Articles | Volume 13, issue 3
https://doi.org/10.5194/esd-13-1157-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-13-1157-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
Riccardo Silini
CORRESPONDING AUTHOR
Departament de Física, Universitat Politècnica de Catalunya, Sant Nebridi 22, 08222 Terrassa, Barcelona, Spain
Sebastian Lerch
Institute of Economics, Karlsruhe Institute of Technology, Blücherstr. 17, 76185 Karlsruhe, Germany
Nikolaos Mastrantonas
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Interdisciplinary Environmental Research Centre, Technische Universität Bergakademie Freiberg (TUBAF), Freiberg, Germany
Holger Kantz
Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
Marcelo Barreiro
Departamento de Ciencias de la Atmósfera, Facultad de Ciencias, Universidad de la República, Igua 4225, 11400 Montevideo, Uruguay
Cristina Masoller
Departament de Física, Universitat Politècnica de Catalunya, Sant Nebridi 22, 08222 Terrassa, Barcelona, Spain
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Weather forecasts 14 days in advance generally have a low skill but not always. We identify reasons thereof depending on the atmospheric flow, shown by Weather Regimes (WRs). If the WRs during the forecasts follow climatological patterns, forecast skill is increased. The forecast of a cold-wave day is better when the European Blocking WR (high pressure around the British Isles) is present a few days before a cold-wave day. These results can be used to assess the reliability of predictions.
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
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A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Imre M. Jánosi, Holger Kantz, Jason A. C. Gallas, and Miklós Vincze
Ocean Sci., 18, 1361–1375, https://doi.org/10.5194/os-18-1361-2022, https://doi.org/10.5194/os-18-1361-2022, 2022
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Surface flow fields of the global oceans are dominated by so-called mesoscale (50–300 km) eddies. They usually drift westward at a few kilometers per day, transporting mass, temperature, chlorophyll, and debris. There are several methods to identify and track eddies based on satellite measurements, some of them very computationally demanding. Here we extend a recently proposed simple procedure to the global scale, which gives quick coarse-grained statistics on mesoscale vortex properties.
Hynek Bednář and Holger Kantz
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A scale-dependent error growth described by a power law or by a quadratic hypothesis is studied in Lorenz’s system with three spatiotemporal levels. The validity of power law is extended by including a saturation effect. The quadratic hypothesis can only serve as a first guess. In addition, we study the initial error growth for the ECMWF forecast system. Fitting the parameters, we conclude that there is an intrinsic limit of predictability after 22 days.
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Spectacular climatic phenomena such as El Nino—La Nina oscillations are connected with large-scale rearrangements of oceanic surface flow patterns. In order to get a better insight into the dynamics of such changes, we performed numerical experiments on the advection of 6600 water parcels in the focal area. Surface flow fields were taken from the AVISO data bank. A simple stochastic model (fractional Brownian motion) with only two parameters nicely reproduced the statistics of advection.
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Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado,
G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp,
A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M.,
Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C.,
Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P.,
Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P.,
Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale
Machine Learning on Heterogeneous Systems, http://tensorflow.org/ (last access: 15 July 2022), 2015. a
Alvarez, M. S., Vera, C. S., and Kiladis, G. N.: MJO Modulating the Activity of the Leading Mode of Intraseasonal Variability in South America, Atmosphere, 8, 232, https://doi.org/10.3390/atmos8120232, 2017. a
Barrett, B. S., Densmore, C. R., Ray, P., and Sanabia, E. R.: Active and
weakening MJO events in the Maritime Continent, Clim. Dynam., 57, 157–172, https://doi.org/10.1007/s00382-021-05699-8, 2021. a
Bergman, J. W., Hendon, H. H., and Weickmann, K. M.: Intraseasonal Air–Sea
Interactions at the Onset of El Niño, J. Climate, 14, 1702–1719, 2001. a
Camargo, S. J., Wheeler, M. C., and Sobel, A. H.: Diagnosis of the MJO
Modulation of Tropical Cyclogenesis Using an Empirical Index, J. Atmos. Sci., 66, 3061–3074, https://doi.org/10.1175/2009JAS3101.1, 2009. a
Dasgupta, P., Metya, A., Naidu, C. V., Singh, M., and Roxy, M. K.: Exploring
the long-term changes in the Madden Julian Oscillation using machine learning, Scient. Rep., 10, 18567, https://doi.org/10.1038/s41598-020-75508-5, 2020. a
Díaz, N., Barreiro, M., and Rubido, N.: Intraseasonal Predictions for the
South American Rainfall Dipole, Geophys. Res. Lett., 47, e2020GL089985, https://doi.org/10.1029/2020GL089985, 2020. a
Dijkstra, H. A., Petersik, P., Hernández-García, E., and López, C.: The Application of Machine Learning Techniques to Improve El Niño Prediction Skill, Front. Phys., 7, 153, https://doi.org/10.3389/fphy.2019.00153, 2019. a
Ferranti, L., Magnusson, L., Vitart, F., and Richardson, D. S.: How far in
advance can we predict changes in large-scale flow leading to severe cold
conditions over Europe?, Q. J. Roy. Meteorol. Soc., 144, 1788–1802, https://doi.org/10.1002/qj.3341, 2018. a
Fowler, M. D. and Pritchard, M. S.: Regional MJO Modulation of Northwest
Pacific Tropical Cyclones Driven by Multiple Transient Controls, Geophys. Res. Lett., 47, e2020GL087148, https://doi.org/10.1029/2020GL087148, 2020. a
Gagne II, D. J., Christensen, H. M., Subramanian, A. C., and Monahan, A. H.:
Machine Learning for Stochastic Parameterization: Generative Adversarial
Networks in the Lorenz'96 Model, J. Adv. Model. Earth Syst., 12, e2019MS001896, https://doi.org/10.1029/2019MS001896, 2020. a
Granger, C. W. .: Investigating Causal Relations by Econometric Models and
Cross-spectral Methods, Econometrica, 37, 424–459, 1969. a
Ham, Y.-G., Kim, J.-H., and Luo, J.-J.: Deep learning for multi-year ENSO
forecasts, Nature, 573, 568–572, https://doi.org/10.1038/s41586-019-1559-7, 2019. a
Haupt, S. E., Chapman, W., Adams, S. V., Kirkwood, C., Hosking, J. S.,
Robinson, N. H., Lerch, S., and Subramanian, A. C.: Towards implementing
artificial intelligence post-processing in weather and climate: proposed
actions from the Oxford 2019 workshop, Philos. T. Roy. Soc. A, 379, 20200091, https://doi.org/10.1098/rsta.2020.0091, 2021. a, b
Jiang, X., Adames, A. F., Kim, D., Maloney, E. D., Lin, H., Kim, H., Zhang, C., DeMott, C. A., and Klingaman, N. P.: Fifty Years of Research on the
Madden–Julian Oscillation: Recent Progress, Challenges, and Perspectives, J. Geophys. Res.-Atmos., 125, e2019JD030911, https://doi.org/10.1029/2019JD030911, 2020. a, b, c, d, e
Kim, H., Vitart, F., and Waliser, D. E.: Prediction of the Madden–Julian
Oscillation: A Review, J. Climate, 31, 9425–9443, https://doi.org/10.1175/JCLI-D-18-0210.1, 2018. a
Kim, H.-M., Webster, P. J., Toma, V. E., and Kim, D.: Predictability and
Prediction Skill of the MJO in Two Operational Forecasting Systems, J. Climate, 27, 5364–5378, https://doi.org/10.1175/JCLI-D-13-00480.1, 2014. a
Kim, H.-M., Kim, D., Vitart, F., Toma, V. E., Kug, J.-S., and Webster, P. J.:
MJO Propagation across the Maritime Continent in the ECMWF Ensemble Prediction System, J.Climate, 29, 3973–3988, https://doi.org/10.1175/JCLI-D-15-0862.1, 2016. a
Klotzbach, P. J.: On the Madden–Julian Oscillation–Atlantic Hurricane
Relationship, J. Climate, 23, 282–293, https://doi.org/10.1175/2009JCLI2978.1, 2010. a
Lau, W. K. M. and Waliser, D. E.: Predictability and forecasting, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-13914-7_12, 2011. a
Lin, H., Brunet, G., and Derome, J.: Forecast Skill of the Madden–Julian
Oscillation in Two Canadian Atmospheric Models, Mon. Weather Rev., 136, 4130–4149, https://doi.org/10.1175/2008MWR2459.1, 2008. a
Madden, R. A. and Julian, P. R.: Detection of a 40–50 Day Oscillation in the
Zonal Wind in the Tropical Pacific, J. Atmos. Sci., 28, 702 –708,
https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2, 1971. a
Madden, R. A. and Julian, P. R.: Description of Global-Scale Circulation Cells in the Tropics with a 40–50 Day Period, J. Atmos. Sci., 29, 1109–1123, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2, 1972. a
Martin, Z. K., Barnes, E. A., and Maloney, E. D.: Using simple, explainable
neural networks to predict the Madden-Julian oscillation, Earth and Space
Science Open Archive, https://doi.org/10.1002/essoar.10507439.1, 2021a. a
Martin, Z. K., Son, S.-W., Butler, A., Hendon, H., Kim, H., Sobel, A., Yoden,
S., and Zhang, C.: The influence of the quasi-biennial oscillation on the
Madden-Julian oscillation, Nature Rev. Earth Environ., 2, 477–489, https://doi.org/10.1038/s43017-021-00173-9, 2021b. a
McGovern, A., Lagerquist II, R. D. J. G., Jergensen, G. E., Elmore, K. L.,
Homeyer, C. R., and Smith, T.: Making the Black Box More Transparent:
Understanding the Physical Implications of Machine Learning, B. Am. Meteorol. Soc., 100, 2175–2199, https://doi.org/10.1175/BAMS-D-18-0195.1, 2019. a
Nooteboom, P. D., Feng, Q. Y., López, C., Hernández-García, E., and Dijkstra, H. A.: Using network theory and machine learning to predict El Niño, Earth Syst. Dynam., 9, 969–983, https://doi.org/10.5194/esd-9-969-2018, 2018. a
O'Gorman, P. A. and Dwyer, J. G.: Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events, J. Adv. Model. Earth Syst., 10, 2548–2563,
https://doi.org/10.1029/2018MS001351, 2018. a
Paluš, M. and Vejmelka, M.: Directionality of coupling from bivariate time series: How to avoid false causalities and missed connections, Phys.
Rev. E, 75, 056211, https://doi.org/10.1103/PhysRevE.75.056211, 2007. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Rashid, H. A., Hendon, H. H., Wheeler, M. C., and Alves, O.: Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system,
Clim. Dynam., 36, 649–661, https://doi.org/10.1007/s00382-010-0754-x, 2011. a, b, c, d
Rasp, S. and Lerch, S.: Neural networks for postprocessing ensemble weather
forecasts, Mon. Weather Rev., 146, 3885–3900, https://doi.org/10.1175/MWR-D-18-0187.1, 2018. a, b, c
Scheuerer, M., Switanek, M. B., Worsnop, R. P., and Hamill, T. M.: Using
Artificial Neural Networks for Generating Probabilistic Subseasonal
Precipitation Forecasts over California, Mon. Weather Rev., 148, 3489–3506, https://doi.org/10.1175/MWR-D-20-0096.1, 2020. a
Schreiber, T.: Measuring Information Transfer, Phys. Rev. Lett., 85, 461–464, 2000. a
Silini, R.: MJO post-processing artificial neural networks, Zenodo [code], https://doi.org/10.5281/zenodo.5801453, 2021a. a
Silini, R.: Wheeler–Hendon phase diagrams, Zenodo [video supplement], https://doi.org/10.5281/zenodo.5801415, 2021b. a
Silini, R. and Masoller, C.: Fast and effective pseudo transfer entropy for
bivariate data-driven causal inference, Scient. Rep., 11, 1–13, 2021. a
Silini, R., Barreiro, M., and Masoller, C.: Machine learning prediction of the Madden-Julian Oscillation, npj Clim. Atmos. Sci., 4, 57, https://doi.org/10.1038/s41612-021-00214-6, 2021. a, b
Silini, R., Tirabassi, G., Barreiro, M., Ferranti, L., and Masoller, C.:
Assessing causal dependencies in climatic indices, Clim. Dynam., in review, 2022. a
Sugihara, G., May, R., Ye, H., Hsieh, C. H., Deyle, E., Fogarty, M., and Munch, S.: Detecting causality in complex ecosystems, Science, 338, 496–500,
https://doi.org/10.1126/science.1227079, 2012.
a
Taraphdar, S., Zhang, F., Leung, L. R., Chen, X., and Pauluis, O. M.: MJO
affects the Monsoon Onset Timing Over the Indian Region, Geophys. Res. Lett., 45, 10011–10018, https://doi.org/10.1029/2018GL078804, 2018. a
Tseng, K.-C., Barnes, E. A., and Maloney, E.: The Importance of Past MJO
Activity in Determining the Future State of the Midlatitude Circulation, J. Climate, 33, 2131–2147, https://doi.org/10.1175/JCLI-D-19-0512.1, 2020. a
Ungerovich, M., Barreiro, M., and Masoller, C.: Influence of Madden–Julian
Oscillation on extreme rainfall events in Spring in southern Uruguay, Int. J. Climatol., 41, 3339–3351, https://doi.org/10.1002/joc.7022, 2021. a
Vannitsem, S., Bremnes, J. B., Demaeyer, J., Evans, G. R., Flowerdew, J.,
Hemri, S., Lerch, S., Roberts, N., Theis, S., Atencia, A., Bouallègue,
Z. B., Bhend, J., Dabernig, M., Cruz, L D., Hieta, L., Mestre, O., Moret,
L., Plenković, I., Schmeits, M., Taillardat, M., den Bergh, J. V.,
Schaeybroeck, B. V., Whan, K., and Ylhaisi, J.: Statistical Postprocessing
for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World, B. Am. Meteorol. Soc., 102, E681–E699, https://doi.org/10.1175/BAMS-D-19-0308.1, 2021. a, b, c
Vitart, F.: Impact of the Madden Julian Oscillation on tropical storms and risk of landfall in the ECMWF forecast system, Geophys. Res. Lett., 36,
L15802, https://doi.org/10.1029/2009GL039089, 2009. a
Wang, S., Tippett, M. K., Sobel, A. H., Martin, Z. K., and Vitart, F.: Impact
of the QBO on Prediction and Predictability of the MJO Convection, J. Geophys. Res.-Atmos., 124, 11766–11782, https://doi.org/10.1029/2019JD030575, 2019. a
Wheeler, M. C. and Hendon, H. H.: An All-Season Real-Time Multivariate MJO
Index: Development of an Index for Monitoring and Prediction, Mon. Weather Rev., 132, 1917–1932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2, 2004. a
Wheeler, M. C., Hendon, H. H., Cleland, S., Meinke, H., and Donald, A.: Impacts of the Madden-Julian Oscillation on Australian Rainfall and Circulation, J. Climate, 22, 1482–1498, https://doi.org/10.1175/2008JCLI2595.1, 2009. a
Wu, C.-H. and Hsu, H.-H.: Topographic Influence on the MJO in hte Maritime
Continent, J. Climate, 22, 5433–5448, https://doi.org/10.1175/2009JCLI2825.1, 2009. a
Wu, J. and Jin, F.-F.: Improving the MJO Forecast of S2S Operation Models by
Correcting Their Biases in Linear Dynamics, Geophys. Res. Lett., 48, e2020GL091930, https://doi.org/10.1029/2020GL091930, 2021. a
Zhang, C., Gottschalck, J., Maloney, E. D., Moncrieff, M. W., Vitart, F.,
Waliser, D. E., Wang, B., and Wheeler, M. C.: Cracking the MJO nut, Geophys. Res. Lett., 40, 1223–1230, https://doi.org/10.1002/grl.50244, 2013. a, b
Short summary
The Madden–Julian Oscillation (MJO) has important socioeconomic impacts due to its influence on both tropical and extratropical weather extremes. In this study, we use machine learning (ML) to correct the predictions of the weather model holding the best performance, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We show that the ML post-processing leads to an improved prediction of the MJO geographical location and intensity.
The Madden–Julian Oscillation (MJO) has important socioeconomic impacts due to its influence on...
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