scholarly journals Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems

2000 ◽  
Vol 15 (5) ◽  
pp. 559-570 ◽  
Author(s):  
Hans Hersbach
Keyword(s):  
Ensemble Prediction ◽  
Probability Score ◽  
Continuous Ranked Probability Score ◽  
Prediction Systems

scholarly journals The Multiensemble Approach: The NAEFS Example

2009 ◽  
Vol 137 (5) ◽  
pp. 1655-1665 ◽  
Author(s):  
Guillem Candille
Keyword(s):  
Random Variable ◽  
Ensemble Prediction ◽  
Ensemble Size ◽  
The North ◽  
Continuous Ranked Probability Score ◽  
Verification Tools ◽  
Forecasting System ◽  
Skill Improvement ◽  
Prediction Systems ◽  
Environmental Prediction

Abstract The North American Ensemble Forecasting System (NAEFS) is the combination of two Ensemble Prediction Systems (EPS) coming from two operational centers: the Canadian Meteorological Centre (CMC) and the National Centers for Environmental Prediction (NCEP). This system provides forecasts of up to 16 days and should improve the predictability skill of the probabilistic system, especially for the second week. First, a comparison between the two components of the NAEFS is performed for several atmospheric variables with “objective” verification tools developed at CMC [i.e., the continuous ranked probability score (CRPS) and its reliability-resolution decomposition, the reduced centered random variable, and confidence intervals estimated with bootstrap methods]. The CMC system is more reliable, especially because of a better ensemble dispersion, while the NCEP system has better probabilistic resolution. The NAEFS, compared to the CMC and NCEP EPSs, shows significant improvements both in terms of reliability and resolution. The predictability has been improved by 1–2 forecast days in the second week. That improvement is not only a result of the increased ensemble size in the EPS—from 20 members to 40 in the present case—but also to the combination of different models and initial condition perturbations. By randomly mixing members from the CMC and NCEP systems in a 20-member EPS, an intrinsic skill improvement of the system is observed.

scholarly journals An approach for tuning ensemble prediction systems

2013 ◽  
Vol 65 (1) ◽  
pp. 20594 ◽  
Author(s):  
Antti Solonen ◽  
Heikki Järvinen
Keyword(s):  
Ensemble Prediction ◽  
Prediction Systems

scholarly journals Optimization of continuous ranked probability score using PSO

2015 ◽  
pp. 373-378 ◽  
Author(s):  
Seyedeh Atefeh Mohammadi ◽  
Morteza Rahmani ◽  
Majid Azadi
Keyword(s):  
Probability Score ◽  
Continuous Ranked Probability Score

scholarly journals Evaluation of two hydrometeorological ensemble strategies for flash-flood forecasting over a catchment of the eastern Pyrenees

2020 ◽  
Vol 20 (2) ◽  
pp. 425-450 ◽  
Author(s):  
Hélène Roux ◽  
Arnau Amengual ◽  
Romu Romero ◽  
Ernest Bladé ◽  
Marcos Sanz-Ramos
Keyword(s):  
Mesoscale Model ◽  
Flash Flood ◽  
Ensemble Prediction ◽  
Flood Warning ◽  
Eastern Pyrenees ◽  
Streamflow Forecasts ◽  
Prediction Systems ◽  
Rainfall Runoff Model ◽  
Alert Threshold ◽  
Threshold Exceedance

Abstract. This study aims at evaluating the performances of flash-flood forecasts issued from deterministic and ensemble meteorological prognostic systems. The hydrometeorological modeling chain includes the Weather Research and Forecasting Model (WRF) forcing the rainfall-runoff model MARINE dedicated to flash floods. Two distinct ensemble prediction systems accounting for (i) perturbed initial and lateral boundary conditions of the meteorological state and (ii) mesoscale model physical parameterizations have been implemented on the Agly catchment of the eastern Pyrenees with three subcatchments exhibiting different rainfall regimes. Different evaluations of the performance of the hydrometeorological strategies have been performed: (i) verification of short-range ensemble prediction systems and corresponding streamflow forecasts, for a better understanding of how forecasts behave; (ii) usual measures derived from a contingency table approach, to test an alert threshold exceedance; and (iii) overall evaluation of the hydrometeorological chain using the continuous rank probability score, for a general quantification of the ensemble performances. Results show that the overall discharge forecast is improved by both ensemble strategies with respect to the deterministic forecast. Threshold exceedance detections for flood warning also benefit from large hydrometeorological ensemble spread. There are no substantial differences between both ensemble strategies on these test cases in terms of both the issuance of flood warnings and the overall performances, suggesting that both sources of external-scale uncertainty are important to take into account.

scholarly journals PostProcessing and Visualization Techniques for Convection-Allowing Ensembles

2019 ◽  
Vol 100 (7) ◽  
pp. 1245-1258 ◽  
Author(s):  
Brett Roberts ◽  
Israel L. Jirak ◽  
Adam J. Clark ◽  
Steven J. Weiss ◽  
John S. Kain
Keyword(s):  
Real Time ◽  
Data Extraction ◽  
Deep Convection ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Data Volume ◽  
Explicit Simulation ◽  
Prediction Systems ◽  
Visualization Techniques

AbstractSince the early 2000s, growing computing resources for numerical weather prediction (NWP) and scientific advances enabled development and testing of experimental, real-time deterministic convection-allowing models (CAMs). By the late 2000s, continued advancements spurred development of CAM ensemble forecast systems, through which a broad range of successful forecasting applications have been demonstrated. This work has prepared the National Weather Service (NWS) for practical usage of the High Resolution Ensemble Forecast (HREF) system, which was implemented operationally in November 2017. Historically, methods for postprocessing and visualizing products from regional and global ensemble prediction systems (e.g., ensemble means and spaghetti plots) have been applied to fields that provide information on mesoscale to synoptic-scale processes. However, much of the value from CAMs is derived from the explicit simulation of deep convection and associated storm-attribute fields like updraft helicity and simulated reflectivity. Thus, fully exploiting CAM ensembles for forecasting applications has required the development of fundamentally new data extraction, postprocessing, and visualization strategies. In the process, challenges imposed by the immense data volume inherent to these systems required new approaches when considering diverse factors like forecaster interpretation and computational expense. In this article, we review the current state of postprocessing and visualization for CAM ensembles, with a particular focus on forecast applications for severe convective hazards that have been evaluated within NOAA’s Hazardous Weather Testbed. The HREF web viewer implemented at the NWS Storm Prediction Center (SPC) is presented as a prototype for deploying these techniques in real time on a flexible and widely accessible platform.

scholarly journals Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments

2010 ◽  
Vol 14 (11) ◽  
pp. 2303-2317 ◽  
Author(s):  
J. A. Velázquez ◽  
F. Anctil ◽  
C. Perrin
Keyword(s):  
Predictive Distribution ◽  
Added Value ◽  
Average Error ◽  
Hydrological Models ◽  
Relative Operating Characteristic ◽  
Probability Score ◽  
Ensemble Simulations ◽  
Continuous Ranked Probability Score ◽  
The Mean ◽  
Improved Performance

Abstract. This work investigates the added value of ensembles constructed from seventeen lumped hydrological models against their simple average counterparts. It is thus hypothesized that there is more information provided by all the outputs of these models than by their single aggregated predictors. For all available 1061 catchments, results showed that the mean continuous ranked probability score of the ensemble simulations were better than the mean average error of the aggregated simulations, confirming the added value of retaining all the components of the model outputs. Reliability of the simulation ensembles is also achieved for about 30% of the catchments, as assessed by rank histograms and reliability plots. Nonetheless this imperfection, the ensemble simulations were shown to have better skills than the deterministic simulations at discriminating between events and non-events, as confirmed by relative operating characteristic scores especially for larger streamflows. From 7 to 10 models are deemed sufficient to construct ensembles with improved performance, based on a genetic algorithm search optimizing the continuous ranked probability score. In fact, many model subsets were found improving the performance of the reference ensemble. This is thus not essential to implement as much as seventeen lumped hydrological models. The gain in performance of the optimized subsets is accompanied by some improvement of the ensemble reliability in most cases. Nonetheless, a calibration of the predictive distribution is still needed for many catchments.

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