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>

scholarly journals Evaluating U.S. East Coast Winter Storms in a Multimodel Ensemble Using EOF and Clustering Approaches

2019 ◽  
Vol 147 (6) ◽  
pp. 1967-1987 ◽  
Author(s):  
Minghua Zheng ◽  
Edmund K. M. Chang ◽  
Brian A. Colle
Keyword(s):  
Lead Time ◽  
East Coast ◽  
Time Frame ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Winter Storms ◽  
East Coast Winter Storms ◽  
Prediction Systems ◽  
Environmental Prediction ◽  
The Individual

Abstract Empirical orthogonal function (EOF) and fuzzy clustering tools were applied to generate and validate scenarios in operational ensemble prediction systems (EPSs) for U.S. East Coast winter storms. The National Centers for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF), and Canadian Meteorological Centre (CMC) EPSs were validated in their ability to capture the analysis scenarios for historical East Coast cyclone cases at lead times of 1–9 days. The ECMWF ensemble has the best performance for the medium- to extended-range forecasts. During this time frame, NCEP and CMC did not perform as well, but a combination of the two models helps reduce the missing rate and alleviates the underdispersion. All ensembles are underdispersed at all ranges, with combined ensembles being less underdispersed than the individual EPSs. The number of outside-of-envelope cases increases with lead time. For a majority of the cases beyond the short range, the verifying analysis does not lie within the ensemble mean group of the multimodel ensemble or within the same direction indicated by any of the individual model means, suggesting that all possible scenarios need to be taken into account. Using the EOF patterns to validate the cyclone properties, the NCEP model tends to show less intensity and displacement biases during 1–3-day lead time, while the ECMWF model has the smallest biases during 4–6 days. Nevertheless, the ECMWF forecast position tends to be biased toward the southwest of the other two models and the analysis.

scholarly journals A Comparison of the Canadian Global and Regional Meteorological Ensemble Prediction Systems for Short-Term Hydrological Forecasting

2013 ◽  
Vol 141 (10) ◽  
pp. 3462-3476 ◽  
Author(s):  
Mabrouk Abaza ◽  
François Anctil ◽  
Vincent Fortin ◽  
Richard Turcotte
Keyword(s):  
Absolute Error ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Hydrological Models ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Study Results ◽  
Prediction Systems ◽  
Set Up ◽  
Meteorological Models

Abstract Meteorological ensemble prediction systems (M-EPS) are generally set up at lower resolution than for their deterministic counterparts. Operational hydrologists are thus more prone to selecting deterministic meteorological forecasts for driving their hydrological models. Limited-area implementation of meteorological models may become a convenient way of providing the sought after higher-resolution meteorological ensemble forecasts. This study aims to compare the Canadian operational global EPS (M-GEPS) and the experimental regional EPS (M-REPS) for short-term operational hydrological ensemble forecasting over eight watersheds, for which performance and reliability was assessed. Higher-resolution deterministic forecasts were also available for the study. Results showed that both M-EPS provided better performance than their deterministic counterparts when comparing their mean continuous ranked probability score (MCRPS) and mean absolute error (MAE), especially beyond a 24-h horizon. The global and regional M-EPS led to very similar performance in terms of RMSE, but the latter produced a larger spread and improved reliability. The M-REPS was deemed superior to its operational global counterpart, especially for its ability to better depict forecast uncertainty.

Comparison and Combination of Regional and Global Ensemble Prediction Systems for Probabilistic Predictions of Hub-Height Wind Speed

2015 ◽  
Vol 30 (5) ◽  
pp. 1234-1253 ◽  
Author(s):  
Constantin Junk ◽  
Stephan Späth ◽  
Lueder von Bremen ◽  
Luca Delle Monache
Keyword(s):  
Wind Speed ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Discrimination Ability ◽  
Adaptive Calibration ◽  
Error Correlation ◽  
Multimodel Ensembles ◽  
Prediction Systems

Abstract The objective of this paper is to compare probabilistic 100-m wind speed forecasts, which are relevant for wind energy applications, from different regional and global ensemble prediction systems (EPSs) at six measurement towers in central Europe and to evaluate the benefits of combining single-model ensembles into multimodel ensembles. The global 51-member EPS from the European Centre for Medium-Range Weather Forecasts (ECMWF EPS) is compared against the Consortium for Small-Scale Modelling’s (COSMO) limited-area 16-member EPS (COSMO-LEPS) and a regional, high-resolution 20-member EPS centered over Germany (COSMO-DE EPS). The ensemble forecasts are calibrated with univariate (wind speed) ensemble model output statistics (EMOS) and bivariate (wind vector) recursive and adaptive calibration (AUV). The multimodel ensembles are constructed by pooling together raw or best-calibrated ensemble forecasts. An additional postprocessing of these multimodel ensembles with both EMOS and AUV is also tested. The best-performing calibration methodology for ECMWF EPS is AUV, while EMOS performs better than AUV for the calibration of COSMO-DE EPS. COSMO-LEPS has similar skill when calibrated with both EMOS and AUV. The AUV ECMWF EPS outperforms the EMOS COSMO-LEPS and COSMO-DE EPS for deterministic and probabilistic wind speed forecast skill. For most thresholds, ECMWF EPS has a comparable reliability and sharpness but higher discrimination ability. Multimodel ensembles, which are constructed by pooling together the best-calibrated EPSs, improve the skill relative to the AUV ECMWF EPS. An analysis of the error correlation among the EPSs indicates that multimodel ensemble skill can be considerably higher when the error correlation is low.

Ensemble Prediction with Different Spatial Resolutions for the 2014 Sochi Winter Olympic Games: The Effects of Calibration and Multimodel Approaches

2016 ◽  
Vol 31 (6) ◽  
pp. 1833-1851 ◽  
Author(s):  
Inger-Lise Frogner ◽  
Thomas Nipen ◽  
Andrew Singleton ◽  
John Bjørnar Bremnes ◽  
Ole Vignes
Keyword(s):  
Complex Terrain ◽  
Olympic Games ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Medium Range ◽  
Winter Olympic Games ◽  
Prediction Systems

Abstract Three ensemble prediction systems (EPSs) with different grid spacings are compared and evaluated with respect to their ability to predict wintertime weather in complex terrain. The experiment period was two-and-a-half winter months in 2014, coinciding with the Forecast and Research in the Olympic Sochi Testbed (FROST) project, which took place during the Winter Olympic Games in Sochi, Russia. The global, synoptic-scale ensemble system used is the IFS ENS from the European Centre for Medium-Range Weather Forecasts (ECMWF), and its performance is compared with both the operational pan-European Grand Limited Area Ensemble Prediction System (GLAMEPS) at 11-km horizontal resolution and the experimental regional convection-permitting HIRLAM–ALADIN Regional Mesoscale Operational NWP in Europe (HARMONIE) EPS (HarmonEPS) at 2.5 km. Both GLAMEPS and HarmonEPS are multimodel systems, and it is seen that a large part of the skill in these systems comes from the multimodel approach, as long as all subensembles are performing reasonably. The number of members has less impact on the overall skill measurement. The relative importance of resolution and calibration is also assessed. Statistical calibration was applied and evaluated. In contrast to what is seen for the raw ensembles, the number of members, as well as the number of subensembles, is important for the calibrated ensembles. HarmonEPS shows greater potential than GLAMEPS for predicting wintertime weather, and also has an advantage after calibration.

scholarly journals MEDEX: a general overview

2014 ◽  
Vol 2 (1) ◽  
pp. 535-580 ◽  
Author(s):  
A. Jansa ◽  
P. Alpert ◽  
P. Arbogast ◽  
A. Buzzi ◽  
B. Ivancan-Picek ◽  
...  
Keyword(s):  
Social Impact ◽  
High Impact ◽  
Ensemble Prediction ◽  
Physical Processes ◽  
Innovative Methods ◽  
Weather Events ◽  
High Impact Weather ◽  
Prediction Systems ◽  
Two Phases ◽  
Scientific Achievements

Abstract. The general objective of the international MEDiterranean EXperiment (MEDEX) was the better understanding and forecasting of cyclones that produce high impact weather in the Mediterranean. This paper reviews the motivation and foundation of MEDEX, the gestation, history and organisation of the project, as well as the main products and scientific achievements obtained from it. MEDEX obtained the approval of WMO and can be considered as framed within other WMO actions, such as ALPEX, MCP and, to certain extent, THORPEX and HyMeX. Through two phases (2000–2005 and 2006–2010) MEDEX has produced a specific database, with information about cyclones and high impact weather events, several main reports and a specific field campaign (DTS-MEDEX-2009). The scientific achievements are significant in fields like climatology, dynamical understanding of the physical processes and social impact of cyclones, as well as on aspects related to the location of sensitive zones for individual cases, climatology of sensitivity zones and the improvement of the forecasts through innovative methods like mesoscale ensemble prediction systems.

scholarly journals Combining 2-m temperature nowcasting and short range ensemble forecasting

2011 ◽  
Vol 18 (6) ◽  
pp. 903-910 ◽  
Author(s):  
A. Kann ◽  
T. Haiden ◽  
C. Wittmann
Keyword(s):  
Statistical Method ◽  
Statistical Methods ◽  
Short Range ◽  
Statistical Approach ◽  
Forecast Skill ◽  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Spread ◽  
Prediction Systems

Abstract. During recent years, numerical ensemble prediction systems have become an important tool for estimating the uncertainties of dynamical and physical processes as represented in numerical weather models. The latest generation of limited area ensemble prediction systems (LAM-EPSs) allows for probabilistic forecasts at high resolution in both space and time. However, these systems still suffer from systematic deficiencies. Especially for nowcasting (0–6 h) applications the ensemble spread is smaller than the actual forecast error. This paper tries to generate probabilistic short range 2-m temperature forecasts by combining a state-of-the-art nowcasting method and a limited area ensemble system, and compares the results with statistical methods. The Integrated Nowcasting Through Comprehensive Analysis (INCA) system, which has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG) since 2006 (Haiden et al., 2011), provides short range deterministic forecasts at high temporal (15 min–60 min) and spatial (1 km) resolution. An INCA Ensemble (INCA-EPS) of 2-m temperature forecasts is constructed by applying a dynamical approach, a statistical approach, and a combined dynamic-statistical method. The dynamical method takes uncertainty information (i.e. ensemble variance) from the operational limited area ensemble system ALADIN-LAEF (Aire Limitée Adaptation Dynamique Développement InterNational Limited Area Ensemble Forecasting) which is running operationally at ZAMG (Wang et al., 2011). The purely statistical method assumes a well-calibrated spread-skill relation and applies ensemble spread according to the skill of the INCA forecast of the most recent past. The combined dynamic-statistical approach adapts the ensemble variance gained from ALADIN-LAEF with non-homogeneous Gaussian regression (NGR) which yields a statistical \\mbox{correction} of the first and second moment (mean bias and dispersion) for Gaussian distributed continuous variables. Validation results indicate that all three methods produce sharp and reliable probabilistic 2-m temperature forecasts. However, the statistical and combined dynamic-statistical methods slightly outperform the pure dynamical approach, mainly due to the under-dispersive behavior of ALADIN-LAEF outside the nowcasting range. The training length does not have a pronounced impact on forecast skill, but a spread re-scaling improves the forecast skill substantially. Refinements of the statistical methods yield a slight further improvement.

scholarly journals Stochastic versus Dynamical Downscaling of Ensemble Precipitation Forecasts

2009 ◽  
Vol 10 (4) ◽  
pp. 1051-1061 ◽  
Author(s):  
Elisa Brussolo ◽  
Jost von Hardenberg ◽  
Nicola Rebora
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Forecast Skill ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Prediction Systems ◽  
Stochastic Downscaling ◽  
Precipitation Events

Abstract The assessment of hydrometeorological risk in small basins requires the availability of skillful, high-resolution quantitative precipitation forecasts to predict the probability of occurrence of severe, localized precipitation events. Large-scale ensemble prediction systems (EPS) currently provide forecast scenarios down to a resolution of about 50 km. High-resolution, nonhydrostatic, limited-area ensemble prediction systems provide dynamically based forecasts by extending these scenarios to smaller scales, typically on the order of 10 km. This work explores an alternative approach to the use of limited-area ensemble prediction systems, by directly applying a stochastic downscaling technique to large-scale ensemble forecasts. The performances of these two different approaches for three well-predicted precipitation events in northwestern Italy during 2006 are compared. Ensemble forecasts provided by the ECMWF EPS, downscaled using the Rainfall Filtered Autoregressive Model (RainFARM) stochastic technique, and ensemble forecasts obtained from the Consortium for Small-Scale Modeling Limited-Area Ensemble Prediction System (COSMO-LEPS) are considered. A dense network of rain gauges is used for verification. It is found that the probabilistic forecast skill of stochastically downscaled ensembles may be comparable with that of dynamically downscaled ensembles, using a range of standard forecast skill measures. Stochastic downscaling is suggested as a tool for benchmarking the performance of dynamical ensemble downscaling systems.

scholarly journals A flood episode in northern Italy: multi-model and single-model mesoscale meteorological ensembles for hydrological predictions

2013 ◽  
Vol 17 (6) ◽  
pp. 2107-2120 ◽  
Author(s):  
S. Davolio ◽  
M. M. Miglietta ◽  
T. Diomede ◽  
C. Marsigli ◽  
A. Montani
Keyword(s):  
Horizontal Resolution ◽  
Northern Italy ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Model Ensemble ◽  
Single Model ◽  
Ensemble Prediction System ◽  
Forecasting System ◽  
Meteorological Models

Abstract. Numerical weather prediction models can be coupled with hydrological models to generate streamflow forecasts. Several ensemble approaches have been recently developed in order to take into account the different sources of errors and provide probabilistic forecasts feeding a flood forecasting system. Within this framework, the present study aims at comparing two high-resolution limited-area meteorological ensembles, covering short and medium range, obtained via different methodologies, but implemented with similar number of members, horizontal resolution (about 7 km), and driving global ensemble prediction system. The former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter, following a single-model approach, is the operational ensemble forecasting system developed within the COSMO consortium, COSMO-LEPS (limited-area ensemble prediction system). The meteorological models are coupled with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno River (northern Italy), for a recent severe weather episode affecting northern Apennines. The evaluation of the ensemble systems is performed both from a meteorological perspective over northern Italy and in terms of discharge prediction over the Reno River basin during two periods of heavy precipitation between 29 November and 2 December 2008. For each period, ensemble performance has been compared at two different forecast ranges. It is found that, for the intercomparison undertaken in this specific study, both mesoscale model ensembles outperform the global ensemble for application at basin scale. Horizontal resolution is found to play a relevant role in modulating the precipitation distribution. Moreover, the multi-model ensemble provides a better indication concerning the occurrence, intensity and timing of the two observed discharge peaks, with respect to COSMO-LEPS. This seems to be ascribable to the different behaviour of the involved meteorological models. Finally, a different behaviour comes out at different forecast ranges. For short ranges, the impact of boundary conditions is weaker and the spread can be mainly attributed to the different characteristics of the models. At longer forecast ranges, the similar behaviour of the multi-model members forced by the same large-scale conditions indicates that the systems are governed mainly by the boundary conditions, although the different limited area models' characteristics may still have a non-negligible impact.

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