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

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.

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

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

<p>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).  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).</p><p><span>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. </span>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.</p><p><span><br></span></p><p><span> </span> <span> </span></p><p><strong>Gneiting</strong>, <strong>T.</strong>, 2014: Calibration of medium-range weather forecasts. <em>ECMWF Technical Memorandum</em> <strong>No. 719</strong></p><p><span> </span> <span> </span></p><p><strong>Van Schaeybroeck</strong>, <strong>B.</strong> & <strong>Vannitsem</strong>, <strong>S.</strong>, 2015: Ensemble post-processing using member-by-member approaches: theoretical aspects. <em>Quarterly Journal of the Royal Meteorological Society</em>, <strong>141</strong>, 807–818.</p>

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

2018 ◽  
Vol 19 (3) ◽  
pp. 575-598 ◽  
Author(s):  
Limin Wu ◽  
Yu Zhang ◽  
Thomas Adams ◽  
Haksu Lee ◽  
Yuqiong Liu ◽  
...  
Keyword(s):  
The United States ◽  
Spatiotemporal Variability ◽  
Precipitation Event ◽  
Precipitation Pattern ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Temporal Correlations ◽  
Extreme Precipitation Events ◽  
Proper Size ◽  
Ensemble Weather 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.

scholarly journals Simulation-based comparison of multivariate ensemble post-processing methods

2020 ◽  
Vol 27 (2) ◽  
pp. 349-371 ◽  
Author(s):  
Sebastian Lerch ◽  
Sándor Baran ◽  
Annette Möller ◽  
Jürgen Groß ◽  
Roman Schefzik ◽  
...  
Keyword(s):  
Covariance Structure ◽  
Post Processing ◽  
Copula Functions ◽  
Ensemble Forecasts ◽  
Processing Methods ◽  
Practical Applications ◽  
Weather Forecasts ◽  
Simulation Based ◽  
Mean Variance

Abstract. Many practical applications of statistical post-processing methods for ensemble weather forecasts require accurate modeling of spatial, temporal, and inter-variable dependencies. Over the past years, a variety of approaches has been proposed to address this need. We provide a comprehensive review and comparison of state-of-the-art methods for multivariate ensemble post-processing. We focus on generally applicable two-step approaches where ensemble predictions are first post-processed separately in each margin and multivariate dependencies are restored via copula functions in a second step. The comparisons are based on simulation studies tailored to mimic challenges occurring in practical applications and allow ready interpretation of the effects of different types of misspecifications in the mean, variance, and covariance structure of the ensemble forecasts on the performance of the post-processing methods. Overall, we find that the Schaake shuffle provides a compelling benchmark that is difficult to outperform, whereas the forecast quality of parametric copula approaches and variants of ensemble copula coupling strongly depend on the misspecifications at hand.

scholarly journals A low-cost post-processing technique improves weather forecasts around the world

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Timothy David Hewson ◽  
Fatima Maria Pillosu
Keyword(s):  
Flash Flood ◽  
Low Cost ◽  
Extreme Rainfall ◽  
Processing Technique ◽  
Processing Method ◽  
Weather Type ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Strong Focus

AbstractComputer-generated weather forecasts divide the Earth’s surface into gridboxes, each currently spanning about 400 km2, and predict one value per gridbox. If weather varies markedly within a gridbox, forecasts for specific sites inevitably fail. Here we present a statistical post-processing method for ensemble forecasts that accounts for the degree of variation within each gridbox, bias on the gridbox scale, and the weather dependence of each. When applying this post-processing, skill improves substantially across the globe; for extreme rainfall, for example, useful forecasts extend 5 days ahead, compared to less than 1 day without post-processing. Skill improvements are attributed to creation of huge calibration datasets by aggregating, globally rather than locally, forecast-observation differences wherever and whenever the observed “weather type” was similar. A strong focus on meteorological understanding also contributes. We suggest that applications for our methodology include improved flash flood warnings, physics-related insights into model weaknesses and global pointwise re-analyses.

scholarly journals Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System

2021 ◽  
Author(s):  
Montgomery L. Flora ◽  
Corey K. Potvin ◽  
Patrick S. Skinner ◽  
Shawn Handler ◽  
Amy McGovern
Keyword(s):  
Machine Learning ◽  
Storm Track ◽  
Severe Weather ◽  
Storm Tracks ◽  
State Variables ◽  
Post Processing ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Environment Variables

AbstractA primary goal of the National Oceanic and Atmospheric Administration Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0-3 h) severe weather forecasts. Post-processing is required to maximize the usefulness of probabilistic guidance from an ensemble of convection-allowing model forecasts. Machine learning (ML) models have become popular methods for post-processing severe weather guidance since they can leverage numerous variables to discover useful patterns in complex datasets. In this study, we develop and evaluate a series of ML models to produce calibrated, probabilistic severe weather guidance from WoF System (WoFS) output.Our dataset includes WoFS ensemble forecasts available every 5 minutes out to 150 min of lead time from the 2017-2019 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (81 dates). Using a novel ensemble storm track identification method, we extracted three sets of predictors from the WoFS forecasts: intra-storm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. We then trained random forests, gradient-boosted trees, and logistic regression algorithms to predict which WoFS 30-min ensemble storm tracks will overlap a tornado, severe hail, and/or severe wind report. To provide rigorous baselines against which to evaluate the skill of the ML models, we extracted the ensemble probabilities of hazard-relevant WoFS variables exceeding tuned thresholds from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced more reliable probabilities than the baseline predictions. Overall, the results suggest that ML-based post-processing of dynamical ensemble output can improve short term, storm-scale severe weather probabilistic guidance.

scholarly journals Simulation-based comparison of multivariate ensemble post-processing methods

2020 ◽  
Author(s):  
Sebastian Lerch ◽  
Sándor Baran ◽  
Annette Möller ◽  
Jürgen Groß ◽  
Roman Schefzik ◽  
...  
Keyword(s):  
Covariance Structure ◽  
Post Processing ◽  
Copula Functions ◽  
Ensemble Forecasts ◽  
Processing Methods ◽  
Practical Applications ◽  
Weather Forecasts ◽  
Simulation Based ◽  
Mean Variance

Abstract. Many practical applications of statistical post-processing methods for ensemble weather forecasts require to accurately model spatial, temporal and inter-variable dependencies. Over the past years, a variety of approaches has been proposed to address this need. We provide a comprehensive review and comparison of state of the art methods for multivariate ensemble post-processing. We focus on generally applicable two-step approaches where ensemble predictions are first post-processed separately in each margin, and multivariate dependencies are restored via copula functions in a second step. The comparisons are based on simulation studies tailored to mimic challenges occurring in practical applications and allow to readily interpret the effects of different types of misspecifications in the mean, variance and covariance structure of the ensemble forecasts on the performance of the post-processing methods. Overall, we find that the Schaake shuffle provides a compelling benchmark that is difficult to outperform, whereas the forecast quality of parametric copula approaches and variants of ensemble copula coupling strongly depend on the misspecifications at hand.

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

2011 ◽  
Vol 29 ◽  
pp. 85-94 ◽  
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.

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

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.

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

2018 ◽  
Vol 99 (5) ◽  
pp. 1015-1026 ◽  
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.

scholarly journals Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts

2019 ◽  
Vol 20 (7) ◽  
pp. 1379-1398 ◽  
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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