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1.
Summary New models based on (a) Multivariate Principal Component Regression (PCR) (b) Neural Network (NN) and (c) Linear Discriminant Analysis (LDA) techniques were developed for long-range forecasts of summer monsoon (June–September) rainfall over two homogeneous regions of India, viz., North West India and Peninsular India. The PCR and NN models were developed with two different data sets. One set consisted 42 years (1958–1999) of data with 8 predictors and the other, 49 years (1951–1999) of data with 6 predictors. The predictors were subjected to the Principal Component Analysis (PCA) before model development. Two different neural networks were designed with 2 and 3 hidden neurons. To avoid the nonlinear instability, 20 ensemble runs were made while training the network and the ensemble mean results are discussed. The LDA model was developed with 42 years of data (1958–1999) for classifying three rainfall intervals with equal prior probability of 0.33. Both the PCR and NN models showed useful forecast skill for NW India and Peninsular India. Models with 8 predictors performed better than the models with only 6 predictors. The NN model with 3 hidden neurons performed better than model with 2 hidden neurons. For NW India, the NN model performed better than the PCR model. The RMSE of the NN model and PCR model with 8 predictors for NW India (Peninsular India) during the independent period 1984–99 was 12.5% (12.2%) and 12.6% (11.5%), respectively. Corresponding figures for the models with 6 predictors are 15.0% (13.0%) and 13.9% (11.4%) respectively. During the independent period, model errors were large in 1991, 1994, 1997 and 1999. However all the models showed deteriorating predictive skill after 1988, both for NW India and Peninsular India. The LDA model correctly classified 62% of grouped cases for NW India and Peninsular India. The LDA model showed better skill in classifying deficient rainfall (< − 8%) over NW India and excess rainfall (> 3%) over Peninsular India. Received October 2, 1999 Revised December 28, 1999  相似文献   

2.
Summary The interannual and decadal scale variability in the North Atlantic Oscillation (NAO) and its relationship with Indian Summer monsoon rainfall has been investigated using 108 years (1881–1988) of data. The analysis is carried out for two homogeneous regions in India, (Peninsular India and Northwest India) and the whole of India. The analysis reveals that the NAO of the preceding year in January has a statistically significant inverse relationship with the summer monsoon rainfall for the whole of India and Peninsular India, but not with the rainfall of Northwest India. The decadal scale analysis reveals that the NAO during winter (December–January–February) and spring (March–April–May) has a statistically significant inverse relationship with the summer monsoon rainfall of Northwest India, Peninsular India and the whole of India. The highest correlation is observed with the winter NAO. The NAO and Northwest India rainfall relationship is stronger than that for the Peninsular and whole of India rainfall on climatological and sub-climatological scales.Trend analysis of summer monsoon rainfall over the three regions has also been carried out. From the early 1930s the Peninsular India and whole of India rainfall show a significant decreasing trend (1% level) whereas the Northwest India rainfall shows an increasing trend from 1896 onwards.Interestingly, the NAO on both climatological and subclimatological scales during winter, reveals periods of trends very similar to that of Northwest Indian summer monsoon rainfall but with opposite phases.The decadal scale variability in ridge position at 500 hPa over India in April at 75° E (an important parameter used for the long-range forecast of monsoon) and NAO is also investigated.With 4 Figures  相似文献   

3.
Summary Zonally averaged surface air temperatures have been analysed to form time series of surface air temperature anomalies over the tropics (TTA), extratropics (ETA), the poles (PTA) and the whole northern hemisphere (NHTA) for the period 1901–1990. The temporal statistical relationships between these temperature time series and Indian monsoon rainfall over all India (AIR), northwest India (NWR) and peninsular India (PIR) have been examined for the above period.The northern hemispheric January–February (JF) temperature correlates significantly and positively with all the three monsoon rainfall series, the regional peninsular rainfall series (PIR) displaying the best correlation. The Strongest correlation is observed during 1951–1980 for both AIR and NWR but weakened in 1961–1990. For PIR, the highest correlation is observed during 1961–1990, remaining almost stable since 1951–1980. The JF series AIR monsoon relationship showed the highest correlation over the tropics during 1901–1940, over the polar region during 1941–1980 and over the northern hemisphere during 1951–1980. AIR and NWR moreover show a significant negative relationship with simultaneous, succeeding autumn and following year TTA series, while AIR and PIR monsoon rainfall series show significant positive association with the following year PTA series.The results also suggest that cooler January–February NHTA not only lead to a poor monsoon, but a poor monsoon also leads to warmer temperatures over the tropics and cooler temperatures over the polar region in the following year.With 1 Figure  相似文献   

4.
王秀英  王俊杰 《气象科技》2021,49(2):200-210
云南夏季降水年际变化较大,影响因子众多,夏季降水的预测较为困难。使用1965—2017年云南省122个气象观测站的逐日降水资料和NCEP大气环流资料,采用年际增量的方法来预测云南夏季降水。文中基于云南夏季降水年际增量变化规律和影响夏季降水的环流形势及物理过程,选取了6个具有物理意义的预测因子,包括:前期2月南太平洋海温异常、前期2月东亚北部海平面气压异常、前期4月北美500hPa位势高度异常、前期5月太平洋北部海平面气压异常、前期1月印度半岛北部500hPa位势高度异常及前期2月澳洲以南地区200hPa高度场偶极子异常,来建立云南夏季降水预测模型。并对预测模型进行逐年交叉检验和1998—2017年逐年独立样本检验。交叉检验中夏季降水年际增量预测值和观测值的相关系数为0.85,相对均方根误差为8.0%。回报检验中夏季降水年际增量的相对均方根误差为9.1%,63.0%的异常年份预测值能够准确地预报出夏季降水异常。该预测模型有较好的预测能力。  相似文献   

5.
Summary Based on observed rainfall data of India Meteorological Department (IMD), correlation coefficients (CCs) have been computed between Indian summer monsoon rainfall (ISMR) and sea surface temperature (SST) anomalies over different Nino regions and standardised pressure difference between Tahiti and Darwin. Significant positive CCs are found between the Southern Oscillation Index (SOI) in winter and subsequent June rainfall over India. Concurrent with and subsequent to Indian summer monsoon, SOI shows significant positive CC with the mean rainfall of July to September (JAS). Significant negative CCs are found between JAS mean rain and the concurrent and following SST anomalies over Nino-3.4 region. On the basis of these correlations, it is proposed that the entire period of summer monsoon from June to September could be divided into two sub-periods such as: early summer (June) and mid-late summer (July to September) monsoon for prediction of ISMR in the extended range.In order to examine the characteristics of atmospheric circulation during some El-Nino years, divergent flow at 200hPa and omega field at 500hPa based on NCEP/NCAR reanalysis have been studied in detail. Major significant southward shift of upper level divergent field from India are related to El-Nino and this shift may be responsible for causing droughts during several El-Nino years over India. Also vertical wind fields at 500hPa show sinking motion over large parts of India and west Pacific and ascending motion over southern Indian Ocean, central and eastern Pacific during major drought years.  相似文献   

6.
Peninsular India and Sri Lanka receive major part of their annual rainfall during the northeast monsoon season (October–December). The long-term trend in the northeast monsoon rainfall over the Indian Ocean and peninsular India is examined in the vicinity of global warming scenario using the Global Precipitation Climatology Project (GPCP) dataset available for the period 1979–2010. The result shows a significant increasing trend in rainfall rate of about 0.5 mm day?1 decade?1 over a large region bounded by 10 °S–10 °N and 55 °E–100 °E. The interannual variability of seasonal rainfall rate over peninsular India using conventional rain gauge data is also investigated in conjunction to the Indian Ocean dipole. The homogeneous rain gauge data developed by Indian Institute of Tropical Meteorology over peninsular India also exhibit the considerable upward rainfall trend of about 0.4 mm day?1 decade?1 during this period. The associated outgoing longwave radiation shows coherent decrease in the order of 2 W?m?2 decade?1 over the rainfall increase region.  相似文献   

7.
In this paper, a diagnostic study is carried out with global analysis data sets to determine how the large scale atmospheric circulation affecting the anomalous drought of the Indian summer monsoon 2002. The daily analysis obtained from National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) for the month of July is used to investigate the mean circulation characteristics and the large scale energetics over the Indian monsoon domain. Examination of rainfall revealed that the summer monsoon (JJAS) rainfall of 2002 over India is 22% below normal in which the large deficit of 56% below normal rainfall in July. The recent past drought during summer season of 2004 and 2009 are 12 and 23%, respectively, below normal rainfall. The large deficit of rainfall in 2009 is from the June month with 48% below normal rainfall, where as 2004 drought contributed from July (19%) and August (24%). Another significant facet of the rainfall in July 2002 is lowest ever recorded in the past 138 years (1871–2008). The circulation features illustrated weak low level westerly wind at 850 hPa (Somali Jet) in July during large deficit rainfall years of 1987 and 2002 with a reduction of about 30% when compared with the excess and normal rainfall years of 1988 and 2003. Also, tropical easterly jet at 150 hPa reduced by 15% during the deficit rainfall year of 2002 against the excess rainfall year of 1988. Both the jet streams are responsible for low level convergence and upper level divergence leading to build up moisture and convective activity to sustain the strength of the monsoon circulation. These changes are well reflected in reduction of tropospheric moisture profile considerably. It is found that the maximum number of west pacific cyclonic system during July 2002 is also influenced for large deficit rainfall over India. The dynamic, thermodynamic and energetic clearly show the monsoon break type situation over India in the month of July 2002 resulting less convective activity and the reduction of moisture. The large diabatic heating, flux convergence of heat and moisture over south east equatorial Indian Ocean are also responsible for drought situation in July 2002 over the Indian region.  相似文献   

8.
The India Meteorological Department (IMD) has been issuing long-range forecasts (LRF) based on statistical methods for the southwest monsoon rainfall over India (ISMR) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the recent deficient monsoon years of 2002 and 2004. In this paper, we report the improved results of new experimental statistical models developed for LRF of southwest monsoon seasonal (June–September) rainfall. These models were developed to facilitate the IMD’s present two-stage operational forecast strategy. Models based on the ensemble multiple linear regression (EMR) and projection pursuit regression (PPR) techniques were developed to forecast the ISMR. These models used new methods of predictor selection and model development. After carrying out a detailed analysis of various global climate data sets; two predictor sets, each consisting of six predictors were selected. Our model performance was evaluated for the period from 1981 to 2004 by sliding the model training period with a window length of 23 years. The new models showed better performance in their hindcast, compared to the model based on climatology. The Heidke scores for the three category forecasts during the verification period by the first stage models based on EMR and PPR methods were 0.5 and 0.44, respectively, and those of June models were 0.63 and 0.38, respectively. Root mean square error of these models during the verification period (1981–2004) varied between 4.56 and 6.75% from long period average (LPA) as against 10.0% from the LPA of the model based on climatology alone. These models were able to provide correct forecasts of the recent two deficient monsoon rainfall events (2002 and 2004). The experimental forecasts for the 2005 southwest monsoon season based on these models were also found to be accurate.  相似文献   

9.
For central India and its west coast, rainfall in the early (15 May–20 June) and late (15 September–20 October) monsoon season correlates with Pacific Ocean sea-surface temperature (SST) anomalies in the preceding month (April and August, respectively) sufficiently well, that those SST anomalies can be used to predict such rainfall. The patterns of SST anomalies that correlate best include the equatorial region near the dateline, and for the early monsoon season (especially since ~1980), a band of opposite correlation stretching from near the equator at 120°E to ~25°N at the dateline. Such correlations for both early and late monsoon rainfall and for both regions approach, if not exceed, 0.5. Although correlations between All India Summer Monsoon Rainfall and typical indices for the El Ni?o-Southern Oscillation (ENSO) commonly are stronger for the period before than since 1980, these correlations with early and late monsoon seasons suggest that ENSO continues to affect the monsoon in these seasons. We exploit these patterns to assess predictability, and we find that SSTs averages in specified regions of the Pacific Ocean in April (August) offer predictors that can forecast rainfall amounts in the early (late) monsoon season period with a ~25% improvement in skill relative to climatology. The same predictors offer somewhat less skill (~20% better than climatology) for predicting the number of days in these periods with rainfall greater than 2.5?mm. These results demonstrate that although the correlation of ENSO indices with All India Rainfall has decreased during the past few decades, the connections with ENSO in the early and late parts have not declined; that for the early monsoon season, in fact, has grown stronger in recent decades.  相似文献   

10.
Summary In this study the relationship between mid-tropospheric geopotential heights over the Northern Hemisphere (20° N to 90° N, around the globe) and Indian summer monsoon rainfall (ISMR: June to September total rainfall) have been examined. For this purpose, the monthly 500 hPa geopotential heights in a 2.5° lat./lon. grid over the Northern Hemisphere and the ISMR data for the period 1958 to 2003 have been used.The analysis demonstrates a dipole structure in the correlation pattern over the East Pacific Ocean in the month of January which intensifies in February and weakens in March.The average 500 hPa geopotential height over the eastern tropical Pacific Ocean during February (index one), has a significant positive relationship (r = 0.72) with the ISMR. In addition, the surface air temperature (SAT) anomaly over North-west Eurasia during January (index two) is found to be strongly related with the subsequent summer monsoon rainfall. These relationships are found to be consistent and robust during the period of analysis and these indices are found to be independent of each other.Hence, using index one and index two, a multiple linear regression model is developed for the prediction of the ISMR and the empirical relationships are verified on independent data. The forecast of the ISMR, using the above model, is found to be satisfactory.The dipole structure in the correlation pattern over the East Pacific region during February weakens once the ENSO (El-Nino and Southern Oscillation) events are excluded from the analysis. This suggests that the dipole type relationship between mid-tropospheric geopotential heights over the East Pacific Ocean and the ISMR may be a manifestation of the ENSO cycle.  相似文献   

11.
前期高度场和海温场变化对我国汛期降水的影响   总被引：14，自引：1，他引：13  
严华生  严小冬 《大气科学》2004,28(3):405-414
利用1952～2001年我国160个测站汛期降水和前期500 hPa高度场和太平洋海温场资料以及三因子最佳子集回归求最大复相关系数的方法,把前期不同时间步长、不同时段的高度场和海温场同时作为预报因子与汛期降水求相关.结果发现:前期两个场共同作为预报因子比把其中某场单独作为预报因子的相关要好.并且存在着较好的"隔多季度相关"现象.预报因子具有实际预报意义的最佳时段为上一年的6～9月.影响我国汛期降水的最佳预报因子主要集中于高度场和海温场具有重要天气气候意义的关键区域.汛期降水可预报性在北方和长江以南均较好.  相似文献   

12.
500 hPa ridge positions over the Indian and the West Pacific regions during April are related with the summer monsoon rainfall over India. The ridge position over the Indian region shows better relation with monsoon rainfall than that shown by the ridge over the Pacific region. The multiple correlation of these ridge positions with monsoon rainfall exceeds 0.7. These predictive relationships are better than those shown by other parameters, viz. (1) Northern Hemispheric surface temperature; (2) East-Pacific sea surface tempera-ture; (3) El-Nino events and (4) Tahiti-Darwin pressure difference, and index of southern oscillation, over the 30-year samples analysed.  相似文献   

13.
Summary Analysis of mean sea level pressure (1925 to 1988) over the North Pacific Ocean (NPP) for the winter period (November to March) revealed a significant correlation with Indian Monsoon rainfall during the later period. Its correlation coefficients (CC) for different periods (during 1951–1988) are significant at the 1% to 5% levels. The temporal stability of these CCs is examined using 11, 21 and 31 year sliding windows. NPP is seen to play an important role in the regression models as revealed by the relative significance of its partial regression coefficients. The regression models developed are seen to perform well for the independent period.With 5 Figures  相似文献   

14.
The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.  相似文献   

15.
Summary The relationship between the Indian Ocean Sea-Surface Temperature Anomalies (SSTA) and the Indian Summer Monsoon Rainfall (ISMR) have been examined for the period, 1983–2006. High and positive correlation (0.51; significant at >99% level) is noticed between ISMR and SSTA over southeastern Arabian Sea (AS) in the preceding January. Significant and positive correlation (0.61: significant at >99% level) is also observed with the SSTA over northwest of Australia (NWA) in the preceding February. The combined SSTA index (AS + NWA) showed a very high correlation of 0.71 with the ISMR. The correlation between East Asia sea-level pressure (average during February and March in the region, 35° N–45° N; 120° E–130° E) and ISMR is found to be 0.62. The multiple correlation using the above two parameters is 0.85 which explains 72% variance in ISMR. Using the above two parameters a linear multiple regression model to predict ISMR is developed. Our results are comparable with those obtained from the power regression (developed with 16, 8 and 10 parameters) and ensemble models (using 3 to 6 parameters) of the Indian Meteorological Department (IMD) (Rajeevan et al. 2004; 2006). The rainfall during 2002 and 2004 could be predicted accurately from the present model. It is well known fact that most of the dynamical/statistical methods failed to predict the rainfall in 2002. However, as for associations between SST and ISMR, the index is quite susceptible to inter decadal fluctuations and markedly reduced skill is found in the decades preceding 1983. The RMS error for 24 years is 5.56 (% of long period average, LPA) and the correlation between the predicted and observed rainfall is 0.79. Correspondence: Y. Sadhuram, Regional Centre, National Institute of Oceanography, 176, Lawson’s Bay Colony, Visakhapatnam-530017, India  相似文献   

16.
Simulation of Indian summer monsoon circulation and rainfall using RegCM3   总被引：5，自引：2，他引：5  
Summary The Regional Climate Model RegCM3 has been used to examine its suitability in simulating the Indian summer monsoon circulation features and associated rainfall. The model is integrated at 55 km horizontal resolution over a South Asia domain for the period April–September of the years 1993 to 1996. The characteristics of wind at 850 hPa and 200 hPa, temperature at 500 hPa, surface pressure and rainfall simulated by the model over the Indian region are examined for two convective schemes (a Kuo-type and a mass flux scheme). The monsoon circulation features simulated by RegCM3 are compared with those of the NCEP/NCAR reanalysis and the simulated rainfall is validated against observations from the Global Precipitation Climatology Centre (GPCC) and the India Meteorological Department (IMD). Validation of the wind and temperature fields shows that the use of the Grell convection scheme yields results close to the NCEP/NCAR reanalysis. Similarly, the Indian Summer Monsoon Rainfall (ISMR) simulated by the model with the Grell convection scheme is close to the corresponding observed values. In order to test the model response to land surface changes such as the Tibetan snow depth, a sensitivity study has also been conducted. For such sensitivity experiment, NIMBUS-7 SMMR snow depth data in spring are used as initial conditions in the RegCM3. Preliminary results indicate that RegCM3 is very much sensitive to Tibetan snow. The model simulated Indian summer monsoon circulation becomes weaker and the associated rainfall is reduced by about 30% with the introduction of 10 cm of snow over the Tibetan region in the month of April.  相似文献   

17.
Summary  The interannual variability of the Indian summer monsoon (June–September) rainfall is examined in relation to the stratospheric zonal wind and temperature fluctuations at three stations, widely spaced apart. The data analyzed are for Balboa, Ascension and Singapore, equatorial stations using recent period (1964–1994) data, at each of the 10, 30 and 50 hPa levels. The 10 hPa zonal wind for Balboa and Ascension during January and the 30 hPa zonal wind for Balboa during April are found to be positively correlated with the subsequent Indian summer monsoon rainfall, whereas the temperature at 10 hPa for Ascension during May is negatively correlated with Indian summer monsoon rainfall. The relationship with stratospheric temperatures appears to be the best, and is found to be stable over the period of analysis. Stratospheric temperature is also significantly correlated with the summer monsoon rainfall over a large and coherent region, in the north-west of India. Thus, the 10 hPa temperature for Ascension in May appears to be useful for forecasting summer monsoon rainfall for not only the whole of India, but also for a smaller region lying to the north-west of India. Received July 30, 1999 Revised March 17, 2000  相似文献   

18.
Summary  The existing methods based on statistical techniques for long range forecasts of Indian monsoon rainfall have shown reasonably accurate performance, for last 11 years. Because of the limitation of such statistical techniques, new techniques may have to be tried to obtain better results. In this paper, we discuss the results of an artificial neural network model by combining two different neural networks, one explaining assumed deterministic dynamics within the time series of Indian monsoon rainfall (Model I) and other using eight regional and global predictors (Model II). The model I has been developed by using the data of past 50 years (1901–50) and the data for recent period (1951–97) has been used for verification. The model II has been developed by using the 30 year (1958–87) data and the verification of this model has been carried out using the independent data of 10 year period (1988–97). In model II, instead of using eight parameters directly as inputs, we have carried out Principal Component Analysis (PCA) of the eight parameters with 30 years of data, 1958–87, and the first five principal components are included as input parameters. By combining model I and model II, a hybrid principal component neural network model (Model III) has been developed by using 30 year (1958–87) data as training period and recent 10 year period (1988–97) as verification period. Performance of the hybrid model (Model III) has been found the best among all three models developed. Rootmean square error (RMSE) of this hybrid model during the independent period (1988–97) is 4.93% as against 6.83%of the operational forecasts of the India Meteorological Department (IMD) using the 16 parameter Power Regression model. As this hybrid model is showing good results, it is now used by the IMD for experimental long-range forecasts of summer monsoon rainfall over India as a whole. Received August 20, 1998/Revised April 20, 1999  相似文献   

19.
统计预报海温场驱动的CAM3.1模式预报试验   总被引：2，自引：0，他引：2       下载免费PDF全文
基于动力气候模式进行月一季尺度预报的“两步法”思想,提出一种新的预报海温场统计模型,并以该统计模型预报的海温场驱动NCAR CAM3.1模式对1981-2000年月时间尺度的东亚500 hPa高度距平场进行客观回报试验;在此基础上,提出了对预报结果的订正方法。结果表明:统计预报海温模型的预报海温场能够反映出全球海温空间分布的基本特征,并对表征ENSO事件的Ni?o3.4区海温变化的预报能力较强。该统计模型预报的海温场驱动的CAM3.1模式可以较好地预报出东亚500 hPa环流的主要分布特征,试验表明:适当的统计订正方法可以在一定程度上提高CAM3.1模式对东亚夏季500 hPa环流背景的预报技巧。  相似文献   

20.
Summary The main objective of this study was to develop empirical models with different seasonal lead time periods for the long range prediction of seasonal (June to September) Indian summer monsoon rainfall (ISMR). For this purpose, 13 predictors having significant and stable relationships with ISMR were derived by the correlation analysis of global grid point seasonal Sea-Surface Temperature (SST) anomalies and the tendency in the SST anomalies. The time lags of the seasonal SST anomalies were varied from 1 season to 4 years behind the reference monsoon season. The basic SST data set used was the monthly NOAA Extended Reconstructed Global SST (ERSST) data at 2° × 2° spatial grid for the period 1951–2003. The time lags of the 13 predictors derived from various areas of all three tropical ocean basins (Indian, Pacific and Atlantic Oceans) varied from 1 season to 3 years. Based on these inter-correlated predictors, 3 predictor sub sets A, B and C were formed with prediction lead time periods of 0, 1 and 2 seasons, respectively, from the beginning of the monsoon season. The selected principal components (PCs) of these predictor sets were used as the input parameters for the models A, B and C, respectively. The model development period was 1955–1984. The correct model size was derived using all-possible regressions procedure and Mallow’s “Cp” statistics. Various model statistics computed for the independent period (1985–2003) showed that model B had the best prediction skill among the three models. The root mean square error (RMSE) of model B during the independent test period (6.03% of Long Period Average (LPA)) was much less than that during the development period (7.49% of LPA). The performance of model B was reasonably good during both ENSO and non-ENSO years particularly when the magnitudes of actual ISMR were large. In general, the predicted ISMR during years following the El Ni?o (La Ni?a) years were above (below) LPA as were the actual ISMR. By including an NAO related predictor (WEPR) derived from the surface pressure anomalies over West Europe as an additional input parameter into model B, the skill of the predictions were found to be substantially improved (RMSE of 4.86% of LPA).  相似文献   

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