Breakdown of hydrostatic balance at convective scales in the forecast errors in the Met Office Unified Model

Quarterly Journal of the Royal Meteorological Society

Volumn 138, Issue 668, 2012, Pages 1709-1720

  Vetra Carvalho, S ^a   Dixon, M ^a   Migliorini, S ^a   Nichols, N K ^a   Ballard, S P ^a  

^a UNIVERSITY OF READING   (United Kingdom)

Author keywords

Background covariances; Ensemble data assimilation; High resolution numerical weather prediction; MOGREPS

Indexed keywords

ATMOSPHERIC FLOWS; BACKGROUND COVARIANCES; CONVECTIVE ACTIVITY; COVARIANCE MODELS; DATA ASSIMILATION; ENSEMBLE DATA ASSIMILATION; ENSEMBLE PREDICTION SYSTEMS; FORECAST ERRORS; HIGH RESOLUTION; HIGH-RESOLUTION MODELS; HYDROSTATIC BALANCE; MATRIX P; MOGREPS; NUMERICAL WEATHER PREDICTION; ONSET OF CONVECTION; UNIFIED MODEL; VARIATIONAL ASSIMILATION; VARIATIONAL DATA ASSIMILATION;

ATMOSPHERIC MOVEMENTS; COVARIANCE MATRIX; HYDRAULICS; HYDRODYNAMICS; VALUE ENGINEERING;

FORECASTING;

ACCURACY ASSESSMENT; ATMOSPHERIC MODELING; CONVECTIVE SYSTEM; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; FOUR-DIMENSIONAL MODELING; HYDROSTATICS; MODEL TEST; MODEL VALIDATION; THREE-DIMENSIONAL MODELING; WEATHER FORECASTING;

EID: 84868194248     PISSN: 00359009     EISSN: 1477870X     Source Type: Journal    
DOI: 10.1002/qj.1925     Document Type: Article

References (10)

1. Bloom SC, Takacs LL, da Silva AM, Ledvina D. 1996. Data assimilation using incremental analysis updates. Mon. Weather Rev. 124: 1256-1271.
2. Bowler NE, Arribas A, Mylne KR, Robertson KB, Beare SE. 2008. The MOGREPS short-range ensemble prediction system. Q. J. R. Meteorol. Soc. 134: 703-722.
3. Davies T, Cullen MJP, Malcolm AJ, Mawson MH, Staniforth A, White AA, Wood N. 2006. A new dynamical core for the Met Office's global and regional modelling of the atmosphere. Q. J. R. Meteorol. Soc. 131: 1759-1782.
4. Dixon M, Zhihong L, Lean H, Roberts N, Ballard SP. 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.
5. Evensen G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. J. Geophys. Res. 99: 10143-10162.
6. Kalman RE. 1960. A new approach to linear filtering and prediction problems. Trans. AMSE-J. Basic Engineering 82D: 35-45.
7. Migliorini S, Dixon M, Bannister R, Ballard SP. 2011. Ensemble prediction for nowcasting with a convection-permitting model-I: description of the system and the impact of radar-derived surface precipitation rates. Tellus A 63: 468-496.
8. Montmerle T, Berre L. 2010. Diagnosis and formulation of heterogeneous backgroud-error covariances at the mesoscale. Q. J. R. Meteorol. Soc. 136: 1408-1420.
9. Wallace JM. 2006. Atmospheric Science: An Introductory Survey, 2nd edn. Elsevier: Amsterdam.
10. Wang XG, Bishop CH, Julier SJ. 2004. Which is better, an ensemble of positive-negative pairs or a centered spherical simplex ensemble? Mon. Weather Rev. 132: 1590-1605.