Process‐oriented stochastic perturbations applied to the parametrization of turbulence and shallow convection for ensemble prediction.

2022 ◽  
Author(s):  
Axelle Fleury ◽  
François Bouttier ◽  
Fleur Couvreux
Keyword(s):  
Ensemble Prediction ◽  
Shallow Convection ◽  
Stochastic Perturbations ◽  
Process Oriented

scholarly journals A Regional Ensemble Prediction System Based on Moist Targeted Singular Vectors and Stochastic Parameter Perturbations

2008 ◽  
Vol 136 (2) ◽  
pp. 443-462 ◽  
Author(s):  
Xiaoli Li ◽  
Martin Charron ◽  
Lubos Spacek ◽  
Guillem Candille
Keyword(s):  
Deep Convection ◽  
Horizontal Resolution ◽  
Forecast Skill ◽  
Convective Precipitation ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Singular Vectors

Abstract A regional ensemble prediction system (REPS) with the limited-area version of the Canadian Global Environmental Multiscale (GEM) model at 15-km horizontal resolution is developed and tested. The total energy norm singular vectors (SVs) targeted over northeastern North America are used for initial and boundary perturbations. Two SV perturbation strategies are tested: dry SVs with dry simplified physics and moist SVs with simplified physics, including stratiform condensation and convective precipitation as well as dry processes. Model physics uncertainties are partly accounted for by stochastically perturbing two parameters: the threshold vertical velocity in the trigger function of the Kain–Fritsch deep convection scheme, and the threshold humidity in the Sundqvist explicit scheme. The perturbations are obtained from first-order Markov processes. Short-range ensemble forecasts in summer with 16 members are performed for five different experiments. The experiments employ different perturbation and piloting strategies, and two different surface schemes. Verification focuses on quantitative precipitation forecasts and is done using a range of probabilistic measures. Results indicate that using moist SVs instead of dry SVs has a stronger impact on precipitation than on dynamical fields. Forecast skill for precipitation is greatly influenced by the dominant synoptic weather systems. For stratiform precipitation caused by strong baroclinic systems, the forecast skill is improved in the moist SV experiments relative to the dry SV experiments. For convective precipitation rates in the range 15–50 mm (24 h)−1 produced by weak synoptic baroclinic systems, all experiments exhibit noticeably poorer forecast skills. Skill improvements due to the Interactions between Soil, Biosphere, and Atmosphere (ISBA) surface scheme and stochastic perturbations are also observed.

scholarly journals New Developments in Ensemble-based Probabilistic Forecasting of Solar Radiation: The WRF-Solar Ensemble Prediction System

2021 ◽  
Author(s):  
Manajit Sengupta ◽  
Pedro Jimenez ◽  
Jaemo Yang ◽  
Ju-Hye Kim ◽  
Yu Xie
Keyword(s):  
Sensitivity Analysis ◽  
Solar Radiation ◽  
Solar Irradiance ◽  
Solar Power ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Radiation Data ◽  
Solar Generation

<p>The demand for increased accuracy in predicting solar power has grown considerably over recent years due to a rapid growth in grid-tied solar generation both utility scale and distributed. To increase confidence in forecasting solar power there is a need to provide reliable probabilistic solar radiation information that also minimizes error and uncertainty. Funded by the U.S. Department of Energy, the Weather Research and Forecasting (WRF)-Solar ensemble prediction system (WRF-Solar EPS) has been recently developed by a collaboration between the National Renewable Energy Laboratory and the National Center for Atmospheric Research. The WRF-Solar EPS is now ready to be disseminated to support the integration of solar generation resources and improve accuracy of day-ahead and intraday probabilistic solar forecasts. The first stage of our framework in developing WRF-Solar EPS required a specially designed method using a tangent linear (TL) sensitivity analysis to efficiently investigate uncertainties of WRF-Solar variables in forecasting clouds and solar irradiance. For the second stage, we applied a methodology to introduce stochastic perturbations in 14 key variables ascertained through the TL sensitivity analysis in generating ensemble members. A user-friendly interface is provided in WRF-Solar EPS, in which the parameters of stochastic perturbations can be controlled by configuration files. Lastly, we implemented an analog technique as an ensemble post-processing method to improve the performance of ensemble solar irradiance forecasts. This presentation will summarize the work performed in the past 3 years to understand the interactions between cloud physics, land surface, boundary layer and radiative transfer models through the development of a probabilistic cloud optimized day-ahead forecasting system based on WRF-Solar. For evaluation of forecasts, we adapt and use satellite-derived solar radiation data, e.g., the National Solar Radiation Data Base (NSRDB) as well as ground-measured observations. A comprehensive analysis to assess gridded model outputs over the Contiguous U.S is performed. The importance of evaluation of the WRF-Solar model with the NSRDB lies in the fact that the knowledge of the cloud-caused uncertainties in predicting solar irradiance over a wide range of regions provides model developers a detailed understanding of model strength and weaknesses in predicting clouds. Overall, we will present the detailed research steps that resulted in the development of the WRF-Solar EPS. We will also present a detailed validation demonstrating the improvements provided by this model. Moreover, we will also introduce the user’s guide for WRF-Solar EPS and future extension of this research.</p>

scholarly journals Stochastic perturbations for parametrisation tendencies in a convection-permitting ensemble

2018 ◽  
Author(s):  
Clemens Wastl ◽  
Yong Wang ◽  
Aitor Atencia ◽  
Christoph Wittman
Keyword(s):  
Ensemble Forecasting ◽  
Limited Area ◽  
Test Cases ◽  
Shallow Convection ◽  
Stochastic Perturbations ◽  
Forecasting System ◽  
The Stability ◽  
Positive Effect ◽  
Total Model ◽  
Stochastic Physics

Abstract. A modification of the widely used SPPT (Stochastically Perturbed Parametrisation Tendencies) scheme is proposed and tested in a Convection-permitting – Limited Area Ensemble Forecasting system (C-LAEF) developed at ZAMG (Zentralanstalt für Meteorologie und Geodynamik). The tendencies from four physical parametrisation schemes are perturbed: radiation, shallow convection, turbulence and microphysics. Whereas in SPPT the total model tendencies are perturbed, in the present approach (pSPPT hereinafter) the partial tendencies of the physics parametrisation schemes are sequentially perturbed. Thus, in pSPPT an interaction between the uncertainties of the different physics parametrisation schemes is sustained and a more physically consistent relationship between the processes is kept. Two configurations of pSPPT are evaluated over two months (one of summer and another of winter). Both schemes increase the stability of the model and lead to statistically significant improvements in the probabilistic performance compared to the original SPPT. An evaluation of selected test cases shows that the positive effect of stochastic physics is much more pronounced on days with high convective activity. Small discrepancies in the humidity analysis can be dedicated to the use of a very simple supersaturation adjustment. This and other adjustments are discussed to provide some suggestions for future investigations.

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.

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>

Options and extensions for the stochastic shallow convection scheme in ICON

2021 ◽  
Author(s):  
Maike Ahlgrimm ◽  
Daniel Klocke ◽  
Alberto de Lozar ◽  
Ekaterina Machulskaya ◽  
Mirjana Sakradzija ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Convective Transport ◽  
Limited Area ◽  
Stable Operation ◽  
Shallow Convection ◽  
Max Planck ◽  
Convection Scheme ◽  
Stochastic Perturbations ◽  
Convection Parameterization ◽  
Flux Limiters

<p>The Icosahedral Model (ICON) of the German Weather Service (Deutscher Wetterdienst, DWD) is used for numerical weather prediction at global and regional scales. In the limited area mode, resolution is typically on the order of a few kilometers horizontal grid spacing. Deep convective transport is partially resolved at these scales, but shallow convection remains poorly represented without a parameterization.</p><p>A stochastic shallow convection scheme was developed in collaboration with the Max Planck Institute for Meteorology, and is now being implemented in ICON with a view towards operational use. The scheme is scale-adaptive and renders resolution-dependent tuning of the convection parameterization unnecessary. Mass flux limiters essential for the stable operation of the unaltered convection scheme can be removed when the stochastic perturbations are introduced.</p><p>Alongside the original, explicit stochastic scheme an approximation using stochastic differential equations (SDE) has been developed. The advantage of the SDE version is a lower computational and memory cost, and the ability to save and restart the model‘s stochastic cloud state easily.</p><p>Equivalence of the two versions can be demonstrated by running one version interactively, the other passively (“piggy-backing”). While the SDE approximation is computationally more efficient, the explicit version of the scheme can be easily extended to keep track of additional properties of the shallow cloud ensemble. For example, the convective updraft core fraction can be calculated for use in the diagnostic subgrid cloud scheme. Or knowledge of individual clouds’ depth can be used to derive a more realistic lateral detrainment profile than is currently in use.</p><p>We demonstrate the performance of the scheme and illustrate options and applications in single column mode, case studies and month-long hindcasts.</p>

scholarly journals Model Error Representation in an Operational Ensemble Kalman Filter

2009 ◽  
Vol 137 (7) ◽  
pp. 2126-2143 ◽  
Author(s):  
P. L. Houtekamer ◽  
Herschel L. Mitchell ◽  
Xingxiu Deng
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Model Error ◽  
Ensemble Prediction ◽  
Isotropic Model ◽  
Error Sources ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Ensemble Mean ◽  
The Impact

Since 12 January 2005, an ensemble Kalman filter (EnKF) has been used operationally at the Meteorological Service of Canada to provide the initial conditions for the medium-range forecasts of the ensemble prediction system. One issue in EnKF development is how to best account for model error. It is shown that in a perfect-model environment, without any model error or model error simulation, the EnKF spread remains representative of the ensemble mean error with respect to a truth integration. Consequently, the EnKF can be used to quantify the impact of the various error sources in a data-assimilation cycle on the quality of the ensemble mean. Using real rather than simulated observations, but still not simulating model error in any manner, the rms ensemble spread is found to be too small by approximately a factor of 2. It is then attempted to account for model error by using various combinations of the following four different approaches: (i) additive isotropic model error perturbations; (ii) different versions of the model for different ensemble members; (iii) stochastic perturbations to physical tendencies; and (iv) stochastic kinetic energy backscatter. The addition of isotropic model error perturbations is found to have the biggest impact. The identification of model error sources could lead to a more realistic, likely anisotropic, parameterization. Using different versions of the model has a small but clearly positive impact and consequently both (i) and (ii) are used in the operational EnKF. The use of approaches (iii) and (iv) did not lead to further improvements.

scholarly journals Impact of Stochastic Physics in a Convection-Permitting Ensemble

2012 ◽  
Vol 140 (11) ◽  
pp. 3706-3721 ◽  
Author(s):  
François Bouttier ◽  
Benoît Vié ◽  
Olivier Nuissier ◽  
Laure Raynaud
Keyword(s):  
Stochastic Perturbation ◽  
Atmospheric Model ◽  
Ensemble Prediction ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Score Improvement ◽  
Time Correlations ◽  
Systematic Effects ◽  
Stochastic Physics

A stochastic physics scheme is tested in the Application of Research to Operations at Mesoscale (AROME) short-range convection-permitting ensemble prediction system. It is an adaptation of ECMWF’s stochastic perturbation of physics tendencies (SPPT) scheme. The probabilistic performance of the AROME model ensemble is found to be significantly improved, when verified against observations over two 2-week periods. The main improvement lies in the ensemble reliability and the spread–skill consistency. Probabilistic scores for several weather parameters are improved. The tendency perturbations have zero mean, but the stochastic perturbations have systematic effects on the model output, which explains much of the score improvement. Ensemble spread is an increasing function of the SPPT space and time correlations. A case study reveals that stochastic physics do not simply increase ensemble spread, they also tend to smooth out high-spread areas over wider geographical areas. Although the ensemble design lacks surface perturbations, there is a significant end impact of SPPT on low-level fields through physical interactions in the atmospheric model.

scholarly journals The importance of atmosphere-ocean-wave coupling in ensemble regional convective-scale forecasts of midlatitude cyclones

2021 ◽  
Author(s):  
Emanuele Silvio Gentile ◽  
Suzanne L. Gray ◽  
Janet F. Barlow ◽  
Huw W. Lewis
Keyword(s):  
Wind Speed ◽  
Ocean Wave ◽  
Ensemble Prediction ◽  
Initial Condition ◽  
Wave Coupling ◽  
Stochastic Perturbations ◽  
Wind Speeds ◽  
Deterministic Systems ◽  
Convective Scale ◽  
The Impact

<p>Convective-scale ensemble prediction systems (EPS) are critical tools to accurately forecast damaging surface winds in the short range,  capturing the local details of their variability and providing guidance on the associated forecast uncertainty. Due to computational cost, operational convective-scale EPS are atmosphere-only models, which represent ocean and wave effects through sea-state independent parametrizations, and therefore do not account for the impact of an evolving ocean and wave state during the forecast. Benefits of integrating atmosphere, ocean, and wave feedbacks into a single coupled multimodel system have been shown by global-scale deterministic systems and EPS, and convective-scale deterministic systems. These benefits lead to the question of what are the corresponding benefits of coupling in convective-scale EPS. To address this question, we present the first convective-scale regional ensemble coupled system focused on the UK domain and surrounding seas (termed RECS-UK). We demonstrate the robustness of the impact of atmosphere-ocean-wave coupling and stochastic perturbations to model physics parametrizations on forecasts of extratropical cyclone Ciara and quantify the importance of these coupling impacts relative to initial condition error. </p><p>Coupling to the ocean leads to localised reductions in the 10-m wind speeds due to cooling of sea surface temperatures, which increase the stability in the surface layer. However, these localised impacts on coupling to ocean are not apparent when comparing the ensemble strike probabilities of exceeding a storm wind threshold (set to 20 m s<sup>-1</sup><sup>)</sup> for the atmosphere-ocean-coupled and control (atmosphere-only) ensembles. In contrast, coupling additionally to waves leads to substantial reductions in wind strike probability and consistently reduces, by up to 1 m s<sup>-1</sup><sup>,</sup> the ensemble forecast median and mean of Ciara’s wind speeds at all simulation hours during which Ciara is in the model domain. Each atmosphere-ocean-wave coupled ensemble member simulates the dynamical response of wind speeds to the forced young ocean waves, with maximum reductions in high wind speed regions. The largest 10-m wind speed spread from stochastic and initial condition perturbations is found away from the strongest wind speed regions of Ciara, but the impact of coupling to waves is more enhanced in these strongest wind speed regions, and is also comparable in size there with the largest sensitivity to stochastic and initial condition perturbations. The implications of this work are that the impacts on 10-m wind speed of coupling convective-scale atmospheric models to ocean and wave models can be robust across an ensemble and be of comparable size to those of initial condition and stochastic physics perturbations. However, convection-permitting atmosphere-ocean-wave coupled EPS should be assessed in different meteorological conditions and further tested on longer timescales prior to operational implementation. </p>

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