Multimodel Ensemble Forecasts for Weather and Seasonal Climate

By T. N. Krishnamurti, C. Kishtawal, Zhanzai Zhang, T. LaRow, D. Bachiochi, Eric Williford, S. Gadgil, S. Surendran. 2000

678 citations recorded by the source corpus.

Abstract In this paper the performance of a multimodel ensemble forecast analysis that shows superior forecast skills is illustrated and compared to all individual models used. The model comparisons include global weather, hurricane track and intensity forecasts, and seasonal climate simulations. The performance improvements are completely attributed to the collective information of all models used in the statistical algorithm. The proposed concept is first illustrated for a low-order spectral model from which the multimodels and a “nature run” were constructed. Two hundred time units are divided into a training period (70 time units) and a forecast period (130 time units). The multimodel forecasts and the observed fields (the nature run) during the training period are subjected to a simple linear multiple regression to derive the statistical weights for the member models. The multimodel forecasts, generated for the next 130 forecast units, outperform all the individual models. This procedure was deployed...

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/49834d2c36dade3955a9e9bf401cd911f6e2e0e2

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Citations in the corpus

Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments (2006, methodology): “One popular (yet ad hoc) approach involves the use of different forecast models (e.g., Evans et al. 2000; Krishnamurti et al. 2000 ) or different physical parameterization schemes (e.g., Stensrud et al. 2000).”
Weighting climate model ensembles for mean and variance estimates (2015, background): “Performance weighting is in widespread use, and is an active area in ensemble research, yet almost all weighting procedures consider only the weighted mean, and ignore the effect on variance (e.g. Giorgi 2005; Krishnamurti et al. 2000).”
Categorical Climate Forecasts through Regularization and Optimal Combination of Multiple GCM Ensembles (2002, methodology): “Another approach uses best linear unbiased estimates (Fraedrich and Smith 1989; Pavan and Doblas-Reyes 2000) or linear regression (Krishnamurti et al. 1999, 2000).”
The use of the multi-model ensemble in probabilistic climate projections (2007, methodology): “In fact, the paper by Lopez et al. (2006) which compares the probabilistic estimates derived from Tebaldi et al.”
The Multiensemble Approach: The NAEFS Example (2009, background): “Several studies have shown the positive impact of that process in the climate and seasonal prediction fields (Palmer et al. 2004; Barnston et al. 2003; Krishnamurti et al. 2000).”
Multimodel Simulation of Water Flow in a Field Soil Using Pedotransfer Functions (2009, methodology): “Such a method of obtaining multimodel forecasts has been called superensemble forecasting (Krishnamurti et al., 2000).”
Weighting of model results for improving best estimates of climate change (2010, methodology): “In weather prediction, the expected accuracy of the forecasts can be estimated from verification statistics gathered from previous cases (e.g., Krishnamurti et al. 2000 ).”
Comparison of Some Statistical Methods of Probabilistic Forecasting of ENSO. (2002, methodology): “More sophisticated methods of forecast combination, and/or careful and detailed diagnostics of the strengths and weaknesses of each model enabling subjective forecast combination, may effect further improvements in forecast quality (Krishnamurti et al. 1999, 2000).”
Retrieval of Latent Heating from TRMM Measurements (2006, methodology): “…an effective technique for improving predictive skill in global weather prediction models (e.g., Bauer et al. 2002; Hou et al. 2000a,b, 2001, 2004; Krishnamurti et al. 2000a,b, 2001; Marecal et al. 2002), the use of explicit LH information for initialization and/or assimilation in global models…”