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.

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.

scholarly journals Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

2017 ◽  
Author(s):  
Sanjib Sharma ◽  
Ridwan Siddique ◽  
Seann Reed ◽  
Peter Ahnert ◽  
Pablo Mendoza ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Warm Season ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Near Surface

Abstract. The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised by the following components: i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); iii) NOAA’s Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the U.S. middle Atlantic region, ranging in size from 381 to 12,362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble flood forecasts but QR outperforms ARX(1,1). Preprocessing alone has little effect on improving the skill of the ensemble flood forecasts. Indeed, postprocessing alone performs similar, in terms of the relative mean error, skill, and reliability, to the more involved scenario that includes both preprocessing and postprocessing. We conclude that statistical preprocessing may not always be a necessary component of the ensemble flood forecasting chain.

scholarly journals Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 253
Author(s):  
Luying Ji ◽  
Qixiang Luo ◽  
Yan Ji ◽  
Xiefei Zhi
Keyword(s):  
Northern Hemisphere ◽  
Geopotential Height ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Height Distribution ◽  
Height Field ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Geopotential Height Field

Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.

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 Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble Forecasting

2019 ◽  
Vol 34 (3) ◽  
pp. 617-634 ◽  
Author(s):  
Maxime Taillardat ◽  
Anne-Laure Fougères ◽  
Philippe Naveau ◽  
Olivier Mestre
Keyword(s):  
Heavy Rainfall ◽  
Hybrid Methods ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Wide Range ◽  
Selection Step ◽  
Heavy Tailed ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract To satisfy a wide range of end users, rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We introduce local statistical postprocessing methods based on quantile regression forests and gradient forests with a semiparametric extension for heavy-tailed distributions. These hybrid methods make use of the forest-based outputs to fit a parametric distribution that is suitable to model jointly low, medium, and heavy rainfall intensities. Our goal is to improve ensemble quality and value for all rainfall intensities. The proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the Météo-France ensemble prediction system called Prévision d’Ensemble ARPEGE (PEARP). They are verified with a cross-validation strategy and compete favorably with state-of-the-art methods like analog ensemble or ensemble model output statistics. Our methods do not assume any parametric links between the variables to calibrate and possible covariates. They do not require any variable selection step and can make use of more than 60 predictors available such as summary statistics on the raw ensemble, deterministic forecasts of other parameters of interest, or probabilities of convective rainfall. In addition to improvements in overall performance, hybrid forest-based procedures produced the largest skill improvements for forecasting heavy rainfall events.

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 Increasing the horizontal resolution of ensemble forecasts at CMC

2003 ◽  
Vol 10 (6) ◽  
pp. 463-468 ◽  
Author(s):  
G. Pellerin ◽  
L. Lefaivre ◽  
P. Houtekamer ◽  
C. Girard
Keyword(s):  
Operating Characteristic ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Ensemble Size ◽  
Ensemble Forecasts ◽  
Relative Operating Characteristic ◽  
Sea Level Pressure ◽  
Ensemble Prediction System ◽  
Probability Of Precipitation ◽  
Model Approach

Abstract. Ensemble forecasts are run operationally since February 1998 at the Canadian Meteorological Centre, with outputs up to ten days. The ensemble size was increased from eight to sixteen members in August 1999. The method of producing the perturbed analyses consists of running independent assimilation cycles that use perturbed sets of observations and are driven by eight different models, mainly different in their physical parameterizations. Perturbed analyses are doubled by taking opposite pairs. A multi-model approach is then used to obtain the forecasts. The ensemble output has been used to generate several products. In view of increasing computing facilities, the ensemble prediction system horizontal resolution was increased to TL149 in June 2001. Heights at 500 hPa and mean sea-level pressure maps are regularly used. Charts of precipitation with the probability of precipitation being above various thresholds are also produced at each run. The probabilistic forecast of the 24-h accumulated precipitation has shown skill as demonstrated by the relative operating characteristic (ROC). Verifications of the ensemble forecasts will be presented.

scholarly journals Opportunistically recorded acoustic data support Northeast Atlantic mackerel expansion theory

2015 ◽  
Vol 73 (4) ◽  
pp. 1115-1126 ◽  
Author(s):  
Jeroen van der Kooij ◽  
Sascha M.M. Fässler ◽  
David Stephens ◽  
Lisa Readdy ◽  
Beth E. Scott ◽  
...  
Keyword(s):  
North Sea ◽  
Stock Assessment ◽  
Detection Algorithm ◽  
Trawl Survey ◽  
Northeast Atlantic ◽  
Acoustic Data ◽  
The North ◽  
The North Sea ◽  
Northern North Sea ◽  
Feeding Grounds

Abstract Fisheries independent monitoring of widely distributed pelagic fish species which conduct large seasonal migrations is logistically complex and expensive. One of the commercially most important examples of such a species in the Northeast Atlantic Ocean is mackerel for which up to recently only an international triennial egg survey contributed to the stock assessment. In this study, we explore whether fisheries acoustic data, recorded opportunistically during the English component of the North Sea International Bottom Trawl Survey, can contribute to an improved understanding of mackerel distribution and provide supplementary data to existing dedicated monitoring surveys. Using a previously published multifrequency acoustic mackerel detection algorithm, we extracted the distribution and abundance of schooling mackerel for the whole of the North Sea during August and September between 2007 and 2013. The spatio-temporal coverage of this unique dataset is of particular interest because it includes part of the unsurveyed summer mackerel feeding grounds in the northern North Sea. Recent increases in landings in Icelandic waters during this season suggested that changes have occurred in the mackerel feeding distribution. Thus far it is poorly understood whether these changes are due to a shift, i.e. mackerel moving away from their traditional feeding grounds in the northern North Sea and southern Norwegian Sea, or whether the species' distribution has expanded. We therefore explored whether acoustically derived biomass of schooling mackerel declined in the northern North Sea during the study period, which would suggest a shift in mackerel distribution rather than an expansion. The results of this study show that in the North Sea, schooling mackerel abundance has increased and that its distribution in this area has not changed over this period. Both of these findings provide, to our knowledge, the first evidence in support of the hypothesis that mackerel have expanded their distribution rather than moved away.

scholarly journals The Soverato flood in Southern Italy: performance of global and limited-area ensemble forecasts

2003 ◽  
Vol 10 (3) ◽  
pp. 261-274 ◽  
Author(s):  
A. Montani ◽  
C. Marsigli ◽  
F. Nerozzi ◽  
T. Paccagnella ◽  
S. Tibaldi ◽  
...  
Keyword(s):  
High Resolution ◽  
Southern Italy ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Probabilistic Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Limited Area Model ◽  
Area Model

Abstract. The predictability of the flood event affecting Soverato (Southern Italy) in September 2000 is investigated by considering three different configurations of ECMWF ensemble: the operational Ensemble Prediction System (EPS), the targeted EPS and a high-resolution version of EPS. For each configuration, three successive runs of ECMWF ensemble with the same verification time are grouped together so as to generate a highly-populated "super-ensemble". Then, five members are selected from the super-ensemble and used to provide initial and boundary conditions for the integrations with a limited-area model, whose runs generate a Limited-area Ensemble Prediction System (LEPS). The relative impact of targeting the initial perturbations against increasing the horizontal resolution is assessed for the global ensembles as well as for the properties transferred to LEPS integrations, the attention being focussed on the probabilistic prediction of rainfall over a localised area. At the 108, 84 and 60- hour forecast ranges, the overall performance of the global ensembles is not particularly accurate and the best results are obtained by the high-resolution version of EPS. The LEPS performance is very satisfactory in all configurations and the rainfall maps show probability peaks in the correct regions. LEPS products would have been of great assistance to issue flood risk alerts on the basis of limited-area ensemble forecasts. For the 60-hour forecast range, the sensitivity of the results to the LEPS ensemble size is discussed by comparing a 5-member against a 51-member LEPS, where the limited-area model is nested on all EPS members. Little sensitivity is found as concerns the detection of the regions most likely affected by heavy precipitation, the probability peaks being approximately the same in both configurations.

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