Convection-permitting models: A step-change in rainfall forecasting

Meteorological Applications

Volumn 23, Issue 2, 2016, Pages 165-181

  Clark, Peter ^a   Roberts, Nigel ^a   Lean, Humphrey ^a   Ballard, Susan P ^a   Charlton Perez, Cristina ^a  

^a UNIVERSITY OF READING   (United Kingdom)

Author keywords

Convection permitting models; Convective ensembles; Explicit convection; High resolution data assimilation; High resolution NWP; Rainfall forecasts forecasting; Resolved processes

Indexed keywords

NATURAL CONVECTION; RAIN;

CONVECTIVE ENSEMBLES; DATA ASSIMILATION TECHNIQUES; FORECAST PROBABILITIES; HIGH-RESOLUTION DATA ASSIMILATIONS; HIGH-RESOLUTION NWP; HISTORICAL DEVELOPMENT; NUMERICAL WEATHER PREDICTION; RAINFALL FORECASTS;

WEATHER FORECASTING;

CLIMATE MODELING; CONVECTIVE SYSTEM; DATA ASSIMILATION; NOWCASTING; PRECIPITATION INTENSITY; RAINFALL; WEATHER FORECASTING; WEATHER STATION;

UNITED KINGDOM;

EID: 84962788280     PISSN: 13504827     EISSN: 14698080     Source Type: Journal    
DOI: 10.1002/met.1538     Document Type: Review

References (124)

1. Anthes RA, Warner TT. 1978. Development of hydrodynamic models suitable for air pollution and other mesometeorological studies. Mon. Weather Rev. 106: 1045-1078.
2. Arakawa A, Schubert WH. 1974. Interaction of a cumulus cloud ensemble with large-scale environment, part 1. J. Atmos. Sci. 31: 674-701.
3. Arakawa A, Wu C-M. 2013. A unified representation of deep moist convection in numerical modelling of the atmosphere-part 1. J. Atmos. Sci. 70(7): 1929-1953.
4. Baker L, Rudd A, Migliorini S, Bannister R. 2014. Representation of model error in a convective-scale ensemble prediction system. Nonlinear Processes Geophys. 21: 19-39.
5. Baldauf M, Seifert A, Förstner J, Majewski D, Raschendorfer M, Reinhardt T. 2011. Operational convective-scale numerical weather prediction with the COSMO model: description and sensitivities. Mon. Weather Rev. 139: 3887-3905.
6. Ballard SP, Golding BW, Smith RNB. 1991. Mesoscale model experimental forecasts of the Haar of North East Scotland. Mon. Weather Rev. 119: 2107-2123.
7. Ballard SP, Li Z, Simonin D, Buttery H, Charlton-Perez C, Gaussiat N, Hawkness-Smith L. 2012a. Use of radar data in NWP-based nowcasting in the Met Office. In Proceedings of Weather Radar and Hydrology, Vol. 352. 18-21 Exeter, UK; 336-341.
8. Ballard SP, Macpherson B, Li Z, Simonin D, Caron J-F, Buttery H, Charlton-Perez C, Gaussiat N, Hawkness-Smith L, Piccolo C, Kelly G, Tubbs R, Dow G, Renshaw R. 2012b. Convective scale data assimilation and nowcasting. In Proceedings of ECMWF Seminar on Data Assimilation for Atmosphere and Ocean, 6-9 September 2011, Shinfield Park, Reading.
9. Ballard SP, Li Z, Simonin D, Caron J-F. 2015. Performance of 4D-Var NWP-based nowcasting of precipitation at the Met Office for summer 2012. Q. J. R. Meteorol. Soc. DOI: 10.1002/qj.2665.
10. Barrett AI, Gray SL, Kirshbaum DJ, Roberts NM, Schultz DM, Fairman JG. 2015. Synoptic versus orographic control on stationary convective banding. Q. J. R. Meteorol. Soc. 141: 1101-1113.
11. Ben Bouallègue Z, Theis SE. 2014. Spatial techniques applied to precipitation ensemble forecasts: from verification results to probabilistic products. Meteorol. Appl. 21: 922-929.
12. Bloom SC, Takaks LL, Da Silva AM, Ledvina D. 1996. Data assimilation using incremental analysis updates. Mon. Weather Rev. 124: 1256-1271.
13. Blyth AM, Bennett LJ, Collier CG. 2015. High-resolution observations of precipitation from cumulonimbus clouds. Meteorol. Appl. 22: 75-89.
14. Bouttier F, Vie B, Nuissier O, Raynaud L. 2012. Impact of stochastic physics in a convection-permitting ensemble. Mon. Weather Rev. 140: 3706-3721.
15. Bowler NE, Pierce CE, Seed AW. 2006. STEPS: a probabilistic precipitation forecasting scheme which merged an extrapolation nowcast with downscaled NWP. Q. J. R. Meteorol. Soc. 132: 2127-2155.
16. Brousseau P, Berre L, Bouttier F, Desroziers G. 2011. Background-error covariances for a convective-scale data-assimilation system: AROME-France 3D-Var. Q. J. R. Meteorol. Soc. 137: 409-422.
17. Bryan GH, Wyngaard JC, Fritsch JM. 2003. Resolution requirements for the simulation of deep moist convection. Mon. Weather Rev. 131: 2394-2416.
18. Caron J-F. 2013. Mismatching perturbations at the lateral boundaries in limited-area ensemble forecasting: a case study. Mon. Weather Rev. 141: 356-374.
19. Casati B, Ross G, Stephenson DB. 2004. A new intensity-scale approach for the verification of spatial precipitation forecasts. Meteorol. Appl. 11: 141-154.
20. Clark PA, Browning KA, Forbes RM, Morcrette CJ, Blyth AM, Lean HW. 2014. The evolution of an MCS over southern England. Part 2: model simulations and sensitivity to microphysics. Q. J. R. Meteorol. Soc. 140: 458-479.
21. Clark AJ, Gallus WA Jr, Weisman ML. 2010. Neighborhood-based verification of precipitation forecasts from convection-allowing NCAR WRF model simulations and the operational NAM. Weather Forecast. 25: 1495-1509.
22. Clark AJ, Kain JS, Stenstrud DJ, Xue M, Kong F, Coniglio MC, et al. 2011. Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble. Mon. Weather Rev. 139: 1052-1081.
23. Crook NA. 2001. Understanding Hector: the dynamics of island thunderstorms. Mon. Weather Rev. 129: 1550-1563.
24. Cullen MJP. 1993. The unified forecast/climate model. Meteorol. Mag. 122: 81-94.
25. Cullen MJP, Davies T. 1991. A conservative split-explicit integration scheme with fourth-order horizontal advection. Q. J. R. Meteorol. Soc. 117: 993-1002.
26. Davies T, Cullen MJP, Malcolm AJ, Mawson MH, Staniforth A, White AA, et al. 2005. A new dynamical core for the Met Office's global and regional modelling of the atmosphere. Q. J. R. Meteorol. Soc. 131: 1759-1782.
27. Davis CA, Brown BG, Bullock R, Halley-Gotway J. 2009. The method for object-based diagnostic evaluation (MODE) applied to numerical forecasts from the 2005 NSSL/SPC Spring program. Weather Forecast. 24: 1252-1267.
28. de Haan S. 2011. High-resolution wind and temperature observations from aircraft tracked by Mode-S air traffic control radar. J. Geophys. Res. 116: D10111, DOI:10.1029/2010JD015264.
29. de Haan S, Stoffelen A. 2012. Assimilation of high-resolution mode-S wind and temperature observations in a regional NWP model. Weather Forecast. 27: 918-937.
30. Dixon M, Li Z, Lean H, Roberts N, Ballard S. 2009. Impact of data assimilation on forecasting convection over the United Kingdom using a high-resolution version of the Met Office Unified Model. Mon. Weather Rev. 137: 1562-1584.
31. Done J, Davis CA, Weisman ML. 2004. The next generation of NWP: explicit forecasts of convectioning the weather research and forecasting (WRF) model. Atmos.Sci. Lett. 5: 110-117.
32. Dow G, Macpherson B. 2013. Benefit of convective-scale data assimilation and observing systems in the UK Models. Forecasting Research Technical Report no 585. Met Office: Exeter, UK.
33. Droegemeier KK. 1990. Toward a science of storm scale prediction. Preprint, 16th Conference on Severe Local Storms. American Meteorological Society: Kananaskis Park, Alberta, Canada.
34. Duc L, Saito K, Seko H. 2013. Spatial-temporal fractions verification for high-resolution ensemble forecasts. Tellus A 65: 18171-18193.
35. Ebert EE. 2008. Fuzzy verification of high-resolution gridded forecasts: a review and proposed framework. Meteorol. Appl. 15: 51-64.
36. Ebert EE. 2009. Neighbourhood verification: a strategy for rewarding close forecasts. Weather Forecast. 24: 1498-1510.
37. Essery RLH, Best MJ, Betts RA, Cox PM, Taylor CM. 2003. Explicit representation of subgrid heterogeneity in a GCM land surface scheme. J. Hydrometeorol. 4: 530-543.
38. Forbes R, Halliwell C. 2003. Assessment of the performance of an enhanced microphysics parametrization scheme in the Unified Model at 1km resolution. Met Office internal report. Met Office: Exeter, UK.
39. Fritsch JM, Carbone RE. 2004. Improving quantitative precipitation forecasts in the warm season: a USRWP research and development strategy. Bull. Am. Meteorol. Soc. 85: 955-965.
40. Fritsch JM, Chappell CF. 1980. Numerical prediction of convectively driven mesoscale pressure systems. Part I: convective parametrization. J. Atmos. Sci. 37: 1722-1733.
41. Gebhardt C, Theis S, Krahe P, Renner V. 2008. Experimental ensemble forecasts of precipitation based on a convection-resolving model. Atmos. Sci. Lett. 9: 67-72.
42. Gebhardt C, Theis SE, Paulat ME, Ben Bouallègue Z. 2011. Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Atmos. Res. 100: 168-177.
43. Gerard L, Piriou J-M, Brožková R, Geleyn J-F, Banciu D. 2009. Cloud and precipitation parameterization in a meso-gamma-scale operational weather prediction model. Mon. Weather Rev. 137(11): 3960-3977.
44. Gilleland E, Ahijevych DA, Brown BG, Ebert EE. 2010. Verifying forecasts spatially. Bull. Am. Meteorol. Soc. 91: 1365-1373.
45. Golding BW. 1990. The Meteorological Office mesoscale model. Meteorol. Mag. 119: 81-96.
46. Golding BW. 1992. An efficient non-hydrostatic forecast model. Meteorol. Atmos. Phys. 50: 89-103.
47. Golding BW. 1993. A numerical investigation of tropical island thunderstorms. Mon. Weather Rev. 121: 1417-1433.
48. Golding BW. 1998. Nimrod: a system for generating automated very short range forecasts. Meteorol. Appl. 5: 1-16.
49. Golding BW, Ballard SP, Mylne K, Roberts N, Saulter A, Wilson C, Agnew P, Davis LS, Trice J, Jones C, Simonin D, Li Z, Pierce C, Bennett A, Weeks M, Moseley S. 2014. Forecasting Capabilities for the London 2012 Olympics. Bull. Amer. Meteor. Soc. 95: 883-896.
50. Gregory D, Rowntree PR. 1990. A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Weather Rev. 118: 1483-1506.
51. Hanley KE, Kirshbaum DJ, Belcher SE, Roberts NM, Leoncini G. 2011. Ensemble predictability of an isolated mountain thunderstorm in a high-resolution model. Q. J. R. Meteorol. Soc. 137: 2124-2137.
52. Hanley KE, Kirshbaum DJ, Roberts NM, Leoncini G. 2013. Sensitivities of a squall line over Central Europe in a convective-scale ensemble. Mon. Weather Rev. 141: 112-133.
53. Hanley KE, Plant RS, Stein TH M, Hogan RJ, Nicol JC, Lean HW, Halliwell C, Clark P A. 2015. Mixing-length controls on high-resolution simulations of convective storms. Q. J. R. Meteorol. Soc. 141: 272-284.
54. th Conference on Radar Meteorology, 19 September 2013, Breckenridge, CO, USA. American Meteorological Society: Boston, MA.
55. Honda Y, Nishijima M, Koizumi K, Ohta Y, Tamiya K, Kawabata T, et al. 2005. A pre-operational variational data assimilation system for a non-hydrostatic model at the Japan Meteorological Agency: formulation and preliminary results. Q. J. R. Meteorol. Soc. 131: 3465-3475.
56. Jones CD, Macpherson B. 1997. A latent heat nudging scheme for the assimilation of precipitation data into an operational mesoscale model. Meteorol. Appl. 4: 269-277.
57. Kain JS, Fritsch JM. 1993. Convective parameterization for mesoscale models: the Kain-Fritsch scheme. The representation of cumulus convection in numerical models. Meteorol. Monogr. 24: 165-170.
58. Kain JS, Weiss SJ, Bright DR, Baldwin ME, Levit JJ, Carbin GW, et al. 2008. Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Weather Forecast. 23: 931-952.
59. Keil C, Craig GC. 2009. A displacement and amplitude score employing an optical flow technique. Weather Forecast. 24: 1297-1308.
60. Keil C, Heinlein F, Craig GC. 2014. The convective adjustment time-scale as indicator of predictability of convective precipitation. Q. J. R. Meteorol. Soc. 140: 480-490.
61. Kendon EJ, Roberts NM, Fowler HJ, Roberts MJ, Chan S, Senior CA. 2014. Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat. Clim. Change 4: 570-576.
62. Kendon EJ, Roberts NM, Senior CA, Roberts MJ. 2012. Realism of rainfall in a very high-resolution regional climate model. J. Clim. 25: 5791-5806.
63. Khairoutdinov M, Randall D. 2006. High-resolution simulation of shallow-to-deep convection transition over land. J. Atmos. Sci. 63: 3421-3436.
64. Klemp JB, Wilhelmson RB. 1978. The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci. 35: 1070-1096.
65. Kühnlein C, Keil C, Craig GC, Gebhardt C. 2014. The impact of downscaled initial condition perturbations on convective-scale ensemble forecasts of precipitation. Q. J. R. Meteorol. Soc. 140: 1552-1562.
66. Lean HW, Clark PA, Dixon M, Roberts NM, Fitch A, Forbes R, et al. 2008. Characteristics of high-resolution versions of the Met Office Unified Model for forecasting convection over the United Kingdom. Mon. Weather Rev. 136: 3408-3424.
67. Leoncini G, Plant RS, Gray SL, Clark PA. 2013. Ensemble forecasts of a flood-producing storm: comparison of the influence of model-state perturbations and parameter modifications. Q. J. R. Meteorol. Soc. 139: 198-211.
68. Leroyer S, Dorval EC, Canada QC, Bélair S, Husain Z, Vionnet V. 2014. Urban, water bodies, and orography induced circulations and their impact on extreme weather forecasting. In Proceedings of 11th Symposium on the Urban Environment, 2-6 February 2014, Atlanta, GA, USA. American Meteorological Society: Boston, MA.
69. Li Z, Ballard SP, Simonin D. 2015. Comparison of 3D-Var and 4D-Var data assimilation in an NWP-based nowcasting system of precipitation at the Met Office, Forecasting Research Technical Report no 607. Met Office: Exeter, UK
70. Lilly DK. 1990. Numerical prediction of thunderstorms - has its time come? Q. J. R. Meteorol. Soc. 116: 779-798.
71. Lorenc AC, Ballard SP, Bell RS, Ingleby NB, Andrews PLF, Barker DM, et al. 2000. The Met. Office global 3-Dimensional variational data assimilation scheme. Q. J. R. Meteorol. Soc. 12: 2991-3012.
72. Lorenc AC, Bell RS, Macpherson B. 1991. The Meteorological Office analysis corrective data assimilation scheme. Q. J. R. Meteorol. Soc. 117: 59-89.
73. Lorenz EN. 1969. The predictability of a flow which possesses many scales of motion. Tellus 21: 289-307.
74. Macpherson B, Wright BJ, Hand WH, Maycock AJ. 1996. The impact of MOPS moisture data in the U.K. Meteorological Office mesoscale data assimilation scheme. Mon. Weather Rev. 124: 1746-1766.
75. Manners J, Vosper SB, Roberts N. 2012. Radiative transfer over resolved topographic features for high-resolution weather prediction. Q. J. R. Meteorol. Soc. 138: 720-733.
76. Michalakes J, Chen S, Dudhia J, Hart L, Klemp J, Middlecoff J, et al. 2001. "Development of a next generation regional weather research and forecast model" in developments in teracomputing. In Proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology. World Scientific: Singapore.
77. Miller MJ, Pearce RP. 1974. A three-dimensional primitive equation model of cumulonimbus convection. Q. J. R. Meteorol. Soc. 100: 133-154.
78. Mittermaier M, Roberts NM, Thompson SA. 2013. A long term assessment of precipitation forecast skill using the fractions skill score. Meteorol. Appl. 20: 176-186.
79. Molteni F, Buizza R, Palmer TN, Petroliagis T. 1996. The ECMWF ensemble prediction system: methodology and validation. Q. J. R. Meteorol. Soc. 122: 73-119.
80. Müller M, Scherer D. 2005. A grid- and subgrid-scale radiation parameterization of topographic effects for mesoscale weather forecast models. Mon. Weather Rev. 133: 1431-1442.
81. Nicol JC, Illingworth AJ, Bartholomew K. 2014. The potential of 1h refractivity changes from an operational C-band magnetron-based radar for numerical weather prediction validation and data assimilation. Q. J. R. Meteorol. Soc. 140: 1209-1218.
82. Oliphant AJ, Spronken-Smith RA, Sturman AP. 2003. Spatial variability of surface radiation fluxes in mountainous terrain. J. Appl. Meteorol. 42: 113-128.
83. Pielke RA. 1974. A three-dimensional numerical model of the sea breezes over south Florida. Mon. Weather Rev. 102: 115-139.
84. Pielke RA, Cotton WR, Walko RL, Tremback CJ, Lyons WA, Grasso LD, et al. 1992. A comprehensive meteorological modeling system-RAMS. Meteorol. Atmos. Phys. 49: 69-91.
85. Porson A, Clark PA, Harman IN, Best MJ, Belcher SE. 2010. Implementation of a new urban energy budget scheme in the MetUM. Part I: description and idealized simulations. Q. J. R. Meteorol. Soc. 136: 1514-1529.
86. Rawlins FR, Ballard SP, Bovis KR, Clayton AM, Li D, Inverarity GW, et al. 2007. The Met Office global 4-Dimensional data assimilation system. Q. J. R. Meteorol. Soc. 133: 347-362.
87. Renshaw R, Francis PN. 2011. Variational assimilation of cloud fraction in the operational Met Office Unified Model. Q. J. R. Meteorol. Soc. 137: 1963-1974.
88. Richardson LF. 1922. Weather Prediction by Numerical Process. Cambridge University Press.
89. Roberts NM. 2003a. The impact of a change to the use of the convection scheme to high resolution simulations of convective events (stage 2 report from the storm scale numerical modelling project). Met Office Technical Report, 407; 31 (available online).
90. Roberts N M. 2003b. Precipitation diagnostics for a high resolution forecasting system. Met Office Technical Report, 423; 45 (available online).
91. Roberts NM. 2008. Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model. Meteorol. Appl. 15: 163-169.
92. Roberts NM, Cole SJ, Forbes RM, Moore RJ, Boswell D. 2009. Use of high-resolution NWP rainfall and river flow forecasts for advance warning of the Carlisle flood, north-west England. Meteorol. Appl. 16: 23-34.
93. Roberts NM, Lean HW. 2008. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Weather Rev. 136: 78-97.
94. Saito K, Fujita T, Yamada Y, Ishida J, Kumagai Y, Aranami K, et al. 2006. The operational JMA nonhydrostatic mesoscale model. Mon. Weather Rev. 134: 1266-1298.
95. Schlesinger RE. 1975. A three-dimensional numerical model of an isolated deep convective cloud: preliminary results. J. Atmos. Sci. 32: 934-957.
96. Schwartz CS, Kain JS, Weiss SJ, Xue M, Bright DR, Kong F, et al. 2009. Next-day convection-allowing WRF model guidance: a second look at 2-km versus 4-km grid spacing. Mon. Weather Rev. 137: 3351-3372.
97. Schwartz CS, Kain JS, Weiss SJ, Xue M, Bright DR, Kong F, et al. 2010. Toward improved convection-allowing ensembles: model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Weather Forecast. 25: 263-280.
98. Schwartz CS, Liu Z. 2014. Convection-permitting forecasts initialized with continuously cycling limited-area 3DVAR, ensemble Kalman filter, and "hybrid" variational-ensemble data assimilation systems. Mon. Weather Rev. 142: 716-773.
99. Seed AW, Pierce CE, Norman K. 2013. Formulation and evaluation of a scale decomposition-based stochastic precipitation nowcast scheme. Water Resour. Res. 49: 6624-6641.
100. Seity Y, Brosseau P, Malardel S, Hello G, Bénard P, Bouttier F, et al. 2011. The AROME-France convective-scale operational model. Mon. Weather Rev. 139: 976-991.
101. Simonin D, Ballard SP, Li Z. 2014. Doppler radar radial wind assimilation using an hourly cycling 3D-Var with a 1.5 km resolution version of the Met Office Unified Model for nowcasting. Q. J. R. Meteorol. Soc. 140: 2298-2314.
102. Skamarock WC. 2004. Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Weather Rev. 132: 3019-3032.
103. Sobash RA, Kain JS, Bright DR, Dean AR, Coniglio MC, Weiss SJ. 2011. Probabilistic forecast guidance for severe thunderstorms based on the identification of extreme phenomena in convection-allowing model forecasts. Weather Forecast. 26: 714-728.
104. Stensrud DJ, Wicker LJ, Kelleher KE, Xue M, Foster MP, Schaefer JT, et al. 2009. Convective-scale warn-on-forecast system. Bull. Am. Meteorol. Soc. 90: 1487-1499.
105. Stephan K, Klink S, Schraff C. 2008. Assimilation of radar derived rain rates into the convective scale model COSMO-DE at DWD. Q. J. R. Meteorol. Soc. 134: 1315-1326.
106. Sun J, Xue M, Wilson J, Zawadzki I, Ballard SP, Onvlee-Hooimeyer J, et al. 2014. Use of NWP for nowcasting precipitation: recent progress and challenges. Bull. Am. Meteorol. Soc. 95: 409-426.
107. Tang Y, Lean H, Bornemann J. 2013. The benefits of the Met Office variable resolution NWP model for forecasting convection. Meteorol. Appl. 20: 417-426.
108. Tapp MC, White PW. 1976. A non-hydrostatic mesoscale model. Q. J. R. Meteorol. Soc. 102: 277-296.
109. Theis SE, Hense H, Damrath U. 2005. Probabilistic precipitation forecasts from a deterministic model: a pragmatic approach. Meteorol. Appl. 12: 257-268.
110. Tremback CJ, Tripoli GJ, Cotton WR. 1985. A regional scale atmospheric numerical model including explicit moist physics and a hydrostatic time-split scheme. In Proceedings of 7th AMS Conference on Numerical Weather Prediction, June 17-20, Montreal, Quebec, Canada. American Meteorological Society: Boston, MA.
111. Tripoli GJ, Cotton WR. 1980. A numerical investigation of several factors contributing to the observed variable intensity of deep convection over south Florida. J. Appl. Meteorol. 19: 1037-1063.
112. Tripoli GJ, Cotton WR. 1982. The Colorado State University three-dimensional cloud/mesoscale model-1982. Part I: general theoretical framework and sensitivity experiments. J. de Rech. Atmos. 16: 185-220.
113. Vetra-Carvalho S, Dixon M, Migliorini S, Nichols NK, Ballard SP. 2012. Breakdown of hydrostatic balance at convective scales in the forecast errors in the Met Office Unified Model. Q. J. R. Meteorol. Soc. 138: 1709-1720.
114. Wang C, Jones R, Perry M, Johnson C, Clark P. 2013. Using an ultrahigh-resolution regional climate model to predict local climatology. Q. J. R. Meteorol. Soc. 139: 1964-1976.
115. Weisman ML, Davis C, Wang W, Manning KW, Klemp JB. 2008. Experiences with 0-36 hour explicit convective forecasts with the WRF-ARW model. Weather Forecast. 23: 407-437.
116. Wernli H, Paulat M, Hagen M, Frei C. 2008. SAL-a novel quality measure for the verification of quantitative precipitation forecasts. Mon. Weather Rev. 136: 4470-4487.
117. White AA, Hoskins BJ, Roulstone I, Staniforth A. 2005. Consistent approximate models of the global atmosphere: shallow, deep, hydrostatic, quasi hydrostatic and non-hydrostatic. Q. J. R. Meteorol. Soc. 131: 2081-2107.
118. Wilkinson JM, Bornemann FJ. 2014. A lightning forecast for the London 2012 Olympics opening ceremony. Weather 69: 16-19.
119. Wilkinson J, Wilson D, Forbes R. 2011. The large-scale precipitation parametrization scheme. UM documentation paper 26, Model version 8.1. Met Office: Exeter, UK.
120. Wilson DR, Ballard SP. 1999. A microphysically based precipitation scheme for the UK Meteorological Office unified model. Q. J. R. Meteorol. Soc. 125: 1607-1636.
121. Wilson D, Forbes R. 2004. The large-scale precipitation parametrization scheme. UM documentation paper 26, Model version 5.5. Met Office: Exeter, UK.
122. Wright BJ, Golding BW. 1990. The interactive mesoscale initialization. Meteorol. Mag. 119: 234-244.
123. Xue M, Wang D-H, Gao J-D, Brewster K, Droegemeier KK. 2003. The advanced regional prediction system (ARPS), storm-scale numerical weather prediction and data assimilation. Meteorol. Atmos. Phys. 82: 139-170.
124. Yu X, Lee T-Y. 2010. Role of convective parameterization in simulations of a convection band at grey-zone resolutions. Tellus A 62: 617-632.