scholarly journals Hydrometeorological Ensemble Forecast of a Highly Localized Convective Event in the Mediterranean

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1545
Author(s):  
Luca Furnari ◽  
Giuseppe Mendicino ◽  
Alfonso Senatore
Keyword(s):  
Ensemble Prediction ◽  
Lead Times ◽  
Forecast Uncertainty ◽  
Ensemble Prediction System ◽  
Distributed Information ◽  
Convective Event ◽  
Spatially Distributed ◽  
Accumulated Rainfall ◽  
Clear Information

The uncertainties that affect hydrometeorological modelling chains can be addressed through ensemble approaches. In this paper, a convection-permitting ensemble system was assessed based on the downscaling of all members of the ECMWF ensemble prediction system through the coupled atmospheric-hydrological WRF-Hydro modelling system. An exemplary highly localized convective event that occurred in a morphologically complex area of the southern Italian coast was selected as a case study, evaluating the performance of the system for two consecutive lead times up to the hydrological forecast on a very small (11.4 km2) catchment. The proposed approach accurately downscales the signal provided by the global model, improving up to almost 200% the quantitative forecast of the accumulated rainfall peak in the area affected by the event and supplying clear information about the forecast uncertainty. Some members of the ensemble simulations provide accurate results up to the hydrological scale over the catchment, with unit peak discharge forecasts up to 3 m3∙s−1∙km−2. Overall, the study highlights that for highly localized convective events in coastal Mediterranean catchments, ensemble approaches should be preferred to a classic single-based simulation approach, because they improve the forecast skills and provide spatially distributed information about the forecast uncertainty, which can be particularly useful for operational purposes.

scholarly journals A Probabilistic Tropical Cyclone Track Forecast Scheme Based on the Selective Consensus of Ensemble Prediction Systems

2017 ◽  
Vol 32 (6) ◽  
pp. 2143-2157 ◽  
Author(s):  
Xiping Zhang ◽  
Hui Yu
Keyword(s):  
Tropical Cyclone ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Track Forecast ◽  
Forecast Errors ◽  
Forecast Uncertainty ◽  
Ensemble Prediction System ◽  
The Mean ◽  
Environmental Prediction ◽  
Uncertainty Information

Abstract Selective consensus and a grand ensemble based on an ensemble prediction system (EPS) have been found to be effective in improving deterministic tropical cyclone (TC) track forecasts, while little attention has been paid to quantitative applications of the forecast uncertainty information provided by EPSs. In this paper the forecast uncertainty information is evaluated for two operational EPSs and their grand ensemble. Then, a probabilistic TC track forecast scheme is proposed based on the selective consensus of the two EPSs; this scheme is composed of member picking, mean track shifting, and probability ellipses. The operational EPSs are from the European Centre for Medium-Range Weather Forecasts (ECMWF-EPS) and the National Centers for Environmental Prediction (NCEP-GEFS). Evaluation exhibits that the hit ratios of ECMWF-EPS are above 80% for the 70% probability ellipses at all lead times until 120 h and are used in the proposed scheme. The other components of the proposed scheme are about picking potentially good EPS members. A picking ratio of 1/2 is found to be the best choice, and the member-picking technique is used for the grand ensemble but only for lead times out to 48 h. For lead times longer than 48 h, all of the grand ensemble members are used in obtaining the mean track. The effectiveness of the proposed scheme shows a 10% improvement in the mean track forecast errors over the grand ensemble and a 4.5% improvement in the hit ratio of 70% probability ellipses over the ECMWF-EPS at 24 h, demonstrating its good potential to be applied in operations.

scholarly journals An analysis of uncertainties and skill in forecasts of winter storm losses

2016 ◽  
Vol 16 (11) ◽  
pp. 2391-2402 ◽  
Author(s):  
Tobias Pardowitz ◽  
Robert Osinski ◽  
Tim Kruschke ◽  
Uwe Ulbrich
Keyword(s):  
Model Uncertainty ◽  
Damage Model ◽  
Calibration Method ◽  
Ensemble Prediction ◽  
Brier Score ◽  
Lead Times ◽  
Storm Damage ◽  
Winter Storm ◽  
Forecast Uncertainty ◽  
Ensemble Prediction System

Abstract. This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences from a global medium-range ensemble prediction system (EPS). Predictions of storm damage occurrences are subject to large uncertainty due to meteorological forecast uncertainty (typically addressed by means of ensemble predictions) and uncertainties in modelling weather impacts. The latter uncertainty arises from the fact that local vulnerabilities are not known in sufficient detail to allow for a deterministic prediction of damages, even if the forecasted gust wind speed contains no uncertainty. Thus, to estimate the damage model uncertainty, a statistical model based on logistic regression analysis is employed, relating meteorological analyses to historical damage records. A quantification of the two individual contributions (meteorological and damage model uncertainty) to the total forecast uncertainty is achieved by neglecting individual uncertainty sources and analysing resulting predictions. Results show an increase in forecast skill measured by means of a reduced Brier score if both meteorological and damage model uncertainties are taken into account. It is demonstrated that skilful predictions on district level (dividing the area of Germany into 439 administrative districts) are possible on lead times of several days. Skill is increased through the application of a proper ensemble calibration method, extending the range of lead times for which skilful damage predictions can be made.

scholarly journals Uncertainties in Forecasts of Winter Storm Losses

2016 ◽  
Author(s):  
Tobias Pardowitz ◽  
Robert Osinski ◽  
Tim Kruschke ◽  
Uwe Ulbrich
Keyword(s):  
Damage Model ◽  
Calibration Method ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Storm Damage ◽  
Winter Storm ◽  
Forecast Uncertainty ◽  
Ensemble Prediction System ◽  
Uncertainty Sources ◽  
Sufficient Detail

Abstract. This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences from a global medium-range ensemble prediction system (EPS). Predictions of storm damage occurrences are subject to large uncertainty due to meteorological forecast uncertainty (typically addressed by means of ensemble predictions) and uncertainties in modelling weather impacts. The latter uncertainty arises from the fact that local vulnerabilities are not known in sufficient detail to allow for a deterministic assessment of damages. Thus to estimate the damage model uncertainty, a statistical model based on logistic regression analysis is employed, relating meteorological analyses to historical damage records. A quantification of the two individual contributions to the total forecast uncertainty is achieved by neglecting individual uncertainty sources and analysing resulting predictions. Results show an increase in forecast skill if both meteorological and damage occurrence uncertainties are taken into account. It is demonstrated that skilful predictions on district level are possible on lead times of several days. Skill is increased through the application of a proper ensemble calibration method, extending the range of lead times for which skilful damage predictions can be made.

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

Probabilistic precipitation nowcast for flash flooding purposes across Australia: verification of a new version of Short-Term Ensemble Prediction System (STEPS)

2021 ◽  
Author(s):  
Carlos Velasco-Forero ◽  
Jayaram Pudashine ◽  
Mark Curtis ◽  
Alan Seed
Keyword(s):  
Lead Time ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Short Term ◽  
Radar Rainfall ◽  
Flash Flooding ◽  
Ensemble Prediction System ◽  
Study Results

<div> <p>Short-term precipitation forecast plays a vital role for minimizing the adverse effects of heavy precipitation events such as flash flooding.  Radar rainfall nowcasting techniques based on statistical extrapolations are used to overcome current limitations of precipitation forecasts from numerical weather models, as they provide high spatial and temporal resolutions forecasts within minutes of the observation time. Among various algorithms, the Short-Term Ensemble Prediction System (STEPS) provides rainfall fields nowcasts in a probabilistic sense by accounting the uncertainty in the precipitation forecasts by means of ensembles, with spatial and temporal characteristic very similar to those in the observed radar rainfall fields. The Australian Bureau of Meteorology uses STEPS to generate ensembles of forecast rainfall ensembles in real-time from its extensive weather radar network. </p> </div><div> <p>In this study, results of a large probabilistic verification exercise to a new version of STEPS (hereafter named STEPS-3) are reported. An extensive dataset of more than 47000 individual 5-minute radar rainfall fields (the equivalent of more than 163 days of rain) from ten weather radars across Australia (covering tropical to mid-latitude regions) were used to generate (and verify) 96-member rainfall ensembles nowcasts with up to a 90-minute lead time. STEPS-3 was found to be more than 15-times faster in delivering results compared with previous version of STEPS and an open-source algorithm called pySTEPS. Interestingly, significant variations were observed in the quality of predictions and verification results from one radar to other, from one event to other, depending on the characteristics and location of the radar, nature of the rainfall event, accumulation threshold and lead time. For example, CRPS and RMSE of ensembles of 5-min rainfall forecasts for radars located in mid-latitude regions are better (lower) than those ones from radars located in tropical areas for all lead-times. Also, rainfall fields from S-band radars seem to produce rainfall forecasts able to successfully identify extreme rainfall events for lead times up to 10 minutes longer than those produced using C-band radar datasets for the same rain rate thresholds. Some details of the new STEPS-3 version, case studies and examples of the verification results will be presented. </p> </div>

scholarly journals Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 458 ◽  
Author(s):  
Zhenzhen Liu ◽  
Qiang Dai ◽  
Lu Zhuo
Keyword(s):  
Lead Time ◽  
Rainfall Variability ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Short Term ◽  
Radar Rainfall ◽  
Ensemble Prediction System ◽  
Predictive Skill ◽  
Many Sources

Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.

scholarly journals Wave Extremes in the Northeast Atlantic from Ensemble Forecasts

2013 ◽  
Vol 26 (19) ◽  
pp. 7525-7540 ◽  
Author(s):  
Øyvind Breivik ◽  
Ole Johan Aarnes ◽  
Jean-Raymond Bidlot ◽  
Ana Carrasco ◽  
Øyvind Saetra
Keyword(s):  
Ensemble Prediction ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Northeast Atlantic ◽  
The North ◽  
Time Period ◽  
The North Sea ◽  
Mean And Variance ◽  
Buoy Data

Abstract A method for estimating return values from ensembles of forecasts at advanced lead times is presented. Return values of significant wave height in the northeast Atlantic, the Norwegian Sea, and the North Sea are computed from archived +240-h forecasts of the ECMWF Ensemble Prediction System (EPS) from 1999 to 2009. Three assumptions are made: First, each forecast is representative of a 6-h interval and collectively the dataset is then comparable to a time period of 226 years. Second, the model climate matches the observed distribution, which is confirmed by comparing with buoy data. Third, the ensemble members are sufficiently uncorrelated to be considered independent realizations of the model climate. Anomaly correlations of 0.20 are found, but peak events (>P97) are entirely uncorrelated. By comparing return values from individual members with return values of subsamples of the dataset it is also found that the estimates follow the same distribution and appear unaffected by correlations in the ensemble. The annual mean and variance over the 11-yr archived period exhibit no significant departures from stationarity compared with a recent reforecast; that is, there is no spurious trend because of model upgrades. The EPS yields significantly higher return values than the 40-yr ECMWF Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) and is in good agreement with the high-resolution 10-km Norwegian Reanalyses (NORA10) hindcast, except in the lee of unresolved islands where EPS overestimates and in enclosed seas where it has low bias. Confidence intervals are half the width of those found for ERA-Interim because of the magnitude of the dataset.

scholarly journals Study on Optimization of the Operation of Dams Using Ensemble Prediction and a Distributed Rainfall-Runoff Model

10.29007/tkhk ◽  
2018 ◽  
Author(s):  
Satoru Oishi ◽  
Toshihiko Tahara ◽  
Mariko Ogawa
Keyword(s):  
Water Supply ◽  
Flood Control ◽  
Action Plan ◽  
Normal Operation ◽  
Ensemble Prediction ◽  
Global Spectral Model ◽  
Ensemble Prediction System ◽  
Rainfall Forecast ◽  
Accumulated Rainfall ◽  
Dam Operation

Recently, heavy rain by typhoon increases risk of disaster everywhere in Japan. There has been considerable interest to improve the flood control function of dams by prior releasing because an action plan was enacted to fully use the existing hydraulic structures to prevent flood disasters. Carrying out prior releasing has a risk in terms of water supply purpose, in other words, it may cause artificial drought. Therefore, it should be taken into consideration that releasing larger amount of water than rain gives water shortage. In the present study, we suggested the method of dam operation based on rainfall forecast including prior releasing considering the risk in terms of water supply purpose. Concretely, first, we investigated that it could estimate the accuracy of forecasted accumulated rainfall based on Global Spectral Model (GSM) by adding the information of spreads calculated by Weekly Ensemble Prediction System (WEPS) in the Yodo river basin. Second, accumulated rainfall based on GSM errors using gamma distribution was analyzed. Third, the method of dam operation based on rainfall forecast including prior releasing was applied to past examples and the effect was verified. As a result, peak discharge in Hirakata point was reduced than normal operation in case of rainfall prediction was accurate.

scholarly journals Ensemble-Based Forecast Uncertainty Analysis of Diverse Heavy Rainfall Events

2010 ◽  
Vol 25 (4) ◽  
pp. 1103-1122 ◽  
Author(s):  
Russ S. Schumacher ◽  
Christopher A. Davis
Keyword(s):  
United States ◽  
Tropical Cyclones ◽  
Heavy Rainfall ◽  
Warm Season ◽  
The United States ◽  
Forecast Skill ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Cool Season ◽  
Ensemble Prediction System

Abstract This study examines widespread heavy rainfall over 5-day periods in the central and eastern United States. First, a climatology is presented that identifies events in which more than 100 mm of precipitation fell over more than 800 000 km2 in 5 days. This climatology shows that such events are most common in the cool season near the Gulf of Mexico coast and are rare in the warm season. Then, the focus turns to the years 2007 and 2008, when nine such events occurred in the United States, all of them leading to flooding. Three of these were associated with warm-season convection, three took place in the cool season, and three were caused by landfalling tropical cyclones. Global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System are used to assess forecast skill and uncertainty for these nine events, and to identify the types of weather systems associated with their relative levels of skill and uncertainty. Objective verification metrics and subjective examination are used to determine how far in advance the ensemble identified the threat of widespread heavy rains. Specific conclusions depend on the rainfall threshold and the metric chosen, but, in general, predictive skill was highest for rainfall associated with tropical cyclones and lowest for the warm-season cases. In almost all cases, the ensemble provides very skillful 5-day forecasts when initialized at the beginning of the event. In some of the events—particularly the tropical cyclones and strong baroclinic cyclones—the ensemble still shows considerable skill in 96–216-h precipitation forecasts. In other cases, however, the skill drops off much more rapidly as lead time increases. In particular, forecast skill at long lead times was the lowest and spread was the largest in the two cases associated with meso-α-scale to synoptic-scale vortices that were cut off from the primary upper-level jet. In these cases, it appears that when the vortex is present in the initial conditions, the resulting precipitation forecasts are quite accurate and certain, but at longer lead times when the model is required to both develop and correctly evolve the vortex, forecast quality is low and uncertainty is large. These results motivate further investigation of the events that were poorly predicted.

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>

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