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Challenges in Tropical Numerical Weather Prediction at ECMWF

  • Peter BechtoldEmail author
Chapter
Part of the Springer Atmospheric Sciences book series (SPRINGERATMO)

Abstract

We describe the challenges in the coming decade in global numerical weather prediction and in the tropics in particular. The ECMWF forecasting system is our benchmark. These challenges comprise four main areas of developments: making optimal use of the available observational data to obtain the best analysis, advanced ensemble methods to predict the uncertainties in the analyses and forecasts, model developments to better represent shallow and deep convection and associated circulations and finally necessary advances in computational efficiency called scalability.

Keywords

Numerical weather prediction Tropical convection Observation feedback 

References

  1. Arakawa, A., and C.-M. Wu. 2013. A unified representation of deep moist convection in numerical modelling of the atmosphere. Part I. Journal of the Atmospheric Sciences 70: 1977–1992.CrossRefGoogle Scholar
  2. Bauer, P., A. Thorpe, and G. Brunet. 2015. The quiet revolution of numerical weather prediction. Nature 525: 47–55.CrossRefGoogle Scholar
  3. Bauer, P., A.J. Geer, P. Lopez, and D. Salmond. 2010. Direct 4D-Var assimilation of all-sky radiances: Part I. Implementation. Quarterly Journal of the Royal Meteorological Society 136: 1868–1885.CrossRefGoogle Scholar
  4. Bechtold, P., M. Köhler, T. Jung, M. Leutbecher, M. Rodwell, F. Vitart, and G. Balsamo. 2008. Advances in predicting atmospheric variability with the ECMWF model, 2008: From synoptic to decadal time-scales. Quarterly Journal of the Royal Meteorological Society 134: 1337–1351.CrossRefGoogle Scholar
  5. Bechtold, P., N. Semane, P. Lopez, J.-P. Chaboureau, A. Beljaars, and N. Bormann. 2014. Representing equilibrium and non-equilibrium convection in large-scale models. Journal of the Atmospheric Sciences 71: 734–753.CrossRefGoogle Scholar
  6. Benedict, J.J., and D.A. Randall. 2007. Observed characteristics of the MJO relative to maximum rainfall. Journal of the Atmospheric Sciences 64: 2332–2354.CrossRefGoogle Scholar
  7. Birch, C.E., D.J. Parker, J.H. Marsham, D. Copsey, and L. Garcia-Carrera. 2014. A seamless assessment of the role of convection in the water cycle of the West African monsoon. Journal Geophysical Research 119: 2890–2912.Google Scholar
  8. Blake, B.J., D.B. Parsons, K.R. Haghi, and S.G. Castleberry. 2017. The structure, evolution and dynamics of a nocturnal convective system simulated using the WRF-ARW model. Monthly Weather Review (early online release).CrossRefGoogle Scholar
  9. Forbes, R., A. Geer, K. Lonitz, and M. Ahlgrimm. 2016. Reducing systematic errors in cold-air outbreaks. ECMWF Newsletter 146: 17–22.Google Scholar
  10. Geer, A.J., P. Bauer, and C.W. O’Dell. 2009. A revised cloud overlap scheme for fast microwave radiative transfer in rain and cloud. Journal of Applied Meteorology and Climatology 48: 2257–2270.CrossRefGoogle Scholar
  11. Gerard, L. 2015. Bulk mass-flux perturbation formulation for a unified approach of deep convection at high resolution. Monthly Weather Review 143: 4038–4063.CrossRefGoogle Scholar
  12. Grell, G.A., and S.R. Freitas. 2014. A scale and aerosol aware stochastic convective parameterization for weather and air quality modelling. Atmospheric Chemistry and Physics 14: 5233–5250.CrossRefGoogle Scholar
  13. Hagos, S.M., C. Zhang, Z. Feng, C.D. Burleyson, C. De Mott, B. Kerns, J.J. Benedict, and M.N. Martini. 2016. The impact of the diurnal cycle on the propagation of Madden-Julian oscillation convection across the maritime continent. Journal of Advances in Modeling Earth Systems 8: 1552–1564.  https://doi.org/10.1002/2016MS000725.CrossRefGoogle Scholar
  14. Herman, M.J., Ž. Fuchs, D. Raymond, and P. Bechtold. 2016. Convectively coupled Kelvin waves: From linear theory to global models. Journal of the Atmospheric Sciences 73: 407–428.CrossRefGoogle Scholar
  15. Hirons, L.C., P. Inness, F. Vitart, and P. Bechtold. 2013. Understanding advances in the simulation of intraseasonal variability in the ECMWF model. Part II: The application of process-based diagnostics. Quarterly Journal of the Royal Meteorological Society 139: 1427–1444.  https://doi.org/10.1002/qj.2059.CrossRefGoogle Scholar
  16. Hu, Y., S. Rodier, K.-M. Xu, W. Sun, J. Huang, B. Lin, P. Zhai, and D. Josset. 2010. Occurrence, liquid water content and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements. Journal of Geophysical Research 115 (D4): D00H34.Google Scholar
  17. Kalmus, P., M. Lebsock, and J. Teixeira. 2014. Observational boundary layer energy and water budgets of the stratocumulus-to-cumulus transition. Journal of Climate 27: 9155–9170.CrossRefGoogle Scholar
  18. Kessler, E. 1969. On the distribution and continuity of water substance in atmospheric circulation. In Meteorological monographs, vol. 10. Boston, MA: American Meteorological Society.CrossRefGoogle Scholar
  19. Kwon, Y.C., and S.-Y. Hong. 2017. A mass flux parameterization scheme across gray-zone resolutions. Monthly Weather Review 145: 583–598.CrossRefGoogle Scholar
  20. Laing, A.G., S.B. Trier, and C.A. Davis. 2012. Numerical simulation of episodes of organized convection in tropical northern Africa. Monthly Weather Review 140: 2874–2885.CrossRefGoogle Scholar
  21. Leutbecher, M., and T. Palmer. 2008. Ensemble forecasting. Journal of Computational Physics 227 (7): 3515–3539.CrossRefGoogle Scholar
  22. Lewis, E.R. 2016. Marine ARM GPCI investigation of clouds (MAGIC) field campaign report. In DOE ARM climate research facility, ed. R. Stafford. DOE/SC-ARM-16-057.Google Scholar
  23. Li, J.-L.F., D.E. Walliser, G. Stephens, S. Lee, T. L’Ecuyer, S. Kato, N. Loeb, and H.-Y. Ma. 2013. Characterizing and understanding radiation budget biases in CMIP3/CMIP5 GCMs, contemporary GCM, and reanalysis. Journal Geophysical Research 118: 8166–8184.  https://doi.org/10.1002/jgrd.50378.CrossRefGoogle Scholar
  24. Loeb, N.G., B.A. Wielicki, D.R. Doelling, G.L. Smith, D.F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong. 2009. Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. Journal of Climate 22: 748–766.CrossRefGoogle Scholar
  25. Ollinaho, P., S.-J. Lock, M. Leutbecher, P. Bechtold, A. Beljaars, A. Bozzo, R. Forbes, T. Haiden, R. Hogan, and I. Sandu. 2017. Towards process-level representation of model uncertainties: Stochastically perturbed parametrisations in the ECMWF ensemble. Quarterly Journal of the Royal Meteorological Society 143: 408–422.CrossRefGoogle Scholar
  26. Rabier, F., H. Järvinen, E. Klinker, J.-F. Mahfouf, and A. Simmons. 2000. The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Quarterly Journal of the Royal Meteorological Society 126: 1143–1170.CrossRefGoogle Scholar
  27. Schlemmer, L., P. Bechtold, I. Sandu, and M. Ahlgrimm. 2017. Uncertainties related to the representation of momentum transport in shallow convection. Journal of Advances in Modeling Earth Systems.Google Scholar
  28. Vitart, F., and F. Molteni. 2010. Simulation of the MJO and its teleconnections in the ECMWF forecast system. Quarterly Journal of the Royal Meteorological Society 136: 842–855.CrossRefGoogle Scholar
  29. Žagar, N. 2017. A global perspective of the limits of prediction skill of NWP models. Tellus A (early online release).  https://doi.org/10.1080/16000870.2017.1317573.CrossRefGoogle Scholar
  30. Žagar, N., E. Andersson, and M. Fisher. 2005. Balanced tropical data assimilation based on a study of equatorial waves in ECMWF short-range forecast errors. Quarterly Journal Royal Meteorological Society 131: 987–1011.CrossRefGoogle Scholar
  31. Zhang, G., K.H. Cook, and E.K. Vizy. 2016. The diurnal cycle of warm season rainfall over West Africa. Part II: Convection-permitting simulations. Journal of Climate 29: 8439–8454.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.ECMWFShinfield Park, ReadingUK

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