报因子,通过典型相关分析(canonical correlation analysis,简称CCA)建立预报关系,然后用集合典型相关分析预报(ensemble canonical correlation prediction,简称ECC)方法预报中国冬季气温,并分析预报技巧及进行独立样本检验.结果表明,不同的预报因子对各个地区有不同的预报技巧,以欧亚大陆地面温度为预报因子预报技巧较高,而ECC模式对中国冬季气温有更好的预报能力,预报技巧高于任何一个单因子场的CCA预报;采用回归法的集合平均比简单的等权集合平均预报技巧更稳定. 相似文献
8.
基于BCC_CSM模式的中国东部夏季降水预测检验及订正 总被引：1，自引：1，他引：0
基于国家气候中心第二代季节预测模式的历史回报试验数据,检验了模式对我国东部夏季降水的预测能力,探讨了预测误差形成的可能原因,并应用降尺度方法提高了模式的降水预测技巧。分析表明:（1）模式能在一定程度上把握我国东部夏季降水时空变率的两个主要模态（偶极子型模态和全区一致型模态）,但是不同超前时间的预测在刻画模态方差贡献、异常空间分布特征、时间系数的年际变化等方面存在明显误差;（2）模式能够合理预测大尺度环流和海表温度（SST）的变化特征,但是对中国东部夏季降水的总体预测技巧有限,这与模式不能准确刻画西太平洋副热带高压、大陆高压、中高纬阻塞高压等环流系统以及热带太平洋、印度洋SST变率对中国东部降水模态的影响有关;（3）针对1991~2003年回报试验数据中的500 hPa位势高度、850 hPa纬向风和经向风、SST变量,在全球范围内寻找并定位与中国东部站点降水关系最密切的预报因子,进而建立针对降水预测的单因子线性回归、多因子逐步和多元回归模型。采用2004~2013年回报试验对所建立的降水预测模型进行了独立检验,结果表明:所建立的降尺度预测模型能显著提高中国东部地区夏季降水的预报技巧。以6月1日起报试验为例,预测的第一模态（第二模态）与观测的空间相关系数由原始的0.12（0.48）提高到了0.58（0.80）,时间相关系数则从0.47（0.15）提高到0.80（0.67）;其它超前时间的预测试验中,降尺度预测模型的降水预测技巧相比模式原始预测技巧也同样明显提高。 相似文献
9.
范可 《热带气象学报(英文版)》2012,18(4):543-550
The spring (March-April-May) rainfall over northern China (SPRNC) is predicted by using the interannual increment approach. DY denotes the difference between the current year and previous years. The seasonal forecast model for the DY of SPRNC is constructed based on the data that are taken from the 1965-2002 period (38 years), in which six predictors are available no later than the current month of February. This is favorable so that the seasonal forecasts can be made one month ahead. Then, SPRNC and the percentage anomaly of SPRNC are obtained by the predicted DY of SPRNC. The model performs well in the prediction of the inter-annual variation of the DY of SPRNC during 1965-2002, with a correlation coefficient between the predicted and observed DY of SPRNC of 0.87. This accounts for 76% of the total variance, with a low value for the average root mean square error (RMSE) of 20%. Both the results of the hindcast for the period of 2003-2010 (eight years) and the cross-validation test for the period of 1965-2009 (45 years) illustrate the good prediction capability of the model, with a small mean relative error of 10%, an RMSE of 17% and a high rate of coherence of 87.5% for the hindcasts of the percentage anomaly of SPRNC. 相似文献
10.
Based on NCEP/NCAR daily reanalysis data, climate trend rate and other methods are used to quantitatively analyze the change trend of China's summer observed temperature in 1983—2012. Moreover, a dynamics-statistics-combined seasonal forecast method with optimal multi-factor portfolio is applied to analyze the impact of this trend on summer temperature forecast. The results show that: in the three decades, the summer temperature shows a clear upward trend under the condition of global warming, especially over South China, East China, Northeast China and Xinjiang Region, and the trend rate of national average summer temperature was 0.27℃ per decade. However, it is found that the current business model forecast(Coupled Global Climate Model) of National Climate Centre is unable to forecast summer warming trends in China, so that the post-processing forecast effect of dynamics-statistics-combined method is relatively poor. In this study, observed temperatures are processed first by removing linear fitting trend, and then adding it after forecast to offset the deficiency of model forecast indirectly. After test, ACC average value in the latest decade was 0.44 through dynamics-statistics-combined independent sample return forecast. The temporal correlation(TCC) between forecast and observed temperature was significantly improved compared with direct forecast results in most regions, and effectively improved the skill of the dynamics-statistics-combined forecast method in seasonal temperature forecast. 相似文献
11.
基于高温日数存在受不同物理因子影响不同时间尺度变率的特征,应用滤波对华南夏季高温日数进行时间尺度分离,得到高温日数的年代际分量和年际分量。统计分析高温日数总量、年代际分量和年际分量在各自对应时间尺度上的影响因子,采用"向前"交叉检验逐步回归法,分别建立高温日数总量、年代际分量和年际分量的回归模型。高温日数总量的回归模型即为高温日数不区分时间尺度的直接回归模型,而两个分量回归模型拟合结果的叠加,即为高温日数时间尺度分离统计模型对总量的拟合。利用十折交叉检验法,对高温日数直接回归模型和时间尺度分离统计模型的拟合结果进行比较:相比高温日数直接回归模型,时间尺度分离统计模型的年代际分量均方根误差由2.6降低到2.3,与观测数据的相关系数由0.69提高到0.73（显著性水平α=0.01）;年际分量均方根误差由3.2降低到2.9,与观测数据的相关系数由0.4（α=0.1）提高到0.48（α=0.01）;高温日数总量均方根误差由4.1降低到3.7,与观测数据的相关系数由0.48提高到0.62（α=0.01）。1979~2010年拟合时段华南夏季高温日数的回报结果表明:两模型回报结果与观测数据均存在明显相关（α=0.01）,直接回归模型的相关系数为0.57,时间尺度分离统计模型提高到0.72。2011~2013年独立检验时段的预测结果表明:直接回归模型预测结果的平均均方根误差为26.4%,时间尺度分离统计模型降低到12.3%。初步结果表明,两模型对华南夏季高温日数均有一定的预测能力,而时间尺度分离统计模型的预测结果有所改进。 相似文献
12.
Summary Estimates of the predictability of New Zealand monthly and seasonal temperature and rainfall anomalies are calculated using
a cross-validated linear regression procedure. Predictors are indices of the large scale circulation, sea-surface temperatures,
the Southern Oscillation Index and persistence. Statistical significance is estimated through a series of Monte Carlo trials.
No significant forecast relationships are found for rainfall anomalies at either the monthly or seasonal time scale. Temperature
forecasts are however considered to exhibit significant skill, with variance reductions of the order of 10–20% in independent
trials. Temperature anomalies are most skilfully predicted over the North Island, and skill is greatest in Spring and Summer
in most areas. At the monthly time scale, predictors local to the New Zealand region account for most of the forecast skill,
while at the seasonal time scale, skill depends strongly upon “remote” predictors defined over regions of the southern hemisphere
distant from New Zealand. Indices of meridional flow over the Tasman Sea/New Zealand region are found to be useful predictors,
especially for monthly forecasts, perhaps as a proxy for atmospherically-forced sea surface temperature anomalies. Sea surface
temperature anomalies to the west of New Zealand and in the tropical Indian Ocean are also useful, especially for seasonal
predictions. Forecast skill is more reliably estimated at the monthly time scale than at the seasonal time scale, as a result
of the larger sample size of monthly mean data. While long-term mean levels of skill may be estimated reliably over the whole
data set, statistically significant decadal-scale variations are found in the predictability of temperature anomalies. Therefore,
even if long-term forecast skill levels are reliably estimated, it may be impossible to predict the short-term skill of operational
seasonal climate forecasts. Implications for operational climate predictions in mid-latitudes are discussed.
Received July 18, 1997 Revised April 2, 1998 相似文献
13.
Prediction of summer monsoon rainfall over India using the NCEP climate forecast system 总被引：1，自引：0，他引：1
The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the
Indian region during June to September (JJAS). The evaluation is based on the National Centre for Environmental Prediction’s
(NCEP) climate forecast system (CFS) initialized during March, April and May and integrated for a period of 9 months with
a 15 ensemble members for 25 years period from 1981 to 2005. The CFS’s hindcast climatology during JJAS of March (lag-3),
April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of verification
climatology with the rainfall maxima (one over the west-coast of India and the other over the head Bay of Bengal region) well
simulated. The pattern correlation between verification and forecast climatology over the global tropics and Indian monsoon
region (IMR) bounded by 50°E–110°E and 10°S–35°N shows significant correlation coefficient (CCs). The skill of simulation
of broad scale monsoon circulation index (Webster and Yang; WY index) is quite good in the CFS with highly significant CC
between the observed and predicted by the CFS from the March, April and May forecasts. High skill in forecasting El Nino event
is also noted for the CFS March, April and May initial conditions, whereas, the skill of the simulation of Indian Ocean Dipole
is poor and is basically due to the poor skill of prediction of sea surface temperature (SST) anomalies over the eastern equatorial
Indian Ocean. Over the IMR the skill of monsoon rainfall forecast during JJAS as measured by the spatial Anomaly CC between
forecast rainfall anomaly and the observed rainfall anomaly during 1991, 1994, 1997 and 1998 is high (almost of the order
of 0.6), whereas, during the year 1982, 1984, 1985, 1987 and 1989 the ACC is only around 0.3. By using lower and upper tropospheric
forecast winds during JJAS over the regions of significant CCs as predictors for the All India Summer Monsoon Rainfall (AISMR;
only the land stations of India during JJAS), the predicted mean AISMR with March, April and May initial conditions is found
to be well correlated with actual AISMR and is found to provide skillful prediction. Thus, the calibrated CFS forecast could
be used as a better tool for the real time prediction of AISMR. 相似文献
14.
Summary Separate predictive models are created for the Caribbean early wet season (May–June–July) and late wet season (August–September–October). Simple correlations are used to select predictors for a Caribbean rainfall index and predictive equations are formulated using multiple linear regression. The process is repeated after long term trends are removed from the Caribbean rainfall index and the models validated using a number of statistical methods. Four variables are confirmed as predictors for the early season: Caribbean sea surface temperature anomalies, tropical North Atlantic sea level pressure anomalies, vertical shear anomalies in the equatorial Atlantic, and the size of the Atlantic portion of the Western Hemisphere Warm Pool. Only the first two are retained in the late season model. On the interannual time-scale, equatorial Pacific sea surface temperature anomalies become significant in both seasons. The NINO3 index is retained among the predictors for the early season, and zonal gradients of sea surface temperature between the equatorial Pacific and tropical Atlantic are retained for the late season. The results also indicate spatial variation in the importance of the seasonal predictors. 相似文献
15.
The boreal spring Antarctic Oscillation(AAO) has a significant impact on the spring and summer climate in China. This study evaluates the capability of the NCEP's Climate Forecast System, version 2(CFSv2), in predicting the boreal spring AAO for the period 1983–2015. The results indicate that CFSv2 has poor skill in predicting the spring AAO, failing to predict the zonally symmetric spatial pattern of the AAO, with an insignificant correlation of 0.02 between the predicted and observed AAO Index(AAOI). Considering the interannual increment approach can amplify the prediction signals, we firstly establish a dynamical–statistical model to improve the interannual increment of the AAOI(DY AAOI), with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice. This dynamical–statistical model demonstrates good capability in predicting DY AAOI, with a significant correlation coefficient of 0.58 between the observation and prediction during 1983–2015 in the two-year-out cross-validation. Then, we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI. The improved AAOI shows a significant correlation coefficient of 0.45 with the observed AAOI during 1983–2015. Moreover, the unrealistic atmospheric response to March–April–May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO. This study gives new clues regarding AAO prediction and short-term climate prediction. 相似文献
16.
以江西省376个气象自动观测站的逐小时气温数据为基准,采用偏差、相关性和平均绝对误差等评价指标,对比分析2017—2022年CLDAS陆面同化和ERA5 Land再分析气温资料在江西省的适用性。结果表明： 1） ERA5 Land、CLDAS资料均能很好反映大部分站点的气温变化,CLDAS资料与观测资料的相关系数为0.99,相关系数区间分布较为集中;ERA5 Land资料与观测资料的相关系数为0.97,分布较为分散。2） 相较于观测站点多年平均气温,CLDAS资料较为接近,ERA5 Land资料则偏离较大。3） CLDAS资料的平均绝对误差明显低于ERA5 Land资料,二者均存在平原、盆地部分站点平均绝对误差较小而局部高海拔山区站点异常偏大的空间特征,以及秋季最大而冬季最小的季节特征。4） ERA5 Land资料偏差的日变化范围为-0.65—0.39 ℃,整体呈现单谷形分布;CLDAS资料偏差日变化范围为-0.05—0.05 ℃,波动幅度较小,没有明显的变化特征。5） 两种格点资料均能较好反映大部分站点的低温日数变化,但对于高温日数变化,ERA5 Land资料偏差较大,CLDAS资料偏差较小。 相似文献
17.
利用全国逐日天气预报产品和对应实况数据, 分析了目前普遍使用的晴雨(雪) 和气温预报评分方法存在的问题, 并进行了改进尝试和研究。结果表明:由于没有考虑降水概率的影响, 在降水概率全国差异较大的多数月份, 晴雨(雪) 预报正确率与单站无降水频率表现为正相关, 具有无降水频率越大评分越高的趋势; 采用绝对标准值(1 ℃或2 ℃) 作为阈值进行气温预报准确率评分, 评分结果与气温日际变化呈明显负相关, 气温日际变化偏小则评分值偏高的趋势比较明显。该文提出的晴雨(雪) 和气温预报改进评分方法能有效减少降水概率和气温日际变化对晴雨(雪) 和气温预报评分的影响, 提高不同气候背景地区天气预报评分结果的可比性, 在天气预报质量检验和评估业务中具有一定的应用和推广价值。 相似文献
18.
The spatial and temporal distributions of marine cold air outbreaks (MCAOs) over the northern North Atlantic have been investigated using re-analysis data for the period from 1958 to 2007. MCAOs are large-scale outbreaks of cold air over a relatively warm ocean surface. Such conditions are known to increase the severity of particular types of hazardous mesoscale weather phenomena. We used a simple index for identifying MCAOs: the vertical potential temperature gradient between the sea surface and 700 hPa. It was found that atmospheric temperature variability is considerably more important than the sea surface temperature variability in governing both the seasonal and the inter-annual variability of MCAOs. Furthermore, a composite analysis revealed that a few well-defined and robust synoptic patterns are evident during MCAOs in winter. Over the Labrador and Irminger Seas the MCAO index was found to have a correlation of 0.70 with the North Atlantic Oscillation index, while over the Barents Sea a negative correlation of 0.42 was found. 相似文献
19.
The quasi-biennial oscillation (QBO) in the zonal wind in the tropical stratosphere is one of the most predictable aspects of the circulation anywhere in the atmosphere and can be accurately forecast for many months in advance. If the stratospheric QBO systematically (and significantly) affects the tropospheric circulation, it potentially provides a predictable signal useful for seasonal forecasting. The stratospheric QBO itself is generally not well represented in current numerical models, however, including those used for seasonal prediction and this potential may not be exploited by current numerical-model based forecast systems. The purpose of the present study is to ascertain if a knowledge of the state of the QBO can contribute to extratropical boreal winter seasonal forecast skill and, if so, to motivate further research in this area. The investigation is in the context of the second Historical Forecasting Project (HFP2), a state-of-the-art multimodel two-tier ensemble seasonal forecasting system. The first tier, consisting of a prediction of sea surface temperature anomalies (SSTAs), is followed by the second tier which is a prediction of the state of the atmosphere and surface using an AGCM initialized from atmospheric analyses and using the predicted SSTs as boundary conditions. The HFP2 forecasts are successful in capturing the extratropical effects of sea surface temperature anomalies in the equatorial Pacific to the extent that a linear statistical correction based on the NINO3.4 index does not provide additional extratropical skill. By contrast, knowledge of the state of the stratospheric QBO can be used statistically to add extratropical skill centred in the region of the North Atlantic Oscillation. Although the additional skill is modest, the result supports the contention that taking account of the QBO could improve extratropical seasonal forecasting skill. This might be done statistically after the fact, by forcing the QBO state into the forecast model as it runs or, preferably, by using models which correctly represent the physical processes and behaviour of the QBO. 相似文献
20.
利用人工神经网络模型预测西北太平洋热带气旋生成频数 总被引：1，自引：0，他引：1
通过对60年(1950~2009年)北半球夏、秋季(6~10月)热带气旋(TC)频数与春季(3~5月)大尺度环境变量的相关分析,挑选出8个相关性较高的前期预报因子建立人工神经网络(ANN)模型,对2010~2017年8年夏、秋季TC频数进行回报,并将回报结果与传统多元线性回归(MLR)方法所得结果进行对比分析。结果表明,ANN模型对60年历史数据的拟合精度高,相关系数高达0.99,平均绝对误差低至0.77。在8年回报中,ANN模型相关系数为0.80,平均绝对误差为1.97;而MLR模型相关系数仅为0.46,平均绝对误差为3.30。ANN模型在历史数据拟合和回报中的表现都明显优于MLR模型,未来可考虑应用于实际的业务预测中。 相似文献