Forecast Uncertainty Research Articles

Energy supply-demand interaction model integrating uncertainty forecasting and peer-to-peer energy trading

With the penetration of large amounts of renewable energy resources into energy system, the interaction between energy supply and demand has become more complex and diverse. The complexity and diversity make it more difficult to achieve real-time, efficient, accurate and dynamic matching of energy supply and demand. Therefore, the study proposes an efficient energy supply-demand interaction model integrating uncertainty forecasting and peer-to-peer energy trading. First, to reduce the impact of supply and demand uncertainty on the energy supply and demand matching, gate recurrent unit and long short-term memory models are used to forecast power generation and consumption. Then, based on the results of forecasting, an energy supply-demand interaction model is proposed to assist the energy system in achieving dynamic energy supply-demand matching. Finally, the effectiveness of the proposed energy supply-demand interaction model has been verified through experiments. The proposed energy supply-demand interaction model that considers supply and demand uncertainty and economic benefits helps to better achieve transparent, efficient, stable, and sustainable matching of supply and demand. This study can reduce the impact of supply and demand uncertainty by forecasting power generation and consumption. In addition, this study considers the preferences of prosumers in their trading, reduces the cost of electricity for prosumers, and realizes the profitability of multiple subjects involved in the trading.

Time series modeling for production prediction of shale gas wells

Accurate production prediction of shale gas wells in the early stage is the key to optimizing development plans and effectively extending the life cycle of gas wells. The existing models pay less attention to the production prediction of gas wells with short production history in the early stage, and the long-term shut-in intervals in the early production process have not been taken into account. In addition, uncertainty in production forecasts is often not mentioned because of difficulties in characterizing. In this paper, a new early production prediction model is developed that integrates the long short-term memory (LSTM) model and deep learning autoregressive (DeepAR) model. The LSTM model makes a deterministic prediction of early production. At the same time, the Bidirectional LSTM (Bi-LSTM) model is applied to the interpolation of long shut-in interval data. For the problem of lag and difficult to deal with production mutation in LSTM model, DeepAR model uses probabilistic prediction to quantify the uncertainty in early production prediction. To verify the performance of the proposed model, the production time series of production case wells are analyzed. The experimental results indicate that the early production prediction model established in this paper can effectively realize the production dynamic tracking with limited production data, and can reliably quantify the uncertainty of early production prediction.

Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy

To mitigate the impact of market uncertainty on trading investments, this paper proposes a forecasting and investing framework for crude oil market by integrating interval models and machine learning models. Firstly, natural language processing technique is employed to analyze text information from social and news media, enabling the capture of market and societal sentiment. Subsequently, deep learning models are integrated to combine sentiment data with other economic variables for more accurate predictions of crude oil prices. Furthermore, this paper introduces a trading strategy with interval constraints based on interval prediction models to reduce trading risk arising from the uncertainty of point forecasts in investments. Through trading simulations, it is discovered that employing the interval constrained strategy is more effective in reducing trading risk and enhancing investment returns compared to point-based trading strategies. This interval-based strategy offers a novel approach to mitigating investment risk in the crude oil market.

Quantitative evaluation of the impact of hydrological forecasting uncertainty on reservoir real-time optimal operation

Quantitative evaluation of the impact of hydrological forecasting uncertainty on reservoir real-time optimal operation

Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification

Data-driven modeling is useful for reconstructing nonlinear dynamical systems when the underlying process is unknown or too expensive to compute. Having reliable uncertainty assessment of the forecast enables tools to be deployed to predict new scenarios unobserved before. In this work, we first extend parallel partial Gaussian processes for predicting the vector-valued transition function that links the observations between the current and next time points, and quantify the uncertainty of predictions by posterior sampling. Second, we show the equivalence between the dynamic mode decomposition and the maximum likelihood estimator of the linear mapping matrix in the linear state space model. The connection provides a probabilistic generative model of dynamic mode decomposition and thus, uncertainty of predictions can be obtained. Furthermore, we draw close connections between different data-driven models for approximating nonlinear dynamics, through a unified view of generative models. We study two numerical examples, where the inputs of the dynamics are assumed to be known in the first example and the inputs are unknown in the second example. The examples indicate that uncertainty of forecast can be properly quantified, whereas model or input misspecification can degrade the accuracy of uncertainty quantification.

Using synthetic case studies to explore the spread and calibration of ensemble atmospheric dispersion forecasts

Abstract. Ensemble predictions of atmospheric dispersion that account for the meteorological uncertainties in a weather forecast are constructed by propagating the individual members of an ensemble numerical weather prediction forecast through an atmospheric dispersion model. Two event scenarios involving hypothetical atmospheric releases are considered: a near-surface radiological release from a nuclear power plant accident and a large eruption of an Icelandic volcano releasing volcanic ash into the upper air. Simulations were run twice-daily in real time over a 4-month period to create a large dataset of cases for this study. The performance of the ensemble predictions is measured against retrospective simulations using a sequence of meteorological fields analysed against observations. The focus of this paper is on comparing the spread of the ensemble members against forecast errors and on the calibration of probabilistic forecasts derived from the ensemble distribution. Results show good overall performance by the dispersion ensembles in both studies but with simulations for the upper-air ash release generally performing better than those for the near-surface release of radiological material. The near-surface results demonstrate a sensitivity to the release location, with good performance in areas dominated by the synoptic-scale meteorology and generally poorer performance at some other sites where, we speculate, the global-scale meteorological ensemble used in this study has difficulty in adequately capturing the uncertainty from local- and regional-scale influences on the boundary layer. The ensemble tends to be under-spread, or over-confident, for the radiological case in general, especially at earlier forecast steps. The limited ensemble size of 18 members may also affect its ability to fully resolve peak values or adequately sample outlier regions. Probability forecasts of threshold exceedances show a reasonable degree of calibration, though the over-confident nature of the ensemble means that it tends to be too keen on using the extreme forecast probabilities. Ensemble forecasts for the volcanic ash study demonstrate an appropriate degree of spread and are generally well-calibrated, particularly for ash concentration forecasts in the troposphere. The ensemble is slightly over-spread, or under-confident, within the troposphere at the first output time step T + 6, thought to be attributable to a known deficiency in the ensemble perturbation scheme in use at the time of this study, but improves with probability forecasts becoming well-calibrated here by the end of the period. Conversely, an increasing tendency towards over-confident forecasts is seen in the stratosphere, which again mirrors an expectation for ensemble spread to fall away at higher altitudes in the meteorological ensemble. Results in the volcanic ash case are also broadly similar between the three different eruption scenarios considered in the study, suggesting that good ensemble performance might apply to a wide range of eruptions with different heights and mass eruption rates.

A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams

Abstract. Ice sheet models are the main tool to generate forecasts of ice sheet mass loss, a significant contributor to sea level rise; thus, knowing the likelihood of such projections is of critical societal importance. However, to capture the complete range of possible projections of mass loss, ice sheet models need efficient methods to quantify the forecast uncertainty. Uncertainties originate from the model structure, from the climate and ocean forcing used to run the model, and from model calibration. Here we quantify the latter, applying an error propagation framework to a realistic setting in West Antarctica. As in many other ice sheet modelling studies we use a control method to calibrate grid-scale flow parameters (parameters describing the basal drag and ice stiffness) with remotely sensed observations. Yet our framework augments the control method with a Hessian-based Bayesian approach that estimates the posterior covariance of the inverted parameters. This enables us to quantify the impact of the calibration uncertainty on forecasts of sea level rise contribution or volume above flotation (VAF) due to the choice of different regularization strengths (prior strengths), sliding laws, and velocity inputs. We find that by choosing different satellite ice velocity products our model leads to different estimates of VAF after 40 years. We use this difference in model output to quantify the variance that projections of VAF are expected to have after 40 years and identify prior strengths that can reproduce that variability. We demonstrate that if we use prior strengths suggested by L-curve analysis, as is typically done in ice sheet calibration studies, our uncertainty quantification is not able to reproduce that same variability. The regularization suggested by the L curves is too strong, and thus propagating the observational error through to VAF uncertainties under this choice of prior leads to errors that are smaller than those suggested by our two-member “sample” of observed velocity fields.

Evaluation of Ensemble Snowfall Forecasts Using Operationally Used Snow-to-Liquid Ratios in Five Winter Storms

Abstract The prediction of snow accumulation remains a forecasting challenge. While the adoption of ensemble numerical weather prediction has enabled the development of probabilistic guidance, the challenges associated with snow accumulation, particularly snow-to-liquid ratio (SLR), still remain when building snow-accumulation tools. In operations, SLR is generally assumed to either fit a simple mathematical relationship or conform to a historic average. In this paper, the impacts of the choice of SLR on ensemble snow forecasts are tested. Ensemble forecasts from the nine-member High-Resolution Rapid Refresh Ensemble (HRRRE) were used to create 24-h snowfall forecasts for five snowfall events associated with winter cyclones. These snowfall forecasts were derived from model liquid precipitation forecasts using five SLR relationships. These forecasts were evaluated against daily new snowfall observations from the Community Collaborative Rain Hail and Snow network. The results of this analysis show that the forecast error associated with individual members is similar to the error associated with choice of SLR. The SLR with the lowest forecast error showed regional agreement across nearby observations. This suggests that, while there is no one SLR that works best everywhere, it may be possible to improve ensemble snow forecasts if regions where SLRs perform best can be determined ahead of time. The implications of these findings for future ensemble snowfall tools will be discussed. Significance Statement Snowfall prediction remains a challenge. Computer models are used to address the inherent uncertainty in forecasts. This uncertainty includes aspects like the location and rate of snowfall. Meteorologists run multiple similar computer models to understand the range of possible weather outcomes. One aspect of uncertainty is the snow-to-liquid ratio, or the ratio of snow depth to the amount of liquid water it melts into. This study tests how common predictions of snow-to-liquid ratio impact snowfall forecasts. The results show that snow-to-liquid ratio choices are as impactful as the models’ differing snow rate or snow location forecasts, and that no particular snow-to-liquid ratio is most accurate. These results underscore the importance of better snow-to-liquid ratio prediction to improve snowfall forecasts.

Impact of social vulnerability assessment on flood risk management processes in the urban environment in Annaba province


 
 
 Considering that flood risk is one of the greatest natural hazards threatening human beings, the standard approach of flood risk management is mostly based on structural mea- sures giving less importance to uncertainty as a main factor. The robustness leads frequently to failures in the structural measures as a factor of uncertainty, in addition to other factors like changes in hazard comportment. The current study represents an evaluation of flood policy in Annaba city, how decision-makers deal with this issue, and how to reduce exposure and susceptibility factors. The evaluation is based on the study of flood risk components, hazard amount, vulnerability degree and people’s perception of flood risk. The study also assesses how people handle flood risk and their capability to cope with extreme events. A survey method was used to assess the potential risks as it involves analyzing different flood risk aspects by conducting a survey through questioning local residents. We can state that exposure is highly affected by the number and density of the population characterized by the high probability of human and material losses in the study area. On the other hand, the vulnerability operates on a smaller scale that increases the capacity of flood risk to affect humans and other components of the environment. This study affirms that the uncertainty in hazard determination forecasting and misevaluation of social vulnerability leads inevitably to devastating consequences in the case of an exceptional event. Moreover, the behaviour of citizens during and after disaster phases can have a key role in flood risk management. Taking into consideration complexities related to social behaviour enhances the outcome of scenarios that lead consequently to more realistic forecasting. These findings concluded that the risk has always existed, i.e., the notion of «zero risk» never exists by natural laws. Knowing that risk management is the only way to deal with such issues, we highlight the importance of urban governance that integrates all actors without exclusion, based on a partnership aimed at obtaining optimal results. The essential principle of this mode of management is the reduction of economic and human losses.
 
 

The 30 December 2021 Colorado Front Range windstorm and Marshall Fire: Evolution of surface and 3-d structure, NWP guidance, NWS forecasts and decision support

Abstract The Marshall Fire on 30 December 2021 became the most destructive wildfire cost-wise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly eleven hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the mid-level inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a high-wind warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a county-wide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts – important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.

Тривалий COVID у дітей: частота та виклики діагностики

Purpose - to analyze the prevalence of symptoms of long COVID-19 in the pediatric population and the methods of their diagnosis. Electronic search of scientific research using known databases from PubMed, SCOPUS, ResearchGate, Wiley Online Library and Google Scholar from 2019 to February 2023. The keywords for this review: long COVID, post COVID, COVID-19, pediatrics, children, adolescents, post-acute sequelae of SARS‐CoV‐2 infection (PASC). Exclusion criteria were: duplicated, dedicated exclusively to adults, analyzed only acute COVID-19. In the analysis were included research from the post-Covid period in children and adolescents, which contained the results of the assessment of their state of health and displayed certain clinical manifestations that remained after the end of the acute infection within 4-12 weeks. Optimistic forecasts regarding the course of SARS-CoV-2 infection in the child population at the beginning of the pandemic quite quickly passed into the stage of uncertainty in forecasts of the course of the post-Covid period and the consequences of the transferred disease. Most children infected with COVID-19 recover, but some of them have persistent symptoms after an infection. The true prevalence of “long-term COVID” is under investigation study. Reports on the range of its manifestations are very diverse and differ in conclusions about the intensity of their impact on the quality of children’s life. Hence, there is an obvious need for long-term clinical observations with a mandatory comparison with the data of control groups appropriate age. Because this category of convalescents will need of a multidisciplinary approach in monitoring them, and therefore they will bear a significant burden on the health care system. No conflict of interests was declared by the authors.

A new intelligent hybrid forecasting method for power load considering uncertainty

Accurate load forecasting plays an important role in promoting low-carbon energy and high-quality utilization of electricity, as well as carbon reduction and safety in energy and power systems. The complexity of power system trends leads to widespread uncertainty in load forecasting. This article analyzes the internal mechanisms of load and meteorological factors at the level of multidimensional uncertainty and proposes a bidirectional memory feature hybrid model based on a new intelligent optimization method. The VMD optimized by gray wolf decomposes the electricity load data into different information modal components. The decomposed components are statistically analyzed with multidimensional uncertainty analysis to extract statistical patterns. The GTO part is optimized according to the fitness value to update the population and global optimal solution. Furthermore, convolutional neural networks (CNN) are used to extract potential features from load and meteorological data and enhance the correlation between input and output data. Then, short-term load forecasting is realized by using bi-directional short-term memory neural network (BiLSTM). The results show that the proposed model has smaller errors than several commonly used models, and has higher prediction accuracy. It can provide some new ideas for relevant departments to carry out power economic dispatch work in low-carbon modes.

Analysts’ Earnings per Share Forecasts: The Effects of Forecast Uncertainty and Forecast Precision on Investor Judgements

This study uses controlled experiments to investigate the joint effects of forecast uncertainty and forecast precision on investor judgements. It finds that forecast precision moderates the effects of forecast uncertainty on investors’ forecast reliability judgements such that the effects of forecast uncertainty on investors’ judgements of forecast reliability are more negative when an analyst's point earnings per share (EPS) forecast is rounded than when it is precise. In addition, the relationship between forecast precision and investors’ judgements of forecast reliability is mediated by investors’ perceptions of forecast attributes. The evidence also suggests that while forecast uncertainty exerts a negative effect on investment judgements, forecast precision does not play a role in mitigating these negative effects. Using a supplementary within‐participants experiment, the study further finds that investors may not be consciously aware of how forecast precision influences their judgements of forecast reliability.

Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection

Solar energy supply is usually highly volatile, limiting its integration into the power grid. Accurate probabilistic intraday forecasts of solar resources are essential to increase the share of photovoltaic (PV) energy in the grid and enable cost-efficient balancing of power demand and supply. Solar PV production mainly depends on downwelling surface solar radiation (SSR). By estimating SSR from geostationary satellites, we can cover large areas with high spatial and temporal resolutions, allowing us to track cloud motion. State-of-the-art probabilistic forecasts of solar resources from satellite imagery account only for the advective motion of clouds. They do not consider the evolution of clouds over time, their growth, and dissipation, even though these are major sources of forecast uncertainty. To address the uncertainty of cloudiness evolution, we present SolarSTEPS, the first optical-flow probabilistic model able to simulate the temporal variability of cloudiness. We demonstrate that forecasting the autocorrelated scale-dependent evolution of cloudiness outperforms state-of-the-art probabilistic advection-based forecasts by 9% in continuous ranked probability score (CRPS). This corresponds to an extension of the forecast lead time by about 45 min at constant CRPS. Our work is motivated by the scale-dependent predictability of cloud growth and decay. We demonstrate that cloudiness is more variable in time at smaller spatial scales than at larger ones. Specifically, we show that the temporal autocorrelation of cloudiness is related to its spatial scale by an inverse power law. We also demonstrate that decomposing cloudiness into multiple spatial scales in the forecasts further improves the forecast skill, reducing the CRPS by 10% and the RMSE by 9%.

Time-Varying Uncertainty of the Federal Reserve's Output Gap Estimate

AbstractA factor stochastic volatility model estimates the common component to output gap estimates produced by the staff of the Federal Reserve, its time-varying volatility, and time-varying, horizon-specific forecast uncertainty. The output gap estimates are uncertain even well after the fact. Nevertheless, the common component is clearly procyclical, and positive innovations to the common component produce movements in macroeconomic variables consistent with an increase in aggregate demand. Heightened macroeconomic uncertainty, as measured by the common component's volatility, leads to persistently negative economic responses.

FORECAST VERIFICATION OF AN ENSEMBLE TROPICAL CYCLONE STORM SURGE SYSTEM

Tropical Cyclone (TC) induced storm surges can be especially damaging and require emergency preparations. A unique challenge of providing TC storm surge advice to emergency services relates to the sensitivity of storm surge forecasts to uncertainty in the TC forecast. Probabilistic forecast products are a good fit for such advice, as long as forecast skill characteristics are known and understood. The Australian Bureau of Meteorology operates a dynamical ensemble prediction system to provide forecasts of storm surge driven by TCs. This is a step change from the legacy parametric and/or scenario-based techniques previously used at the Bureau. There are a number of benefits to be gained from dynamic storm surge ensemble forecasts over more traditional systems. Most importantly, the surge forecast is very sensitive to errors in the TC location, velocity, intensity, and size. Ensemble storm surge forecasting is required in order to take account of this uncertainty in the TC forecast.

Probabilistic Solar Proxy Forecasting With Neural Network Ensembles

AbstractSpace weather indices are used commonly to drive forecasts of thermosphere density, which affects objects in low‐Earth orbit (LEO) through atmospheric drag. One commonly used space weather proxy, F10.7cm, correlates well with solar extreme ultra‐violet (EUV) energy deposition into the thermosphere. Currently, the USAF contracts Space Environment Technologies (SET), which uses a linear algorithm to forecast F10.7cm. In this work, we introduce methods using neural network ensembles with multi‐layer perceptrons (MLPs) and long‐short term memory (LSTMs) to improve on the SET predictions. We make predictions only from historical F10.7cm values. We investigate data manipulation methods (backwards averaging and lookback) as well as multi step and dynamic forecasting. This work shows an improvement over the popular persistence and the operational SET model when using ensemble methods. The best models found in this work are ensemble approaches using multi step or a combination of multi step and dynamic predictions. Nearly all approaches offer an improvement, with the best models improving between 48% and 59% on relative MSE with respect to persistence. Other relative error metrics were shown to improve greatly when ensembles methods were used. We were also able to leverage the ensemble approach to provide a distribution of predicted values; allowing an investigation into forecast uncertainty. Our work found models that produced less biased predictions at elevated and high solar activity levels. Uncertainty was also investigated through the use of a calibration error score metric (CES), our best ensemble reached similar CES as other work.

REAL-TIME MECHANISTIC BAYESIAN FORECASTS OF COVID-19 MORTALITY.

The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the U.S. reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real time represent a nonstationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes nonparametric modeling of varying transmission rates, nonparametric modeling of case and death discrepancies due to testing and reporting issues, and a joint observation likelihood on new case counts and new deaths; it is implemented in a probabilistic programming language to automate the use of Bayesian reasoning for quantifying uncertainty in probabilistic forecasts. The model has been used to submit forecasts to the U.S. Centers for Disease Control through the COVID-19 Forecast Hub under the name MechBayes. We examine the performance relative to a baseline model as well as alternate models submitted to the forecast hub. Additionally, we include an ablation test of our extensions to the classic SEIR model. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes, when compared to a baseline model, and show that MechBayes ranks as one of the top two models out of nine which regularly submitted to the COVID-19 Forecast Hub for the duration of the pandemic, trailing only the COVID-19 Forecast Hub ensemble model of which which MechBayes is a part.

Possibilistic Aggregate Production Planning Considering Dynamic Workforce with Fuzzy Demand

This Aggregate Production Planning (APP) is a combination of Possibilistic Linear Programming (PLP), Fuzzy Goal Programming (FGP), and the Perfume Accounting System (PAS) to maximize profit. APP involves strategic decisions on Production levels, Inventory management, and Resource allocation to meet client demand while maximizing profit. Traditional planning models face significant challenges due to the uncertainties and complexities inherent in real world production environments. In this paper, there is an integration of PLP, Fuzzy Goal Programing, and throughput accounting system to overcome these challenges. At the very end of the paper, based on the data received from the company, the derived findings were by using Lingo Version 18 software. The model includes possibility distributions of the input parameters. Decision-makers can take into account the uncertainty and imprecision in demand forecasts and dynamic workforce in maximizing profit while taking into account risk tolerance.

A Bayesian hierarchical model framework to quantify uncertainty of tropical cyclone precipitation forecasts

A Bayesian hierarchical model framework to quantify uncertainty of tropical cyclone precipitation forecasts