Comparative Evaluation of Three Schaake Shuffle Schemes in Postprocessing GEFS Precipitation Ensemble Forecasts

Abstract Natural weather systems possess certain spatiotemporal variability and correlations. Preserving these spatiotemporal properties is a significant challenge in postprocessing ensemble weather forecasts. To address this challenge, several rank-based methods, the Schaake Shuffle and its variants, have been developed in recent years. This paper presents an extensive assessment of the Schaake Shuffle and its two variants. These schemes differ in how the reference multivariate rank structure is established. The first scheme (SS-CLM), an implementation of the original Schaake Shuffle method, relies on climatological observations to construct rank structures. The second scheme (SS-ANA) utilizes precipitation event analogs obtained from a historical archive of observations. The third scheme (SS-ENS) employs ensemble members from the Global Ensemble Forecast System (GEFS). Each of the three schemes is applied to postprocess precipitation ensemble forecasts from the GEFS for its first three forecast days over the mid-Atlantic region of the United States. In general, the effectiveness of these schemes depends on several factors, including the season (or precipitation pattern) and the level of gridcell aggregation. It is found that 1) the SS-CLM and SS-ANA behave similarly in spatial and temporal correlations; 2) by a measure for capturing spatial variability, the SS-ENS outperforms the SS-ANA, which in turn outperforms the SS-CLM; and 3), overall, the SS-ANA performs better than the SS-CLM. The study also reveals that it is important to choose a proper size for the postprocessed ensembles in order to capture extreme precipitation events.

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Statistical post-processing of ensemble forecasts at the Belgian met service

10.5194/egusphere-egu21-2495 ◽

2021 ◽

Author(s):

Jonathan Demaeyer ◽

Bert Van schaeybroeck ◽

Stéphane Vannitsem

Keyword(s):

Maximum Temperature ◽

Post Processing ◽

Ensemble Forecasts ◽

Weather Forecasts ◽

Operational Application ◽

Uncertainty Estimates ◽

The Mean ◽

Model Output Statistics ◽

Ensemble Weather Forecasts ◽

Meteorological Institute

Statistical post-processing of ensemble weather forecasts has become an essential step in the forecasting chain as it enables the correction of biases and reliable uncertainty estimates of ensembles (Gneiting, 2014).&#160;&#160;One algorithm recently proposed to perform the correction of ensemble weather forecasts is a linear member-by-member (MBM) Model Output Statistics (MOS) system, post-processing each member of the ECMWF ensemble (Van Schaeybroeck & Vannitsem, 2015). This method consists in correcting the mean and variability of the ensemble members in line with the observed climatology. At the same time, it calibrates the ensemble spread such as to match, on average, the mean square error of the ensemble mean. The MBM method calibrates the ensemble forecasts based on the station observations by minimizing the continuous ranked probability score (CRPS).Using this method, the Royal Meteorological Institute of Belgium has started in 2020 its new postprocessing program by developing an operational application to perform the calibration of the ECMWF ensemble forecasts at the stations points for the minimum and maximum temperature, and for wind gusts.&#160;In this report, we will first describe briefly the postprocessing methods being used and the architecture of the application. We will then present the results over the first few months of operation. Finally, we will discuss the future developments of this application and of the program. Gneiting, T., 2014: Calibration of medium-range weather forecasts. ECMWF Technical Memorandum No. 719 Van Schaeybroeck, B. & Vannitsem, S., 2015: Ensemble post-processing using member-by-member approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society, 141, 807&#8211;818.

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A comparison between ensemble and deterministic hydrological forecasts in an operational context

Advances in Geosciences ◽

10.5194/adgeo-29-85-2011 ◽

2011 ◽

Vol 29 ◽

pp. 85-94 ◽

Cited By ~ 26

Author(s):

M.-A. Boucher ◽

F. Anctil ◽

L. Perreault ◽

D. Tremblay

Keyword(s):

Weather Forecasting ◽

Ensemble Forecasts ◽

Meteorological Model ◽

Resources Management ◽

Weather Forecasts ◽

Hydropower Production ◽

Forecasting System ◽

The Cost ◽

Ensemble Weather Forecasts

Abstract. Ensemble forecasts can greatly benefit water resources management as they provide useful information regarding the uncertainty of the situation at hand. However, weather forecasting systems are evolving and the cost for reanalysis and reforecasts is prohibitive. Consequently, series of ensemble weather forecasts from a particular version of the forecasting system are often short. In this case study, we consider a hydrological event that took place in 2003 on the Gatineau watershed in Canada and caused management difficulties in a hydropower production context. The weather ensemble forecasting system in place at that time is now obsolete, but we show that with minimal post-processing of the forecasts, it is still beneficial to exploit ensemble rather than deterministic forecasts, even if the latter emerge from a more advanced meteorological model and possess superior spatial resolution.

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The similarity-based method: a new object detection method for deterministic and ensemble weather forecasts

Advances in Science and Research ◽

10.5194/asr-16-209-2019 ◽

2019 ◽

Vol 16 ◽

pp. 209-213

Author(s):

Lucie Rottner ◽

Philippe Arbogast ◽

Mayeul Destouches ◽

Yamina Hamidi ◽

Laure Raynaud

Keyword(s):

Detection Method ◽

Weather Prediction ◽

Object Oriented ◽

Small Scale ◽

Object Oriented Method ◽

Ensemble Forecasts ◽

Weather Forecasts ◽

Mesoscale Structures ◽

Ensemble Weather Forecasts ◽

Meteorological Models

Abstract. A new object-oriented method has been developed to detect hazardous phenomena predicted by Numerical Weather Prediction (NWP) models. This method, called similarity-based method, is looking for specific meteorological objects in the forecasts, which are defined by a reference histogram representing the meteorological phenomena to be detected. The similarity-based method enables to cope with small scale unpredictable details of mesoscale structures in meteorological models and to quantify the uncertainties on the location of the predicted phenomena. Applied to ensemble forecasts, the similarity-based method can be viewed as a particular case of neighborhood processing, allowing spatialized probabilities to be computed. An application to rainfall detection using forecasts from the AROME deterministic and ensemble models is presented.

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Flow-Dependent Reliability: A Path to More Skillful Ensemble Forecasts

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-17-0027.1 ◽

2018 ◽

Vol 99 (5) ◽

pp. 1015-1026 ◽

Cited By ~ 13

Author(s):

M. J. Rodwell ◽

D. S. Richardson ◽

D. B. Parsons ◽

H. Wernli

Keyword(s):

Lead Times ◽

Jet Stream ◽

Ensemble Forecasts ◽

Weather Forecasts ◽

Convective Systems ◽

Flow Type ◽

Medium Range ◽

Mesoscale Convective ◽

Ensemble Weather Forecasts ◽

Start Dates

AbstractWhile chaos ensures that probabilistic weather forecasts cannot always be “sharp,” it is important for users and developers that they are reliable. For example, they should not be overconfident or underconfident. The “spread–error” relationship is often used as a first-order assessment of the reliability of ensemble weather forecasts. This states that the ensemble standard deviation (a measure of forecast uncertainty) should match the root-mean-square error on the ensemble mean (when averaged over a sufficient number of forecast start dates). It is shown here that this relationship is now largely satisfied at the European Centre for Medium-Range Weather Forecasts (ECMWF) for ensemble forecasts of the midlatitude, midtropospheric flow out to lead times of at least 10 days when averaged over all flow situations throughout the year. This study proposes a practical framework for continued improvement in the reliability (and skill) of such forecasts. This involves the diagnosis of flow-dependent deficiencies in short-range (∼12 h) reliability for a range of synoptic-scale flow types and the prioritization of modeling research to address these deficiencies. The approach is demonstrated for a previously identified flow type, a trough over the Rockies with warm, moist air ahead. The mesoscale convective systems that can ensue are difficult to predict and, by perturbing the jet stream, are thought to lead to deterministic forecast “busts” for Europe several days later. The results here suggest that jet stream spread is insufficient during this flow type, and thus unreliable. This is likely to mean that the uncertain forecasts for Europe may, nevertheless, still be overconfident.

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Post-Processing Ensemble Weather Forecasts for Introducing Multisite and Multivariable Correlations using Rank Shuffle and Copula Theory

Monthly Weather Review ◽

10.1175/mwr-d-21-0100.1 ◽

2021 ◽

Keyword(s):

Latent Variables ◽

Gaussian Copula ◽

Post Processing ◽

Ensemble Forecasts ◽

Copula Theory ◽

Weather Forecasts ◽

Empirical Copula ◽

Post Process ◽

Reconstruction Methods ◽

Ensemble Weather Forecasts

Abstract Statistical methods have been widely used to post-process ensemble weather forecasts for hydrological predictions. However, most of the statistical post-processing methods apply to a single weather variable at a single location, thus neglecting the inter-site and inter-variable dependence structures of forecast variables. This study synthesized a multisite and multivariate (MSMV) post-processing framework that extends the univariate method to the MSMV version by directly rearranging the post-processed ensemble members (post-reordering strategy) or by rearranging the latent variables used in univariate method (pre-reordering strategy). Based on the univariate Generator-based Post-Processing (GPP) method, the two reordering strategies and three dependence reconstruction methods (Rank shuffle (RS), Gaussian Copula (GC), and Empirical Copula (EC)) totaling 6 MSMV methods (RS-Pre, GC-Pre, EC-Pre, RS-Post, GC-Post, and EC-Post) were evaluated in post-processing ensemble precipitation and temperature forecasts for the Xiangjiang Basin in China using the 11-member ensemble forecasts from the Global Ensemble Forecasting System (GEFS). The results showed that raw GEFS forecasts tend to be biased for both the forecast ensembles and the inter-site and inter-variable dependencies. Univariate method can improve the univariate performance of ensemble mean and spread but misrepresent the inter-site and inter-variable dependence among the forecast variables. The MSMV framework can well utilize the advantages of the univariate method and also reconstruct the inter-site and inter-variable dependencies. Among the six methods, RS-Pre, RS-Post, GC-Post, and EC-Post perform better than the others with respect to reproducing the univariate statistics and multivariable dependences. The post-reordering strategy is recommended to combine the univariate method (i.e. GPP) and reconstruction methods.

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Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0251.1 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1379-1398 ◽

Cited By ~ 10

Author(s):

Shasha Han ◽

Paulin Coulibaly

Keyword(s):

Bias Correction ◽

Short Range ◽

Flood Forecasting ◽

Ensemble Prediction ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Weather Forecasts ◽

Hydrologic Uncertainty ◽

Ensemble Weather Forecasts ◽

Range Forecasts

Recent advances in the field of flood forecasting have shown increased interests in probabilistic forecasting as it provides not only the point forecast but also the assessment of associated uncertainty. Here, an investigation of a hydrologic uncertainty processor (HUP) as a postprocessor of ensemble forecasts to generate probabilistic flood forecasts is presented. The main purpose is to quantify dominant uncertainties and enhance flood forecast reliability. HUP is based on Bayes’s theorem and designed to capture hydrologic uncertainty. Ensemble forecasts are forced by ensemble weather forecasts from the Global Ensemble Prediction System (GEPS) that are inherently uncertain, and the input uncertainty propagates through the model chain and integrates with hydrologic uncertainty in HUP. The bias of GEPS was removed using multivariate bias correction, and several scenarios were developed by different combinations of GEPS with HUP. The performance of different forecast horizons for these scenarios was compared using multifaceted evaluation metrics. Results show that HUP is able to improve the performance for both short- and medium-range forecasts; the improvement is significant for short lead times and becomes less obvious with increasing lead time. Overall, the performances for short-range forecasts when using HUP are promising, and the most satisfactory result for the short range is obtained by applying bias correction to each ensemble member plus applying the HUP postprocessor.

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Sequential Aggregation of Probabilistic Forecasts - Applicaton to Wind Speed Ensemble Forecasts

10.5194/egusphere-egu21-11193 ◽

2021 ◽

Author(s):

Michael Zamo ◽

Liliane Bel ◽

Olivier Mestre

Keyword(s):

Distribution Function ◽

Wind Speed ◽

Cumulative Distribution Function ◽

Performance Measure ◽

Cumulative Distribution ◽

Ensemble Forecasts ◽

Forecast Performance ◽

Weather Forecasts ◽

Probabilistic Forecasts ◽

Ensemble Weather Forecasts

Sequential aggregation is a theoretically-grounded means to combine several forecasts of a quantity to achieve better forecast performance as evaluated by a loss function. This theory has been mainly applied to point forecasts with a scalar forecast quantity, but rarely to forecasts expressed in a probabilistic form. In this work, we show how this theory can be readily adapted to forecasts expressed as step-wise cumulative distribution function (CDF), with the continuous ranked probabilistic score (CRPS) as performance measure.Ensemble weather forecasts estimate the outcome of future observed quantities in a way that can be interpreted as step-wise CDF. Since those forecast CDFs are biased, statistical postprocessing methods are used to improve their statistical coherency with the observed quantity. Since many ensembles and many postprocessing methods exist, one can combine raw and post-processed ensembles in order to get even better forecast performance. To illustrate this point and the advantages of blending CDFs, sequential aggregation is applied to wind-speed ensemble weather forecasts with the CRPS as a performance measure alongside the Jolliffe-Primo test to assess the reliability of the various (raw, post-processed or aggregated) forecasts.

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Postprocessing of Ensemble Weather Forecasts Using a Stochastic Weather Generator

Monthly Weather Review ◽

10.1175/mwr-d-13-00180.1 ◽

2014 ◽

Vol 142 (3) ◽

pp. 1106-1124 ◽

Cited By ~ 13

Author(s):

Jie Chen ◽

François P. Brissette ◽

Zhi Li

Keyword(s):

Bias Correction ◽

Weather Forecast ◽

Correction Method ◽

Weather Generator ◽

Ensemble Forecasts ◽

Stochastic Weather Generator ◽

Weather Forecasts ◽

Bias Correction Method ◽

Ensemble Weather Forecasts ◽

Precipitation And Temperature

Abstract This study proposes a new statistical method for postprocessing ensemble weather forecasts using a stochastic weather generator. Key parameters of the weather generator were linked to the ensemble forecast means for both precipitation and temperature, allowing the generation of an infinite number of daily times series that are fully coherent with the ensemble weather forecast. This method was verified through postprocessing reforecast datasets derived from the Global Forecast System (GFS) for forecast leads ranging between 1 and 7 days over two Canadian watersheds in the Province of Quebec. The calibration of the ensemble weather forecasts was based on a cross-validation approach that leaves one year out for validation and uses the remaining years for training the model. The proposed method was compared with a simple bias correction method for ensemble precipitation and temperature forecasts using a set of deterministic and probabilistic metrics. The results show underdispersion and biases for the raw GFS ensemble weather forecasts, which indicated that they were poorly calibrated. The proposed method significantly increased the predictive power of ensemble weather forecasts for forecast leads ranging between 1 and 7 days, and was consistently better than the bias correction method. The ability to generate discrete, autocorrelated daily time series leads to ensemble weather forecasts’ straightforward use in forecasting models commonly used in the fields of hydrology or agriculture. This study further indicates that the calibration of ensemble forecasts for a period up to one week is reasonable for precipitation, and for temperature it could be reasonable for another week.

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A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts

Applied Energy ◽

10.1016/j.apenergy.2017.05.068 ◽

2017 ◽

Vol 201 ◽

pp. 135-147 ◽

Cited By ~ 49

Author(s):

Emily Craparo ◽

Mumtaz Karatas ◽

Dashi I. Singham

Keyword(s):

Robust Optimization ◽

Optimization Approach ◽

Weather Forecasts ◽

Hybrid Microgrid ◽

Ensemble Weather Forecasts

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Anomalies of continental precipitation associated with El Niño Southern Oscillation: the role of moisture contribution from oceanic and terrestrial sources

10.5194/egusphere-egu21-12386 ◽

2021 ◽

Author(s):

Rogert Sorí ◽

Raquel Nieto ◽

Margarida L.R. Liberato ◽

Luis Gimeno

Keyword(s):

South America ◽

Southern Oscillation ◽

The United States ◽

Precipitation Pattern ◽

Two Phase ◽

The North ◽

Terrestrial Origin ◽

Precipitation Anomalies ◽

June July

The regional and global precipitation pattern is highly modulated by the influence of El Ni&#241;o Southern Oscillation (ENSO), which is considered the most important mode of climate variability on the planet. In this study was investigated the asymmetry of the continental precipitation anomalies during El Ni&#241;o and La Ni&#241;a. To do it, a Lagrangian approach already validated was used to determine the proportion of the total Lagrangian precipitation that is of oceanic and terrestrial origin. During both, El Ni&#241;o and La Ni&#241;a, the Lagrangian precipitation in regions such as the northeast of South America, the east and west coast of North America, Europe, the south of West Africa, Southeast Asia, and Oceania is generally determined by the oceanic component of the precipitation, while that from terrestrial origin provides a major percentage of the average Lagrangian precipitation towards the interior of the continents. The role of the moisture contribution to precipitation from terrestrial and oceanic origin was evaluated in regions with statistically significant precipitation anomalies during El Ni&#241;o and La Ni&#241;a. Two-phase asymmetric behavior of the precipitation was found in regions such the northeast of South America, South Africa, the north of Mexico, and southeast of the United States, etc. principally for December-January-February and June-July-August. For some of these regions was also calculated the anomalies of the precipitation from other datasets to confirm the changes. Besides, for these regions was calculated the anomaly of the Lagrangian precipitation, which agrees in all the cases with the precipitation change. For these regions, it was determined which component of the Lagrangian precipitation, whether oceanic or terrestrial, controlled the precipitation anomalies. A schematic figure represents the extent of the most important seasonal oceanic and terrestrial sources for each subregion during El Ni&#241;o and La Ni&#241;a.

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