scholarly journals Evaluation of ensemble precipitation forecasts generated through postprocessing in a Canadian catchment

2017 ◽  
Author(s):  
Sanjeev K. Jha ◽  
Durga Lal Shrestha ◽  
Tricia Stadnyk ◽  
Paulin Coulibaly
Keyword(s):  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Forecast System ◽  
Forecast Performance ◽  
Continuous Ranked Probability Score ◽  
Forecasting System ◽  
Hydrologic Forecast

Abstract. Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effect over diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall-post processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global ensemble forecasting system (GEFS) Reforecast 2 project from National Centers for Environmental Protection (NCEP), and Global deterministic forecast system (GDPS) from Environment and Climate Change Canada (ECCC) are used in this study. The study period from Jan 2013 to Dec 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias, and reduce the continuous ranked probability score of both GEFS and GDPS forecasts. Ensembles generated from the RPP better depict the forecast uncertainty.

scholarly journals Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

2018 ◽  
Vol 22 (3) ◽  
pp. 1957-1969 ◽  
Author(s):  
Sanjeev K. Jha ◽  
Durga L. Shrestha ◽  
Tricia A. Stadnyk ◽  
Paulin Coulibaly
Keyword(s):  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Forecast System ◽  
Forecast Performance ◽  
Forecasting System ◽  
Environmental Prediction ◽  
Hydrologic Forecast

Abstract. Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

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 Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 188
Author(s):  
Rodrigo Valdés-Pineda ◽  
Juan B. Valdés ◽  
Sungwook Wi ◽  
Aleix Serrat-Capdevila ◽  
Tirthankar Roy
Keyword(s):  
Real Time ◽  
Optimal Parameter ◽  
Ensemble Forecasting ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Satellite Precipitation ◽  
Daily Streamflow ◽  
Medium Range ◽  
Streamflow Forecasts ◽  
Forecasting System

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

Does the application of multiple hydrological models improve seasonal streamflow forecasting skill?

2020 ◽  
Author(s):  
Bastian Klein ◽  
Ilias Pechlivanidis ◽  
Louise Arnal ◽  
Louise Crochemore ◽  
Dennis Meissner ◽  
...  
Keyword(s):  
Central Europe ◽  
Bayesian Model Averaging ◽  
Forecast Skill ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Seasonal Streamflow

<p>Many sectors, such as hydropower, agriculture, water supply and waterway transport, need information about the possible evolution of meteorological and hydrological conditions in the next weeks and months to optimize their decision processes on a long term. With increasing availability of meteorological seasonal forecasts, hydrological seasonal forecasting systems have been developed all over the world in the last years. Many of them are running in operational mode. On European scale the European Flood Awareness System EFAS and SMHI are operationally providing seasonal streamflow forecasts. In the context of the EU-Horizon2020 project IMPREX additionally a national scale forecasting system for German waterways operated by BfG was available for the analysis of seasonal forecasts from multiple hydrological models.</p><p>Statistical post processing tools could be used to estimate the predictive uncertainty of the forecasted variable from deterministic / ensemble forecasts of a single / multi-model forecasting system. Raw forecasts shouldn’t be used directly by users without statistical post-processing because of various biases. To assess the added potential benefit of the application of a hydrological multi-model ensemble, the forecasting systems from EFAS, SMHI and BfG were forced by re-forecasts of the ECMWF’s Seasonal Forecast System 4 and the resulting seasonal streamflow forecasts have been verified for 24 gauges across Central Europe. Additionally two statistical forecasting methods - Ensemble Model Output Statistics EMOS and Bayesian Model Averaging BMA - have been applied to post-process the forecasts.</p><p>Overall, seasonal flow forecast skill is limited in Central Europe before and after post-processing with a current predictability of 1-2 months. The results of the multi-model analysis indicate that post-processing of raw forecasts is necessary when observations are used as reference. Post-processing improves forecast skill significantly for all gauges, lead times and seasons. The multi-model combination of all models showed the highest skill compared to the skill of the raw forecasts and the skill of the post-processed results of the individual models, i.e. the application of several hydrological models for the same region improves skill, due to the different model strengths.</p>

scholarly journals Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

2013 ◽  
Vol 17 (9) ◽  
pp. 3587-3603 ◽  
Author(s):  
D. E. Robertson ◽  
D. L. Shrestha ◽  
Q. J. Wang
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Probability Distributions ◽  
Weather Prediction ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Numerical Weather

Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.

scholarly journals Operational Performance of a New Barotropic Model (WBAR) in the Western North Pacific Basin

2006 ◽  
Vol 21 (4) ◽  
pp. 656-662 ◽  
Author(s):  
Charles R. Sampson ◽  
James S. Goerss ◽  
Harry C. Weber
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Prediction Models ◽  
Weather Prediction ◽  
Track Forecast ◽  
Forecast Performance ◽  
Barotropic Model ◽  
The North ◽  
Forecasting System ◽  
The Impact

Abstract The Weber barotropic model (WBAR) was originally developed using predefined 850–200-hPa analyses and forecasts from the NCEP Global Forecasting System. The WBAR tropical cyclone (TC) track forecast performance was found to be competitive with that of more complex numerical weather prediction models in the North Atlantic. As a result, WBAR was revised to incorporate the Navy Operational Global Atmospheric Prediction System (NOGAPS) analyses and forecasts for use at the Joint Typhoon Warning Center (JTWC). The model was also modified to analyze its own storm-dependent deep-layer mean fields from standard NOGAPS pressure levels. Since its operational installation at the JTWC in May 2003, WBAR TC track forecast performance has been competitive with the performance of other more complex NWP models in the western North Pacific. Its TC track forecast performance combined with its high availability rate (93%–95%) has warranted its inclusion in the JTWC operational consensus. The impact of WBAR on consensus TC track forecast performance has been positive and WBAR has added to the consensus forecast availability (i.e., having at least two models to provide a consensus forecast).

scholarly journals The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps

2013 ◽  
Vol 17 (10) ◽  
pp. 3853-3869 ◽  
Author(s):  
K. Liechti ◽  
L. Panziera ◽  
U. Germann ◽  
M. Zappa
Keyword(s):  
Early Warning ◽  
Initial Conditions ◽  
Flash Flood ◽  
Weather Prediction ◽  
Radar Data ◽  
Ensemble Forecasting ◽  
Swiss Alps ◽  
Ensemble Forecasts ◽  
Orographic Rainfall ◽  
Forecasting System

Abstract. This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA) initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.

scholarly journals Setting up a water quality ensemble forecast for coastal ecosystems: a case study of the southern North Sea

2018 ◽  
Vol 20 (4) ◽  
pp. 846-863 ◽  
Author(s):  
Lőrinc Mészáros ◽  
Ghada El Serafy
Keyword(s):  
Water Quality ◽  
Coastal Ecosystems ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Ecological Processes ◽  
Forecast Performance ◽  
Probabilistic Forecasts ◽  
Forecasting System

Abstract Prediction systems, such as the coastal ecosystem models, often incorporate complex non-linear ecological processes. There is an increasing interest in the use of probabilistic forecasts instead of deterministic forecasts in cases where the inherent uncertainties in the prediction system are important. The primary goal of this study is to set up an operational ensemble forecasting system for the prediction of the Chlorophyll-a concentration in coastal waters, using the Generic Ecological Model. The input ensemble is generated from perturbed model process parameters and external forcings through Latin Hypercube Sampling with Dependence. The forecast performance of the ensemble prediction is assessed using several forecast verification metrics that can describe the forecast accuracy, reliability and discrimination. The verification is performed against in-situ measurements and remote sensing data. The ensemble forecast moderately outperforms the deterministic prediction at the coastal in-situ measurement stations. The proposed ensemble forecasting system is therefore a promising tool to provide enhanced water quality prediction for coastal ecosystems which, with further inclusion of other uncertainty sources, could be used for operational forecasting.

scholarly journals On the Orthogonalization of Bred Vectors

2010 ◽  
Vol 25 (4) ◽  
pp. 1219-1234 ◽  
Author(s):  
Jan D. Keller ◽  
Andreas Hense ◽  
Luis Kornblueh ◽  
Andreas Rhodin
Keyword(s):  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Forecast Performance ◽  
Ensemble Prediction System ◽  
Study Results ◽  
Bred Vectors ◽  
Forecasting System ◽  
Value Decomposition

Abstract The key to the improvement of the quality of ensemble forecasts assessing the inherent flow uncertainties is the choice of the initial ensemble perturbations. To generate such perturbations, the breeding of growing modes approach has been used for the past two decades. Here, the fastest-growing error modes of the initial model state are estimated. However, the resulting bred vectors (BVs) mainly point in the phase space direction of the leading Lyapunov vector and therefore favor one direction of growing errors. To overcome this characteristic and obtain growing modes pointing to Lyapunov vectors different from the leading one, an orthogonalization implemented as a singular value decomposition based on the similarity between the BVs is applied. This transformation is similar to that used in the ensemble transform technique currently in operational use at NCEP but with certain differences in the metric used and in the implementation. In this study, results of this approach using BVs generated in the Ensemble Forecasting System (EFS) based on the global numerical weather prediction model GME of the German Meteorological Service are presented. The gain in forecast performance achieved with the orthogonalized BV initialization is shown by using different probabilistic forecast scores evaluating ensemble reliability, variance, and resolution. For a 3-month period in summer 2007, the results are compared to forecasts generated with simple BV initializations of the same ensemble prediction system as well as operational ensemble forecasts from ECMWF and NCEP. The orthogonalization vastly improves the GME–EFS scores and makes them competitive with the two other centers.

scholarly journals Ensemble flood forecasting considering dominant runoff processes: I. Setup and application to nested basins (Emme, Switzerland)

2018 ◽  
Author(s):  
Manuel Antonetti ◽  
Christoph Horat ◽  
Ioannis V. Sideris ◽  
Massimiliano Zappa
Keyword(s):  
Predictive Power ◽  
Hydrological Model ◽  
Prediction Models ◽  
Expert Knowledge ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Medium Size ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Forecast Performance

Abstract. Flash floods (FFs) evolve rapidly during and after heavy precipitation events and represent a risk for society. To predict the timing and magnitude of a peak runoff, it is common to couple meteorological and hydrological models in a forecasting chain. However, hydrological models rely on strong simplifying assumptions and hence need to be calibrated. This makes their application difficult in catchments where no direct observation of runoff is available. To address this gap, a FF forecasting chain is presented based on: (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip), (ii) meteorological data from state-of-the-art numerical weather prediction models (COSMO-1, COSMO-E), (iii) operationally available soil moisture estimations from the PREVAH hydrological model, and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes from the so-called maps of runoff types (RTs), which can be derived with different mapping approaches with increasing involvement of expert knowledge. RGM-PRO is then parametrised a priori based on the results of sprinkling experiments. This prediction chain has been evaluated using data from April to September 2016 in the Emme catchment, a medium-size FF prone basin in the Swiss Prealps. Two novel forecasting chains were set up with two different maps of RTs, which allowed sensitivity of the forecast performance on the mapping approaches to be analysed. Furthermore, special emphasis was placed on the predictive power of the new forecasting chains in nested subcatchments when compared with a prediction chain including a conventional hydrological model relying on calibration. Results showed a low sensitivity of the predictive power on the amount of expert knowledge included for the mapping approach. The forecasting chain including a map of RTs with high involvement of expert knowledge did not guarantee more skill. In the larger basins of the Emme region, process-based forecasting chains revealed comparable skill as a prediction system including a conventional hydrological model. In the small nested subcatchments, the process-based forecasting chains outperformed the conventional system, however, no forecasting chain showed satisfying skill. The outcomes of this study show that operational FF predictions in ungauged basins can benefit from the use of information on runoff processes, as no long-term runoff measurements are needed for calibration.

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