scholarly journals Skill and relative economic value of medium-range hydrological ensemble predictions

2007 ◽  
Vol 11 (2) ◽  
pp. 725-737 ◽  
Author(s):  
E. Roulin
Keyword(s):  
Economic Value ◽  
Skill Score ◽  
Balance Model ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Probability Forecast ◽  
Ensemble Prediction System ◽  
Verification Methods ◽  
Medium Range ◽  
The One

Abstract. A hydrological ensemble prediction system, integrating a water balance model with ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS), is evaluated for two Belgian catchments using verification methods borrowed from meteorology. The skill of the probability forecast that the streamflow exceeds a given level is measured with the Brier Skill Score. Then the value of the system is assessed using a cost-loss decision model. The verification results of the hydrological ensemble predictions are compared with the corresponding results obtained for simpler alternatives as the one obtained by using of the deterministic forecast of ECMWF which is characterized by a higher spatial resolution or by using of the EPS ensemble mean.

scholarly journals Skill and relative economic value of medium-range hydrological ensemble predictions

2006 ◽  
Vol 3 (4) ◽  
pp. 1369-1406 ◽  
Author(s):  
E. Roulin
Keyword(s):  
Economic Value ◽  
Skill Score ◽  
Balance Model ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Probability Forecast ◽  
Ensemble Prediction System ◽  
Verification Methods ◽  
Medium Range ◽  
The One

Abstract. A hydrological ensemble prediction system, integrating a water balance model with ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS), is evaluated for two Belgian catchments using verification methods borrowed from meteorology. The skill of the probability forecast that the streamflow exceeds a given level is measured with the Brier Skill Score. Then the value of the system is assessed using a cost-loss decision model. The verification results of the hydrological ensemble predictions are compared with the corresponding results obtained for simpler alternatives as the one obtained by using of the deterministic forecast of ECMWF which is characterized by a higher spatial resolution or by using of the EPS ensemble mean.

scholarly journals Skill of Medium-Range Hydrological Ensemble Predictions

2005 ◽  
Vol 6 (5) ◽  
pp. 729-744 ◽  
Author(s):  
Emmanuel Roulin ◽  
Stéphane Vannitsem
Keyword(s):  
Balance Model ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Operational Forecasts ◽  
Medium Range ◽  
High Flows ◽  
Hydrological Prediction ◽  
The One

Abstract A hydrological ensemble prediction system, integrating a water balance model with ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS), is evaluated for two Belgian catchments. The skill of streamflow forecast for high flows is analyzed using a 6-yr period of archived EPS forecasts. The probabilistic skill of this hydrological prediction system is much better than the one based on historical precipitation inputs and extends beyond 9 days for both catchments. The skill is larger in winter than in summer. The use of this approach for operational forecasts is briefly discussed.

scholarly journals Multivariate Probabilistic Analysis and Predictability of Medium-Range Ensemble Weather Forecasts

2014 ◽  
Vol 142 (11) ◽  
pp. 4074-4090 ◽  
Author(s):  
Jessica Keune ◽  
Christian Ohlwein ◽  
Andreas Hense
Keyword(s):  
Covariance Matrix ◽  
Full Rank ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Verification Methods ◽  
Uncertainty Estimates ◽  
Medium Range ◽  
Prediction Systems

Abstract Ensemble weather forecasting has been operational for two decades now. However, the related uncertainty analysis in terms of probabilistic postprocessing still focuses on single variables, grid points, or stations. Inevitable dependencies in space and time and between variables are often ignored. To address this problem, two probabilistic postprocessing methods are presented, which are multivariate versions of Gaussian fit and kernel dressing, respectively. The multivariate case requires the estimation of a full rank, invertible covariance matrix. For this purpose, a Graphical Least Absolute Shrinkage and Selection Operators (GLASSO) estimator has been employed that is based on sparse undirected graphical models regularized by an L1 penalty term in order to parameterize the full rank inverse covariance. In all cases, the result is a multidimensional probability density. The forecasts used to test the approach are station forecasts of 2-m temperature and surface pressure from four main global ensemble prediction systems (EPS) with medium-range weather forecasts: the NCEP Global Ensemble Forecast System (GEFS), the Met Office Global and Regional Ensemble Prediction System (MOGREPS), the Canadian Meteorological Centre (CMC) Global Ensemble Prediction System (GEPS), and the ECMWF EPS. To evaluate the multivariate probabilistic postprocessing, especially the uncertainty estimates, common verification methods such as the analysis rank histogram and the continuous ranked probability score (CRPS) are applied. Furthermore, a multivariate extension of the CRPS, the energy score, allows for the verification of a complete medium-range forecast as well as for determining its predictability. It is shown that the predictability is similar for all of the examined ensemble prediction systems, whereas the GLASSO proved to be a useful tool for calibrating the commonly observed underdispersion of ensemble forecasts during the first few lead days by using information from the full covariance matrix.

scholarly journals The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2

2021 ◽  
Vol 893 (1) ◽  
pp. 012047
Author(s):  
R Rahmat ◽  
A M Setiawan ◽  
Supari
Keyword(s):  
Southern Oscillation ◽  
Skill Score ◽  
Skill Assessment ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
In Situ Observation ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Enso Prediction ◽  
The Government

Abstract Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.

scholarly journals Fire weather index: the skill provided by ECMWF ensemble prediction system

2020 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Claudia Vitolo ◽  
Blazej Krzeminski ◽  
Jesús San-Miguel
Keyword(s):  
Weather Forecast ◽  
Research Centre ◽  
Ensemble Prediction ◽  
Fire Weather ◽  
Prediction System ◽  
Weather Index ◽  
Fire Weather Index ◽  
Ensemble Prediction System ◽  
Medium Range Weather Forecast ◽  
Medium Range

Abstract. In the framework of the EU Copernicus program, the European Centre for Medium-range Weather Forecast (ECMWF) on behalf of the Joint Research Centre (JRC) is forecasting daily fire weather indices using its medium range ensemble prediction system. The use of weather forecast in place of local observations can extend early warnings up to 1–2 weeks allowing for greater proactive coordination of resource-sharing and mobilization within and across countries. Using one year of pre-operational service in 2017 and the fire weather index (FWI) here we assess the capability of the system globally and analyze in detail three major events in Chile, Portugal and California. The analysis shows that the skill provided by the ensemble forecast system extends to more than 10 days when compared to the use of mean climate making a case of extending the forecast range to the sub-seasonal to seasonal time scale. However accurate FWI prediction does not translate into accuracy in the forecast of fire activity globally. Indeed when all 2017 detected fires are considered, including agricultural and human induced burning, high FWI values only occurs in 50 % of the cases and only in Boreal regions. Nevertheless for very important events mostly driven by weather condition, FWI forecast provides advance warning that could be instrumental in setting up management strategies.

Application and Verification of the ECMWF Precipitation Type Forecast Product (PTYPE) in China

2020 ◽  
Author(s):  
Quan Dong ◽  
Feng Zhang ◽  
Ning Hu ◽  
Zhiping Zong
Keyword(s):  
High Resolution ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Probability Forecast ◽  
Freezing Rain ◽  
Ensemble Prediction System ◽  
Optimal Probability ◽  
Probability Threshold ◽  
Precipitation Type

<p>The ECMWF (European Centre for Medium-Range Weather Forecasts) precipitation type forecast products—PTYPE are verified using the weather observations of more than 2000 stations in China of the past three winter half years (October to next March). The products include the deterministic forecast from High-resolution model (HRE) and the probability forecast from ensemble prediction system (EPS). Based on the verification results, optimal probability thresholds approaches under criteria of TS maximization (TSmax), frequency match (Bias1) and HSS maximization (HSSmax) are used to improve the deterministic precipitation type forecast skill. The researched precipitation types include rain, sleet, snow and freezing rain.</p><p>The verification results show that the proportion correct of deterministic forecast of ECMWF high-resolution model is mostly larger than 90% and the TSs of rain and snow are high, next is freezing rain, and the TS of sleet is small indicating that the forecast skill of sleet is limited. The rain and snow separating line of deterministic forecasts show errors of a little south in short-range and more and more significant north following elongating lead times in medium-range. The area of sleet forecasts is smaller than observations and the freezing rain is bigger for the high-resolution deterministic forecast. The ensemble prediction system offsets these errors partly by probability forecast. The probability forecast of rain from the ensemble prediction system is smaller than the observation frequency and the probability forecast of snow is larger in short-range and smaller in medium-range than the observation frequency. However, there are some forecast skills for all of these probability forecasts. There are advantages of ensemble prediction system compared to the high-resolution deterministic model. For rain and snow, for some special cost/loss ratio events the EPS is better than the HRD. For sleet and freezing rain, the EPS is better than the HRD significantly, especially for the freezing rain.</p><p>The optimal thresholds of snow and freezing rain are largest which are about 50%~90%, decreasing with elongating lead times. The thresholds of rain are small which are about 10%~20%, increasing with elongating lead times. The thresholds of sleet are the smallest which are under 10%. The verifications show that the approach of optimal probability threshold based on EPS can improve the forecast skill of precipitation type. The proportion correct of HRD is about 92%. Bias1 and TSmax improve it and the improvement of HSSmax is the most significant which is about 94%. The HSS of HRD is about 0.77~0.65. Bias1 increases 0.02 and TSmax increases more. The improvement of HSSmax is the biggest which is about 0.81~0.68 and the increasing rate is around 4%. From the verifications of every kinds of precipitation types, it is demonstrated that the approach of optimal probability threshold improves the performance of rain and snow forecasts significantly compared to the HRD and decreases the forecast area and missing of freezing rain and sleet which are forecasted more areas and false alarms by the HRD.</p><p><strong>Key words: </strong>ECMWF; ensemble prediction system；precipitation type forecast; approach of optimal probability threshold; verification</p>

Bayesian Approach to Decision Making Using Ensemble Weather Forecasts

2006 ◽  
Vol 21 (2) ◽  
pp. 220-231 ◽  
Author(s):  
Richard W. Katz ◽  
Martin Ehrendorfer
Keyword(s):  
Decision Making ◽  
Prior Distribution ◽  
Economic Value ◽  
Bayesian Estimator ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Size ◽  
Ensemble Prediction System ◽  
The Face ◽  
The Cost

Abstract The economic value of ensemble-based weather or climate forecasts is generally assessed by taking the ensembles at “face value.” That is, the forecast probability is estimated as the relative frequency of occurrence of an event among a limited number of ensemble members. Despite the economic value of probability forecasts being based on the concept of decision making under uncertainty, in effect, the decision maker is assumed to ignore the uncertainty in estimating this probability. Nevertheless, many users are certainly aware of the uncertainty inherent in a limited ensemble size. Bayesian prediction is used instead in this paper, incorporating such additional forecast uncertainty into the decision process. The face-value forecast probability estimator would correspond to a Bayesian analysis, with a prior distribution on the actual forecast probability only being appropriate if it were believed that the ensemble prediction system produces perfect forecasts. For the cost–loss decision-making model, the economic value of the face-value estimator can be negative for small ensemble sizes from a prediction system with a level of skill that is not sufficiently high. Further, this economic value has the counterintuitive property of sometimes decreasing as the ensemble size increases. For a more plausible form of prior distribution on the actual forecast probability, which could be viewed as a “recalibration” of face-value forecasts, the Bayesian estimator does not exhibit this unexpected behavior. Moreover, it is established that the effects of ensemble size on the reliability, skill, and economic value have been exaggerated by using the face-value, instead of the Bayesian, estimator.

scholarly journals Analysis of the Relative Operating Characteristic and Economic Value Using the LAPS Ensemble Prediction System in Taiwan

2015 ◽  
Vol 143 (5) ◽  
pp. 1833-1848 ◽  
Author(s):  
Hui-Ling Chang ◽  
Shu-Chih Yang ◽  
Huiling Yuan ◽  
Pay-Liam Lin ◽  
Yu-Chieng Liou
Keyword(s):  
Decision Making ◽  
Operating Characteristic ◽  
Economic Value ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Forecast System ◽  
Relative Operating Characteristic ◽  
Ensemble Prediction System ◽  
Discrimination Ability ◽  
Probabilistic Forecasts

Abstract Measurement of the usefulness of numerical weather prediction considers not only the forecast quality but also the possible economic value (EV) in the daily decision-making process of users. Discrimination ability of an ensemble prediction system (EPS) can be assessed by the relative operating characteristic (ROC), which is closely related to the EV provided by the same forecast system. Focusing on short-range probabilistic quantitative precipitation forecasts (PQPFs) for typhoons, this study demonstrates the consistent and strongly related characteristics of ROC and EV based on the Local Analysis and Prediction System (LAPS) EPS operated at the Central Weather Bureau in Taiwan. Sensitivity experiments including the effect of terrain, calibration, and forecast uncertainties on ROC and EV show that the potential EV provided by a forecast system is mainly determined by the discrimination ability of the same system. The ROC and maximum EV (EVmax) of an EPS are insensitive to calibration, but the optimal probability threshold to achieve the EVmax becomes more reliable after calibration. In addition, the LAPS ensemble probabilistic forecasts outperform deterministic forecasts in respect to both ROC and EV, and such an advantage grows with increasing precipitation intensity. Also, even without explicitly knowing the cost–loss ratio, one can still optimize decision-making and obtain the EVmax by using ensemble probabilistic forecasts.

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

2010 ◽  
Vol 14 (8) ◽  
pp. 1639-1653 ◽  
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 ◽  
Ensemble 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.

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