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Uncertainties in Weather Forecast – Reasons and Handling

  • Dirk Schüttemeyer
  • Clemens Simmer
Chapter

Abstract

The generation of precipitation forecasts by means of numerical weather prediction (NWP) models is increasingly becoming an important input for hydrological models. Over the past decades the quality and spatial resolution of meteorological numerical models has been drastically improved, which makes it now possible to incorporate high-resolution NWP output directly into flood forecasting systems. The quality of forecasted precipitation, however, is still close to insufficient because rainfall constitutes merely the very end of a complex of interlinked process chains acting at a broad range of spatial and temporal scales. Consequently the precipitation fields can vary significantly with time and space and inherit wide ranges of uncertainties. For the purpose of flood risk management it is of particular interest to investigate both the potential and implications of the related variations and uncertainties. For this endeavour the general background and current uncertainties in NWP as well as the handling of the uncertainties has to be taken into account. This chapter gives a brief introduction into the generation of weather forecasts with a particular focus on the accuracy of rainfall prediction. It includes in this context the relatively new field of ensemble forecasting and discusses ways to link numerical NWP with radar-based precipitation nowcasting.

Keywords

Probability Density Function Data Assimilation Numerical Weather Prediction Ensemble Forecast Numerical Weather Prediction Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We acknowledge large contributions to this text from the research proposal of the DAQUA PI-team (G. Craig, H. Elbern, D. Leuenberger, C. Simmer, W. Wergen) in the framework of the Priority Programme 1167 of the German Science Foundation (DFG).

References

  1. Alexander GD, Weinman JA, Schols JL (1998) The use of digital warping of microwave integrated water vapor imagery to improve forecasts of marine extratropical cyclones. Mon Weather Rev 126:1469–1496CrossRefGoogle Scholar
  2. Anderson JL (2001) An ensemble adjustment filter for data assimilation. Mon Weather Rev 129:2884–2903CrossRefGoogle Scholar
  3. Andersson E, Fisher M, Munro R, McNally A (2000) Diagnosis of background errors for radiances and other observable quantities in a variational data assimilation scheme, and the explanation of a case of poor convergence. Q J R Meteorol Soc 126:1455–1472CrossRefGoogle Scholar
  4. Andersson E, Haseler J, Undén P, Courtier P, Kelly G, Vasiljevic D, Brankovic C, Cardinali C, Gaffard C, Hollingsworth A, Jakob C, Janssen P, Klinker E, Lanzinger A, Miller M, Rabier F, Simmons A, Strauss B, Thépaut J-N, Viterbo P (1998) The ECMWF implementation of three dimensional variational assimilation (3D-Var). Part III: experimental results. Q J R Meteorol Soc 124:1831–1860CrossRefGoogle Scholar
  5. Anthes RA (1974) Data assimilation and initialization of hurricane prediction models. J Atmospheric Sci 31:702–719CrossRefGoogle Scholar
  6. Bartels H, Weigl E, Reich T, Lang P, Wagner A, Kohler O, Gerlach N (2004) Routineverfahren zur Online-Aneichung der Radarniederschlagsdaten mit Hilfe von automatischen Bodenniederschlagsstationen. – Abschlussbericht, Selbstverlag, Deutscher Wetterdienst OffenbachGoogle Scholar
  7. Bougeault P (2005) The ECMWF data assimilation and forecasting system. Presentation at the celebration “100 Jahre Meteorologisches Observatorium Lindenberg”, 12–16 Oct, Lindenberg, GermanyGoogle Scholar
  8. Bouttier F, Courtier P (1999) Data assimilation concepts and methods. Meteorological Training Course Lecture Series. ECMWF, ReadingGoogle Scholar
  9. Bright DR, Mullen SL (2002) Short-range ensemble forecasts of precipitation during the southwest monsoon. Weather Forecasting 17:1080–1100CrossRefGoogle Scholar
  10. Bryan GH, Wyngaard JC, Fritsch JM (2003) Resolution requirements for the simulation of deep moist convection. Mon Weather Rev 131:2394–2416CrossRefGoogle Scholar
  11. Buizza R (1997) Potential forecast skill of ensemble prediction and spread and skill distributions of the ECMWF ensemble prediction system. Mon Weather Rev 125:99–119CrossRefGoogle Scholar
  12. Cotton WR, Anthes RA (1989) Storm and cloud dynamics. Academic, San Diego, CA, 863 ppGoogle Scholar
  13. Courtier (1997) Dual formulation of four-dimensional variational assimilation. Q J R Meteorol Soc 123:2449–2462CrossRefGoogle Scholar
  14. Craig GC, Cohen BG, Plant RS (2005) Statistical mechanics and stochastic convective parameterisation. Workshop on Representation of sub-grid processes using stochastic dynamic models, ECMWF, Reading, EnglandGoogle Scholar
  15. Dee DP, da Silva A (2003) The choice of variable for atmospheric moisture analysis. Mon Weather Rev 131:155–171CrossRefGoogle Scholar
  16. Dirmeyer PA, Schlosser CA, Brubaker KL (2009) Precipitation, recycling, and land memory: an integrated analysis. J Hydrometeorol 10:278–288CrossRefGoogle Scholar
  17. Dixon M, Wiener G (1993) TITAN: thunderstorm identification, tracking, analysis, and nowcasting – a radar-based methodology. J Atmospheric Oceanic Technol 10(6):785–797CrossRefGoogle Scholar
  18. Douady D, Talagrand O (1990) The impact of threshold processes on variational assimilation. Preprints, international symposium on assimilation of observations in meteorology and oceanography, WMO, Clermont-Ferrand, pp 486–487Google Scholar
  19. Ebert EE (2008) Fuzzy verification of high resolution gridded forecasts: a review and proposed framework. Meteorol Appl 15:51–64CrossRefGoogle Scholar
  20. Ebisuzaki W, Kalnay E (1991) Ensemble experiments with a new lagged average forecasting scheme. WMO Report #15Google Scholar
  21. Ehrendorfer M (1994a) The Liouville Equation and its potential usefulness for the prediction of forecast skill. Part I: Theory. Mon Weather Rev 122:703–713CrossRefGoogle Scholar
  22. Ehrendorfer M (1994b) The Liouville equation and its potential usefulness for the prediction of forecast skill. Part II: Applications. Mon Weather Rev 122:714–728CrossRefGoogle Scholar
  23. Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. J Geophys Res 99(C5):10143–10162CrossRefGoogle Scholar
  24. Fillion L, Mahfouf JF (2003) Jacobians of an operational prognostic cloud scheme. Mon Weather Rev 131:2838–2856CrossRefGoogle Scholar
  25. Grasselt R, Schüttemeyer D, Warrach-Sagi K, Ament F, Simmer (2008) Validation of TERRA-ML with discharge measurements. Meteorol Z 17(6):763–773CrossRefGoogle Scholar
  26. Grell GA, Dévényi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Lett 29:1693CrossRefGoogle Scholar
  27. Grell GA, Dudhia J, Stauffer DR (1994) A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR/TN-398+STR, 122 ppGoogle Scholar
  28. Haase G, Crewell S, Simmer C, Wergen W (2000) Assimilation of radar data in mesoscale models: physical initialization and latent heat nudging. Phys Chem Earth 25:1237–1242Google Scholar
  29. Hoffman RN, Grassotti C, (1996) A technique for assimilating SSM/I observations of marine atmospheric storms: tests with ECMWF analyses. J Appl Meteorol 35:1177–1188CrossRefGoogle Scholar
  30. Hoffman RN, Kalnay E (1983) Lagged average forecasting, an alternative to monte-carlo forecasting. Tellus 35A:100–118CrossRefGoogle Scholar
  31. Hoffman RN, Liu Z, Louis JF, Grassotti C (1995) Distortion representation of forecast errors. Mon Weather Rev 123:2758–2770CrossRefGoogle Scholar
  32. Hoke JE, Anthes RA (1976) The initialization of numerical models by a dynamic-initialization technique. Mon Weather Rev 104(12):1551–1556CrossRefGoogle Scholar
  33. Hou DC, Kalnay E, Droegemeier KK (2001) Objective verification of the SAMEX '98 ensemble forecasts. Mon Weather Rev 129:73–91CrossRefGoogle Scholar
  34. Houtekamer PL, Mitchell HL (1998) Data assimilation using an ensemble Kalman filter technique. Mon Weather Rev 126:796–811CrossRefGoogle Scholar
  35. Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D 230:112–126CrossRefGoogle Scholar
  36. Jolliffe IT, Stephenson DB (2003) Forecast verification. A practitioner's GUIDE in atmospheric science. Wiley, New York, NY, 240 pp.Google Scholar
  37. Jones C, Macpherson B (1997) A latent heat nudging scheme for the assimilation of precipitation data into an operational mesoscale model. Meteorol Appl 4:269–277CrossRefGoogle Scholar
  38. Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, Cambridge, 341 pp.Google Scholar
  39. Krishnamurti TN, Bedi HS, Ingles K (1993) Physical initialization using SSM/I rain rates. Tellus 45A:247–269Google Scholar
  40. Krishnamurti TN, Correa-Torres R, Rohaly G, Oosterhof D, Surgi N (1997) Physical initialization and hurricane ensemble forecasts. Weather Forecasting, 12:503–514CrossRefGoogle Scholar
  41. Krishnamurti TN, Kishtawal CM, LaRow TE, Bachiochi DR, Zhang Z, Williford CE, Gadgil S, Surendran S (1999) Improved weather and seasonal climate forecasts from multimodel superensemble. Science 285:1548–1550CrossRefGoogle Scholar
  42. Krishnamurti TN, Xue J, Bedi HS, Ingles K, Oosterhof D (1991) Physical initialization for numerical weather prediction over the tropics. Tellus 43A:53–81Google Scholar
  43. Lang P, Plörer O, Munier H, Riedl J (2003) KONRAD – Ein operationelles Verfahren zur Analyse von Gewitterzellen und deren Zugbahnen, basierend auf Wetterradarprodukten. Berichte des Deutschen Wetterdienstes 222. Technical Report. DeutscherWetterdienst: OffenbachGoogle Scholar
  44. Leith CE (1974) Theoretical skill of Monte-Carlo forecasts. Mon Weather Rev 102:409–418.CrossRefGoogle Scholar
  45. Lorenz EN (1963) Deterministic nonperiodic flow. J Atmospheric Sci 20:130–141CrossRefGoogle Scholar
  46. Macpherson B (2001) Operational experience with assimilation of rainfall data in the Met Office Mesoscale model. Meteorol Atmospheric Phys 76:3–8CrossRefGoogle Scholar
  47. Manobianco J, Koch S, Karyampudi V, Negri A (1994) The impact of assimilating satellite-derived precipitation rates on numerical simulations of the ERICA IOP 4 cyclone. Mon Weather Rev 122:341–365CrossRefGoogle Scholar
  48. Marécal V, Mahfouf J-F (2002) Four-dimensional variational assimilation of total column water vapour in rainy areas. Mon Weather Rev 130:43–58CrossRefGoogle Scholar
  49. Mass CF, Ovens D, Westrick K, Colle BA (2002) Does increasing horizontal resolution produce more skillful forecasts? Bull Am Meteorol Soc 83:407–430CrossRefGoogle Scholar
  50. Mehrkorn Th, Hoffman RA, Grasotti Ch, Louis J-F (2003) Feature calibration and alignment to represent model forecast errors: empirical regularization. Q J R Meteorol Soc 129:195–218CrossRefGoogle Scholar
  51. Milan M, Venema V, Schüttemeyer D, Simmer C (2008) Assimilation of radar and satellite data in mesoscale models: a physical initialisation scheme. Meteorol Z 17(6):887–902CrossRefGoogle Scholar
  52. Molteni F, Buizza R, Marsigli C, Montani A, Nerozzi F, Paccagnella T (2001) A strategy for high-resolution ensemble prediction. I: definition of representative members and global-model experiments. Q J R Meteorol Soc 127:2069–2094CrossRefGoogle Scholar
  53. Molteni F, Buizza R, Palmer TN, Petroliagis T (1996) The ECMWF ensemble prediction system: methodology and validation. Q J R Meteorol Soc 122:73–119CrossRefGoogle Scholar
  54. Murphy AH (1991) Probabilities, odds, and forecasts of rare events. Weather Forecasting 6:302–307CrossRefGoogle Scholar
  55. Murphy AH, Winkler RL (1987) A general framework for forecast verification. Mon Weather Rev 115:1330–1338CrossRefGoogle Scholar
  56. Ott E, Hunt BR, Szunyogh I, Zimin AV, Kostelich EJ, Corazza M, Kalnay E, Patil DJ, Yorke JA (2004) A local ensemble Kalman Filter for atmospheric data assimilation. Tellus 56A:415–428Google Scholar
  57. Park SK (1999) Nonlinearity and predictability of convective rainfall associated with water vapor perturbations in a numerically simulated storm. J Geophys Res Atmos 104(D24):31575–31587Google Scholar
  58. Park SK, Droegemeier KK (1997) Validity of the tangent linear approximation in a moist convective cloud model. Mon Weather Rev 125:3320–3340CrossRefGoogle Scholar
  59. Park SK, Droegemeier KK (1999) Sensitivity analysis of a moist 1-D Eulerian cloud model using automatic differentiation. Mon Weather Rev 127:2128–2142CrossRefGoogle Scholar
  60. Park SK, Droegemeier KK (2000) Sensitivity analysis of a 3D convective storm: implications for variational data assimilation and forecast error. Mon Weather Rev 128:140–159CrossRefGoogle Scholar
  61. Pierce CE, Hardaker PJ, Collier CG, Haggett CM (2000) GANDOLF: a system for generating automated nowcasts of convective precipitation. Meteorol Appl 7:341–360CrossRefGoogle Scholar
  62. Rinehart RE, Garvey ET (1978) Three-dimensional storm motion detection by conventional weather radar. Nature 273:287–289CrossRefGoogle Scholar
  63. Shin DW, Krishnamurti TN (2003) Short- to medium-range superensemble precipitation forecasts using satellite products: 1. Deterministic forecasting. J Geophys Res 108(D6):8383CrossRefGoogle Scholar
  64. Simmons AJ, Untch A, Jakob C, Kallberg P, Unden P (1999) Stratospheric water vapour and tropical tropopause temperatures in ECMWF analyses and multi-year simulations. Q J R Meteorol Soc 125:353–386CrossRefGoogle Scholar
  65. Stanski H, Wilson LJ, Burrows WR (1989) Survey of common verification methods in meteorology. WMO World Weather Watch Technical Report 8, 114 pp.Google Scholar
  66. Stensrud DJ, Bao JW, Warner TT (2000) Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon Weather Rev 128:2077–2107CrossRefGoogle Scholar
  67. Sun J, Crook NA (1997) Dynamical and microphysical retrieval from doppler radar observations using a cloud model and its adjoint. part 1: model development and simulated data experiments. J Atmospheric Sci 54:1642–1661CrossRefGoogle Scholar
  68. Szunyogh I, Kostelich EJ, Gyarmati G, Kalnay E, Hunt BR, Ott E, Satterfield E, Yorke JA (2008) A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus 60A:113–130Google Scholar
  69. Talagrand O, Vautard R, Strauss B (1997) Evaluation of probabilistic prediction systems, 1999. Proceedings of the ECMWF workshop on predictability, 20–22 October 1997, Reading, pp 372Google Scholar
  70. Theis S (2005) Deriving probabilistic short-range forecasts from a deterministic high-resolution model. PhD-Thesis, Bonn University, BonnGoogle Scholar
  71. Tibaldi S, Paccagnella T, Marsigli C, Montani A, Nerozzi F (2003) Short-to-medium range limited area ensemble prediction: the LEPS system, Quaderno Tecnico ARPA-SMR, 13/2003Google Scholar
  72. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Weather Rev 117(8):1779–1800CrossRefGoogle Scholar
  73. Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Mon Weather Rev 125:3297–3319CrossRefGoogle Scholar
  74. Toth Z, Talagrand O, Candille G, Zhu Y (2003) Probability and ensemble forecasts. In: Jolliffe IT, Stephenson DB (eds) Forecast verification: a practitioner’s guide in atmospheric science. Wiley, West Sussex, pp 137–163Google Scholar
  75. Van Leeuwen PJ (2001) An ensemble smoother with error estimates. Mon Weather Rev 129:709–728CrossRefGoogle Scholar
  76. van Leeuwen PJ (2009) Particle filtering in geophysical systems. Mon Weather Rev 137:4089–4114CrossRefGoogle Scholar
  77. Verlinde J, Cotton WR (1993) Fitting microphysical observations of nonsteady convective clouds to a numerical model: an application of the adjoint technique of data assimilation to a kinematic model. Mon Wea Rev 121:2776–2793CrossRefGoogle Scholar
  78. Wilhelmson RB, Wicker LJ (2001) Severe storm modeling. Severe convective storms. Meteorol Monogr 28(50), Am Meteorol Soc:123–166CrossRefGoogle Scholar
  79. Wilks DS (1995) Statistical methods in the atmospheric sciences. Academic, San Diego, CA, 467 ppGoogle Scholar
  80. Wilson JW, Schreiber WE (1986) Initiation of convective storms at radar-observed boundary-layer convergence lines. Mon Weather Rev 114:2516–2536CrossRefGoogle Scholar
  81. Wulfmeyer V, Behrendt A, Bauer H-S, Kottmeier C, Corsmeier U, Blyth A, Craig G, Schumann U, Hagen M, Crewell S, Di Girolamo P, Flamant C, Miller M, Montani A, Mobbs S, Richard E, Rotach MW, Arpagaus M, Russchenberg H, Schlüssel P, König M, Gärtner V, Steinacker R, Dorninger M, Turner DD, Weckwerth T, Hense A, Simmer C (2008) The convective and orographically-induced precipitation study: a research and development project of the world weather research program for improving quantitative precipitation forecasting in low-mountain regions. Bull Am Meteorol Soc 89(10) 1477–1486CrossRefGoogle Scholar
  82. Zupanski M, Zupanski D, Parrish D, Rogers E, DiMego G (2002) Four-dimensional variational data assimilation for the Blizzard of 2000. Mon Weather Rev 130:1967–1988CrossRefGoogle Scholar
  83. Zus F, Grzeschik M, Bauer H-S, Wulfmeyer V, Dick G, Bender M (2008) Development and optimization of the IPM MM5 GPS slant path 4DVAR system. Meteorol Z 17:867–885CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Meteorological InstituteUniversity of BonnBonnGermany

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