This study presents a model to forecast the Indian summer monsoon rainfall（ISMR）（June-September）based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling ...This study presents a model to forecast the Indian summer monsoon rainfall（ISMR）（June-September）based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,（1） training data set（1871-1960）, and（2） testing data set（1961-2014）.Statistical analyzes reflect the dynamic nature of the ISMR, which couldn＇t be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network（ANN）. Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model.展开更多
The long-range forecasts (LRF) based on statistical methods for southwest monsoon rainfall over India (ISMR) has been issued by the India Meteorological Department (IMD) for more than 100 years. Many statistical and d...The long-range forecasts (LRF) based on statistical methods for southwest monsoon rainfall over India (ISMR) has been issued by the India Meteorological Department (IMD) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the operational models of IMD failed to predict the deficient monsoon years 2002 and 2004 on the earlier occasions and so had happened for monsoon 2009. In this paper a brief of the recent methods being followed for LRF that is 8-parameter and 10-parameter power regression models used from 2003 to 2006 and new statistical ensemble forecasting system are explained. Then the new three stage procedure is explained. In this the most pertinent predictors are selected from the set of all the potential predictors for April, June and July models. The model equations are developed by using the linear regression and neural network techniques based upon training set of the 43 years of data from 1958 to 2000. The skill of the models is evaluated based upon the validation set of 11 years of data from 2001 to 2011, which has shown the high skill on the validation data set. It can be inferred that these models have the potential to provide a prediction of ISMR, which would significantly improve the operational forecast.展开更多
In India,large-scale climatic oscillations have strong influences on the Indian summer monsoon rainfall(ISMR),which plays a crucial role in India’s agricultural production and economic growth.However,there are limite...In India,large-scale climatic oscillations have strong influences on the Indian summer monsoon rainfall(ISMR),which plays a crucial role in India’s agricultural production and economic growth.However,there are limited studies in India that explore the influences of decadal and multidecadal oscillations on the ISMR and associated El Niño–Southern Oscillation(ENSO).Therefore,in this study we carried out a comprehensive and detailed investigation to understand the influences of ENSO,Pacific decadal oscillation(PDO),and Atlantic multidecadal oscillation(AMO)on ISMR across different regions in India.The statistical significance of ISMR associated with different phases(positive/warm and negative/cold)of ENSO,PDO,and AMO(individual analysis),and combined ENSO–AMO,and ENSO–PDO(coupled analysis)were analysed by using the nonparametric Wilcoxon Rank Sum(WRS)test.The individual analysis results indicate that in addition to the ENSO teleconnection,AMO and PDO significantly affect the spatial patterns of ISMR.Coupled analysis was performed to understand how the phase shift of PDO and AMO has modulated the rainfall during El Niño and La Niña phases.The results indicate that the La Niña associated with a positive PDO phase caused excessive precipitation of about 21%–150%in the peninsular,west–central,and hilly regions compared to the individual effect of ENSO/PDO/AMO on ISMR;similarly,the west–central,coastal,and northwest regions received 15%–56%of excessive rainfall.Moreover,during the El Niño combined with PDO positive(AMO positive),above-normal precipitation was observed in hilly,northeast,and coastal(hilly,northeast,west–central,and coastal)regions,opposite to the results obtained from the individual ENSO analysis.This study emphasizes the importance of accounting the decadal and multidecadal forcing when examining variations in the ISMR during different phases of ENSO events.展开更多
El Niño–Southern Oscillation(ENSO)was identified as the dominant predictor for the Indian summer monsoon rainfall(ISMR)in the early 1900s.An apparent weakening of the ENSO–ISMR relationship has been observed si...El Niño–Southern Oscillation(ENSO)was identified as the dominant predictor for the Indian summer monsoon rainfall(ISMR)in the early 1900s.An apparent weakening of the ENSO–ISMR relationship has been observed since the 1970s.Here,we found a clear restoration of the ENSO–ISMR relationship since 1999/2000.This restoring relationship is closely linked to the interdecadal transition of ENSO evolution and the associated sea surface temperature anomalies(SSTAs)over the tropical Atlantic.During 1979–1997,summer ENSO events mainly continued from the previous winter,which can drive apparent Atlantic Niña SSTAs to offset ENSO's impact on ISMR and weaken the ENSO–ISMR relationship.In contrast,when ENSO events newly emerge from late spring,as they have done more recently during 2000–2018,the associated tropical Atlantic SSTAs are weak and shift to the tropical North Atlantic,which can offset the contribution of Atlantic Niña and reinforce the ENSO–ISMR relationship.We identified that the diversity of ENSO's evolution,continuing from the previous winter or emerging from late spring,is the dominant factor perturbing the ENSO–ISMR relationship in recent epochs,with tropical Atlantic SSTAs as the crucial bridge.This finding should be considered in our efforts to improve ISMR prediction.展开更多
基金supported by the Department of Science and Technology (DST)-SERB, Government of India, under Grant EEQ/ 2016/000021
文摘This study presents a model to forecast the Indian summer monsoon rainfall（ISMR）（June-September）based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,（1） training data set（1871-1960）, and（2） testing data set（1961-2014）.Statistical analyzes reflect the dynamic nature of the ISMR, which couldn＇t be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network（ANN）. Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model.
文摘The long-range forecasts (LRF) based on statistical methods for southwest monsoon rainfall over India (ISMR) has been issued by the India Meteorological Department (IMD) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the operational models of IMD failed to predict the deficient monsoon years 2002 and 2004 on the earlier occasions and so had happened for monsoon 2009. In this paper a brief of the recent methods being followed for LRF that is 8-parameter and 10-parameter power regression models used from 2003 to 2006 and new statistical ensemble forecasting system are explained. Then the new three stage procedure is explained. In this the most pertinent predictors are selected from the set of all the potential predictors for April, June and July models. The model equations are developed by using the linear regression and neural network techniques based upon training set of the 43 years of data from 1958 to 2000. The skill of the models is evaluated based upon the validation set of 11 years of data from 2001 to 2011, which has shown the high skill on the validation data set. It can be inferred that these models have the potential to provide a prediction of ISMR, which would significantly improve the operational forecast.
文摘In India,large-scale climatic oscillations have strong influences on the Indian summer monsoon rainfall(ISMR),which plays a crucial role in India’s agricultural production and economic growth.However,there are limited studies in India that explore the influences of decadal and multidecadal oscillations on the ISMR and associated El Niño–Southern Oscillation(ENSO).Therefore,in this study we carried out a comprehensive and detailed investigation to understand the influences of ENSO,Pacific decadal oscillation(PDO),and Atlantic multidecadal oscillation(AMO)on ISMR across different regions in India.The statistical significance of ISMR associated with different phases(positive/warm and negative/cold)of ENSO,PDO,and AMO(individual analysis),and combined ENSO–AMO,and ENSO–PDO(coupled analysis)were analysed by using the nonparametric Wilcoxon Rank Sum(WRS)test.The individual analysis results indicate that in addition to the ENSO teleconnection,AMO and PDO significantly affect the spatial patterns of ISMR.Coupled analysis was performed to understand how the phase shift of PDO and AMO has modulated the rainfall during El Niño and La Niña phases.The results indicate that the La Niña associated with a positive PDO phase caused excessive precipitation of about 21%–150%in the peninsular,west–central,and hilly regions compared to the individual effect of ENSO/PDO/AMO on ISMR;similarly,the west–central,coastal,and northwest regions received 15%–56%of excessive rainfall.Moreover,during the El Niño combined with PDO positive(AMO positive),above-normal precipitation was observed in hilly,northeast,and coastal(hilly,northeast,west–central,and coastal)regions,opposite to the results obtained from the individual ENSO analysis.This study emphasizes the importance of accounting the decadal and multidecadal forcing when examining variations in the ISMR during different phases of ENSO events.
基金This work was supported by the National Key R&D Program of China(grant 2019YFA0606703)the National Natural Science Foundation of China(grants 41722504 and 41975116)and the Youth Innovation Promotion Association of the Chinese Academy of Sciences.Data and codes will be made available upon request to X.Y.
文摘El Niño–Southern Oscillation(ENSO)was identified as the dominant predictor for the Indian summer monsoon rainfall(ISMR)in the early 1900s.An apparent weakening of the ENSO–ISMR relationship has been observed since the 1970s.Here,we found a clear restoration of the ENSO–ISMR relationship since 1999/2000.This restoring relationship is closely linked to the interdecadal transition of ENSO evolution and the associated sea surface temperature anomalies(SSTAs)over the tropical Atlantic.During 1979–1997,summer ENSO events mainly continued from the previous winter,which can drive apparent Atlantic Niña SSTAs to offset ENSO's impact on ISMR and weaken the ENSO–ISMR relationship.In contrast,when ENSO events newly emerge from late spring,as they have done more recently during 2000–2018,the associated tropical Atlantic SSTAs are weak and shift to the tropical North Atlantic,which can offset the contribution of Atlantic Niña and reinforce the ENSO–ISMR relationship.We identified that the diversity of ENSO's evolution,continuing from the previous winter or emerging from late spring,is the dominant factor perturbing the ENSO–ISMR relationship in recent epochs,with tropical Atlantic SSTAs as the crucial bridge.This finding should be considered in our efforts to improve ISMR prediction.
