Term Forecasting Research Articles

A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability

Despite the significant progress in probabilistic forecasting science in the last two decades, particularly in the quantification of predictive uncertainty (PU), most operational flood early warning systems (FEWSs) continue to be based on deterministic forecasts. Thereupon, additional work is needed to demonstrate the advantages of using PU over deterministic forecasting to enhance the uptake of probabilistic forecasts in flood warning decision-making. In this paper, a Monte-Carlo (MC)-based sensitivity analysis is done to explore how the outcomes of flood peak water level-based deterministic and -probabilistic warning strategies behave when factors controlling the forecast quality are perturbed. The flood warning reliability is evaluated through the probability of detection (POD) and false alarm ratio (FAR) based on two criteria: a flooding threshold-based criterion (FTC), and a new floodplain property-based criterion (FPC) based on inundation level forecasting. The results of this work show that the advantage of a probabilistic strategy over a deterministic one is greater when PU is relatively high. The probabilistic strategy is robust to biases in the mean and variance of forecasts by maintaining POD and FAR stability, while this is not the case for a deterministic warning strategy. Likewise, it was concluded that if inundation level forecasting is undertaken (FPC), improved forecasts would be needed to achieve the same reliability level as for FTC, reflecting the more demanding FPC criterion. Also, the levels of correlation needed to achieve acceptable operational values of POD and FAR are shown. These results provide new insight into the advantages of a probabilistic forecasting strategy under several forecast quality scenarios and guidance for the design of operational FEWSs.

Analysts versus the random walk in financial forecasting: evidence from the Czech National Bank’s Financial Market Inflation Expectations survey

ABSTRACT We analyse how financial market analysts’ expectations in the Czech National Bank’s Financial Market Inflation Expectations survey perform relative to the random-walk forecast when it comes to predicting five financial variables. Using data from 2001 to 2022, our results indicate that the analysts are able to significantly outperform the random-walk forecast in terms of forecast precision for the repo rate and Prague Interbank Offered Rate at the one-month forecasting horizon. For the five- and ten-year interest rate swap rates, the random walk significantly outperforms the analysts at both the one-month and one-year forecasting horizons. For the CZK/EUR exchange rate, the random-walk forecast has a lower root mean squared forecast error than that of the analysts’ forecast at the one-month horizon whereas at the one-year horizon the opposite is found; however, none of these differences are statistically significant.

Factors impacting the price of the gold: an empirical study of EGARCH model

Purpose- The gold as a fundamental asset has been displaying a high and sharp price volatility in international financial markets. The price dynamics of gold are believed to be influenced by non-linear dependencies with stock market indices, exchange rates, and commodity prices. Therefore, it is rational to examine the factors contributing to the non linear inferences. As a financial asset for the portfolio investment and hedging, gold prices display multilateral and dynamic data patterns this study aims to focus the analysis of price volatility which will contribute to the volatility structure, persistence, and correlated behaviors of gold prices. Methodology- To display and analyze the persistence level of volatility in the financial markets, a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) specification is employed. GARCH class models are further applied to determine the relevance of the leverage effect of market news, the progress of spillover pattern, and the effects on the risk-premium. Findings- Volatility in gold price has become an important feature of the financial markets in terms of volatility trading in adjusting the international portfolio investments. The correlated structure of the financial markets and the contagion effects of the news have displayed the asymmetric and complex but time-fragmented portfolio returns in terms of short and long term volatility formation and forecasting. Conclusion- Since gold’s safe haven status have played a major stake in the determination of the gold price, it is undeniable that the speculators’ rising power, the artificial intelligence utilisation in robo-trading, global and regional geo-political tensions, the recent developments in the risk sentiment in investment portfolios have an increasing amount of influence on gold prices. Keywords: Gold returns, US Dollar Index, exponential generalized autoregressive conditional heteroskedasticity, volatility persistence. JEL Codes: C58, G12, C22, G17

Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia

The objective of this study is to investigate and perform long–term forecasting of both streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation data are collected from 17 streamflow stations and 34 rainfall gauge stations to forecast future streamflow and hydrological drought from 2026 to 2099. Streamflow forecasting is performed using an artificial neural network (ANN) in conjunction with python software. Observed precipitation and streamflow data from 1973 to 2014 are used to train and test the ANN model by 70 and 30% ratios, respectively. After training the model, future downscaled precipitation data from regional climate models (RCM) have been used as input data to forecast future streamflow. Three RCM models were used to downscale historical and future climate data. RACMO is found a good downscaling model for all selected stations. The linear scaling bias correction technique results in less than 2% error compared to other alternative techniques. The result indicates that ANN is a good tool to forecast streamflow in areas having a good correlation between precipitation and streamflow such as Abbay, Awash, Baro, Omo Gibe, and Tekeze river basins. But in arid areas for example Genale Dawa, Wabishebele, and Rift Valley basins, the model is not suitable because the input data (precipitation) have high variation than the output variable (streamflow). In such areas, meteorological drought analysis and forecasting are better than hydrological drought analysis. Finally, future hydrological drought is analyzed using forecasted streamflow data as input to the streamflow drought index (SDI). The result indicates that 2028, 2036, 2042, 2044, 2062, and 2063 are the expected extreme drought years in most river basins of Ethiopia in the future. This shows that at least one extreme drought is expected in each decade in the future. Therefore, extensive research in drought analysis and forecasting is needed to develop an effective drought early warning system, and water resource management policy.

Особливості управління аграрними підприємствами України в умовах невизначеності та кризової ситуації

The paper is devoted to the importance of the agricultural sector for the development of the economies of the countries of the world. The role of agrarian enterprises in filling the country's GDP is determined, for example, in the GDP of Ukraine, the share of agriculture in the structure of GDP was 7-8% in 2010-2012 and 9-10% in 2020-2021. The key problems of the agrarian sector of the economy of Ukraine are considered, among which the following are highlighted: loss of production potential of land areas, labor shortage, insufficient demand, unfavorable weather conditions. The state of mined territory of Ukraine is given. It was determined that the most mined territories are located in the South and East of Ukraine, a large part of which is currently occupied and cannot be demined. It was established that a significant problem is the destruction of infrastructure facilities of the agro-industrial complex of Ukraine due to the constant shelling, as a result of which a significant number of granaries, warehouses, factories were destroyed, supply logistics were disrupted, in particular, before the full-scale invasion, about 90% of grain and oil crops were exported by sea, whereas now the routes have been changed to rail and road, which not only increases the cost but also limits the number of deliveries. In the paper a change in the structure of employees in the field of plant and animal husbandry during recent years was showed, in particular, a decrease in their number in connection with the comprehensive mobilization of the population and migration processes. The results of surveys of 702 enterprises throughout the territory of Ukraine regarding the prospects for positive changes in the activity of the country's agrarian sector are presented. As a result, expectations have a pessimistic forecast in terms of changes in the volume of produced products, the volume of products sold on foreign markets (within and outside the EU), stocks of products suitable for sale, prices of products and the number of employees for both crop and animal husbandry in general. A set of measures is proposed, among them the following: to adapt Ukraine's agricultural policy to EU standards; to provide financing, as well as comprehensive assistance to the livestock sector, as the most affected and backward sector of the agrarian economy of Ukraine; introduction of modern methods of management of agricultural enterprises, focusing on the experience of the Western colleagues in overcoming crisis situations; continuation of the "grain agreement" as one of the main ways of exporting grain raw materials abroad; provision of mineral fertilizers, as well as the necessary agricultural machinery of low-income farmers; the application of a number of measures to overcome the existing problems should stimulate the financial flow to the agricultural sector, create conditions for the development of a completely new stage of the agrarian policy of Ukraine. As a result, it give possibility to eliminate the negative impact of external factors influencing the functioning of agricultural enterprises and, as a result, to increase revenues to the budget of Ukraine, which are currently extremely low.

A Forecast Cycle–Based Evaluation for Tropical Cyclone Rapid Intensification Forecasts by the Operational HWRF Model

Abstract An evaluation framework for tropical cyclone rapid intensification (RI) forecasts is introduced and applied to evaluate the performance of RI forecasts by the operational Hurricane Weather Research and Forecasting (HWRF) Model. The framework is based on the performance of each 5-day forecast cycle, while the conventional RI evaluation is based on the statistics of successful or false RI forecasts at individual lead times. The framework can be used to compare RI forecasts of different cycles, which helps model developers and forecasters to characterize RI forecasts under different scenarios. It also can provide the evaluation of statistical performance in the context of 5-day forecast cycles. The RI forecast of each cycle is assessed using a modified probability-based approach that takes the absolute errors in intensity changes into account. The overall performance of RI forecasts during a given period is assessed based on the fractions of the individual forecast cycles during which RI events are successfully or falsely predicted. The framework is applied to evaluate the performance of RI forecasts by the HWRF Model for the whole life cycle of a single hurricane, as well as for each of the hurricane seasons from 2009 to 2021. The metric based on the probabilities of detection and false alarm rate of RI is compared with that based on the absolute errors in the intensity and intensity change during RI events. Significance Statement An evaluation framework for tropical cyclone rapid intensification (RI) forecasts is introduced, focusing on the performance of RI forecasts in each 5-day forecast cycle. The cycle-based approach can help to characterize RI forecasts under different conditions such as certain synoptic scenarios, initial conditions, or vortex structures. It also can be used to assess the overall performance of RI forecasts in terms of the percentages of individual forecast cycles that successfully or falsely predict RI events.

О получении стохастических прогнозов в детерминированной модели банковской системы России

In this article we propose the method of obtaining forecasts in stochastic terms for deterministic models. The proposed method is computationally simpler than the one used in dyna­mic stochastic general equilibrium (DSGE) models. The method is based on the estimation of parameters in the deterministic paradigm and the estimation of the vector of sample means and the covariance matrix for the increments of exogeneous variables on the in-sample period. For every realization of exogeneous variables, the trajectories of endogenous variables is calculated. The methods of mathematical statistics such as moments calculation, construction of confidence intervals, testing various hypotheses can be applied to them. The approach is illustrated on the model of the Russian banking system, which successfully reproduces the wide set of its indicators. Several interesting properties of the obtained stochastic forecasts were found, including the violation of their normality and the nontrivial dynamics of confidence intervals. Several scenarios of key rate and exchange rate resembling their actual dynamics in the beginning of the 2022. Several conclusions on the influence of key rate and exchange rate on the basic indicators of the banking systems are made. In particular, several effects are found which could not be discovered in the purely deterministic modelling paradigm.

Johansen model for photovoltaic a very short term prediction to electrical power grids in the Island of Mauritius

<abstract> <p>Sudden variability in solar photovoltaic (PV) power output to electrical grid can not only cause grid instability but can also affect power and frequency quality. Therefore, to study the balance of electrical grid or micro-grid power generated by PV systems in an upstream direction, predicting models can help. The power output conversion is directly proportional to the solar irradiance. Unlike time horizons predictions, many technics of irradiance forecasting have been proposed, long, medium and short term forecasting. For the Island of Mauritius in the Indian Ocean, and regards to key policy decisions, the government has outlined its intention to promote the PV technologies through the local electricity supplier but oversee the technical requirements of PV power output predicts for 1 hour to 15-minutes ahead. So, this paper is illustrating results of the Johansen vector error correction model (VECM) cointegration approach, from the author original and previous studies, but for a very short term prediction of 15-minutes to PV power output in Mauritius. The novelty of this study, is the long run equilibrium relationship of the Johansen model, that was initially determined in previous research works and from dataset in Reunion Island, is then applied to the PV plant in the Island of Mauritius. The proposed prediction model is trained for an hourly and 15-minutes period from year 2019 to year 2022 for a random month and a random day. The experimental results show that the performance metric R<sup>2</sup> values are more than 93% signifying that Johansen model is positively and strongly correlated to onsite measurements. This proposed model is a powerful predicting tool and more accuracy should be attained when associated to a machine learning method that can learn from datasets.</p> </abstract>

Short – Term Forecasting for Daily Stock Market Indices using Discrete Fourier Transforms

The stock market indices are used to gauge the financial movements in the stock markets. If the index rises, the market is growing, and if it falls, the market is declining. The only stock exchange in Sri Lanka is operated by the Colombo Stock Exchange (CSE). Its two primary stock market indices are All Share Price Index (ASPI) and Standard & Poor’s Sri Lanka 20 (S&P SL20). Market indices provide information to investors. So, they can predict the risks and returns of their investments. While ASPI forecasts assist investors in understanding the future direction of the entire market, S&P SL20 forecasts help investors make investment decisions. Therefore, in order to make the correct investment decisions, it is crucial to identify appropriate forecasting methods for those two indices to meet investor expectations. The Discrete Fourier Transform (DFT) is a technique that can be used to convert a time-domain discrete signal into a frequency-domain discrete spectrum. In this study, the ASPI and S&P SL20 indexes were modeled as the Fast Fourier Transform amplitude spectrum using the daily stock values. The daily index data from the years 2017 to 2022 were used to formulate this model. The study also examined the periodic deviations of both indices during the considered period. Additionally, this research predicts the near future of both indices by modeling the daily indices values. To further verify the accuracy of the model, data from the SET (Thailand Stock Index) were employed. According to the results, the ASPI and S&P SL20 datasets show periodic patterns ranging from 4 days to 7 days and the SET datasets show periodic patterns between 5 and 6 days. The forecasting ability of the proposed Fourier model was assessed by using metrices such as Mean Squared Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Finally, it is concluded that the proposed Fourier model is capable of forecasting the daily ASPI and S&P SL20 indices for a short period of time equivalent to their periodicities.

Drifting Streaming Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast

This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6% for the whole year data and at least 37% for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8% for the whole year data and at least 35.2% for the ramp events.

Short–term forecasting of daily evapotranspiration from rice using a modified Priestley–Taylor model and public weather forecasts

Reliable crop evapotranspiration (ET) forecasting is critical for water resource management and allocation. However, most estimation models cannot be directly used for ET forecasting with public weather forecasting information. Here, we propose a modified Priestley–Taylor (PT) model using only solar radiation (Rs) and air temperature (Ta) as inputs that can be easily obtained from public weather forecasts. The commonly used generalized sunshine– and temperature–based Rs models using forecasted weather types (transferred to sunshine duration) and Ta data as inputs were compared to identify the more suitable model for forecasting daily Rs. Results showed that the modified PT model using measured Rs and Ta data provided a reasonable accuracy to estimate daily rice ET across three energy flux sites. The regression coefficient (m), correlation coefficient (R), root mean squared error (RMSE), and index of agreement (dIA) were in the range of 0.949–1.044, 0.916–0.964, 0.396–0.645 mm d–1, and 0.954–0.980, respectively. The generalized temperature–based Rs model is more suitable for forecasting daily Rs for a 1–7 day lead time during the growth period of rice at present as indicated by its superior performance to sunshine–based model at 70% of 30 sites. The forecasted accuracy of rice ET was acceptable and decreased gradually as lead time increased. The mean values of m, R, RMSE, and dIA were decreased from 0.887 to 0.981, 0.641–0.782, 1.160–1.243 mm d–1, and 0.783–0.870 in 1–day lead time to 0.834–0.941, 0.376–0.563, 1.406–1.740 mm d–1, and 0.639–0.743, respectively, in 7–day lead time during 2018–2020 at Nanjing when Rs was forecasted based on the generalized temperature–based model. This study provides an important approach for short–term prediction of rice ET using public weather forecasting information.

Assimilation of Aerosol Optical Depth Into the Warn‐on‐Forecast System for Smoke (WoFS‐Smoke)

AbstractThis research extends the Warn‐on‐Forecast System for Smoke (WoFS‐Smoke) by adding the capability to assimilate aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite Series‐R (GOES‐R) satellites. The WoFS is a rapidly cycling, ensemble‐based analysis and forecasting system designed to generate short term (0–3 hr) forecasts of high impact weather. WoFS‐Smoke provides short‐term forecasts of smoke aerosols injected into the atmosphere from ongoing wildfires. The vertically integrated concentration of smoke aerosols in the atmosphere can be estimated from satellite based AOD retrievals. To assimilate AOD into WoFS‐Smoke, a smoke control variable is added to the prognostic state that is updated during each assimilation cycle. Then, a forward operator is created to relate modeled smoke aerosols to AOD retrievals using a mathematical function developed for the smoke‐AOD conversion used by the High‐Resolution Rapid Refresh for Smoke. Finally, GOES‐R AOD retrievals are quality controlled and are assimilated at 15 min intervals. Comparing analyzed AOD with smoke and other atmospheric variables indicates that assimilating AOD not only directly impacts smoke aerosol concentration in the system, but also has indirect impacts on variables such as temperature, humidity, and wind. Results from two wildfire cases in Oklahoma and Arizona show that assimilating AOD substantially impacts the concentration and distribution of smoke aerosols in the system. Forecast verification against satellite and surface observations indicates the overall impact of assimilating AOD in WoFS‐Smoke can improve forecast skill of smoke and the surrounding environment.

Price Prediction for Bitcoin: Does Periodicity Matter?

Purpose: A major challenge traders, speculators and investors are grappling with is how to accurately forecast Bitcoin price in the cryptocurrency market. This study is aimed to uncover the best model for the forecasts of Bitcoin price as well as to verify the price series that offers the best predictions performance under different periodicity of datasets. Design/methodology/approach: The study adopts three different data periods to verify whether frequency matters in forecasting Bitcoin price. The Bitcoin price, from 01/01/15 to 11/01/2021, is trained and validated on selected forecast models, including the Naïve, Linear, Exponential Smoothing Model, ARIMA, Neural Network, STL and Holt-Winters filters. Five forecast accuracy measures (RSME, MAE, MPE, MAPE and MASE) are applied to confirm the best performing model. The Diebold‐Mariano test is used to compare the forecasts based on the daily price with those based on the weekly and monthly. Findings: Based on the accuracy measures, the results indicate that the Naïve model provides more accurate performance for the daily series, while the linear model outperforms others for the weekly and monthly series. Using the Diebold‐Mariano statistics, there is evidence that forecasting Bitcoin price is not sensitive to the data periodicity. Research limitations/implications: The study has a major limitation, which is the shared sentiment to apply actual Bitcoin price series, and not the returns or log transformation for the forecast models. Notably, actual data may sometimes be loud, hence increasing the possibility of over predictions. Originality/value: In forecasting, different approaches have been used, this paper compares outputs of both statistical and machine learning methods in order to arrive at the best option for the Bitcoin price forecasts. Hence, we investigate whether the machine learning tools offer better forecasts in terms of lower error and higher model’s accuracy relative to the traditional models.

Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity

AbstractBased on the idealized supercell and real case studies in Part I and II, the purpose of this subsequent study is to further investigate the impact of assimilating Geostationary Operational Environmental Satellites‐16 (GOES‐16) derived atmospheric motion vectors (AMVs) in addition to WSR_88D Doppler radar observations on convective scale numerical weather prediction. Five high‐impact weather events that occurred in spring 2018 and 2019 are analyzed using the National Severe Storms Laboratory three‐dimensional variational data assimilation (DA) system. Four types of experiments are implemented and compared: (a) the control experiment (NoDA) without assimilating any observation, (b) the radar DA experiment (RAD), (c) the GOES‐16 AMV DA experiment (AMV), and (d) the experiment assimilating AMVs together with radar data (AMV_RAD). Score metrics aggregated over all cases indicate that AMV_RAD performs slightly better than RAD in 0–3 hr reflectivity and precipitation forecasts especially at higher thresholds, suggesting the added value of GOES‐16 AMVs on radar data. Detailed case examinations also show that AMV_RAD generally exhibits slightly more skillful storm forecast in terms of the areal coverage, storm mode, and storm orientations, owing to improvements in the analysis of boundary locations and localized enhanced divergence signatures. In spite of encouraging objective and subjective evaluation results, AMV_RAD has difficulty in adjusting the moisture gradient associated with dryline and tends to underpredict the associated weak discrete storms.

Reduction of time series forecast variance by the case of ArDL(p, q) models

Combination of time series forecasts or time series models is often considered nowadays in various research papers and is usually a good technique in practice. Still it has got weak theoretical explanation and there’s a lot of work that is going to be done in this field. In the research optimal combination of forecasters in terms of minimal variance is investigated. These results don’t depend on nature of combined models but in the practical part ArDL models describing connections between pairs of time series are considered. Weighted sum of models’ predictions is used as a new prediction with variance that should be less than individual models’ predictions variance. Optimal weights for combination of two models are calculated theoretically. They are based on correlation of combined models and their forecasts’ variance. Optimal model combination variance (variance of the combined models with optimal weights) has been investigated and it has been shown that it doesn’t exceed variances of the combined models. Practical experiments and conditions under which combined time series model is expected to improve forecasts of original ones are in scope of this research. Forecasts of two ArDL models are combined in order to construct forecast with lower variety. This approach can be used in case of more models but theoretical expression of optimal weights is going to be a part of further research. Russian macroeconomical time series statistics and economic data of the USA are used as experimental time series. Combined models have got predictions close to the forecasts of the best models and better than the worst models used in combinations. It’s shown that optimal combination should make predictions of high quality if combined predictors describe different aspects of investigated time series and correlation of their forecasts isn’t very high. Otherwise, it turns into mean forecast of two models.

Circularity and LCA - material pathways: cascade potential and cascade environmental impact of an in-use building product

Improving circularity in the building sector entails ensuring greater material efficiency to avoid virgin material extraction. To assist stakeholders in decisions regarding salvaging an in-use building product, requires to predict and assess the potential further productive uses of that product and its materials. The range of possible cascade material paths originating from the in-use building product X and their assessments comprise the cascade potential of product X. Method: To determine the cascade potential and impact, we work further on existing efforts done in the field of circularity and life cycle assessment (LCA). This entails discussing scenario models to predict cascade material pathways over time, and multifunctionality solutions to assess those pathways. Due to the fact that the environment is a complex system and long term forecasting is required, the cascade potential can never be exactly determined. Therefore, we first set up conceptual formulas and then discuss steps to make these formulas feasible. Furthermore, the effort to generate the cascade paths originating from a product, can also be used to form circular systems that adhere to carbon mitigation pathways.

On the verification of ensemble precipitation forecasts over the Godavari River basin

The ensemble forecasts from Numerical Weather Prediction (NWP) models are considered as essential elements for operational hydrologic forecasting. However, these forecasts are burdened with significant inherent biases and cannot be used directly for hydrologic forecasting. Therefore, the present study examines the capability of ensemble precipitation forecasts obtained from two NWP models i.e., a 20-member ensemble from the National Center for Environmental Prediction (NCEP) and a 50-member ensemble from the European center for Medium-term Weather Forecasts (ECMWF). The skill of 70-member multi-model grand ensemble (MME) generated from the NCEP and ECMWF forecasts is also verified in this study. Furthermore, post-processing of the raw ensemble members was carried out using two methods (Quantile Mapping (QM) and Quantile Regression Forests (QRF)) in order to minimize the inherent biases. The analysis was performed over subbasins of an Indian river basin namely the Godavari River Basin. Here, a set of deterministic and probabilistic verification measures (forecast error box plots, correlation coefficient, Relative Mean Error, mean Continuous Ranked Probability Score, spread-skill relationship, rank histograms, reliability diagram, Area under Relative Operating Characteristic Curve) were employed to assess various quality attributes of the forecasts. The findings from the study suggests that QRF post-processed NCEP and MME forecasts are comparatively better than other forecasts in terms of all employed verification measures over all subbasins. The QRF post-processed forecasts were found to be superior than QM post-processed and raw forecasts over all subbasins. The adopted post-processing methods resulted in improved forecasts at shorter lead times. However, the skill of both raw and post-processed forecasts declines at higher lead times over all subbasins.

Evaluation of Tianji and ECMWF high-resolution precipitation forecasts for extreme rainfall event in Henan in July 2021

The extreme rainfall event of July 17 to 22, 2021 in Henan Province, China, led to severe urban waterlogging and flood disasters. This study investigated the performance of high-resolution weather forecasts in predicting this extreme event and the feasibility of weather forecast-based hydrological forecasts. To achieve this goal, high-resolution precipitation forecasts from the Tianji weather system and the forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) were evaluated with the spatial verification metrics of structure, amplitude, and location. The results showed that Tianji weather forecasts accurately predicted the amplitude of 12-h accumulated precipitation with a lead time of 12 h. The location and structure of the rainfall areas in Tianji forecasts were closer to the observations than ECMWF forecasts. Tianji hourly precipitation forecasts were also more accurate than ECMWF hourly forecasts, especially at lead times shorter than 8 h. The precipitation forecasts were used as the inputs to a hydrological model to evaluate their hydrological applications. The results showed that the runoff forecasts driven by Tianji weather forecasts could effectively predict the extreme flood event. The runoff forecasts driven by Tianji forecasts were more accurate than those driven by ECMWF forecasts in terms of amplitude and location. This study demonstrates that high-resolution weather forecasts and corresponding hydrological forecasts can provide valuable information in advance for disaster warnings and leave time for people to act on the event. The results encourage further hydrological applications of high-resolution weather forecasts, such as Tianji weather forecasts, in the future.

On the Predictive Value of the (Shadow) Real Interest Rate for the Realized Volatility of Gold-Price Returns

We use a quasi-out-of-sample forecasting experiment to study the predictive value of a short-term real interest rate for the volatility of gold-price returns. To this end, we use monthly U.S. data for the sample period from 1990/1 to 2022/2, and we study a standard effective-federal-funds-based real interest rate as well as a shadow real interest rate, which accounts for the recent extended zero-lower-bound period. We find that the real interest rate has predictive value for the subsequent realized volatility, and this predictive value turns out to be stronger in several specifications of our forecasting experiment for the shadow real interest rate than for the standard real interest rate. We evaluate the predictive value of forecasts in terms of an asymmetric loss function. Because gold is considered as a safe-haven asset, our results provide some important implications for portfolio decisions of investors.

A significant wave height forecast framework with end-to-end dynamic modeling and lag features length optimization

Ocean wave energy is attracting more and more attention from researchers on observation of its clean and sustainable properties. Significant wave height (SWH) is one of the key wave parameters, making accurately forecasting the SWH important for coastal/ocean engineers. In this paper, we propose a new framework for forecasting the SWH. The data were decomposed and reconstructed, and the lag features length of the input data were adaptively optimized using the Bayesian optimization (BO) algorithm. A new paradigm for end-to-end dynamic modeling (EEDM) forecast is then proposed, where data are modeled and forecasted separately for buoys at various geographical locations, with automated machine learning (AutoML) as the back-end modeling support for the paradigm. It was also trained and tested on nine buoys from NOAA National Data Buoy Center, which are located at sites with different water depths. The results show that the forecast framework has provided reliable forecasts. We also discussed the reasons for the buoy with the worst forecast in terms of model interpretability and data quality. Finally, we compared three deep learning models (simple recurrent network, long short-term memory and gate recurrent unit) and three machine learning models (principal component regression, support vector machine and K-nearest neighbor). The comparisons indicate that the AutoML turns out to be the best.