Evaluation of a short-term ensemble water level forecasting system: case of the Chaudière River

Sources Of Uncertainty ◽

Forecasting System

The quality of water level predictions is highly dependent on the success of the flow forecasts that inform the hydraulic model. Ensemble predictions, by considering several sources of uncertainty, provide more accurate and reliable forecasts. In this project, we aim to evaluate a water level ensemble prediction system coupling a hydraulic model to an ensemble streamflow prediction system accounting for 3 sources of uncertainty: meteorological data, hydrological processing (multimodel) and data assimilation to update the initial conditions. The hydraulic model is previously calibrated and validated and the roughness coefficients are adapted as a function of flow according to predefined relationships developed for several river segments. The forecasts reliability and accuracy are then assessed at each layer of the forecasting system and the outcomes are illustrated comparing the ensembles skills and reliability for the considered events. Overall, the results show that accounting of the hydrometeorological uncertainty improves the performances of the water level forecasts for different lead times.

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

10.5194/hess-17-2107-2013 ◽

2013 ◽

Vol 17 (6) ◽

pp. 2107-2120 ◽

Cited By ~ 13

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 ◽

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.

Dynamical Extended-Range Prediction of Early Monsoon Rainfall over India

Monthly Weather Review ◽

10.1175/2008mwr2761.1 ◽

2009 ◽

Vol 137 (4) ◽

pp. 1480-1492 ◽

Cited By ~ 18

Author(s):

Frédéric Vitart ◽

Franco Molteni

Keyword(s):

Lead Time ◽

Monsoon Rainfall ◽

Horizontal Resolution ◽

Ensemble Prediction ◽

Prediction System ◽

Variable Resolution ◽

Sensitivity Experiments ◽

Extended Range ◽

Forecasting System

Abstract The 15-member ensembles of 46-day dynamical forecasts starting on each 15 May from 1991 to 2007 have been produced, using the ECMWF Variable Resolution Ensemble Prediction System monthly forecasting system (VarEPS-monthy). The dynamical model simulates a realistic interannual variability of Indian precipitation averaged over the month of June. It also displays some skill to predict Indian precipitation averaged over pentads up to a lead time of about 30 days. This skill exceeds the skill of the ECMWF seasonal forecasting System 3 starting on 1 June. Sensitivity experiments indicate that this is likely due to the higher horizontal resolution of VarEPS-monthly. Another series of sensitivity experiments suggests that the ocean–atmosphere coupling has an important impact on the skill of the monthly forecasting system to predict June rainfall over India.

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

10.5194/egusphere-egu2020-22659 ◽

2020 ◽

Author(s):

Emixi Valdez ◽

Francois Anctil ◽

Maria-Helena Ramos

Keyword(s):

Ensemble Forecasting ◽

Ensemble Prediction ◽

Hydrological Models ◽

Streamflow Forecasting ◽

Forecast Performance ◽

Sources Of Uncertainty ◽

Skill Scores ◽

Forecasting System ◽

The Relationship

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&#8217;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.&#160;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.&#160;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.&#160;

An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-4891-2009 ◽

2009 ◽

Vol 6 (4) ◽

pp. 4891-4917

Author(s):

J. A. Velázquez ◽

T. Petit ◽

A. Lavoie ◽

M.-A. Boucher ◽

R. Turcotte ◽

...

Keyword(s):

Initial Conditions ◽

Ensemble Prediction ◽

Prediction System ◽

Forecast Horizon ◽

Time Step ◽

Ensemble Forecasts ◽

Hydrological Forecasting ◽

Updating Procedure ◽

First Time

Abstract. Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach

10.5194/hess-23-493-2019 ◽

2019 ◽

Vol 23 (1) ◽

pp. 493-513 ◽

Cited By ~ 7

Author(s):

Samuel Monhart ◽

Massimiliano Zappa ◽

Christoph Spirig ◽

Christoph Schär ◽

Konrad Bogner

Keyword(s):

Initial Conditions ◽

Weather Prediction ◽

Added Value ◽

Ensemble Prediction ◽

Late Winter ◽

Streamflow Prediction ◽

Mountainous Regions ◽

Temperature And Precipitation ◽

Nwp Model

Abstract. Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions.

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

Journal of Hydrometeorology ◽

10.1175/2008jhm959.1 ◽

2008 ◽

Vol 9 (6) ◽

pp. 1301-1317 ◽

Cited By ~ 53

Author(s):

Guillaume Thirel ◽

Fabienne Rousset-Regimbeau ◽

Eric Martin ◽

Florence Habets

Keyword(s):

Short Range ◽

Ensemble Prediction ◽

Prediction System ◽

Streamflow Prediction ◽

Ensemble Forecasts ◽

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.

Ensemble Prediction of the Dispersion of Volcanic Ash from the 13 February 2014 Eruption of Kelut, Indonesia

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-15-0079.1 ◽

2016 ◽

Vol 55 (1) ◽

pp. 61-78 ◽

Cited By ~ 11

Author(s):

Richard A. Dare ◽

David H. Smith ◽

Michael J. Naughton

Keyword(s):

Qualitative Agreement ◽

Volcanic Ash ◽

Initial Conditions ◽

Ensemble Prediction ◽

Individual Member ◽

Prediction System ◽

Hysplit Model ◽

2014 Eruption ◽

Deterministic Control

AbstractA meteorological ensemble prediction system that represents uncertainties in both initial conditions and model formulations is coupled with a modified version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. This coupled dispersion ensemble prediction system (DEPS) is used to generate a 24-member ensemble forecast of the dispersion of the volcanic ash cloud produced by the 13 February 2014 eruption of Kelut, Indonesia. Uncertainties in the volcanic ash source are not represented. For predictions up to 12 h from the start of the eruption, forecasts from the deterministic control member and from the DEPS both show very good qualitative agreement with satellite observations. By 18–24 h the DEPS forecast shows better qualitative agreement with observations than does the deterministic forecast. Although composited fields such as the ensemble mean and probability present information concisely, experiments here show that it is very important to also consider results from individual member forecasts in order to identify features that may be underrepresented. For example, an area of relatively high ash concentration that was forecast by most of the members was not particularly evident in the composited fields because the location of this feature was highly variable between member forecasts. To fully understand a DEPS forecast, it is necessary to consider both atmospheric column load and concentration fields, individual member forecasts, and a range of thresholds in computing and interpreting probabilities.

An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting

10.5194/hess-13-2221-2009 ◽

2009 ◽

Vol 13 (11) ◽

pp. 2221-2231 ◽

Cited By ~ 23

Author(s):

J. A. Velázquez ◽

T. Petit ◽

A. Lavoie ◽

M.-A. Boucher ◽

R. Turcotte ◽

...

Keyword(s):

Initial Conditions ◽

Ensemble Prediction ◽

Prediction System ◽

Forecast Horizon ◽

Time Step ◽

Ensemble Forecasts ◽

Hydrological Forecasting ◽

Updating Procedure ◽

First Time

A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts

10.5194/hess-14-1639-2010 ◽

2010 ◽

Vol 14 (8) ◽

pp. 1639-1653 ◽

Cited By ~ 39

Author(s):

G. Thirel ◽

E. Martin ◽

J.-F. Mahfouf ◽

S. Massart ◽

S. Ricci ◽

...

Keyword(s):

Soil Moisture ◽

Meteorological Data ◽

Skill Score ◽

Ensemble Prediction ◽

Time Range ◽

Prediction Errors ◽

Prediction System ◽

Streamflow Forecasts ◽

Assimilation System

Abstract. The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.

A probabilistic view on the August 2005 floods in the upper Rhine catchment

Natural Hazards and Earth System Science ◽

10.5194/nhess-8-281-2008 ◽

2008 ◽

Vol 8 (2) ◽

pp. 281-291 ◽

Cited By ~ 51

Author(s):

S. Jaun ◽

B. Ahrens ◽

A. Walser ◽

T. Ewen ◽

C. Schär

Keyword(s):

Probabilistic Approach ◽

Precipitation Forecast ◽

Flood Event ◽

Ensemble Prediction ◽

Limited Area ◽

Prediction System ◽

Extreme Flood ◽

Forecasting System ◽

Rhine Catchment

Abstract. Appropriate precautions in the case of flood occurrence often require long lead times (several days) in hydrological forecasting. This in turn implies large uncertainties that are mainly inherited from the meteorological precipitation forecast. Here we present a case study of the extreme flood event of August 2005 in the Swiss part of the Rhine catchment (total area 34 550 km2). This event caused tremendous damage and was associated with precipitation amounts and flood peaks with return periods beyond 10 to 100 years. To deal with the underlying intrinsic predictability limitations, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area COSMO-LEPS that downscales the ECMWF ensemble prediction system to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m. We document the setup of the coupled system and assess its performance for the flood event under consideration. We show that the probabilistic meteorological-hydrological ensemble prediction chain is quite effective and provides additional guidance for extreme event forecasting, in comparison to a purely deterministic forecasting system. For the case studied, it is also shown that most of the benefits of the probabilistic approach may be realized with a comparatively small ensemble size of 10 members.