Using Analog Ensembles with Alternative Metrics for Hindcasting with Multistations
Abstract
This study concerns the problem of making weather predictions for a location where no data is available, using meteorological datasets from nearby stations. The hindcast with multiple stations is performed with different variants of the Analog Ensemble (AnEn) method. In addition to the traditional Monache metric used to identify analogs in datasets from one or two stations, several new metrics are explored, namely cosine similarity, normalization and k-means clustering. These were analyzed and benchmarked to find the ones that bring improvements. The best results were obtained with the k-means metric, yielding between 3% and 30% of lower quadratic error when compared against the Monache metric. Also, making the predictors include two stations improved the performance of the hindcast, leading up to 16% of lower error, depending on the correlation between the predictor stations.
Downloads
References
E. N. Lorenz, “Atmospheric Predictability as Revealed by Naturally Occurring Analogues,” Journal of the Atmospheric Sciences, vol. 26, no. 4, pp. 636–646, 1969, doi: 10.1175/1520-0469(1969)26<636:aparbn>2.0.co;2.
H. M. V. D. Dool, “A New Look at Weather Forecasting through Analogues,” Monthly Weather Review, vol. 117, no. 10, pp. 2230–2247, 1989, doi: 10.1175/1520-0493(1989)117<2230:anlawf>2.0.co;2.
H. M. V. D. Dool, “Searching for analogues, how long must we wait?” Tellus A: Dynamic Meteorology and Oceanography, vol. 46, no. 3, pp. 314–324, 1994, doi: 10.3402/tellusa.v46i3.15481.
L. D. Monache, F. A. Eckel, D. L. Rife, B. Nagarajan, and K. Searight, “Probabilistic Weather Prediction with an Analog Ensemble,” Monthly Weather Review, vol. 141, no. 10, pp. 3498–3516, 2013, doi: 10.1175/mwr-d-12-00281.1.
S. Alessandrini, L. D. Monache, S. Sperati, and J. N. Nissen, “A novel application of an analog ensemble for short-term wind power forecasting,” Renewable Energy, vol. 76, pp. 768–781, 2015, doi: 10.1016/j.renene.2014.11.061.
L. D. Monache, T. Nipen, Y. Liu, G. Roux, and R. Stull, “Kalman filter and analog schemes to postprocess numerical weather predictions,” Monthly Weather Review, vol. 139, no. 11, pp. 3554–3570, 2011, doi: 10.1175/2011mwr3653.1.
S. Alessandrini, L. D. Monache, S. Sperati, and G. Cervone, “An analog ensemble for short-term probabilistic solar power forecast,” Applied Energy, vol. 157, pp. 95–110, 2015, doi: 10.1016/j.apenergy.2015.08.011.
S. Alessandrini, L. D. Monache, C. M. Rozoff, and W. E. Lewis, “Probabilistic prediction of tropical cyclone intensity with an analog ensemble,” Monthly Weather Review, vol. 146, no. 6, pp. 1723–1744, 2018, doi: 10.1175/mwr-d-17-0314.1.
A. Chesneau, C. Balsa, C. V. Rodrigues, and I. M. Lopes, “Hindcasting with multistations using analog ensembles,” in CEUR workshop proceedings, 2019, vol. 2486, pp. 215–229.
E. Vanvyve, L. D. Monache, A. J. Monaghan, and J. O. Pinto, “Wind resource estimates with an analog ensemble approach,” Renewable Energy, vol. 74, pp. 761–773, 2015, doi: 10.1016/j.renene.2014.08.060.
National Center for Atmospheric Research. https://nar.ucar.edu
K. Adachi, Matrix-based introduction to multivariate data analysis. Springer Singapore, 2016.
J. M. Gutiérrez, A. S. Cofiño, R. Cano, and M. A. Rodrı́guez, “Clustering methods for statistical downscaling in short-range weather forecasts,” Monthly Weather Review, vol. 132, no. 9, pp. 2169–2183, 2004, doi: 10.1175/1520-0493(2004)132<2169:cmfsdi>2.0.co;2.
J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the fifth berkeley symposium on mathematical statistics and probability, 1967, vol. 1, pp. 281–297.
L. Eldén, Matrix methods in data mining and pattern recognition. Philadelphia, PA, USA: SIAM, 2007.
T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature,” Geoscientific Model Development, vol. 7, no. 3, pp. 1247–1250, 2014, doi: 10.5194/gmd-7-1247-2014.
National Data Buoy Center. https://www.ndbc.noaa.gov
R Core Team, R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; Vienna, Austria, 2017. http://www.r-project.org
K. E. Taylor, “Summarizing multiple aspects of model performance in a single diagram,” Journal of Geophysical Research: Atmospheres, vol. 106, no. D7, pp. 7183–7192, 2001, doi: 10.1029/2000jd900719.
Copyright (c) 2020 ParadigmPlus
This work is licensed under a Creative Commons Attribution 4.0 International License.
