Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning

Abstract. The present study focuses on identifying the parameters from the Weather Research and Forecasting (WRF) model that strongly influence the prediction of tropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity analysis (SA) methods, namely the Morris One-at-A-Time (MOAT), Multivariate Adaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to identify the most sensitive parameters out of 24 tunable parameters corresponding to seven parameterization schemes of the WRF model. Ten tropical cyclones across different categories, such as cyclonic storms, severe cyclonic storms, and very severe cyclonic storms over BoB between 2011 and 2018, are selected in this study. The sensitivity scores of 24 parameters are evaluated for eight meteorological variables. The parameter sensitivity results are consistent across three SA methods for all the variables, and 8 out of the 24 parameters contribute 80 %–90 % to the overall sensitivity scores. It is found that the Sobol' method with Gaussian progress regression as a surrogate model can produce reliable sensitivity results when the available samples exceed 200. The parameters with which the model simulations have the least RMSE values when compared with the observations are considered as the optimal parameters. Comparing observations and model simulations with the default and optimal parameters shows that predictions with the optimal set of parameters yield a 16.74 % improvement in the 10 m wind speed, 3.13 % in surface air temperature, 0.73 % in surface air pressure, and 9.18 % in precipitation predictions compared to the default set of parameters.

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Climate change impacts parameter sensitivity - What does this mean for model calibration?

10.5194/egusphere-egu2020-2609 ◽

2020 ◽

Author(s):

Lieke Melsen ◽

Björn Guse

Keyword(s):

Climate Change ◽

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Parameter Sensitivity ◽

Rate Of Change ◽

Hydrological Models ◽

Global Sensitivity ◽

The Future ◽

Sensitive Parameters ◽

The Impact

Many hydrological models that are used for long term projections require calibration of at least a few parameters. When calibrated on discharge only, a general rule of thumb is that 4 to 5 parameters can be calibrated. The general approach is to conduct a global sensitivity analysis, to determine the four to five most sensitive parameters, and to select these for calibration.Parameter sensitivity differs over models, target variables, sensitivity analysis methods, and also over climates. This would also imply that parameter sensitivity could change in a changing climate, and that would interfere with the current standard calibration procedure for hydrological models. Therefore, the question is whether, within a plausible rate of change, climate change propagates into a change in parameter sensitivity.&#160;&#160;&#160;We investigated how parameter sensitivity changes as a consequence of climate change, and if and how this has consequences for the calibration strategy. We applied a hybrid local-global sensitivity analysis method to three frequently used hydrological models (SAC, VIC, and HBV) in 605 basins across the US, and link changes in sensitivity to changes in climate. Finally, we evaluated the impact on the top five most sensitive parameters.The results show that in all three models especially snow parameters tend to become less sensitive in the future. However, the models differ in which parameters increase in sensitivity; for some models ET parameters increase, while for others deep layer parameters increase. Evaluating the top 5 most sensitive parameters per basin, we found that in 43% to 49% of the basins at least one parameter changes in the top 5 in the future, while a maximum of two parameter changes in the top 5 was observed over all basins (in 2 to 4% of the basins).Overall, the results indicate that in about half of the investigated basins one parameter would have been chosen differently for calibration. If a particular model parameter is, within the current climate, not or hardly sensitive to discharge, it is not possible to calibrate this parameter &#8211; notwithstanding whether this parameter becomes sensitive in the future. Therefore, the consequence of these results is that for parameters that will become sensitive in the future, a range of feasible parameter values have to be sampled for future projections, thereby capturing predictive uncertainty as a consequence of changing sensitivities.

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Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models

BMC Bioinformatics ◽

10.1186/s12859-021-04002-0 ◽

2021 ◽

Vol 22 (S2) ◽

Author(s):

Marco S. Nobile ◽

Vasco Coelho ◽

Dario Pescini ◽

Chiara Damiani

Keyword(s):

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Global Strategy ◽

Rational Approach ◽

Order Effects ◽

Model Parameters ◽

Global Sensitivity ◽

Genome Wide ◽

Parameterized Model ◽

Sensitive Parameters

Abstract Background Genome-wide reconstructions of metabolism opened the way to thorough investigations of cell metabolism for health care and industrial purposes. However, the predictions offered by Flux Balance Analysis (FBA) can be strongly affected by the choice of flux boundaries, with particular regard to the flux of reactions that sink nutrients into the system. To mitigate possible errors introduced by a poor selection of such boundaries, a rational approach suggests to focus the modeling efforts on the pivotal ones. Methods In this work, we present a methodology for the automatic identification of the key fluxes in genome-wide constraint-based models, by means of variance-based sensitivity analysis. The goal is to identify the parameters for which a small perturbation entails a large variation of the model outcomes, also referred to as sensitive parameters. Due to the high number of FBA simulations that are necessary to assess sensitivity coefficients on genome-wide models, our method exploits a master-slave methodology that distributes the computation on massively multi-core architectures. We performed the following steps: (1) we determined the putative parameterizations of the genome-wide metabolic constraint-based model, using Saltelli’s method; (2) we applied FBA to each parameterized model, distributing the massive amount of calculations over multiple nodes by means of MPI; (3) we then recollected and exploited the results of all FBA runs to assess a global sensitivity analysis. Results We show a proof-of-concept of our approach on latest genome-wide reconstructions of human metabolism Recon2.2 and Recon3D. We report that most sensitive parameters are mainly associated with the intake of essential amino acids in Recon2.2, whereas in Recon 3D they are associated largely with phospholipids. We also illustrate that in most cases there is a significant contribution of higher order effects. Conclusion Our results indicate that interaction effects between different model parameters exist, which should be taken into account especially at the stage of calibration of genome-wide models, supporting the importance of a global strategy of sensitivity analysis.

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