scholarly journals Reliable Probabilistic Quantitative Precipitation Forecasts from a Short-Range Ensemble Forecasting System

2007 ◽  
Vol 22 (1) ◽  
pp. 3-17 ◽  
Author(s):  
David J. Stensrud ◽  
Nusrat Yussouf
Keyword(s):  
Short Range ◽  
Ensemble Forecasting ◽  
Operating Characteristics ◽  
Rainfall Events ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Quantitative Precipitation Forecasts ◽  
Forecasting System ◽  
Environmental Prediction

Abstract A simple binning technique is developed to produce reliable 3-h probabilistic quantitative precipitation forecasts (PQPFs) from the National Centers for Environmental Prediction (NCEP) multimodel short-range ensemble forecasting system obtained during the summer of 2004. The past 12 days’ worth of forecast 3-h accumulated precipitation amounts and observed 3-h accumulated precipitation amounts from the NCEP stage-II multisensor analyses are used to adjust today’s 3-h precipitation forecasts. These adjustments are done individually to each of ensemble members for the 95 days studied. Performance of the adjusted ensemble precipitation forecasts is compared with the raw (original) ensemble predictions. Results show that the simple binning technique provides significantly more skillful and reliable PQPFs of rainfall events than the raw forecast probabilities. This is true for the base 3-h accumulation period as well as for accumulation periods up to 48 h. Brier skill scores and the area under the relative operating characteristics curve also indicate that this technique yields skillful probabilistic forecasts. The performance of the adjusted forecasts also progressively improves with the increased accumulation period. In addition, the adjusted ensemble mean QPFs are very similar to the raw ensemble mean QPFs, suggesting that the method does not significantly alter the ensemble mean forecast. Therefore, this simple postprocessing scheme is very promising as a method to provide reliable PQPFs for rainfall events without degrading the ensemble mean forecast.

scholarly journals Reliable Probabilistic Quantitative Precipitation Forecasts from a Short-Range Ensemble Forecasting System during the 2005/06 Cool Season

2008 ◽  
Vol 136 (6) ◽  
pp. 2157-2172 ◽  
Author(s):  
Nusrat Yussouf ◽  
David J. Stensrud
Keyword(s):  
Short Range ◽  
Small Sample Size ◽  
Ensemble Forecast ◽  
Small Sample ◽  
Ensemble Forecasting ◽  
Cool Season ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Quantitative Precipitation Forecasts ◽  
Forecasting System

Abstract A simple binning technique developed to produce reliable probabilistic quantitative precipitation forecasts (PQPFs) from a multimodel short-range ensemble forecasting system is evaluated during the cool season of 2005/06. The technique uses forecasts and observations of 3-h accumulated precipitation amounts from the past 12 days to adjust the present day’s 3-h quantitative precipitation forecasts from each ensemble member for each 3-h forecast period. Results indicate that the PQPFs obtained from this simple binning technique are significantly more reliable than the raw (original) ensemble forecast probabilities. Brier skill scores and areas under the relative operating characteristic curve also reveal that this technique yields skillful probabilistic forecasts of rainfall amounts during the cool season. This holds true for accumulation periods of up to 48 h. The results obtained from this wintertime experiment parallel those obtained during the summer of 2004. In an attempt to reduce the effects of a small sample size on two-dimensional probability maps, the simple binning technique is modified by implementing 5- and 9-point smoothing schemes on the adjusted precipitation forecasts. Results indicate that the smoothed ensemble probabilities remain an improvement over the raw (original) ensemble forecast probabilities, although the smoothed probabilities are not as reliable as the unsmoothed adjusted probabilities. The skill of the PQPFs also is increased as the ensemble is expanded from 16 to 22 members during the period of study. These results reveal that simple postprocessing techniques have the potential to provide greatly improved probabilistic guidance of rainfall events for all seasons of the year.

scholarly journals Impact of Sea Surface Temperature Forcing on Weeks 3 and 4 Forecast Skill in the NCEP Global Ensemble Forecasting System

2017 ◽  
Vol 32 (6) ◽  
pp. 2159-2174 ◽  
Author(s):  
Yuejian Zhu ◽  
Xiaqiong Zhou ◽  
Malaquias Peña ◽  
Wei Li ◽  
Christopher Melhauser ◽  
...  
Keyword(s):  
North America ◽  
Forecast Skill ◽  
Ensemble Forecasting ◽  
Superior Performance ◽  
Seasonal Forecasts ◽  
Skill Scores ◽  
Short Period ◽  
Forecasting System ◽  
Sst Forcing ◽  
The Impact

Abstract The Global Ensemble Forecasting System (GEFS) is being extended from 16 to 35 days to cover the subseasonal period, bridging weather and seasonal forecasts. In this study, the impact of SST forcing on the extended-range land-only global 2-m temperature, continental United States (CONUS) accumulated precipitation, and MJO skill are explored with version 11 of the GEFS (GEFSv11) under various SST forcing configurations. The configurations consist of 1) the operational GEFS 90-day e-folding time of the observed real-time global SST (RTG-SST) anomaly relaxed to climatology, 2) an optimal AMIP configuration using the observed daily RTG-SST analysis, 3) a two-tier approach using the CFSv2-predicted daily SST, and 4) a two-tier approach using bias-corrected CFSv2-predicted SST, updated every 24 h. The experimental period covers the fall of 2013 and the winter of 2013/14. The results indicate that there are small differences in the ranked probability skill scores (RPSSs) between the various SST forcing experiments. The improvements in forecast skill of the Northern Hemisphere 2-m temperature and precipitation for weeks 3 and 4 are marginal, especially for North America. The bias-corrected CFSv2-predicted SST experiment generally delivers superior performance with statistically significant improvement in spatially and temporally aggregated 2-m temperature RPSSs over North America. Improved representation of the SST forcing (AMIP) increased the forecast skill for MJO indices up through week 2, but there is no significant improvement of the MJO forecast skill for weeks 3 and 4. These results are obtained over a short period with weak MJO activity and are also subject to internal model weaknesses in representing the MJO. Additional studies covering longer periods with upgraded model physics are warranted.

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 Short-Range Numerical Weather Prediction Using Time-Lagged Ensembles

2007 ◽  
Vol 22 (3) ◽  
pp. 580-595 ◽  
Author(s):  
Chungu Lu ◽  
Huiling Yuan ◽  
Barry E. Schwartz ◽  
Stanley G. Benjamin
Keyword(s):  
Short Range ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Cycle Model ◽  
Forecast System ◽  
Multilinear Regression ◽  
Probabilistic Forecasts ◽  
Regression Approach ◽  
Ensemble Mean ◽  
Time Lagged

Abstract A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1–3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The time-lagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts.

scholarly journals Short-Range Ensemble Forecasts of Precipitation Type

2005 ◽  
Vol 20 (4) ◽  
pp. 609-626 ◽  
Author(s):  
Matthew S. Wandishin ◽  
Michael E. Baldwin ◽  
Steven L. Mullen ◽  
John V. Cortinas
Keyword(s):  
Short Range ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Precipitation Forecast ◽  
Initial Condition ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Precipitation Type ◽  
Forecast Quality

Abstract Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type algorithms to produce probabilistic forecasts from January through March 2002. Thus the ensemble combines model diversity, initial condition diversity, and postprocessing algorithm diversity. All verification numbers are conditioned on both the ensemble and observations recording some form of precipitation. This separates the forecast of type from the yes–no precipitation forecast. The ensemble is very skillful in forecasting rain and snow but it is only moderately skillful for freezing rain and unskillful for ice pellets. However, even for the unskillful forecasts the ensemble shows some ability to discriminate between the different precipitation types and thus provides some positive value to forecast users. Algorithm diversity is shown to be as important as initial condition diversity in terms of forecast quality, although neither has as big an impact as model diversity. The algorithms have their individual strengths and weaknesses, but no algorithm is clearly better or worse than the others overall.

scholarly journals Short- and Medium-Range Prediction of Tropical and Transitioning Cyclone Tracks within the NCEP Global Ensemble Forecasting System

2010 ◽  
Vol 25 (6) ◽  
pp. 1736-1754 ◽  
Author(s):  
Christian Buckingham ◽  
Timothy Marchok ◽  
Isaac Ginis ◽  
Lewis Rothstein ◽  
Dail Rowe
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Ensemble Forecasting ◽  
Lead Times ◽  
Ensemble Mean ◽  
Medium Range ◽  
Forecasting System ◽  
Left And Right ◽  
Cross Track ◽  
Larger Sample

Abstract The NCEP Global Ensemble Forecasting System (GEFS) is examined in its ability to predict tropical cyclone and extratropical transition (ET) positions. Forecast and observed tracks are compared in Atlantic and western North Pacific basins for 2006–08, and the accuracy and consistency of the ensemble are examined out to 8 days. Accuracy is quantified by the average absolute and along- and cross-track errors of the ensemble mean. Consistency is evaluated through the use of dispersion diagrams, missing rate error, and probability within spread. Homogeneous comparisons are made with the NCEP Global Forecasting System (GFS). The average absolute track error of the GEFS mean increases linearly at a rate of 50 n mi day−1 [where 1 nautical mile (n mi) = 1.852 km] at early lead times in the Atlantic, increasing to 150 n mi day−1 at 144 h (100 n mi day−1 when excluding ET tracks). This trend is 60 n mi day−1 at early lead times in the western North Pacific, increasing to 150 n mi day−1 at longer lead times (130 n mi day−1 when excluding ET tracks). At long lead times, forecasts illustrate left- and right-of-track biases in Atlantic and western North Pacific basins, respectively; bias is reduced (increased) in the Atlantic (western North Pacific) when excluding ET tracks. All forecasts were found to lag behind observed cyclones, on average. The GEFS has good dispersion characteristics in the Atlantic and is underdispersive in the western North Pacific. Homogeneous comparisons suggest that the ensemble mean has value relative to the GFS beyond 96 h in the Atlantic and less value in the western North Pacific; a larger sample size is needed before conclusions can be made.

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.

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 Evaluation of Probabilistic Thunderstorm Forecasts and the Automated Generation of Thunderstorm Threat Areas during Environment Canada Pan Am Science Showcase

2019 ◽  
Vol 34 (5) ◽  
pp. 1295-1319
Author(s):  
Dominique Brunet ◽  
David Sills ◽  
Norbert Driedger
Keyword(s):  
Lead Times ◽  
Unique Contribution ◽  
Environment Canada ◽  
Automated Generation ◽  
Object Based ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Alerting System ◽  
Verification Methodology

Abstract An object-based forecasting, nowcasting, and alerting system prototype was demonstrated during the summer 2015 Environment Canada Pan Am Science Showcase (ECPASS) in Toronto. Part of this demonstration involved the generation of experimental thunderstorm threat areas by both automated NWP postprocessing algorithms and by a pair of human forecasters. This paper first develops a rigorous verification methodology for the intercomparison of continuous as well as categorical probabilistic thunderstorm forecasts. The methodology is then applied to the intercomparison of thunderstorm forecasts made during ECPASS. Statistical postprocessing of forecasts by smoothing with optimal bandwidth followed by recalibration is found to improve the skill scores of all thunderstorm forecasts studied at all lead times between 6 and 48 h. In addition, the calibrated ensemble mean forecasts are found to be better than the calibrated deterministic thunderstorm forecasts for all lead times considered, though postprocessing of the convective rain-rate forecast gives results that are statistically comparable with the ensemble mean forecast. Thunderstorm threat areas that were automatically generated by thresholding the output of NWP-based postprocessed algorithms have better scores than those generated by human forecasters for most lead times beyond 9 h, indicating that they could be integrated as an automated tool for providing high-quality “first-guess” thunderstorm threat areas in an object-based forecasting, nowcasting, and alerting system. A unique contribution of this paper is a novel verification methodology for the fair comparison between continuous and categorical probabilistic forecasts, a methodology that could be used for other experiments involving human- and automatically generated object-based forecasts derived from probabilistic forecasts.

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