Potential of stochastic methods for improving convection-permitting ensemble forecasts of extreme events over the Western Mediterranean

Hermoso, Alejandro; Homar, V.; Plant, Raymond

doi:10.1016/j.atmosres.2021.105571

Cited by 9 publications

(13 citation statements)

References 122 publications

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“…Stanford et al (2019) showed that perturbing the ice microphysics parameters involved in mass-size and fallspeed-size relationships using the SPP (Stochastically Perturbed Parameterization; Ollinaho et al, 2017) scheme resulted in significant impacts on the variability of ice cloud optical depth, rain-rate and precipitation in ensemble simulations of deep convection events. Similarly, Hermoso et al (2021) demonstrated that the Random Parameters (RP) scheme (McCabe et al, 2016) applied to several microphysics parameters in a two-moment bulk microphysics scheme significantly increased ensemble variability of convective storm evolution for the studied case. Thompson et al (2021) reported important localized impacts on the precipitation properties including changes to maximum hail-size from perturbing only two microphysics parameters using the SPP scheme.…”

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confidence: 85%

“…The PR scheme (Hermoso et al, 2021;McCabe et al, 2016) assumes no spatial variability of the parameters and makes use of the uniform distribution for the stochastic variability according to , where 𝐴𝐴 𝐴𝐴 * would be a large value applicable to all parameters. The RP scheme and this configuration are not exact equivalents due to approximating the uniform distribution by the Normal with a large variance, but the correspondence would be close for all practical purposes assuming application of the appropriate boundary conditions (the range of parameter values).…”

Section: Appendix A: Relationship Between Generalized Linear Gaussian...mentioning

confidence: 99%

“…Evaluations of the SP schemes in global and regional models have shown positive impacts on the ensemble prediction skill of various degrees depending on the prediction time range, the modeling system, and verification variables (Christensen et al., 2015; Hermoso et al., 2021; Jankov et al., 2017; Leutbecher et al., 2017; McCabe et al., 2016; Ollinaho et al., 2017; Stanford et al., 2019; Thompson et al., 2021; Wang et al., 2019). In comparison to the SPPT, the impacts of the SP schemes on ensemble spread were found to be smaller, but adding value when the two approaches were combined (Christensen et al., 2015; Hermoso et al., 2021; Jankov et al., 2019; Ollinaho et al., 2017). These findings point to two non‐exclusive conclusions: the unresolved variability has a broader scope than is contained in the parameter uncertainty, and applications of the SP approach may need to capture a larger diversity of processes (i.e., more parameters and parameterizations within a model) with improved process‐specific configurations and better understanding of impact mechanisms to achieve full benefits (Jankov et al., 2019; Leutbecher et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the SP schemes produce physically‐based and physically‐consistent stochastic variability to the tendencies of the resolved states. Evaluations of the SP schemes in global and regional models have shown positive impacts on the ensemble prediction skill of various degrees depending on the prediction time range, the modeling system, and verification variables (Christensen et al., 2015; Hermoso et al., 2021; Jankov et al., 2017; Leutbecher et al., 2017; McCabe et al., 2016; Ollinaho et al., 2017; Stanford et al., 2019; Thompson et al., 2021; Wang et al., 2019). In comparison to the SPPT, the impacts of the SP schemes on ensemble spread were found to be smaller, but adding value when the two approaches were combined (Christensen et al., 2015; Hermoso et al., 2021; Jankov et al., 2019; Ollinaho et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

Vukićević

Vukicevic

Stanković

2022

J Adv Model Earth Syst

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This study examines the characteristics of several model parameter perturbation methodologies for ensemble simulations of cloud microphysical processes in convection. A simplified 1D model is used to focus the results on cloud microphysics without the complication of feedbacks to the dynamics and environment. Several parameter perturbation methods are tested, including non‐stochastic and stochastic with various distributions and parameter covariance. We find that an ensemble comprised of different time‐invariant parameters (non‐stochastic) exhibits little bias, but small spread. In addition, its behavior does not respect the time evolution of convection through its various phases. Stochastic parameter (SP) methods in which no inter‐parameter covariance is applied produce greater spread, but significant bias. The bias is particularly large for lognormal parameter perturbation distributions. The ensemble spread is retained and the bias reduced when time‐varying parameter covariance is applied. In this case, the SP scheme is able to adapt to the time and state‐dependent covariance structures and produce ensemble characteristics that are consistent with the specific microphysical processes operating at any given time. The results suggest that SP schemes would benefit from inclusion of parameter covariances, and specifically those that vary with the state of the system. It also suggests that a Normal or LogNormal SP scheme with no covariance may significantly impact the ensemble bias. Finally, the results indicate that high temporal and spatial resolution observations may be needed to characterize the variability in parameter values and covariance.

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confidence: 85%

Section: Appendix A: Relationship Between Generalized Linear Gaussian...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

Vukićević

Vukicevic

Stanković

2022

J Adv Model Earth Syst

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“…As a result, the SP schemes produce physically-based and physically-consistent stochastic variability to the tendencies of the resolved states. Evaluations of the SP schemes in global and regional models have shown positive impacts on the ensemble prediction skill of various degrees depending on the prediction time range, the modeling system, and verification variables (Christensen et al, 2015;Hermoso et al, 2021;Jankov et al, 2017;Leutbecher et al, 2017;McCabe et al, 2016;Ollinaho et al, 2017;Stanford et al, 2019;Thompson et al, 2021;Wang et al, 2019). In comparison to the SPPT, the impacts of the SP schemes on ensemble spread were found to be smaller, but adding value when the two approaches were combined (Christensen et al, 2015;Hermoso et al, 2021;Jankov et al, 2019;Ollinaho et al, 2017).…”

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confidence: 99%

Sensitivity of modeled microphysics to stochastically perturbed parameters

Vukićević¹,

Stanković²,

Posselt³

2021

Preprint

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This study investigates sensitivity of&#160; cloud and precipitation parameterized microphysics&#160; to stochastic representation of parameter uncertainty as formulated by the stochastically perturbed parameterization (SPP) scheme.&#160; SPP is applied to multiple microphysical parameters within a lagrangian column model, used in several prior published studies to characterize &#160;parameter uncertainty by means of multivariate nonlinear inversions using remote sensing observations. The 1D column microphysics model is forced with prescribed time-varying profiles of temperature, humidity and vertical velocity.&#160; This modeling framework allows for investigation of the effect of changes in model physics parameters on the model output in isolation from any feedback to the cloud-scale dynamics.The test case selected in this study of an idealized representation of mid-latitude squall-line convection is the same as in the prior studies. This enabled using the estimates of multi-parameter distributions from the inversions in the prior studies as the basis for setting the second-moment statistics in the SPP scheme implementation. Additionally impacts of the non-stochastic and stochastic multi-parameter representation of parameterization uncertainty on the microphysics model solution could be directly compared.The sensitivity experiments with the SPP scheme involve ensemble simulations where each member is evolved with a different stochastic sequence of parameter perturbations, as is done in the standard practice with this scheme.&#160; The experiments explore impacts of using different decorrelation times and different estimates of second moment statistics for the parameter perturbations.&#160; These include uncorrelated perturbations between the parameters for several values of variance for each parameter and correlated perturbations based on multi-parameter empirical statistical distributions from the prior studies.&#160; The selection of physical parameters for the perturbations is based on the significance of their impacts derived from the prior studies .&#160;The results are evaluated in terms of changes to the ensemble mean and variance of time evolving profiles of hydrometeor mass quantities, the microphysics processes within the model as well as in terms of the simulated column integral microphysics-sensitive satellite-based&#160; observables. The latter include PR (Precipitation Rate) , LWP (Liquid Water Path), IWP (Ice water path), TOA-LW and TOA-SW (-Long and -Short Wave, respectively).&#160; In each experiment six parameters were perturbed.The analyses performed so far indicate a high sensitivity of the microphysics model to the SPP scheme. The ensemble simulations with the standard uncorrelated parameter perturbations exhibit a significant bias relative to the control simulation which uses the unperturbed parameters.&#160; For the selected test case the skewness toward small parameter values in the SPP sampling based on the underlying log-normal distributions leads to less precipitating ice and more precipitating liquid and accumulated precipitation. The response is due to nonlinear relationships between the parameters and modeled microphysics output. The changes in microphysics output result in large mean changes in PR, LWP, IWP, TOA- LW and SW, suggesting a potential for using these and other microphysics sensitive satellite observations to evaluate and if needed correct properties of the underlying sampling distribution in the stochastic scheme.&#160; Further analyses will be presented at the conference.

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Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members

Khain,

Shtivelman,

Levi

et al. 2023

Meteorological Applications

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Nowadays, several global ensembles (GEs) which consist of several tens of members are being run operationally. In order to locally improve the probabilistic forecasts, various forecasting centers and research institutes utilize the GEs as initial and boundary conditions to drive regional convection permitting ensembles (RCPEs). RCPEs demand significant computer resources and often a limited number of ensemble members is affordable, which is smaller than the size of the driving GE. Since each RCPE member obtains the initial and boundary conditions from a specific GE member, there are many options to select the GE members. The study uses the European Centre for Medium‐Range Weather Forecasts (ECMWF) GE consisting of 50 members, to drive 20 members of COSMO model RCPE over the Eastern Mediterranean. We compare various approaches for automatic selection of the GE members and propose several optimal methods, including a random selection, which consistently lead to a better performance of the driven RCPE. The comparison includes verification of near surface variables and precipitation using various verification metrics. The results are validated using several methods of model physics perturbation. Besides the selection of the optimal ensemble configurations, we show that at high precipitation intensities spatial up‐scaling is recommended in order to obtain useful probabilistic forecasts.

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Potential of stochastic methods for improving convection-permitting ensemble forecasts of extreme events over the Western Mediterranean

Cited by 9 publications

References 122 publications

Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

Sensitivity of modeled microphysics to stochastically perturbed parameters

Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members

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