The Experts below are selected from a list of 6705 Experts worldwide ranked by ideXlab platform
Somen Goswami - One of the best experts on this subject based on the ideXlab platform.
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Long-Range Forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time Forecast for the year 2015
Climate Dynamics, 2016Co-Authors: Sutapa Chaudhuri, Somen Goswami, D. Das, S. K. DasAbstract:All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for Long Range Forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The Forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The Forecast error analyses of the models reveal that ANFIS provides the best Forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in Forecasting AISMR. The Forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time Forecast with ANFIS shows possibility of deficit (65–75 cm) AISMR in the year 2015.
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Long Range Forecast of all india summer monsoon rainfall using adaptive neuro fuzzy inference system skill comparison with cfsv2 model simulation and real time Forecast for the year 2015
Climate Dynamics, 2016Co-Authors: Sutapa Chaudhuri, Somen GoswamiAbstract:All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for Long Range Forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The Forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The Forecast error analyses of the models reveal that ANFIS provides the best Forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in Forecasting AISMR. The Forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time Forecast with ANFIS shows possibility of deficit (65–75 cm) AISMR in the year 2015.
Sutapa Chaudhuri - One of the best experts on this subject based on the ideXlab platform.
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Long-Range Forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time Forecast for the year 2015
Climate Dynamics, 2016Co-Authors: Sutapa Chaudhuri, Somen Goswami, D. Das, S. K. DasAbstract:All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for Long Range Forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The Forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The Forecast error analyses of the models reveal that ANFIS provides the best Forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in Forecasting AISMR. The Forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time Forecast with ANFIS shows possibility of deficit (65–75 cm) AISMR in the year 2015.
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Long Range Forecast of all india summer monsoon rainfall using adaptive neuro fuzzy inference system skill comparison with cfsv2 model simulation and real time Forecast for the year 2015
Climate Dynamics, 2016Co-Authors: Sutapa Chaudhuri, Somen GoswamiAbstract:All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for Long Range Forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The Forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The Forecast error analyses of the models reveal that ANFIS provides the best Forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in Forecasting AISMR. The Forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time Forecast with ANFIS shows possibility of deficit (65–75 cm) AISMR in the year 2015.
S. K. Das - One of the best experts on this subject based on the ideXlab platform.
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Long-Range Forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time Forecast for the year 2015
Climate Dynamics, 2016Co-Authors: Sutapa Chaudhuri, Somen Goswami, D. Das, S. K. DasAbstract:All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for Long Range Forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The Forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The Forecast error analyses of the models reveal that ANFIS provides the best Forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in Forecasting AISMR. The Forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time Forecast with ANFIS shows possibility of deficit (65–75 cm) AISMR in the year 2015.
David Cyranoski - One of the best experts on this subject based on the ideXlab platform.
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climate change the Long Range Forecast
Nature, 2005Co-Authors: David CyranoskiAbstract:The Himalayas, roof of the world, are springing a leak. As the climate warms up, melting glaciers are threatening the livelihoods of millions. David Cyranoski reports.
Gayoung Kim - One of the best experts on this subject based on the ideXlab platform.
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global and regional skill of the seasonal predictions by wmo lead centre for Long Range Forecast multi model ensemble
International Journal of Climatology, 2016Co-Authors: Gayoung Kim, Joongbae Ahn, Vladimir N Kryjov, Soojin Sohn, Wontae Yun, Richard Graham, Rupa Kumar Kolli, Arun Kumar, Jeanpierre CeronAbstract:The World Meteorological Organization (WMO) Lead Centre for Long-Range Forecast Multi-Model Ensemble (WMO LC-LRFMME) has been established to collect and share Long-Range Forecasts from the WMO designated Global Producing Centres (GPC). In this study, the seasonal skill of the deterministic multi-model prediction of GPCs in WMO LC-LRFMME is investigated. The GPC models included in the analysis cover 30 years of common hindcast period from 1981 to 2010 and real-time Forecast for the period from DJF2011/2012 to SON2014. The equal-weighted multi-model ensemble (MME) method is used to produce the MME Forecast. We show that the GPC models generally capture the observed climatological patterns and seasonal variations in temperature and precipitation. However, some systematic biases/errors in simulation of the climatological mean patterns and zonal mean profiles are also found, most of which are located in mid-latitudes or high latitudes. The temporal correlation coefficients both of 2 m temperature and precipitation in the tropical region (especially over the ocean) exceed 95%, but drop gradually towards high latitudes and are even negative in the polar region for precipitation. The prediction skills of individual models and the MME over 13 regional climate outlook forum (RCOF) regions for four calendar seasons are also assessed. The prediction skills vary with season and region, with the highest skill being demonstrated by the MME Forecasts for the regions of the tropical RCOFs. These predictions are strongly affected by the ENSO over Pacific Islands, Southeast Asia and Central America. Additionally, Southeast of South America and North Eurasian regions show relatively low skills for all seasons when compared to other regions.
