Representation of model error through process-based perturbations for ensemble prediction : application to turbulence and shallow convection parametrisations

2021 ◽  
Author(s):  
Axelle Fleury ◽  
François Bouttier
Keyword(s):  
Model Uncertainty ◽  
Perturbation Methods ◽  
Stochastic Perturbation ◽  
Research Field ◽  
Ensemble Prediction ◽  
Model Errors ◽  
Shallow Convection ◽  
Physical Processes ◽  
Closure Condition ◽  
Stochastically Perturbed

<p>The boundary layer is the place of many complex physical processes spanning various time and space scales, part of which need to be parametrised in NWP models. These parametrisations are known sources of uncertainty in the models, due to the difficulty of accurately representing the processes, and the resulting simplifications and approximations that have to be done. Model uncertainty is part of what ensemble prediction systems seek to represent. This can be achieved in particular by using stochastic perturbation methods, where noise is introduced during model computations to change its state and produce different simulations. Well-known and widely used perturbation schemes like the Stochastically Perturbed Parametrisation Tendencies (SPPT) scheme have shown their effectiveness and their interest in building ensembles. However, part of the model uncertainty is not yet well represented in current ensemble systems, while some of the assumptions made by SPPT can be questioned. This argues for a diversity of approaches to represent model errors. In this active research field, alternative perturbation methods are investigated, such as the Stochastically Perturbed Parametrisations (SPP) method, or other methods focusing on the perturbation of particular physical processes. The work presented here focuses on the last ones. Based on two examples of methods published in the literature, perturbations have been applied to the turbulence and shallow convection parametrisation schemes of the mesoscale NWP model Arome from Météo-France. The perturbation of turbulence is based on the use of subgrid-scale variances to regulate the amplitude of an additive noise, while shallow convection is perturbed through a stochastic closure condition of the scheme. A simplified 1D framework has been used, in order to assess the ability of the method to produce an ensemble with sufficient dispersion and to compare its results with those from existing methods like SPPT.</p>

scholarly journals A Hybrid Stochastically Perturbed Parametrization Scheme in a Convection-Permitting Ensemble

2019 ◽  
Vol 147 (6) ◽  
pp. 2217-2230 ◽  
Author(s):  
Clemens Wastl ◽  
Yong Wang ◽  
Aitor Atencia ◽  
Christoph Wittmann
Keyword(s):  
Ensemble Prediction ◽  
Limited Area ◽  
Perturbation Approach ◽  
Perturbation Scheme ◽  
Shallow Convection ◽  
Physical Processes ◽  
Parametrization Scheme ◽  
Prediction Systems ◽  
The Individual ◽  
Stochastically Perturbed

Abstract Model error in ensemble prediction systems is often represented by either a tendency perturbation approach or a process-based parameter perturbation scheme. In this paper a novel hybrid stochastically perturbed parameterization (HSPP) scheme is proposed and implemented in the Convection Permitting Limited Area Ensemble Forecasting (C-LAEF) system developed at the Zentralanstalt für Meteorologie und Geodynamik (ZAMG). In HSPP, the individual parameterization tendencies of the physical processes radiation, shallow convection, and microphysics are perturbed stochastically by a spatially and temporally varying pattern. Uncertainties in the turbulence scheme are considered by perturbing key parameters on the process level. The proposed scheme HSPP features several advantages compared to the popular stochastically perturbed parameterization tendencies (SPPT) scheme: it considers a more physically consistent relationship between different parameterization schemes, deals with uncertainties especially adapted to the individual physical processes, respects conservation laws of energy and moisture, and eliminates the tapering function that has to be introduced to the SPPT scheme because of mainly numerical reasons. The hybrid scheme HSPP is evaluated over one summer and one winter month and compared to a reference ensemble without any stochastic physics perturbations and to two versions of the SPPT scheme. The results show that HSPP significantly increases the ensemble spread of temperature, humidity, wind speed, and pressure, especially in the lower levels of the atmosphere where a tapering function is active in the original SPPT approach. Precipitation verification yields a generally improved probabilistic performance of the HSPP scheme in summer when convection is dominating, which has also been demonstrated in a case study.

scholarly journals Model Error Representation Using the Stochastically Perturbed Hybrid Physical–Dynamical Tendencies in Ensemble Data Assimilation System

2020 ◽  
Vol 10 (24) ◽  
pp. 9010
Author(s):  
Sujeong Lim ◽  
Myung-Seo Koo ◽  
In-Hyuk Kwon ◽  
Seon Ki Park
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Stochastic Perturbation ◽  
Numerical Weather Prediction Model ◽  
Natural State ◽  
Model Errors ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Stochastically Perturbed

Ensemble data assimilation systems generally suffer from underestimated background error covariance that leads to a filter divergence problem—the analysis diverges from the natural state by ignoring the observation influence due to the diminished estimation of model uncertainty. To alleviate this problem, we have developed and implemented the stochastically perturbed hybrid physical–dynamical tendencies to the local ensemble transform Kalman filter in a global numerical weather prediction model—the Korean Integrated Model (KIM). This approach accounts for the model errors associated with computational representations of underlying partial differential equations and the imperfect physical parameterizations. The new stochastic perturbation hybrid tendencies scheme generally improved the background error covariances in regions where the ensemble spread was not sufficiently expressed by the control experiment that used an additive inflation and the relaxation to prior spread method.

scholarly journals Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

2019 ◽  
Vol 148 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Kevin Bachmann ◽  
Christian Keil ◽  
George C. Craig ◽  
Martin Weissmann ◽  
Christian A. Welzbacher
Keyword(s):  
Data Assimilation ◽  
Deep Convection ◽  
Radar Data ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Beneficial Impact ◽  
The Impact ◽  
Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

Case Dependence of Multiscale Interactions between Multisource Perturbations for Convection-Permitting Ensemble Forecasting during SCMREX

2021 ◽  
Author(s):  
Xubin Zhang
Keyword(s):  
Perturbation Methods ◽  
Monsoon Rainfall ◽  
Southern China ◽  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Synoptic Scale ◽  
Ensemble Prediction System ◽  
Control Member ◽  
Probabilistic Forecasts ◽  
Perturbation Energy

AbstractThis study examines the case dependence of the multiscale characteristics of initial condition (IC) and model physics (MO) perturbations and their interactions in a convection-permitting ensemble prediction system (CPEPS), focusing on the 12-h forecasts of precipitation perturbation energy. The case dependence of forecast performances of various ensemble configurations is also examined to gain guidance for CPEPS design. Heavy-rainfall cases over Southern China during the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014 were discriminated between the strongly and weakly forced events in terms of synoptic-scale forcing, each of which included 10 cases. In the cases with weaker forcing, MO perturbations showed larger influences while the enhancements of convective activities relative to the control member due to IC perturbations were less evident, leading to smaller dispersion reduction due to adding MO perturbations to IC perturbations. Such dispersion reduction was more sensitive to IC and MO perturbation methods in the weakly and strongly forced cases, respectively. The dispersion reduction improved the probabilistic forecasts of precipitation, with more evident improvements in the cases with weaker forcing. To improve the benefits of dispersion reduction in forecasts, it is instructive to elaborately consider the case dependence of dispersion reduction, especially the various sensitivities of dispersion reduction to different-source perturbation methods in various cases, in CPEPS design.

scholarly journals Randomly correcting model errors in the ARPEGE-Climate v6.1 component of CNRM-CM: applications for seasonal forecasts

2016 ◽  
Vol 9 (6) ◽  
pp. 2055-2076 ◽  
Author(s):  
Lauriane Batté ◽  
Michel Déqué
Keyword(s):  
Stochastic Dynamics ◽  
Stochastic Perturbation ◽  
Coupled Model ◽  
Seasonal Forecasting ◽  
Stochastic Methods ◽  
Boreal Winter ◽  
Model Errors ◽  
Seasonal Forecasts ◽  
Error Statistics ◽  
The Impact

Abstract. Stochastic methods are increasingly used in global coupled model climate forecasting systems to account for model uncertainties. In this paper, we describe in more detail the stochastic dynamics technique introduced by Batté and Déqué (2012) in the ARPEGE-Climate atmospheric model. We present new results with an updated version of CNRM-CM using ARPEGE-Climate v6.1, and show that the technique can be used both as a means of analyzing model error statistics and accounting for model inadequacies in a seasonal forecasting framework.The perturbations are designed as corrections of model drift errors estimated from a preliminary weakly nudged re-forecast run over an extended reference period of 34 boreal winter seasons. A detailed statistical analysis of these corrections is provided, and shows that they are mainly made of intra-month variance, thereby justifying their use as in-run perturbations of the model in seasonal forecasts. However, the interannual and systematic error correction terms cannot be neglected. Time correlation of the errors is limited, but some consistency is found between the errors of up to 3 consecutive days.These findings encourage us to test several settings of the random draws of perturbations in seasonal forecast mode. Perturbations are drawn randomly but consistently for all three prognostic variables perturbed. We explore the impact of using monthly mean perturbations throughout a given forecast month in a first ensemble re-forecast (SMM, for stochastic monthly means), and test the use of 5-day sequences of perturbations in a second ensemble re-forecast (S5D, for stochastic 5-day sequences). Both experiments are compared in the light of a REF reference ensemble with initial perturbations only. Results in terms of forecast quality are contrasted depending on the region and variable of interest, but very few areas exhibit a clear degradation of forecasting skill with the introduction of stochastic dynamics. We highlight some positive impacts of the method, mainly on Northern Hemisphere extra-tropics. The 500 hPa geopotential height bias is reduced, and improvements project onto the representation of North Atlantic weather regimes. A modest impact on ensemble spread is found over most regions, which suggests that this method could be complemented by other stochastic perturbation techniques in seasonal forecasting mode.

scholarly journals Model risk – daring to open up the black box

2015 ◽  
Vol 21 (2) ◽  
pp. 229-296 ◽  
Author(s):  
A. Aggarwal ◽  
M. B. Beck ◽  
M. Cann ◽  
T. Ford ◽  
D. Georgescu ◽  
...  
Keyword(s):  
Model Uncertainty ◽  
Risk Governance ◽  
Model Errors ◽  
Model Risk ◽  
Inappropriate Use ◽  
Business Applications ◽  
Deep Model ◽  
Risk Appetite ◽  
Risk Framework ◽  
Investment Advice

AbstractWith the increasing use of complex quantitative models in applications throughout the financial world, model risk has become a major concern. Such risk is generated by the potential inaccuracy and inappropriate use of models in business applications, which can lead to substantial financial losses and reputational damage. In this paper, we deal with the management and measurement of model risk. First, a model risk framework is developed, adapting concepts such as risk appetite, monitoring, and mitigation to the particular case of model risk. The usefulness of such a framework for preventing losses associated with model risk is demonstrated through case studies. Second, we investigate the ways in which different ways of using and perceiving models within an organisation both lead to different model risks. We identify four distinct model cultures and argue that in conditions of deep model uncertainty, each of those cultures makes a valuable contribution to model risk governance. Thus, the space of legitimate challenges to models is expanded, such that, in addition to a technical critique, operational and commercial concerns are also addressed. Third, we discuss through the examples of proxy modelling, longevity risk, and investment advice, common methods and challenges for quantifying model risk. Difficulties arise in mapping model errors to actual financial impact. In the case of irreducible model uncertainty, it is necessary to employ a variety of measurement approaches, based on statistical inference, fitting multiple models, and stress and scenario analysis.

scholarly journals Coupled parameter perturbations in the SPP scheme of the HarmonEPS

2021 ◽  
Author(s):  
Aristofanis Tsiringakis ◽  
Wim de Rooy ◽  
Sibbo van der Veen ◽  
Jan Barkmeijer
Keyword(s):  
Boundary Layer ◽  
Boundary Conditions ◽  
Model Performance ◽  
Ensemble Prediction ◽  
Model Bias ◽  
Ensemble Spread ◽  
Physical Processes ◽  
Parameter Perturbations ◽  
Atmospheric State ◽  
Parameter Values

<p>In an ensemble prediction system (EPS) the uncertainty in the initial atmospheric conditions is usually represented via perturbation of the initial atmospheric state and different boundary conditions at the beginning and throughout the duration of the forecast. These approaches exclude the uncertainty due to the representation of physical processes within the parameterization schemes of a numerical weather prediction model (NWP). Much of the uncertainty in the presentation of physical process arises from uncertain parameter values regulating key physical processes in the boundary-layer and microphysics schemes. This uncertainty can be represented with a Stochastically Perturbed Parameterization (SPP) scheme, where parameter values for the different model grid points are randomly selected from a defined probability density function. The SPP scheme can improve model performance and increase ensemble spread, but may lead to unrealistic parameter values, which can introduce additional model bias. A potential solution is to use coupled/correlated perturbations for relevant SPP parameters to increase the model performance and ensemble spread, while maintaining physically realistic ranges for the parameters. In this study, we investigate the impact of coupled perturbations in key parameters within the boundary-layer and microphysics schemes of the HarmonEPS model using the new SPP scheme. The performance of the coupled perturbations experiment is evaluated against HarmonEPS experiments using independent parameter perturbations, and perturbations in the initial atmospheric state and boundary conditions for both a winter and a summer period.  We find that coupled perturbations in the SPP scheme can decrease model bias and increase the ensemble spread for the 2m temperature and relative humidity, 10m-wind speed and total cloud cover.</p>

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