Affiliation:
1. Department of Logistics, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
2. School for Climate Studies, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
Abstract
General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models’ output, to improve water management and long-term temperature and precipitations forecasting at local level.
Funder
Graduate School of Economics and Management at Stellenbosch University
Subject
Atmospheric Science,Environmental Science (miscellaneous)
Reference53 articles.
1. Interrogating empirical-statistical downscaling;Hewitson;Clim. Chang.,2013
2. VALUE: A framework to validate downscaling approaches for climate change studies;Maraun;Earth’s Future,2015
3. Downscaling general circulation model output: A review of methods and limitations;Wilby;Prog. Phys. Geogr.,1997
4. Madec, G., Delecluse, P., Imbard, M., and Levy, C. (1997). Note du Pôle de Modélisation, CNRS-Université Pierre et Marie Curie-Université Versailles-Saint-Quentin CEA-ORSTOM-Ecole Normale Supérieure-Ecole Polytechnique.
5. A thermosphere/ionosphere general circulation model with coupled electrodynamics;Richmond;Geophys. Res. Lett.,1992
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献