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Microphysics parameterization schemes assimilation system for WRF-EnSRF Impact of model errors

Author: SunQiongBo
Tutor: ZuoJinZhong
School: Nanjing University of Information Engineering
Course: The climate system and global change
Keywords: Data assimilation Ensemble Kalman Filter Model error Microphysical processes Parameterization scheme
CLC: P456.7
Type: Master's thesis
Year: 2011
Downloads: 71
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Abstract


Numerical weather modeling and data assimilation, how to reduce the model error for data assimilation for the numerical simulation of the impact of providing better initial field, has very important significance. So far in the ensemble Kalman filter for filtering the square root of a collection of model errors assimilation is still scarce. Microphysics parameterization uncertainty is caused by small-scale weather patterns assimilation process simulation and a major source of error. Most current domestic and international research on the WRF model parameterization schemes as well as in forecasting performance test is also only stay in a single program or a different class of parameterization schemes (such as single cumulus parameterization scheme and the single microphysics parameterization scheme ) combined effect. Therefore, this paper-based predecessors built WRF model - a collection of square root filter (WRF-EnSRF) assimilation system, construct a multi-parametric combinations of micro-physical processes set assimilation and use of the program conducted a series of simulated radar data and actual radar comparative analysis of the data assimilation experiment, the main conclusions are as follows: (a) by the micro-physical processes parameterized model errors caused by varying degrees reduces WRF-EnSRF assimilation assimilation system. Analog data assimilation experiments, all sensitive test (single / multi-microphysics parameterization schemes) of each variable (model / observation space variable) analysis of the effect worse than the control experiment. (2) simulated radar data assimilation, various multi-microphysical parameterization schemes are able to some extent to improve analysis results. Excluding controlled trial microphysics scheme can improve the multi-program some variable assimilation test results. Containing controlled trial microphysics scheme multi-program tests, analysis of spatial variables the model is better than a single basic test program has increased, the total energy deviation and the root mean square error decreased. Further found that if the multi-program test contains only ice phase microphysics parameterization scheme, and increase control experiment the proportion of microphysics scheme makes assimilation system analysis to further improve the performance of the basic analysis of all variables have positive results, along with analysis of the effect of increasing the number of more reasonable. (3) the actual radar data assimilation, the microphysical parameterization schemes the choice of observational analysis of spatial variable reflectivity greater impact relatively small impact on the radial velocity, which is ideal in a case slightly different effect. Contains only ice-phase microphysical processes parameterized multi-program test assimilation effect is not ideal. Further select the best performing combination of the two to form a single program, which analyzes the effect of a single solution with the best performance tests are basically the same, some variables slightly better. Therefore, in a case of actual use of multi microphysical parameterization schemes can eliminate some of the impact of model error, there is no obvious advantage. But for the actual lack of a priori numerical weather conditions, the use of multi-microphysical parameterization schemes remain its advantages and desirability.

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CLC: > Astronomy,Earth Sciences > Atmospheric science (meteorology ) > Weather Forecast > Forecasting Methods > Numerical prediction methods
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