Sensitivity analysis of the convective‐scale AROME model to physical and dynamical parameters

2021 ◽  
Author(s):  
Meryl Wimmer ◽  
Laure Raynaud ◽  
Laurent Descamps ◽  
Loik Berre ◽  
Yann Seity
Keyword(s):  
Sensitivity Analysis ◽  
Convective Scale ◽  
Dynamical Parameters

Sensitivity Analysis of the Dynamical Parameters on Road Materials

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Hai-bin Wei ◽  
Han-bing Liu ◽  
He-feng Zhang ◽  
Chang-yu Li
Keyword(s):  
Sensitivity Analysis ◽  
Dynamical Parameters ◽  
Road Materials

Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble

2019 ◽  
Vol 148 (3) ◽  
pp. 1229-1249 ◽  
Author(s):  
Tobias Necker ◽  
Martin Weissmann ◽  
Yvonne Ruckstuhl ◽  
Jeffrey Anderson ◽  
Takemasa Miyoshi
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Error Correction ◽  
Degrees Of Freedom ◽  
Sampling Error ◽  
Lookup Table ◽  
Ensemble Prediction ◽  
Sampling Errors ◽  
Convective Scale ◽  
The Impact

Abstract State-of-the-art ensemble prediction systems usually provide ensembles with only 20–250 members for estimating the uncertainty of the forecast and its spatial and spatiotemporal covariance. Given that the degrees of freedom of atmospheric models are several magnitudes higher, the estimates are therefore substantially affected by sampling errors. For error covariances, spurious correlations lead to random sampling errors, but also a systematic overestimation of the correlation. A common approach to mitigate the impact of sampling errors for data assimilation is to localize correlations. However, this is a challenging task given that physical correlations in the atmosphere can extend over long distances. Besides data assimilation, sampling errors pose an issue for the investigation of spatiotemporal correlations using ensemble sensitivity analysis. Our study evaluates a statistical approach for correcting sampling errors. The applied sampling error correction is a lookup table–based approach and therefore computationally very efficient. We show that this approach substantially improves both the estimates of spatial correlations for data assimilation as well as spatiotemporal correlations for ensemble sensitivity analysis. The evaluation is performed using the first convective-scale 1000-member ensemble simulation for central Europe. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of the sampling error correction for smaller subsets of the full ensemble. The sampling error correction strongly reduced both random and systematic errors for all evaluated variables, ensemble sizes, and lead times.

Sensitivity analysis of the 1-D convective cloud model outputs on the dynamical parameters

1989 ◽  
Vol 40 (4) ◽  
pp. 237-246
Author(s):  
M. Ćurić ◽  
D. Janc
Keyword(s):  
Sensitivity Analysis ◽  
Cloud Model ◽  
Convective Cloud ◽  
Dynamical Parameters

Dynamical Sensitivity Analysis on Blades in Turbine Machines

2012 ◽  
Vol 226-228 ◽  
pp. 602-606
Author(s):  
Da Yi Zhang ◽  
Yan Hong Ma ◽  
Jie Hong
Keyword(s):  
Sensitivity Analysis ◽  
Gas Turbines ◽  
Optimization Design ◽  
High Cycle Fatigue ◽  
Design Method ◽  
Mathematic Model ◽  
Cycle Fatigue ◽  
Aerodynamic Forces ◽  
Outstanding Problem ◽  
Dynamical Parameters

High cycle fatigue of blades caused by forced vibration is an outstanding problem in turbine machines such as turbo engines and ground gas turbines. Dynamical sensitivity analysis (DSA) is put forward and the mathematic model is erected according to blades. DSA on blades includes sensitivity analysis on inherent characteristic (DSAI) and sensitivity analysis on excitation environment (DSAE). The influence laws of dynamical parameters are gained through DSA, and therefore the optimization design on blades is feasible. DSAE is presented by dint of numerical methods, aerodynamic forces in engines possess multifrequency and spatial distributing characteristics, so DSAE on blades is performed against these characteristics and some results are obtained. Finally the resonance criteria are advanced and a new dynamical design method is put forward.

Advances in Sensitivity Analysis and Parametric Programming

1998 ◽  
Vol 49 (7) ◽  
pp. 770-770
Author(s):  
J M Wilson
Keyword(s):  
Sensitivity Analysis ◽  
Parametric Programming
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