Forecast Distribution Research Articles

On the use of distribution-adaptive likelihood functions: Generalized and universal likelihood functions, scoring rules and multi-criteria ranking

This paper is concerned with the formulation of an adequate likelihood function in the application of Bayesian epistemology to uncertainty quantification of hydrologic models. We focus our attention on a special class of likelihood functions (hereinafter referred to as distribution-adaptive likelihood functions), which do not require prior assumptions about the expected distribution of the residuals, rather inference takes place over the hypotheses (model parameters) and space of distribution functions. Our goals are threefold. First, we present theory of a revised implementation of the generalized likelihood (GL) function of Schoups and Vrugt (2010) wherein residual standardization precedes the treatment of serial correlation. This so-called GL+ function, enjoys a solid statistical underpinning and guarantees a more robust joint inference of the autoregressive coefficients and residual properties. Then, as secondary goal, we present a further generalization of the GL+ function, coined the universal likelihood (UL) function, which extends applicability to highly asymmetrical lepto- and platy-kurtic residual distributions. The UL function builds on the 5-parameter skewed generalized Student’s t distribution of Theodossiou (2015) which makes up a large family of continuous probability distributions including (but not limited to) symmetric and skewed forms of the generalized normal, generalized t, Laplace, normal, Student’s t, and Cauchy-Lorentz distributions. As our third and last goal, we present the use of strictly proper scoring rules to evaluate, compare and rank likelihood functions. These scoring rules condense the accuracy of a distribution forecast to a single value while retaining attractive statistical properties. The GL+ and UL functions are illustrated using data of a simple autoregressive scheme and benchmarked against the GL function, Student t likelihood (SL) of Scharnagl et al. (2015) and normal likelihood (NL) for a conceptual hydrologic model using measured streamflow data. Our results show that, (i) the GL+ function is superior to the GL function, (ii) the active set of nuisance variables exerts a large control on the performance of the GL+, SL and UL functions, (iii) the treatment of autocorrelation deteriorates the scoring rules and performance metrics of the forecast distribution, (iv) a leptokurtic distribution is favored for discharge residuals, (v) scoring rules are indispensable in our search for the true forecast distribution, and (vi) the use of multiple strictly proper scoring rules turns the selection of an adequate likelihood function into a multi-criteria problem.

The impact of microphysical uncertainty conditional on initial and boundary condition uncertainty under varying synoptic control

Abstract. The relative impact of individual and combined uncertainties of cloud condensation nuclei (CCN) concentration and the shape parameter of the cloud droplet size distribution (CDSD) in the presence of initial and boundary condition uncertainty (IBC) on convection forecasts is quantified using the convection-permitting model ICON-D2 (ICOsahedral Non-hydrostatic). We performed 180-member ensemble simulations for five real case studies representing different synoptic forcing situations over Germany and inspected the precipitation variability on different spatial and temporal scales. During weak synoptic control, the relative impact of combined microphysical uncertainty on daily area-averaged precipitation accounts for about one-third of the variability caused by operational IBC uncertainty. The effect of combined microphysical perturbations exceeds the impact of individual CCN or CDSD perturbations and is twice as large during weak control. The combination of IBC and microphysical uncertainty affects the extremes of daily spatially averaged rainfall of individual members by extending the tails of the forecast distribution by 5 % in weakly forced conditions. The responses are relatively insensitive in strong forcing situations. Visual inspection and objective analysis of the spatial variability in hourly rainfall rates reveal that IBC and microphysical uncertainties alter the spatial variability in precipitation forecasts differently. Microphysical perturbations slightly shift convective cells but affect precipitation intensities, while IBC perturbations scramble the location of convection during weak control. Cloud and rainwater contents are more sensitive to microphysical uncertainty than precipitation and less dependent on synoptic control.

Deriving optimal operating rules for flood control considering pre-release based on forecast information

Pre-releasing before a flood can increase reservoir flood control storage capacity to effectively improve the flood disaster prevention and mitigation effects, but also might bring water shortage risk due to the uncertain forecasts. This paper proposes a framework to determine the optimal operating rules for flood control considering pre-release based on uncertain forecasts. The framework includes three models: a pre-release model at the early stage of the flood to enlarge the flood control storage capacity; a multi-objective optimization model with resilience and risk as objectives at the flood regulating stage to optimize the flood control operating rules; a fuzzy optimum selecting model to determine the satisfied operating rule considering forecast uncertainty and decision-maker's preferences. In the framework, forecast uncertainty has been considered from the perspective of the forecast error distribution. Nierji Reservoir in Northeast China is taken as a case study. Results show that compared with the existing operating rule, the recommended operating rule considering pre-release releases water more evenly and can not only decrease the peak flow at the downstream protection point but also improve the downstream flood resilience without increasing reservoir risk. The proposed framework that considers forecast error distribution provides higher superiority degrees of the rules than the traditional framework that considers the maximum and minimum forecast errors in the real-time operation of the 1969 and 2013 flood hydrographs.

Research Spotlights

The first Research Spotlights article in this issue is concerned with filtering, a task of paramount importance in a great many applications such as numerical weather prediction and geophysical data assimilation. Authors Alessio Spantini, Ricardo Baptista, and Youssef M. Marzouk, in their article “Coupling Techniques for Nonlinear Ensemble Filtering,” describe discrete-time filtering as the act of characterizing the sequence of conditional distributions of the latent field at observation times, given all currently available measurements. Despite the existing literature on filtering, issues such as high-dimensional state spaces and sparse (in both space and time) observations still prove formidable in practice. The traditional approach of ensemble-based data assimilation is the ensemble Kalman filter (EnKF), involving a prediction (forecasting) step followed by an analysis step. However, the authors note an intrinsic bias of EnKF due to the linearity of the transformation, estimated under Gaussian assumptions, that is used in the analysis step, which limits its accuracy. To overcome this, they propose two non-Gaussian generalizations of the EnKF---the so-called stochastic and deterministic map filters---using nonlinear transformations derived from couplings between the forecast distribution and the filtering distribution. What is crucial is that the transformations “can be estimated efficiently...perhaps using only convex optimization,” that they “are easy to `localize' in high dimensions,” and that their computation “should not become increasingly challenging as the variance of the observation noise decreases.” Following a comprehensive description of their new approaches, the authors demonstrate numerically the superiority of their stochastic map filter approach over traditional EnKF. The subsequent discussion offers the reader several jumping off points for future research. Recovery of a sparse solution to a large-scale optimization problem is another ubiquitous problem arising in many applications such as image reconstruction, signal processing, and machine learning. The cost functional typically includes a regularization term in the form of an $\ell_1$ norm term on the solution and/or regularized solution to enforce sparsity. Designing suitable algorithms for such recovery problems is the subject of our second Research Spotlights article. In “Sparse Approximations with Interior Point Methods,” authors Valentina De Simone, Daniela di Serafino, Jacek Gondzio, Spyridon Pougkakiotis, and Marco Viola set out to correct the misconception that first-order methods are to be preferred over second-order methods out of hand. Through case studies, they offer evidence that interior point methods (IPMs) which are constructed to “exploit special features of the problems in the linear algebra of IPMs” and which are designed “to take advantage of the expected sparsity of the optimal solution” can in fact be the method of choice for solving this class of optimization problems. The key to their approach is a reformulation of the original sparse approximation problem to one which is seemingly larger but which has properties upon which one can capitalize for computational gain. For each of four representative applications, the authors show how to take computational advantage of the problem-specific structure of the underlying linear systems involved at each iteration. These efforts are complemented by leveraging the expected sparsity: employing heuristics to drop near zero variables, thereby replacing very large, ill-conditioned intermediate systems by better conditioned, smaller systems. Their conclusion is that time invested in tailoring solvers to structure admitted by the reformulated variant, and in taking advantage of expected sparsity, may be well spent, since their demonstrations have shown it is possible for IPMs to have a “noticeable advantage” over state-of-the-art first-order methods for sparse approximation problems.

Economic forecasting in a pandemic: some evidence from Singapore.

This paper aims to investigate whether the predictive performance and behaviour of professional forecasters are different during the COVID-19 pandemic as compared with the global financial crisis of 2008 and normal times. To this end, we use a survey of professional forecasters in Singapore collated by the central bank to analyse the forecasting records for GDP growth and CPI inflation for the period 2000Q1–2021Q4. We first examine the point forecasts to document the extent of forecast failure during the two crises and explore various explanations for it, such as leader-following and herding behaviour. Then, using percentile-based summary measures of probability distribution forecasts, we study how the degree of consensus and extent of subjective uncertainty among forecasters were affected by crisis conditions. A trend break is observed in the subjective uncertainty associated with growth projections after the onset of the COVID-19 crisis. In contrast, both subjective uncertainty and the degree of consensus in inflation projections were essentially unchanged in crises, suggesting that the short-term inflation expectations of forecasters were strongly anchored.

Empirical Safety Stock Estimation Using GARCH Model, Historical Simulation, and Extreme Value Theory: A Comparative Study

Safety stock (SS) is an appropriate tactic to deal with demand and supply uncertainty with the aim of preventing inventory shortages. In the literature, previous work on SS estimation assumes that the forecast error distributions (FED) are independent and identically distributed (i.i.d) following the normal distribution. In order to assess violations of this assumption, there are many solution methods in the recent literature that include the following: (1) Consider the FED as other distribution models, such as gamma distribution or log-normal distribution, etc. (2) Use the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model to consider the Heteroskedasticity phenomena, (3) Use the extreme value theory (EVT) to take into consideration the occurrence of extreme demands, etc. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Indeed, the estimation of SS is based on the approximation of quantiles of the FED. Such quantiles are related to the cycle service levels (CSL) that are important to achieve company goals. Accordingly, the aim of this research is to propose two combined empirical methods to determine the SS in a more robust fashion and compare them with traditional methods under different supply chain parameters. The first combined method, named Filtered Historical Simulation (FHS), consists of combining the GARCH model with the simulation method. The second combination named Conditional Extreme Value Theory (CEVT) is the GARCH model with EVT. To validate these proposed combined methods, the SS is also estimated using traditional methods, such as simple exponential smoothing (SES), simulation, and kernel density estimation (KDE). The methodology is illustrated with both simulation data and real case study data for different lead times. For the FED, two cases are studied: lognormal distribution and gamma distribution. The results show the superiority of the two proposed combination methods with respect to the tick loss function (TLF) for the different CSL targets and for shorter and longer lead times. Results are confirmed using the ANOVA test.

Subseasonal forecasts provide a powerful tool for dynamic marine mammal management

Adaptive approaches are needed to effectively manage dynamic marine systems, and ecological forecasts can help managers anticipate when and where conservation issues are likely to arise in the future. The recent development of subseasonal global environmental forecasts provides an opportunity to inform management by forecasting species distributions in advance over operational timeframes. We demonstrate the utility of environmental forecasts for managing marine mammals by integrating species distribution models with subseasonal forecasts to predict the arrival of migratory humpback whales (Megaptera novaeangliae) at foraging grounds in the Northeast US. Environmental forecasts showed high model skill at lead times of up to 2 weeks and resulting humpback whale models performed well in predicting humpback arrival. Forecasts of whale distribution can shape management efforts to minimize both impacts on whales and economic costs. Applying subseasonal forecasts to anticipate future risk presents a powerful tool for the dynamic management of marine mammals.Front Ecol Environ 2022;

Value for Equity Index Options: Expected—Not Realized—Volatility and the Distribution of Forecasts

Models of option returns neglect the distribution of expected asset volatility, unfortunately for not only derivatives traders but also investors who monitor options as <i>fear gauges</i>. Six common GARCH (generalized autoregressive conditional heteroskedasticity) models afford estimates of the physical, rather than the risk-neutral, distribution of anticipated—instead of historical—volatility, as well as of volatility disagreement. This value specification covering nine global equity indexes and five expiries from 1 to 12 months fits implied volatilities closely, with sizeable and robust error-correction speeds out of sample, all else equal. Exploratory backtests of delta-neutral trading rules produce high Sharpe ratios and alphas, with modest drawdowns and skew.

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting.

Effective prediction of aircraft failure rate has important guiding significance for formulating reasonable maintenance plans, carrying out reliable maintenance activities, improving health management levels, and ensuring the safety of aircraft flight, etc. Firstly, combining the advantages of time series model in eliminating random accidental factors interference, grey model in dealing with poor information, and the characteristics of artificial neural network in dealing with nonlinear data, the failure rate of aircraft equipment is predicted by ARIMA model, grey Verhulst model, and BP neural network model, and secondly, based on the idea of variable weight, the method of sum of squares of errors is used to reciprocate. Shapley value method and IOWA operator method determine the weighting coefficient and establish three combined forecasting models for aircraft failure rate prediction, so as to improve the accuracy of the algorithm. Finally, taking the data of actual aircraft failure rate as the research object, the performance indexes of design prediction model are judged by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Index of Agreement (IA), Theil Inequality Coefficient (TIC), Equal Coefficient (EC), Nash-Sutcliffe Efficiency coefficient (NSE), Pearson test, and violin diagram of forecast error distribution. The experimental results show that: The forecasting precision of the combination model is better than that of the single model, and the evaluation index of combination forecasting model based on IOWA operator is better than that of other combination forecasting models, thus improving the forecasting accuracy and reliability. Compared with other typical prediction models simultaneously, it is verified that the proposed combined prediction model has strong applicability, high accuracy, and good stability, which provides a practical and effective technical method for aircraft fault prediction and has good application value.

Improvement of displacement error of rainfall and wind field forecast associated with landfalling tropical cyclone AMPHAN

Spatial distribution of rainfall and wind speed forecast errors associated with landfalling tropical cyclones (TC) occur significantly due to incorrect location forecast by numerical models. Two major areas of errors are: (i) over-estimation over the model forecast locations and (ii) underestimation over the observed locations of the TCs. A modification method is proposed for real-time improvement of rainfall and wind field forecasts and demonstrated for the typical TC AMPHAN over the Bay of Bengal in 2020. The proposed method to improve the model forecasts is a relocation method through shifting of model forecast locations of TC to the real-time official forecast locations of India Meteorological Department (IMD). The modification is applied to the forecasts obtained from the operational numerical model, the Global Forecast System (GFS) of IMD. Application of the proposed method shows considerable improvement of both the parameters over both the locations. The rainfall forecast errors due to displacement are found to have improved by 44.1%–69.8% and 72.1%–85.2% over the GFS forecast locations and over the observed locations respectively for the respective forecast lead times 48 h, 72 h, and 96 h. Similarly, the wind speed forecasts have improved by 27.6%–56.0% and 63.7%–84.6% over the GFS forecast locations and over the observed locations respectively for the respective forecast lead times 60 h, 72 h, and 84 h. The results show that the proposed technique has capacity to provide improved spatial distributions of rainfall and wind speed forecasts associated with landfalling TCs and useful guidance to operational forecasters.

Lead time and skill of Australian wheat yield forecasts based on ENSO-analogue or GCM-derived seasonal climate forecasts – A comparative analysis

Foresight of crop yield is fundamental to producers and industry to better manage climate risks and mitigate ebbs and troughs in crop production. Rain-fed grain production in Australia is highly volatile and producers and industry are progressively confronted with projected uncertainties due to climate variability and change, input costs and market prices. Thus, having advance knowledge of the likely impact of the coming season's climate on crop yield and production is critical for decisions across the supply chain. Here we explore and analyse the lead time and skill of a wheat yield forecasting system using a biophysical crop yield simulation model connected to either a statistical ENSO-analogue climate forecasting system or a dynamic general circulation model (GCM) derived climate forecasting system. The comparative skill was investigated for 16 wheat producing districts (shires) of the broad Australian winter cropping region, each containing 9–35 irregularly-spaced simulation points associated with climate stations. Both the ENSO-analogue and GCM-derived systems produced reliable wheat yield forecasts with the GCM-based approach having general improved skill, and particularly during the early months of the season (March to May) before sowing. The shift in the forecast yield distributions relative to the climatology-based yield distribution were dependent on location and time in the season, with the GCM-derived forecast shifts more widespread and earlier in the season. Overall, the GCM-based climate/crop forecasting system showed a significant improvement in lead time (greater than two months before the normal planting time of wheat), across the Australian wheat belt. This result demonstrates an avenue for improved efficacy in future commodity forecasting frameworks via likely enhanced relevance and utility to industry associated with the use of GCM-derived approaches.

Contradictory effect of climate change on American and European populations of Impatiens capensis Meerb. - is this herb a global threat?

AimsThe present study is the first-ever attempt to generate information on the potential present and future distribution of Impatiens capensis (orange balsam) under various climate change scenarios. Moreover, the differences in bioclimatic preferences of native and non-native populations were evaluated. LocationGlobal. TaxonAngiosperms. MethodsA database of I. capensis localities was compiled based on the public database – the Global Biodiversity Information Facility (GBIF), herbarium specimens, and a field survey in Poland. The initial dataset was verified, and each record was assigned to one of two groups – native (3664 records from North America) or non-native (750 records from Europe and the western part of North America). The analyses involved bioclimatic variables in 2.5 arc-minutes of interpolated climate surface downloaded from WorldClim v. 2.1. MaxEnt version 3.3.2 was used to conduct the ecological niche modeling based on presence-only observations of I. capensis. Forecasts of the future distribution of the climatic niches of the studied species in 2080–2100 were made based on climate projections developed by the CNRM/CERFACS modeling and Model for Interdisciplinary Research on Climate (MIROC-6). Main conclusionsDistribution models created for “present time” showed slightly broader potential geographical ranges of both native and invasive populations of orange balsam. On the other hand, some areas (e.g. NW Poland, SW Finland), settled by the species, are far outside the modeled climate niche, which indicates a much greater adaptation potential of I. capensis. In addition, the models have shown that climate change will shift the native range of orange balsam to the north and the range of its European populations to the northwest. Moreover, while the coverage of niches suitable for I. capensis in America will extend due to climate change, the European populations will face 31–95 % habitat loss.

The Elusive Turkestan Lynx at the Northwestern Edge of Geographic Range: Current Suitable Habitats and Distribution Forecast in the Climate Change

The Turkestan lynx (Lynx lynx isabellina Blyth, 1847) is a rare and understudied subspecies of the Eurasian lynx occupying the mountains of Central and South Asia. This elusive felid’s northwestern range includes the Tien Shan and Zhetisu Alatau mountains in the border region of Kazakhstan, China, Kyrgyzstan, and Uzbekistan. As the first step to conserve this vulnerable carnivore, we have conducted the first full-scale research from 2013 until 2022 on its distribution in this region. Using 132 environmental predictors of 359 lynx sightings, we have created species habitat distribution models across the lynx’s northwestern range using machine learning approaches (Maximum Entropy—MaxEnt). Additionally, we created species distribution forecasts based on seven bio-climatic environmental predictors with each three different future global climate model scenarios. To validate these forecasts, we have calculated the changes in the lynx distribution range for the year 2100, making the first species distribution forecast for the Turkestan lynx in the area. Additionally, it provides insight into the possible effects of global climate change on this lynx population. Based on these distribution models, the lynx population in the Northern and Western Tien Shan and Zhetisu Alatau plays a significant role in maintaining the stability of the whole subspecies in its northwestern and global range, while the distribution forecast shows that most lynx distribution ranges will reduce in all future climate scenarios, and we might face the Turkestan lynx’s significant distribution range decline under the ongoing and advancing climate change conditions. For a future (year 2100) warming scenario of 3 deg. C (GCM IPSL), we observe a decrease of 35% in Kazakhstan, 40% in Kyrgyzstan, and 30% in China as the three countries with the highest current predicted distribution range. For a milder temperature increase of 1.5–2 deg. C. (GCM MRI), we observe an increase of 17% Kazakhstan, decrease of 10% in Kyrgyzstan, and 57% in China. For a cooling scenario of approx. 1–1.5 deg. C (GCM MIROC), we observe a decrease of 14% Kazakhstan, increase of 11% in Kyrgyzstan, and a decrease of 13% in China. These modeled declines indicate the necessity to create new and expand the existing protected areas and establish ecological corridors between the countries in Central and South Asia.

Daily return distribution forecast incorporating intraday high frequency information in China’s stock market

The stock market forecast is an important and challenging issue. Its distribution forecast of returns can provide information that is more complete, compared to point forecast and interval forecast. As intraday high-frequency information is available, we incorporate intraday returns into the predictive modelling of daily return distribution in two ways: realized volatility and scale calibration. Three parametric models, EGARCH, EGARCH-X, and realGARCH, and two nonparametric models, NP and realNP, are used. Our improved NP model, the realNP model, is based on intraday returns calibration. The results show that intraday information improves goodness-of-fit and forecasting effect, and the realGARCH model is relatively the best. According to the realNP model results, the intraday returns can only contribute about a 30% description of the daily distribution and less than 1% information for a one-step-ahead distribution forecast. Furthermore, three combinations are considered, and the log-score and CRPS combinations are found to have direction predictability and excess profitability statistically. The non-short-selling situation consistently has more excess profits than the short-selling situation, which implies that the non-short-selling rule protects investors. This study reveals the importance of incorporating intraday information and model combinations for stock market forecast modelling.

Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors

This study presents a hybrid model for forecasting the conditional probability distribution of carbon allowance prices. We primarily use the singular spectrum analysis method to process the non-stationary signals, and the non-crossing composite quantile regression neural network algorithm to achieve accurate, robust, and realistic quantile forecasts. We also include multiple influencing factors to enhance the forecasting performance. Empirical applications to the China and Europe carbon markets show that the proposed model significantly outperforms other benchmark models in terms of point and density forecasting accuracy. In addition, distribution forecasts can lead to economic gains using a simple switching trading strategy. Our hybrid framework is also useful for risk measurement and management.

The Impact of Scale-Aware Parameterization on the Next-Generation Global Prediction System in Taiwan for Front Predictions

In order to improve the precipitation forecast of the next-generation Global Prediction System with the Finite-Volume Cubed-Sphere Dynamical Core in Taiwan’s Central Weather Bureau, this study modified the convective processes in the New Simplified Arakawa-Schubert scheme based on the methodology of scale-aware parameterization developed in Kwon and Hong (2017) and investigated its impacts on a front event, which propagated across Taiwan and produced heavy rainfall in late May of 2020. Results show that the modified scale-aware parameterization has significantly improved the intensity and the spatial distribution of frontal precipitation forecasts due to the proper definition of convective updraft fraction. However, the synoptic-scale features perform a larger warm bias with the modified scale-aware parameterization. Therefore, further modification of the scale-aware capability of convective cloud water detrainment is proposed to reduce the heating from microphysical processes and result in a better overall performance for the medium-range weather forecasts.

Gondwanan survivor lineages and the high‐risk biogeography of Anthropocene Southeast Asia

AbstractThe Southeast Asian rainforest region is extremely complex and biodiverse. Fossils have shown that paleo‐Antarctic rainforest lineages (PARLs) now extant in Asia tracked the ever‐wet conditions needed to survive and diversify through deep time. However, the threat of future climate change to the remaining rainforest and PARLs in Southeast Asia has yet to be evaluated to set conservation priorities. We first quantified the woody‐genus floristic relationships of Southeast Asian Island Groups by vetting and analyzing recent compilations of bioregional species data. We then evaluated the contributions to community assembly of woody fossil lineages and Island Group relationships to environmental gradients. To better understand climatic constraints of fossil lineage distributions and forecast distributions under projected future climate, we used exemplar living woody PARLs, including two angiosperms and two gymnosperms. Generalized linear models were used to project potential distributions under future climate pathways that assume no reduction in carbon dioxide emissions. The floristic analyses highlighted strong similarities among Island Groups in the ever‐wet forest areas of Malesia, where PARLs are often concentrated. Ordination outliers represented more seasonal locations. Species distribution models showed that potential future distributions of ancient lineages are constrained by increasing rainfall seasonality and higher seasonal temperatures, with significant differences among exemplar genera. Notably, potential distributions often mapped onto de facto inaccessible areas, where forest clearing and the ubiquitous marine dispersal barriers that characterize the region will drastically inhibit potential relocation. These realities gravely threaten paleo‐conservation values and contemporary rainforest community assembly processes in Southeast Asia.

Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System

Abstract. Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the model conditional processor and the ensemble model output statistics method. Using 2 years of reforecasts with daily timesteps, this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations in terms of both the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead times (up to 15 d) but is largest at short lead times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end users in making more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models

Many static and dynamic models exist to forecast Value-at-Risk and other quantile-related metrics used in financial risk management. Industry practice favours simpler, static models such as historical simulation or its variants. Most academic research focuses on dynamic models in the GARCH family. While numerous studies examine the accuracy of multivariate models for forecasting risk metrics, there is little research on accurately predicting the entire multivariate distribution. However, this is an essential element of asset pricing or portfolio optimization problems having non-analytic solutions. We approach this highly complex problem using various proper multivariate scoring rules to evaluate forecasts of eight-dimensional multivariate distributions: exchange rates, interest rates and commodity futures. This way, we test the performance of static models, namely, empirical distribution functions and a new factor-quantile model with commonly used dynamic models in the asymmetric multivariate GARCH class.

Adaptive and parameterised 3D BIM model for the tunnel tender and excavation phase

AbstractBuilding information modelling (BIM) is a frequently discussed topic in tunnelling since it promises less loss of information and reduced lifetime cost of underground infrastructure. There is still some way to go as standardisation in this sector is immature, the implementation of three‐dimensional (3D) BIM models is developed for pilot cases of tunnelling only, and data transfer between software tools is a challenge. The long linear structures of tunnels make a specific approach of parameterised and adaptive modelling necessary to meet the requirements of repetitive construction elements and the natural differences of forecast and actual excavation conditions. This approach renders the matching of construction elements in the model and service items feasible for determining quantities for the tender and billing in tunnel projects. In this article, we show that only 57 % of service items can actually be linked to a physical item in traditional two‐dimensional (2D) design and highlight the need to consider how to incorporate these items into a BIM model. We also use a case study to propose an approach for parameterised and adaptive modelling of repetitive construction elements and show a way of a continuous data transfer from the forecast tunnelling class distribution via 3D BIM modelling to a billing software without data loss.