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1.
The real-time multi-model ensemble (MME)-based extended range (up to 3 weeks) forecast of monsoon rainfall over India during the 2012 monsoon season is analyzed using the outputs of European Centre for Medium Range Weather Forecasts (ECMWF) monthly forecast coupled model, National Centre for Environmental Prediction (NCEP) Climate Forecast System version 2 coupled model and Japan Meteorological Agency (JMA)’ ensemble prediction system. Although the individual models show useful skill in predicting the extended range forecast of monsoon, the MME forecast is found to be superior compared to these. For the country as a whole, the correlation coefficient (CC) between the observed and MME forecast rainfall departure is found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days). Over the four homogeneous regions of India, the CC is found to be significant (above 95 % level) up to 2 weeks except in case of northeast India, which shows significant CC for week 1 (days 5–11) only. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the mean percentage of correct forecast is found to be much higher than the climatology forecast. Considering the complex problem of forecasting of monsoon in the extended range timescales, the MME-based predictions for 2–3 weeks provide skillful results and useful guidance for application in agriculture and other sectors in India.  相似文献   

2.
Summary The study provides a concise and synthesized documentation of the current level of skill of the operational NWP model of India Meteorological Department based on daily 24 hours forecast run of the model during two normal monsoon years 2001 and 2003 making detailed inter-comparison with daily rainfall analysis from the use of high dense land rain gauge observations. The study shows that the model, in general, is able to capture three regions of climatologically heavy rainfall domains along Western Ghats, Northeast India and over east central India, over the domain of monsoon trough. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The inter-comparison reveals that performance of the model rainfall forecast deteriorated in 2003 when rainfall over most parts of the region was significantly under-predicted. These features are also reflected in the error statistics. The study suggests that there is a need to maximize the data ingest in the model with a better data assimilation scheme to improve the rainfall forecast skill.  相似文献   

3.
The performance of the new multi-model seasonal prediction system developed in the frame work of the ENSEMBLES EU project for the seasonal forecasts of India summer monsoon variability is compared with the results from the previous EU project, DEMETER. We have considered the results of six participating ocean-atmosphere coupled models with 9 ensemble members each for the common period of 1960–2005 with May initial conditions. The ENSEMBLES multi-model ensemble (MME) results show systematic biases in the representation of mean monsoon seasonal rainfall over the Indian region, which are similar to that of DEMETER. The ENSEMBLES coupled models are characterized by an excessive oceanic forcing on the atmosphere over the equatorial Indian Ocean. The skill of the seasonal forecasts of Indian summer monsoon rainfall by the ENSEMBLES MME has however improved significantly compared to the DEMETER MME. Its performance in the drought years like 1972, 1974, 1982 and the excess year of 1961 was in particular better than the DEMETER MME. The ENSEMBLES MME could not capture the recent weakening of the ENSO-Indian monsoon relationship resulting in a decrease in the prediction skill compared to the “perfect model” skill during the recent years. The ENSEMBLES MME however correctly captures the north Atlantic-Indian monsoon teleconnections, which are independent of ENSO.  相似文献   

4.
During the summer monsoon (1 June to 30 September) 2007, real-time district level rainfall forecasts in short-range time scale were generated for Indian region applying multimodel ensemble technique. The pre-assigned grid point weights on the basis of correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.5° × 0.5° utilizing two seasons datasets (1 June to 30 September, 2005 and 2006), and the multimodel ensemble forecasts (day 1 and day 2 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district taking the average value of all grid points falling in a particular district. In this paper we examined the performance skill of the multimodel ensemble-based real-time district level short-range forecast of rainfall. It has clearly emerged from the results that the multimodel ensemble technique reported in this study is superior to each ensemble member. District wise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most districts of the country, particularly over the districts where the monsoon systems are dominant. Though the procedure shows appreciable skill to predict occurrence or non-occurrence of rainfall at the district level, it always underestimates rainfall amount, particularly in heavy rainfall events. Possible reasons of this failure may be due to model bias and poor data assimilation procedure.  相似文献   

5.
Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.  相似文献   

6.
This article describes a three way inter-comparison of forecast skill on an extended medium-range time scale using the Korea Meteorological Administration (KMA) operational ensemble numerical weather prediction (NWP) systems (i.e., atmosphere-only global ensemble prediction system (EPSG) and ocean-atmosphere coupledEPSG) and KMA operational seasonal prediction system, the Global Seasonal forecast system version 5 (GloSea5). The main motivation is to investigate whether the ensemble NWP system can provide advantage over the existing seasonal prediction system for the extended medium-range forecast (30 days) even with putting extra resources in extended integration or coupling with ocean with NWP system. Two types of evaluation statistics are examined: the basic verification statistics - the anomaly correlation and RMSE of 500-hPa geopotential height and 1.5-meter surface temperature for the global and East Asia area, and the other is the Real-time Multivariate Madden and Julian Oscillation (MJO) indices (RMM1 and RMM2) - which is used to examine the MJO prediction skill. The MJO is regarded as a main source of forecast skill in the tropics linked to the mid-latitude weather on monthly time scale. Under limited number of experiment cases, the coupled NWP extends the forecast skill of the NWP by a few more days, and thereafter such forecast skill is overtaken by that of the seasonal prediction system. At present stage, it seems there is little gain from the coupled NWP even though more resources are put into it. Considering this, the best combination of numerical product guidance for operational forecasters for an extended medium-range is extension of the forecast lead time of the current ensemble NWP (EPSG) up to 20 days and use of the seasonal prediction system (GloSea5) forecast thereafter, though there exists a matter of consistency between the two systems.  相似文献   

7.
Seasonal rainfall predictability over the Huaihe River basin is evaluated in this paper on the basis of 23-year(1981-2003) retrospective forecasts by 10 climate models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) prediction system.It is found that the summer rainfall variance in this basin is largely internal,which leads to lower rainfall predictability for most individual climate models.By dividing the 10 models into three categories according to their sea surface temperature(SST) boundary conditions including observed,predicted,and persistent SSTs,the MME deterministic predictive skill of summer rainfall over Huaihe River basin is investigated.It is shown that the MME is effective for increasing the current seasonal forecast skill.Further analysis shows that the MME averaged over predicted SST models has the highest rainfall prediction skill,which is closely related to model’s capability in reproducing the observed dominant modes of the summer rainfall anomalies in Huaihe River basin.This result can be further ascribed to the fact that the predicted SST MME is the most effective model ensemble for capturing the relationship between the summer rainfall anomalies over Huaihe River basin and the SST anomalies(SSTAs) in equatorial oceans.  相似文献   

8.
Summary A revised 25-point Shuman-Shapiro Spatial Filter (RSSSF) has been applied to six atmospheric circulation models and multi-model ensemble (MME) predictions, and its effect on the improvement of model forecast skill scores of the Asian summer precipitation anomaly is discussed in this paper. On the basis of 21-yr model ensemble predictions, the RSSSF can remove the unpredictable ‘noise’ with respect to the 2-grid wavelength in the model precipitation anomaly fields and maintain the large-scale counterpart, which is related to the response of the model to large-scale boundary forcing. Therefore, this could possibly enhance the forecast skill of the Asian summer rainfall anomaly in the models and the MME. The potential improvement of model forecasting skill is found in the Asian summer monsoon region, where the anomaly correlation coefficient (ACC) has been improved by 7–40%, corresponding to the decreased root mean square error (RMSE) in the model and the MME precipitation anomaly forecasts.  相似文献   

9.
Performance of seven fully coupled models in simulating Indian summer monsoon climatology as well as the inter-annual variability was assessed using multi member 1 month lead hindcasts made by several European climate groups as part of the program called Development of a European multi-model ensemble system for seasonal-to-inter-annual prediction (DEMETER). Dependency of the model simulated Indian summer monsoon rainfall and global sea surface temperatures on model formulation and initial conditions have been studied in detail using the nine ensemble member simulations of the seven different coupled ocean–atmosphere models participated in the DEMETER program. It was found that the skills of the monsoon predictions in these hindcasts are generally positive though they are very modest. Model simulations of India summer monsoon rainfall for the earlier period (1959–1979) are closer to the ‘perfect model’ (attainable) score but, large differences are observed between ‘actual’ skill and ‘perfect model’ skill in the recent period (1980–2001). Spread among the ensemble members are found to be large in simulations of India summer monsoon rainfall (ISMR) and Indian ocean dipole mode (IODM), indicating strong dependency of model simulated Indian summer monsoon on initial conditions. Multi-model ensemble performs better than the individual models in simulating ENSO indices, but does not perform better than the individual models in simulating ISMR and IODM. Decreased skill of multi-model ensemble over the region indicates amplification of errors due to existence of similar errors in the individual models. It appears that large biases in predicted SSTs over Indian Ocean region and the not so perfect ENSO-monsoon (IODM-monsoon) tele-connections are some of the possible reasons for such lower than expected skills in the recent period. The low skill of multi-model ensemble, large spread among the ensemble members of individual models and the not so perfect monsoon tele-connection with global SSTs points towards the importance of improving individual models for better simulation of the Indian monsoon.  相似文献   

10.
基于欧洲中期天气预报中心全球高分辨率预报模式ECMWF、中国自主研发的新一代业务化区域数值模式GRAPES_Meso和WRF中国全国区域预报模式的降水预报结果进行未来3 d降水集成预报。以中国地面-卫星-雷达三源融合逐时降水格点产品(CMPA-Hourly,V2.0)作为"观测值"进行建模,采用消除偏差多模式平均法和基于无偏平均绝对误差集成法对中国大陆地区进行降水集成预报,同时采用2800个国家自动气象站降水观测数据对降水集成预报效果进行检验。结果表明：基于无偏平均绝对误差的降水集成方法能够综合每个模式成员降水预报场的优势,提供一种更为稳定可靠且具有更高分辨率的优质精细化降水预报产品;其在试验期间对中国大陆地区汛期的降水预报ETS评分,优于消除偏差多模式平均降水集成预报和最优单模式降水预报,BIAS评分更接近于1,与"实况"的距平相关系数也明显提高,是对降水大值捕捉能力较优的一种集成方法,尤其对中雨到暴雨量级预报的改进较好。  相似文献   

11.
Forecast skill of the APEC Climate Center (APCC) Multi-Model Ensemble (MME) seasonal forecast system in predicting two main types of El Niño-Southern Oscillation (ENSO), namely canonical (or cold tongue) and Modoki ENSO, and their regional climate impacts is assessed for boreal winter. The APCC MME is constructed by simple composite of ensemble forecasts from five independent coupled ocean-atmosphere climate models. Based on a hindcast set targeting boreal winter prediction for the period 1982–2004, we show that the MME can predict and discern the important differences in the patterns of tropical Pacific sea surface temperature anomaly between the canonical and Modoki ENSO one and four month ahead. Importantly, the four month lead MME beats the persistent forecast. The MME reasonably predicts the distinct impacts of the canonical ENSO, including the strong winter monsoon rainfall over East Asia, the below normal rainfall and above normal temperature over Australia, the anomalously wet conditions across the south and cold conditions over the whole area of USA, and the anomalously dry conditions over South America. However, there are some limitations in capturing its regional impacts, especially, over Australasia and tropical South America at a lead time of one and four months. Nonetheless, forecast skills for rainfall and temperature over East Asia and North America during ENSO Modoki are comparable to or slightly higher than those during canonical ENSO events.  相似文献   

12.
The 2009 drought in India was one of the major droughts that the country faced in the last 100?years. This study describes the anomalous features of 2009 summer monsoon and examines real-time seasonal predictions made using six general circulation models (GCMs). El Ni?o conditions evolved in the Pacific Ocean, and sea surface temperatures (SSTs) over the Indian Ocean were warmer than normal during monsoon 2009. The observed circulation patterns indicate a weaker monsoon in that year over India with weaker than normal convection over the Bay of Bengal and Indian landmass. Skill of the GCMs during hindcast period shows that neither these models simulate the observed interannual variability nor their multi-model ensemble (MME) significantly improves the skill of monsoon rainfall predictions. Except for one model used in this study, the real-time predictions with longer lead (2- and 1-month lead) made for the 2009 monsoon season did not provide any indication of a highly anomalous monsoon. However, with less lead time (zero lead), most of the models as well as the MME had provided predictions of below normal rainfall for that monsoon season. This study indicates that the models could not predict the 2009 drought over India due to the use of less warm SST anomalies over the Pacific in the longer lead runs. Hence, it is proposed that the uncertainties in SST predictions (the lower boundary condition) have to be represented in the model predictions of summer monsoon rainfall over India.  相似文献   

13.
The Northwest Pacific (NWP) circulation (subtropical high) is an important component of the East Asian summer monsoon system. During summer (June–August), anomalous lower tropospheric anticyclonic (cyclonic) circulation appears over NWP in some years, which is an indicative of stronger (weaker) than normal subtropical high. The anomalous NWP cyclonic (anticyclonic) circulation years are associated with negative (positive) precipitation anomalies over most of Indian summer monsoon rainfall (ISMR) region. This indicates concurrent relationship between NWP circulation and convection over the ISMR region. Dry wind advection from subtropical land regions and moisture divergence over the southern peninsular India during the NWP cyclonic circulation years are mainly responsible for the negative rainfall anomalies over the ISMR region. In contrast, during anticyclonic years, warm north Indian Ocean and moisture divergence over the head Bay of Bengal-Gangetic Plain region support moisture instability and convergence in the southern flank of ridge region, which favors positive rainfall over most of the ISMR region. The interaction between NWP circulation (anticyclonic or cyclonic) and ISMR and their predictability during these anomalous years are examined in the present study. Seven coupled ocean–atmosphere general circulation models from the Asia-Pacific Economic Cooperation Climate Center and their multimodel ensemble mean skills in predicting the seasonal rainfall and circulation anomalies over the ISMR region and NWP for the period 1982–2004 are assessed. Analysis reveals that three (two) out of seven models are unable to predict negative (positive) precipitation anomalies over the Indian subcontinent during the NWP cyclonic (anticyclonic) circulation years at 1-month lead (model is initialized on 1 May). The limited westward extension of the NWP circulation and misrepresentation of SST anomalies over the north Indian Ocean are found to be the main reasons for the poor skill (of some models) in rainfall prediction over the Indian subcontinent. This study demonstrates the importance of the NWP circulation variability in predicting summer monsoon precipitation over South Asia. Considering the predictability of the NWP circulation, the current study provides an insight into the predictability of ISMR. Long lead prediction of the ISMR associated with anomalous NWP circulation is also discussed.  相似文献   

14.
Seasonal prediction of summer rainfall over the Yangtze River valley(YRV) is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble(MME) prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function(PDF) prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.  相似文献   

15.
We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.  相似文献   

16.
The performances of various dynamical models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) in predicting station-scale rainfall in South China(SC) in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME's ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output(DMO) over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.  相似文献   

17.
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   

18.
Long-lead prediction of waxing and waning of the Western North Pacific (WNP)-East Asian (EA) summer monsoon (WNP-EASM) precipitation is a major challenge in seasonal time-scale climate prediction. In this study, deficiencies and potential for predicting the WNP-EASM precipitation and circulation one or two seasons ahead were examined using retrospective forecast data for the 26-year period of 1981–2006 from two operational couple models which are the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the Bureau of Meteorology Research Center (BMRC) Predictive Ocean–Atmosphere Model for Australia (POAMA). While both coupled models have difficulty in predicting summer mean precipitation anomalies over the region of interest, even for a 0-month lead forecast, they are capable of predicting zonal wind anomalies at 850 hPa several months ahead and, consequently, satisfactorily predict summer monsoon circulation indices for the EA region (EASMI) and for the WNP region (WNPSMI). It should be noted that the two models’ multi-model ensemble (MME) reaches 0.40 of the correlation skill for the EASMI with a January initial condition and 0.75 for the WNPSMI with a February initial condition. Further analysis indicates that prediction reliability of the EASMI is related not only to the preceding El Niño and Southern Oscillation (ENSO) but also to simultaneous local SST variability. On other hand, better prediction of the WNPSMI is accompanied by a more realistic simulation of lead–lag relationship between the index and ENSO. It should also be noted that current coupled models have difficulty in capturing the interannual variability component of the WNP-EASM system which is not correlated with typical ENSO variability. To improve the long-lead seasonal prediction of the WNP-EASM precipitation, a statistical postprocessing was developed based on the multiple linear regression method. The method utilizes the MME prediction of the EASMI and WNPSMI as predictors. It is shown that the statistical postprocessing is able to improve forecast skill for the summer mean precipitation over most of the WNP-EASM region at all forecast leads. It is noteworthy that the MME prediction, after applying statistical postprocessing, shows the best anomaly pattern correlation skill for the EASM precipitation at a 4-month lead (February initial condition) and for the WNPSM precipitation at a 5-month lead (January initial condition), indicating its potential for improving long-lead prediction of the monsoon precipitation.  相似文献   

19.
A Bayesian probabilistic prediction scheme of the Yangtze River Valley (YRV) summer rainfall is proposed to combine forecast information from multi-model ensemble dataset provided by ENSEMBLES project.Due to the low forecast skill of rainfall in dynamic models,the time series of regressed YRV summer rainfall are selected as ensemble members in the new scheme,instead of commonly-used YRV summer rainfall simulated by models.Each time series of regressed YRV summer rainfall is derived from a simple linear regression.The predictor in each simple linear regression is the skillfully simulated circulation or surface temperature factor which is highly linear with the observed YRV summer rainfall in the training set.The high correlation between the ensemble mean of these regressed YRV summer rainfall and observation benefit extracting more sample information from the ensemble system.The results show that the cross-validated skill of the new scheme over the period of 1960 to 2002 is much higher than equally-weighted ensemble,multiple linear regression,and Bayesian ensemble with simulated YRV summer rainfall as ensemble members.In addition,the new scheme is also more skillful than reference forecasts (random forecast at a 0.01 significance level for ensemble mean and climatology forecast for probability density function).  相似文献   

20.
The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982–2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982–2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson’s and Kendal’s correlation coefficient and Wilmort’s index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.  相似文献   

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