scholarly journals Hydro-meteorological evaluation of a convection-permitting ensemble prediction system for Mediterranean heavy precipitating events

2012 ◽  
Vol 12 (8) ◽  
pp. 2631-2645 ◽  
Author(s):  
B. Vié ◽  
G. Molinié ◽  
O. Nuissier ◽  
B. Vincendon ◽  
V. Ducrocq ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Probabilistic Evaluation ◽  
Convective Scale ◽  
Prediction Systems ◽  
Data Assimilation Technique ◽  
The Impact

Abstract. An assessment of the performance of different convection-permitting ensemble prediction systems (EPSs) is performed, with a focus on Heavy Precipitating Events (HPEs). The convective-scale EPS configuration includes perturbations of lateral boundary conditions (LBCs) by using a global ensemble to provide LBCs, initial conditions (ICs) through an ensemble data assimilation technique and perturbations of microphysical parameterisations to account for part of model errors. A probabilistic evaluation is conducted over an 18-day period. A clear improvement is found when uncertainties on LBCs and ICs are considered together, but the chosen microphysical perturbations have no significant impact on probabilistic scores. Innovative evaluation processes for three HPE case studies are implemented. First, maxima diagrams provide a multi-scale analysis of intense rainfall. Second, an hydrological evaluation is performed through the computation of discharge forecasts using hourly ensemble precipitation forecasts as an input. All ensembles behave similarly, but differences are found highlighting the impact of microphysical perturbations on HPEs forecasts, especially for cases involving complex small-scale processes.

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.

scholarly journals Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

2011 ◽  
Vol 11 (11) ◽  
pp. 30457-30485 ◽  
Author(s):  
P. Groenemeijer ◽  
G. C. Craig
Keyword(s):  
Large Scale ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Deep Convection ◽  
Horizontal Resolution ◽  
Ensemble Forecasting ◽  
Total Variance ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
The Impact

Abstract. The stochastic Plant-Craig scheme for deep convection was implemented in the COSMO mesoscale model and used for ensemble forecasting. Ensembles consisting of 100 48 h forecasts at 7 km horizontal resolution were generated for a 2000 × 2000 km domain covering central Europe. Forecasts were made for seven case studies and characterized by different large-scale meteorological environments. Each 100 member ensemble consisted of 10 groups of 10 members, with each group driven by boundary and initial conditions from a selected member from the global ECMWF Ensemble Prediction System. The precipitation variability within and among these groups of members was computed, and it was found that the relative contribution to the ensemble variance introduced by the stochastic convection scheme was substantial, amounting to as much as 76% of the total variance in the ensemble in one of the studied cases. The impact of the scheme was not confined to the grid scale, and typically contributed 25–50% of the total variance even after the precipitation fields had been smoothed to a resolution of 35 km. The variability of precipitation introduced by the scheme was approximately proportional to the total amount of convection that occurred, while the variability due to large-scale conditions changed from case to case, being highest in cases exhibiting strong mid-tropospheric flow and pronounced meso- to synoptic scale vorticity extrema. The stochastic scheme was thus found to be an important source of variability in precipitation cases of weak large-scale flow lacking strong vorticity extrema, but high convective activity.

scholarly journals Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions

2011 ◽  
Vol 139 (2) ◽  
pp. 403-423 ◽  
Author(s):  
Benoît Vié ◽  
Olivier Nuissier ◽  
Véronique Ducrocq
Keyword(s):  
Boundary Conditions ◽  
Case Studies ◽  
Short Range ◽  
Initial Conditions ◽  
Ensemble Prediction ◽  
Lateral Boundary ◽  
Synoptic Scale ◽  
Lateral Boundary Conditions ◽  
Convective Scale ◽  
The Impact

Abstract This study assesses the impact of uncertainty on convective-scale initial conditions (ICs) and the uncertainty on lateral boundary conditions (LBCs) in cloud-resolving simulations with the Application of Research to Operations at Mesoscale (AROME) model. Special attention is paid to Mediterranean heavy precipitating events (HPEs). The goal is achieved by comparing high-resolution ensembles generated by different methods. First, an ensemble data assimilation technique has been used for assimilation of perturbed observations to generate different convective-scale ICs. Second, three ensembles used LBCs prescribed by the members of a global short-range ensemble prediction system (EPS). All ensembles obtained were then evaluated over 31- and/or 18-day periods, and on 2 specific case studies of HPEs. The ensembles are underdispersive, but both the probabilistic evaluation of their overall performance and the two case studies confirm that they can provide useful probabilistic information for the HPEs considered. The uncertainty on convective-scale ICs is shown to have an impact at short range (under 12 h), and it is strongly dependent on the synoptic-scale context. Specifically, given a marked circulation near the area of interest, the imposed LBCs rapidly overwhelm the initial differences, greatly reducing the spread of the ensemble. The uncertainty on LBCs shows an impact at longer range, as the spread in the coupling global ensemble increases, but it also depends on the synoptic-scale conditions and their predictability.

scholarly journals An Analysis on Perturbation Features of Convection-Allowing Ensemble Prediction Based on the Local Breeding Growth Mode

2019 ◽  
Vol 34 (2) ◽  
pp. 289-304 ◽  
Author(s):  
Shenjia Ma ◽  
Chaohui Chen ◽  
Hongrang He ◽  
Jie Xiang ◽  
Shengjie Chen ◽  
...  
Keyword(s):  
Initial Perturbation ◽  
Growth Mode ◽  
Ensemble Prediction ◽  
Weather System ◽  
Ensemble Prediction System ◽  
System A ◽  
Convective Scale ◽  
Local Breeding ◽  
Prediction Systems ◽  
Convective Weather

Abstract In this study, a convection-allowing ensemble prediction experiment was conducted on a strong convective weather process, based on the local breeding growth mode (LBGM) method proposed according to the strongly local nature of the convective-scale weather system. A comparative analysis of the evolution characteristics of the initial perturbation was also performed, considering the results from the traditional breeding growth mode (BGM) method, to enhance understanding and application of this new initial perturbation generation method. The experimental results showed that LBGM results in the perturbation distribution exhibiting characteristics more evident of flow dependence, and an initial perturbation with greater definite kinetic significance was derived. Information entropy theory could well measure the amount of information contained in the perturbation distribution, indicating that the innovative initial perturbation generation method can increase the amount of local information associated with the initial perturbation. With regard to the physical perturbation quantities, the LBGM method can improve the dispersion of the ensemble prediction system, thereby solving the problem of insufficient ensemble spread of prediction systems obtained by the traditional BGM method. Simultaneously, the root-mean-square error of the prediction can be further reduced, and the predicted precipitation distribution is closer to the observed precipitation, thereby improving the prediction effect of the convection-allowing ensemble prediction. The LBGM method has advantages compared to the traditional method and provides a new theoretical basis for further development of initial perturbation technologies for convection-allowing ensemble prediction.

scholarly journals On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions

2008 ◽  
Vol 9 (6) ◽  
pp. 1301-1317 ◽  
Author(s):  
Guillaume Thirel ◽  
Fabienne Rousset-Regimbeau ◽  
Eric Martin ◽  
Florence Habets
Keyword(s):  
Short Range ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Streamflow Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Ensemble Streamflow Prediction ◽  
Prediction Systems ◽  
Set Up ◽  
The Impact

Abstract Ensemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d’Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins.

scholarly journals Calibration of Limited-Area Ensemble Precipitation Forecasts for Hydrological Predictions

2014 ◽  
Vol 142 (6) ◽  
pp. 2176-2197 ◽  
Author(s):  
Tommaso Diomede ◽  
Chiara Marsigli ◽  
Andrea Montani ◽  
Fabrizio Nerozzi ◽  
Tiziana Paccagnella
Keyword(s):  
Linear Regression ◽  
Training Dataset ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Limited Area ◽  
Quantile Mapping ◽  
Ensemble Prediction System ◽  
Position Errors ◽  
Beneficial Impact ◽  
The Impact

Abstract The main objective of this study is to investigate the impact of calibration for limited-area ensemble precipitation forecasts, to be used for driving discharge predictions up to 5 days in advance. A reforecast dataset, which spans 30 years, based on the Consortium for Small Scale Modeling Limited-Area Ensemble Prediction System (COSMO-LEPS) was used for testing the calibration strategy. Three calibration techniques were applied: quantile-to-quantile mapping, linear regression, and analogs. The performance of these methodologies was evaluated in terms of statistical scores for the precipitation forecasts operationally provided by COSMO-LEPS in the years 2003–07 over Germany, Switzerland, and the Emilia-Romagna region (northern Italy). The calibration provided a beneficial impact for the ensemble forecast over Switzerland and Germany; whereas, it resulted as less effective for Emilia-Romagna. The analog-based method seemed to be preferred because of its capability of correct position errors and spread deficiencies. A suitable spatial domain for the analog search can help to handle model spatial errors as systematic errors. However, the performance of the analog-based method may degrade in cases where a limited training dataset is available. The quantile-to-quantile mapping and linear regression methods were less effective, mainly because the forecast–analysis relation was not so strong for the available training dataset. The verification of the calibration process was then performed by coupling ensemble precipitation forecasts with a distributed rainfall–runoff model. This test was carried out for a medium-sized catchment located in Emilia-Romagna, showing a beneficial impact of the analog-based method on the reduction of missed events for discharge predictions.

scholarly journals Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

2012 ◽  
Vol 12 (10) ◽  
pp. 4555-4565 ◽  
Author(s):  
P. Groenemeijer ◽  
G. C. Craig
Keyword(s):  
Large Scale ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Deep Convection ◽  
Horizontal Resolution ◽  
Ensemble Forecasting ◽  
Total Variance ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
The Impact

Abstract. The stochastic Plant-Craig scheme for deep convection was implemented in the COSMO mesoscale model and used for ensemble forecasting. Ensembles consisting of 100 48-h forecasts at 7 km horizontal resolution were generated for a 2000×2000 km domain covering central Europe. Forecasts were made for seven case studies characterized by different large-scale meteorological environments. Each 100 member ensemble consisted of 10 groups of 10 members, with each group driven by boundary and initial conditions from a selected member from the global ECMWF Ensemble Prediction System. The precipitation variability within and among these groups of members was computed, and it was found that the relative contribution to the ensemble variance introduced by the stochastic convection scheme was substantial, amounting to as much as 76% of the total variance in the ensemble in one of the studied cases. The impact of the scheme was not confined to the grid scale, and typically contributed 25–50% of the total variance even after the precipitation fields had been smoothed to a resolution of 35 km. The variability of precipitation introduced by the scheme was approximately proportional to the total amount of convection that occurred, while the variability due to large-scale conditions changed from case to case, being highest in cases exhibiting strong mid-tropospheric flow and pronounced meso- to synoptic scale vorticity extrema. The stochastic scheme was thus found to be an important source of variability in precipitation cases of weak large-scale flow lacking strong vorticity extrema, but high convective activity.

Forecast-based attribution of a winter heatwave within the limit of predictability

2021 ◽  
Author(s):  
Nicholas Leach ◽  
Antje Weisheimer ◽  
Myles Allen ◽  
Timothy Palmer
Keyword(s):  
Lead Time ◽  
Climate Model ◽  
Initial Conditions ◽  
Forecast Model ◽  
Ensemble Prediction ◽  
Synoptic Situation ◽  
Ensemble Prediction System ◽  
Model Simulations ◽  
Climate Model Simulations ◽  
The Impact

<p>Between the 21st and 27th February 2019, climatologically exceptional warm temperature anomalies of 10-15 °C were experienced throughout Northern and Western Europe. In particular, the 25th - 27th February saw record-breaking temperatures measured at many weather stations over wide areas of Iberia, France, the British Isles, the Netherlands, Germany and Southern Sweden. </p><p>This heatwave was well-predicted by the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system. Their forecasts indicated "extreme" heat was possible at a lead time of around two weeks, and likely at a lead time of around ten days. The performance of these forecasts in predicting the surface heat is also reflected in their ability to predict the synoptic situation. </p><p>We exploit this successful forecast to perform an attribution analysis of the heatwave that differs from "conventional" analyses in several key regards. Firstly, we are not only confident that the model used is able to simulate the event in question; but that we are unequivocally studying the specific winter heatwave that occurred in Europe during February 2019. </p><p>This analysis is carried out using a state-of-the-art coupled high-resolution forecast model ensemble, as opposed to the prescribed-SST experiments within climate model ensembles traditionally used for attribution.</p><p>A crucial distinction between the typical climate model simulations used for attribution and the forecasts used here is that the climate model simulations are usually allowed to spin out for a sufficient length of time such that they have no memory of their initial conditions; an ensemble constructed in this way will therefore be representative of the climatology of the model (possibly conditioned on any prescribed-SST patterns). Unlike these climatological simulations, the successful forecasts used here are clearly heavily dependent on the initial conditions used. Within these simulations, the level of dynamical conditioning can therefore be specified by altering the lead time from initialisation to the event in question. We explore the implications of this aspect of forecast-based attribution, attempting to integrate between the "conventional" climatological and "storyline" frameworks of attribution. </p><p>To simplify the interpretation of our experiments, here we have decided to only change a single feature between our factual and counterfactual experiments. The analysis presented is therefore limited to attributing the impact of diabatic heating due to increased CO<sub>2</sub> concentrations above pre-industrial levels just over the days between the model initialisation date and the event. We carry out simulations at four different lead times from the event, allowing us to investigate the balance between the level of conditioning of the ensemble and the relaxation of the ensemble toward a new equilibrium at the lowered CO<sub>2</sub> concentrations.</p>

scholarly journals Calibrated Short-Range Ensemble Precipitation Forecasts Using Extended Logistic Regression with Interaction Terms

2013 ◽  
Vol 28 (2) ◽  
pp. 515-524 ◽  
Author(s):  
Zied Ben Bouallègue
Keyword(s):  
Logistic Regression ◽  
Short Range ◽  
Positive Impact ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Ensemble Prediction System ◽  
Interaction Terms ◽  
Scale Modeling ◽  
Regression Parameters ◽  
Prediction Systems

Abstract Extended logistic regression has been shown to be a method well suited to calibrating precipitation forecasts from medium-range ensemble prediction systems. The extension of the logistic regression unifies the separate predictive equations for each threshold, introducing the predictive threshold as part of the predictors. Mutually consistent probabilities and a reduction in the total number of regression parameters to be evaluated are part of the benefits of the extended approach. However, considering the predictive threshold as the only “unification” predictor constrains the regression parameters associated with the primary predictors to be constant with the threshold. To alleviate the rigidity of the extended scheme, interaction terms are introduced in the unified predictive equation. Within the framework of the convection-permitting German-focused Consortium for Small-Scale Modeling ensemble prediction system (COSMO-DE-EPS), it is shown that extended logistic regression, applied to short-range precipitation forecasts with the ensemble mean as the primary predictor, improves the performance of the system. Interaction effects are first illustrated through the analysis of regression parameters and then the positive impact on the calibrated forecasts of the new extended logistic regression scheme, including interaction terms, is shown using quantitative and qualitative measures of reliability and sharpness.

scholarly journals Calibrated Precipitation Forecasts for a Limited-Area Ensemble Forecast System Using Reforecasts

2010 ◽  
Vol 138 (1) ◽  
pp. 176-189 ◽  
Author(s):  
Felix Fundel ◽  
Andre Walser ◽  
Mark A. Liniger ◽  
Christoph Frei ◽  
Christof Appenzeller
Keyword(s):  
Return Period ◽  
Lead Time ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Limited Area ◽  
Return Periods ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Forecast Lead Time

Abstract The calibration of numerical weather forecasts using reforecasts has been shown to increase the skill of weather predictions. Here, the precipitation forecasts from the Consortium for Small Scale Modeling Limited Area Ensemble Prediction System (COSMO-LEPS) are improved using a 30-yr-long set of reforecasts. The probabilistic forecasts are calibrated on the exceedance of return periods, independently from available observations. Besides correcting for systematic model errors, the spatial and temporal variability in the amplitude of rare precipitation events is implicitly captured when issuing forecasts of return periods. These forecast products are especially useful for issuing warnings of upcoming events. A way to visualize those calibrated ensemble forecasts conveniently for end users and to present verification results of the return period–based forecasts for Switzerland is proposed. It is presented that, depending on the lead time and return period, calibrating COSMO-LEPS with reforecasts increases the precipitation forecast skill substantially (about 1 day in forecast lead time). The largest improvements are achieved during winter months. The reasonable choice of the length of the reforecast climatology is estimated for an efficient use of this computational expensive calibration method.

Sign in / Sign up

Export Citation Format

Share Document