scholarly journals Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria

2011 ◽  
Vol 15 (11) ◽  
pp. 3307-3325 ◽  
Author(s):  
D. Brochero ◽  
F. Anctil ◽  
C. Gagné
Keyword(s):  
Computational Time ◽  
Ensemble Prediction ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Prediction Systems ◽  
The Relationship ◽  
Selection Of

Abstract. Hydrological Ensemble Prediction Systems (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of the number of hydrological members) that can be achieved with a HEPS configured using 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF). Here, Backward Greedy Selection (BGS) is proposed to assess the weight that each model must represent within a subset that offers similar or better performance than a reference set of 800 hydrological members. These hydrological models' weights represent the participation of each hydrological model within a simplified HEPS which would issue real-time forecasts in a relatively short computational time. The methodology uses a variation of the k-fold cross-validation, allowing an optimal use of the information, and employs a multi-criterion framework that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).

scholarly journals Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria

2011 ◽  
Vol 8 (2) ◽  
pp. 2739-2782 ◽  
Author(s):  
D. Brochero ◽  
F. Anctil ◽  
C. Gagné
Keyword(s):  
Computational Time ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Criterion Score ◽  
Prediction Systems ◽  
The Relationship ◽  
Selection Of

Abstract. Hydrological Ensemble Prediction System (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of members) of a HEPS configured with 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Center for Medium-range Weather Forecasts (ECMWF). Here, the selection of the most relevant members is proposed using a Backward greedy technique with k-fold cross-validation, allowing an optimal use of the information. The methodology draws from a multi-criterion score that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).

scholarly journals Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space

2011 ◽  
Vol 8 (2) ◽  
pp. 2783-2820 ◽  
Author(s):  
D. Brochero ◽  
F. Anctil ◽  
C. Gagné
Keyword(s):  
Cross Validation ◽  
Hydrological Model ◽  
Local Level ◽  
Current Trend ◽  
Critical Factor ◽  
Ensemble Prediction ◽  
Rainfall Runoff ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Prediction Systems

Abstract. An uncertainty cascade model applied to stream flow forecasting seeks to evaluate the different sources of uncertainty of the complex rainfall-runoff process. The current trend focuses on the combination of Meteorological Ensemble Prediction Systems (MEPS) and hydrological model(s). However, the number of members of such a HEPS may rapidly increase to a level that may not be operationally sustainable. This article evaluates a 94% simplification of an initial 800-member HEPS, forcing 16 lumped rainfall-runoff models with the European Center for Medium-range Weather Forecasts (ECMWF MEPS). More specifically, it tests the time (local) and space (regional) generalization ability of the simplified 50-member HEPS obtained using a methodology that combines 4 main aspects: (i) optimizing information of the short-length series using k-folds cross-validation, (ii) implementing a backward greedy selection technique, (iii) guiding the selection with a linear combination of diversified scores, and (iv) formulating combination case studies at the cross-validation stage. At the local level, the transferability of the 9th day member selection was proven for the other 8 forecast horizons at an 82% success rate. At the regional level, a good performance was also achieved when the 50-member HEPS was applied to a neighbouring catchment within the same cluster. Diversity, defined as hydrological model complementarities addressing different aspects of a forecast, was identified as the critical factor for proper selection applications.

A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems

2005 ◽  
Vol 133 (5) ◽  
pp. 1076-1097 ◽  
Author(s):  
Roberto Buizza ◽  
P. L. Houtekamer ◽  
Gerald Pellerin ◽  
Zoltan Toth ◽  
Yuejian Zhu ◽  
...  
Keyword(s):  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Prediction Systems ◽  
Environmental Prediction ◽  
Global Systems

Abstract The present paper summarizes the methodologies used at the European Centre for Medium-Range Weather Forecasts (ECMWF), the Meteorological Service of Canada (MSC), and the National Centers for Environmental Prediction (NCEP) to simulate the effect of initial and model uncertainties in ensemble forecasting. The characteristics of the three systems are compared for a 3-month period between May and July 2002. The main conclusions of the study are the following:the performance of ensemble prediction systems strongly depends on the quality of the data assimilation system used to create the unperturbed (best) initial condition and the numerical model used to generate the forecasts;a successful ensemble prediction system should simulate the effect of both initial and model-related uncertainties on forecast errors; andfor all three global systems, the spread of ensemble forecasts is insufficient to systematically capture reality, suggesting that none of them is able to simulate all sources of forecast uncertainty.The relative strengths and weaknesses of the three systems identified in this study can offer guidelines for the future development of ensemble forecasting techniques.

Predictions of 2010’s Tropical Cyclones Using the GFS and Ensemble-Based Data Assimilation Methods

2011 ◽  
Vol 139 (10) ◽  
pp. 3243-3247 ◽  
Author(s):  
Thomas M. Hamill ◽  
Jeffrey S. Whitaker ◽  
Daryl T. Kleist ◽  
Michael Fiorino ◽  
Stanley G. Benjamin
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Successful Testing ◽  
Gfs Model ◽  
Environmental Prediction ◽  
Statistical Interpolation

Abstract Experimental ensemble predictions of tropical cyclone (TC) tracks from the ensemble Kalman filter (EnKF) using the Global Forecast System (GFS) model were recently validated for the 2009 Northern Hemisphere hurricane season by Hamill et al. A similar suite of tests is described here for the 2010 season. Two major changes were made this season: 1) a reduction in the resolution of the GFS model, from 2009’s T384L64 (~31 km at 25°N) to 2010’s T254L64 (~47 km at 25°N), and some changes in model physics; and 2) the addition of a limited test of deterministic forecasts initialized from a hybrid three-dimensional variational data assimilation (3D-Var)/EnKF method. The GFS/EnKF ensembles continued to produce reduced track errors relative to operational ensemble forecasts created by the National Centers for Environmental Prediction (NCEP), the Met Office (UKMO), and the Canadian Meteorological Centre (CMC). The GFS/EnKF was not uniformly as skillful as the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system. GFS/EnKF track forecasts had slightly higher error than ECMWF at longer leads, especially in the western North Pacific, and exhibited poorer calibration between spread and error than in 2009, perhaps in part because of lower model resolution. Deterministic forecasts from the hybrid were competitive with deterministic EnKF ensemble-mean forecasts and superior in track error to those initialized from the operational variational algorithm, the Gridpoint Statistical Interpolation (GSI). Pending further successful testing, the National Oceanic and Atmospheric Administration (NOAA) intends to implement the global hybrid system operationally for data assimilation.

Exploring the relationship between the skill of hydrological ensemble predictions and catchment descriptors

2020 ◽  
Author(s):  
Emixi Valdez ◽  
Francois Anctil ◽  
Maria-Helena Ramos
Keyword(s):  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Forecast Performance ◽  
Ensemble Prediction System ◽  
Sources Of Uncertainty ◽  
Skill Scores ◽  
Forecasting System ◽  
The Relationship

<p>Skillful hydrological forecasts are essential for decision-making in many areas such as preparedness against natural disasters, water resources management, and hydropower operations. Despite the great technological advances, obtaining skillful predictions from a forecasting system, under a range of conditions and geographic locations, remain a difficult task. It is still unclear why some systems perform better than others at different temporal and spatial scales. Much work has been devoted to investigate the quality of forecasts and the relative contributions of meteorological forcing, catchment’s initial conditions, and hydrological model structure in a streamflow forecasting system. These sources of uncertainty are rarely considered fully and simultaneously in operational systems, and there are still gaps in understanding their relationship with the dominant processes and mechanisms that operate in a given river basin. In this study, we use a multi-model hydrological ensemble prediction system (H-EPS) named HOOPLA (HydrOlOgical Prediction Laboratory), which allows to account separately for these three main sources of uncertainty in hydrological ensemble forecasting. Through the use of EnKF data assimilation, of 20 lumped hydrological models, and of the 50-member ECMWF medium-range weather forecasts, we explore the relationship between the skill of ensemble predictions and the many descriptors (e.g. catchment surface, climatology, morphology, flow threshold and hydrological regime) that influence hydrological predictability. We analyze streamflow forecasts at 50 stations spread across Quebec, France and Colombia, over the period from 2011 to 2015 and for lead times up to 9 days. The forecast performance is assessed using common metrics for forecast quality verification, such as CRPS, Brier skill score, and reliability diagrams. Skill scores are computed using a probabilistic climatology benchmark, which was generated with the hydrological models forced by resampled historical meteorological data. Our results contribute to relevant literature on the topic and bring additional insight into the role of each descriptor in the skill of a hydrometeorological ensemble forecasting chain, serving as a possible guide for potential users to identify the circumstances or conditions in which it is more efficient to implement a given system.</p><p> </p>

scholarly journals A Similarity-Based Implementation of the Schaake Shuffle

2016 ◽  
Vol 144 (5) ◽  
pp. 1909-1921 ◽  
Author(s):  
Roman Schefzik
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Similarity Criterion ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Prediction Horizon ◽  
Prediction Systems ◽  
Specific Implementation

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

scholarly journals Postprocessing ensemble forecasts of vertical temperature profiles

2020 ◽  
Vol 6 (1) ◽  
pp. 45-60
Author(s):  
David Schoenach ◽  
Thorsten Simon ◽  
Georg Johann Mayr
Keyword(s):  
Rank Order ◽  
Probability Distributions ◽  
Ensemble Prediction ◽  
Order Structure ◽  
Temperature Profiles ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Atmospheric Observations ◽  
Prediction Systems

Abstract. Weather forecasts from ensemble prediction systems (EPS) are improved by statistical models trained on past EPS forecasts and their atmospheric observations. Recently these corrections have moved from being univariate to multivariate. The focus has been on (quasi-)horizontal atmospheric variables. This paper extends the correction methods to EPS forecasts of vertical profiles in two steps. First univariate distributional regression methods correct the probability distributions separately at each vertical level. In the second step copula coupling re-installs the dependence among neighboring levels by using the rank order structure of the EPS forecasts. The method is applied to EPS data from the European Centre for Medium-Range Weather Forecasts (ECMWF) at model levels interpolated to four locations in Germany, from which radiosondes are released to measure profiles of temperature and other variables four times a day. A winter case study and a summer case study, respectively, exemplify that univariate postprocessing fails to preserve stable layers, which are crucial for many atmospheric processes. Quantile resampling and a resampling that preserves the relative distance between individual EPS members improve the calibration of the raw forecasts of the temperature profiles as shown by rank histograms. They also improve the multivariate metrics of energy score and variogram score and retain the stable layers. Improvements take place over all times of the day and all seasons. They are largest within the atmospheric boundary layer and for shorter lead times.

scholarly journals Test of a Poor Man’s Ensemble Prediction System for Short-Range Probability Forecasting

2005 ◽  
Vol 133 (7) ◽  
pp. 1825-1839 ◽  
Author(s):  
A. Arribas ◽  
K. B. Robertson ◽  
K. R. Mylne
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Small Subset ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Sea Level Pressure ◽  
Ensemble Prediction System ◽  
To Come ◽  
Prediction Systems

Abstract Current operational ensemble prediction systems (EPSs) are designed specifically for medium-range forecasting, but there is also considerable interest in predictability in the short range, particularly for potential severe-weather developments. A possible option is to use a poor man’s ensemble prediction system (PEPS) comprising output from different numerical weather prediction (NWP) centers. By making use of a range of different models and independent analyses, a PEPS provides essentially a random sampling of both the initial condition and model evolution errors. In this paper the authors investigate the ability of a PEPS using up to 14 models from nine operational NWP centers. The ensemble forecasts are verified for a 101-day period and five variables: mean sea level pressure, 500-hPa geopotential height, temperature at 850 hPa, 2-m temperature, and 10-m wind speed. Results are compared with the operational ECMWF EPS, using the ECMWF analysis as the verifying “truth.” It is shown that, despite its smaller size, PEPS is an efficient way of producing ensemble forecasts and can provide competitive performance in the short range. The best relative performance is found to come from hybrid configurations combining output from a small subset of the ECMWF EPS with other different NWP models.

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 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.

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