Statistical post-processing of hydrological forecasts using Bayesian model averaging

Accurate and reliable probabilistic forecasts of hydrological quantities like runoff or water level are beneficial to various areas of society. Probabilistic state-of-the-art hydrological ensemble prediction models are usually driven with meteorological ensemble forecasts. Hence, biases and dispersion errors of the meteorological forecasts cascade down to the hydrological predictions and add to the errors of the hydrological models. The systematic parts of these errors can be reduced by applying statistical post-processing. For a sound estimation of predictive uncertainty and an optimal correction of systematic errors, statistical post-processing methods should be tailored to the particular forecast variable at hand. Former studies have shown that it can make sense to treat hydrological quantities as bounded variables. In this paper, a doubly truncated Bayesian model averaging (BMA) method, which allows for flexible post-processing of (multi-model) ensemble forecasts of water level, is introduced. A case study based on water level for a gauge of river Rhine, reveals a good predictive skill of doubly truncated BMA compared both to the raw ensemble and the reference ensemble model output statistics approach.Comment: 19 pages, 6 figure

Probabilistic Forecasting Based on Hydrometeorological Ensembles

Over the last two decades the paradigm in hydrometeorological forecasting has shifted from deterministic to probabilistic. Weather prediction and hydrological models are run increasingly as ensemble forecasting systems, which provide a finite sample of forecast scenarios. Ensemble forecasts are often biased and lack calibration. Different statistical methods to correct for bias and miscalibration are developed and applied to real-world problems within the framework of this thesis

Comparison of multivariate post-processing methods using global ECMWF ensemble forecasts

An influential step in weather forecasting was the introduction of ensemble forecasts in operational use due to their capability to account for the uncertainties in the future state of the atmosphere. However, ensemble weather forecasts are often underdispersive and might also contain bias, which calls for some form of post-processing. A popular approach to calibration is the ensemble model output statistics (EMOS) approach resulting in a full predictive distribution for a given weather variable. However, this form of univariate post-processing may ignore the prevailing spatial and/or temporal correlation structures among different dimensions. Since many applications call for spatially and/or temporally coherent forecasts, multivariate post-processing aims to capture these possibly lost dependencies. We compare the forecast skill of different nonparametric multivariate approaches to modeling temporal dependence of ensemble weather forecasts with different forecast horizons. The focus is on two-step methods, where after univariate post-processing, the EMOS predictive distributions corresponding to different forecast horizons are combined to a multivariate calibrated prediction using an empirical copula. Based on global ensemble predictions of temperature, wind speed and precipitation accumulation of the European Centre for Medium-Range Weather Forecasts from January 2002 to March 2014, we investigate the forecast skill of different versions of Ensemble Copula Coupling (ECC) and Schaake Shuffle (SSh). In general, compared with the raw and independently calibrated forecasts, multivariate post-processing substantially improves the forecast skill. While even the simplest ECC approach with low computational cost provides a powerful benchmark method, recently proposed advanced extensions of the ECC and the SSh are found to not provide any significant improvements over their basic counterparts.Comment: 27 pages, 13 figure

Statistical adjustment, calibration and downscaling of seasonal forecasts: a case-study for Southeast Asia

The present paper is a follow-on of the work presented in Manzanas et al. (Clim Dyn 53(3–4):1287–1305, 2019) which provides a comprehensive intercomparison of alternatives for the post-processing (statistical adjustment, calibration and downscaling) of seasonal forecasts for a particularly interesting region, Southeast Asia. To answer the questions that were raised in the preceding work, apart from Bias Adjustment (BA) and ensemble Re-Calibration (RC) methods—which transform directly the variable of interest,—we include here more complex Perfect Prognosis (PP) and Model Outputs Statistics (MOS) downscaling techniques—which operate on a selection of large-scale model circulation variables linked to the local observed variable of interest. Moreover, we test the suitability of BA and PP methods for the post-processing of daily—not only seasonal—time-series, which are often needed in a variety of sectoral applications (crop, hydrology, etc.) or to compute specific climate indices (heat waves, fire weather index, etc.). In addition, we also undertake an assessment of the effect that observational uncertainty may have for statistical post-processing. Our results indicate that PP methods (and to a lesser extent MOS) are highly case-dependent and their application must be carefully analyzed for the region/season/application of interest, since they can either improve or degrade the raw model outputs. Therefore, for those cases for which the use of these methods cannot be carefully tested by experts, our overall recommendation would be the use of BA methods, which seem to be a safe, easy to implement alternative that provide competitive results in most situations. Nevertheless, all methods (including BA ones) seem to be sensitive to observational uncertainty, especially regarding the reproduction of extremes and spells. For MOS and PP methods, this issue can even lead to important regional differences in interannual skill. The lessons learnt from this work can substantially benefit a wide range of end-users in different socio-economic sectors, and can also have important implications for the development of high-quality climate services.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts and the EU project AfriCultuReS (H2020-EU.3.5.5, GA 774652). JMG was partially supported by the Project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613). The authors also acknowledge the SA-OBS dataset and the data providers in the SACA&D Project ( http://saca-bmkg.knmi.nl )

Preface: Advances in post-processing and blending of deterministic and ensemble forecasts

International audienceAbstract. The special issue on advances in post-processing and blending of deterministic and ensemble forecasts is the outcome of several successful successive sessions organized at the General Assembly of the European Geosciences Union. Statistical post-processing and blending of forecasts are currently topics of important attention and development in many countries to produce optimal forecasts. Ten contributions have been received, covering key aspects of current concerns on statistical post-processing, namely the restoration of inter-variable dependences, the impact of model changes on the statistical relationships and how to cope with it, the operational implementation at forecasting centers, the development of appropriate metrics for forecast verification, and finally two specific applications to snow forecasts and seasonal forecasts of the North Atlantic Oscillation

Simulation-based comparison of multivariate ensemble post-processing methods

Lerch S, Baran S, Möller AC, et al. Simulation-based comparison of multivariate ensemble post-processing methods. Nonlinear Processes in Geophysics. 2020;27(2):349-371.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

Supplementary material to "Simulation-based comparison of multivariate ensemble post-processing methods"

Lerch S, Baran S, Möller AC, et al. Supplementary material to "Simulation-based comparison of multivariate ensemble post-processing methods". Copernicus GmbH; 2020

Statistical Postprocessing for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World

International audienceAbstract Statistical postprocessing techniques are nowadays key components of the forecasting suites in many national meteorological services (NMS), with, for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on in this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations, the necessity to preserve space–time correlation of high-dimensional corrected fields, the need to reduce the impact of model changes affecting the parameters of the corrections, the necessity for techniques to merge different types of forecasts and ensembles with different behaviors, and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues are also discussed