The Advantage of Using International Multimodel Ensemble for Seasonal Precipitation Forecast over Israel

Abstract

This study analyzes the results of monthly and seasonal precipitation forecasting from seven different global climate forecast models for major basins in Israel within October-April 1982. The six National Multimodel Ensemble (NMME) models and the ECMWF seasonal model were used to calculate an International Multimodel Ensemble (IMME). The study presents the performance of both monthly and seasonal predictions of precipitation accumulated over three months, with respect to different lead times for the ensemble mean values, one per individual model. Additionally, we analyzed the performance of different combinations of models. We present verification of seasonal forecasting using real forecasts, focusing on a small domain characterized by complex terrain, high annual precipitation variability, and a sharp precipitation gradient from west to east as well as from south to north. The results in this study show that, in general, the monthly analysis does not provide very accurate results, even when using the IMME for one-month lead time. We found that the IMME outperformed any single model prediction. Our analysis indicates that the optimal combinations with the high correlation values contain at least three models. Moreover, prediction with larger number of models in the ensemble produces more robust predictions. The results obtained in this study highlight the advantages of using an ensemble of global models over single models for small domain.

Seasonal rainfall forecasting for the Yangtze River basin using statistical and dynamical models

“…September, the sharply decreasing forecasting reliabilities of all four GCMs obtained when the lead time is extended to one month indicates that a single model has limited forecasting capability for the Yangtze River basin when the lead time is longer than month. In fact, many GCMs share similar atmospheric and oceanic components, which may explain why they exhibit similar trends with respect to the prediction capabilities associated with longer lead times (Givati et al, 2017).…”

“…Several GCMs have been currently developed and adopted for conducting climate forecasts, such as the Climate Forecast System Version 2 (CFSv2) model developed at the U.S. National Centers for Environmental Prediction (Yuan et al, 2011) and the European Centre for Medium-Range Weather Forecast's Systems 4 model (Kim et al, 2012;Vitart, 2014). The North American Multimodel Ensemble (NMME) project has assessed the strengths and weaknesses of more than 10 GCMs (Givati et al, 2017;Kirtman et al, 2014). However, the application of GCMs for seasonal forecasts has demonstrated greater facility for predicting sea surface variables like SST than land surface variables like rainfall (Ding and Ke, 2013;Smith et al, 2012), although the land surface variables have a more direct impact on society than the sea surface variables.…”

“…Firstly, there are limited studies involved in comparing the difference of seasonal rainfall forecasting skill between dynamical models and statistical models over land. Even though GCMs show increasingly accurate forecasting skill in many climate variables, the skill of seasonal rainfall forecasting over land is not so satisfactory (Givati et al, 2017). It is necessary to evaluate the performance of GCMs in seasonal rainfall forecasts.…”

ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load

“…The proposed hybrid deep learning technique gives diverse error distribution. It gives generalized forecasting results as compared to the single model [44]. Consequently, the overfitting issue of the network is reasonably reduced and it increases the model's robustness.…”

Improved Rainfall Prediction through Nonlinear Autoregressive Network with Exogenous Variables: A Case Study in Andes High Mountain Region

“…Some of them have shown accurate results like the multiple linear regression and k-nearestneighbors methods as in [12,13]. Other approaches have used the outputs of global climate models to improve the seasonal and subseasonal forecasting [14][15][16] and the probabilistic forecasts for uncertainty quantification [17][18][19]. Other studies have applied artificial intelligence approach for rainfall prediction such as artificial neural networks (ANNs) [5,[10][11][12][20][21][22][23][24], support vector machine (SVM) [12,13,24,25], logistic regression [26], and random forest [27,28].…”