Impact of Stochastic Physics in a Convection-Permitting Ensemble

被引:182
作者
Bouttier, Francois [1 ,2 ]
Vie, Benoit
Nuissier, Olivier
Raynaud, Laure
机构
[1] CNRS, CNRM GAME, F-31057 Toulouse, France
[2] Meteo France, F-31057 Toulouse, France
关键词
MODEL-ERROR; PREDICTION SYSTEM; PARAMETERIZATION; REPRESENTATION; PRECIPITATION; FORECASTS; SKILL;
D O I
10.1175/MWR-D-12-00031.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
A stochastic physics scheme is tested in the Application of Research to Operations at Mesoscale (AROME) short-range convection-permitting ensemble prediction system. It is an adaptation of ECMWF's stochastic perturbation of physics tendencies (SPPT) scheme. The probabilistic performance of the AROME model ensemble is found to be significantly improved, when verified against observations over two 2-week periods. The main improvement lies in the ensemble reliability and the spread-skill consistency. Probabilistic scores for several weather parameters are improved. The tendency perturbations have zero mean, but the stochastic perturbations have systematic effects on the model output, which explains much of the score improvement. Ensemble spread is an increasing function of the SPPT space and time correlations. A case study reveals that stochastic physics do not simply increase ensemble spread, they also tend to smooth out high-spread areas over wider geographical areas. Although the ensemble design lacks surface perturbations, there is a significant end impact of SPPT on low-level fields through physical interactions in the atmospheric model.
引用
收藏
页码:3706 / 3721
页数:16
相关论文
共 52 条
[31]   The ECMWF ensemble prediction system: Methodology and validation [J].
Molteni, F ;
Buizza, R ;
Palmer, TN ;
Petroliagis, T .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 1996, 122 (529) :73-119
[32]  
Nicolau J., 2002, P TECH C DAT PROC FO, P6
[33]  
Palmer T. N., 2009, 598 ECMWF RD
[34]   A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models [J].
Palmer, TN .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2001, 127 (572) :279-304
[35]   TIGGE: Preliminary results on comparing and combining ensembles [J].
Park, Young-Youn ;
Buizza, Roberto ;
Leutbecher, Martin .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2008, 134 (637) :2029-2050
[36]   A stochastic parameterization for deep convection based on equilibrium statistics [J].
Plant, R. S. ;
Craig, G. C. .
JOURNAL OF THE ATMOSPHERIC SCIENCES, 2008, 65 (01) :87-105
[37]   An extended specification of flow-dependent background error variances in the Meteo-France global 4D-Var system [J].
Raynaud, Laure ;
Berre, Loik ;
Desroziers, Gerald .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2011, 137 (656) :607-619
[38]   Skill and relative economic value of the ECMWF ensemble prediction system [J].
Richardson, DS .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2000, 126 (563) :649-667
[39]   The AROME-France Convective-Scale Operational Model [J].
Seity, Y. ;
Brousseau, P. ;
Malardel, S. ;
Hello, G. ;
Benard, P. ;
Bouttier, F. ;
Lac, C. ;
Masson, V. .
MONTHLY WEATHER REVIEW, 2011, 139 (03) :976-991
[40]   A kinetic energy backscatter algorithm for use in ensemble prediction systems [J].
Shutts, G .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2005, 131 (612) :3079-3102