Russian Rivers Streamflow Forecasting Using Hydrograph Extrapolation Method

This paper presents a method of hydrograph extrapolation, intended for simple and efficient streamflow forecasting with up to 10 days lead time. The forecast of discharges or water levels is expressed by a linear formula depending on their values on the date of the forecast release and the five previous days. Such forecast techniques were developed for more than 2700 stream gauging stations across Russia. Forecast verification has shown that this method can be successfully applied to large rivers with a smooth shape of hydrographs, while for small mountain catchments, the accuracy of the method tends to be lower. The method has been implemented into real-time continuous operations in the Hydrometcentre of Russia. In the territory of Russia, 18 regions have been identified with a single dependency of the maximum lead time of good forecasts on the area and average slope of the catchment surface for different catchments of each region; the possibilities of forecasting river streamflow by the method of hydrograph extrapolation are approximately estimated. The proposed method can be considered as a first approximation while solving the problem of forecasting river flow in conditions of a lack of meteorological information or when it is necessary to quickly develop a forecasting system for a large number of catchments.

On the ROC Area of Ensemble Forecasts for Rare Events

The relative operating characteristic (ROC) curve is a popular diagnostic tool in forecast verification, with the area under the ROC curve (AUC) used as a verification metric measuring the discrimination ability of a forecast. Along with calibration, discrimination is deemed as a fundamental probabilistic forecast attribute. In particular, in ensemble forecast verification, AUC provides a basis for the comparison of potential predictive skill of competing forecasts. While this approach is straightforward when dealing with forecasts of common events (e.g. probability of precipitation), the AUC interpretation can turn out to be oversimplistic or misleading when focusing on rare events (e.g. precipitation exceeding some warning criterion). How should we interpret AUC of ensemble forecasts when focusing on rare events? How can changes in the way probability forecasts are derived from the ensemble forecast affect AUC results? How can we detect a genuine improvement in terms of predictive skill? Based on verification experiments, a critical eye is cast on the AUC interpretation to answer these questions. As well as the traditional trapezoidal approximation and the well-known bi-normal fitting model, we discuss a new approach which embraces the concept of imprecise probabilities and relies on the subdivision of the lowest ensemble probability category.

Methodological and conceptual challenges in rare and severe event forecast-verification

There are distinctive methodological and conceptual challenges in rare and severe event (RSE) forecast-verification, that is, in the assessment of the quality of forecasts involving natural hazards such as avalanches or tornadoes. While some of these challenges have been discussed since the inception of the discipline in the 1880s, there is no consensus about how to assess RSE forecasts. This article offers a comprehensive and critical overview of the many different measures used to capture the quality of an RSE forecast and argues that there is only one proper skill score for RSE forecast-verification. We do so by first focusing on the relationship between accuracy and skill and show why skill is more important than accuracy in the case of RSE forecast-verification. Subsequently, we motivate three adequacy constraints for a proper measure of skill in RSE forecasting. We argue that the Peirce Skill Score is the only score that meets all three adequacy constraints. We then show how our theoretical investigation has important practical implications for avalanche forecasting by discussing a recent study in avalanche forecast-verification using the nearest neighbour method. Lastly, we raise what we call the “scope challenge" that affects all forms of RSE forecasting and highlight how and why the proper skill measure is important not only for local binary RSE forecasts but also for the assessment of different diagnostic tests widely used in avalanche risk management and related operations. Finally, our discussion is also of relevance to the thriving research project of designing methods to assess the quality of regional multi-categorical avalanche forecasts.

Using Metocean Forecast Verification Information to Effectively Enhance Operational Decision-Making

Metocean forecast verification statistics (or ‘skill scores’), for variables such as significant wave height, are typically computed as a means of assessing the (past) weather model performance over the particular area of interest. For developers, this information is important for the measurement of model improvement, while for consumers this is commonly applied for the comparison/evaluation of potential service providers. However, an opportunity missed by many is also its considerable benefit to users in enhancing operational decision-making on a real-time (future) basis, when combined with an awareness of the context of the specific decision being made. Here, we present two categorical verification techniques and demonstrate their application in simplifying the interpretation of ensemble (probabilistic) wave forecasts out to 15 days ahead, as pioneered – in operation – in Summer 2020 to support the recent weather sensitive installation of the first phase of a 36 km subsea pipeline in the Fenja field in the North Sea. Categorical verification information (based on whether forecast and observations exceed the user-defined operational weather limits) was constructed from 1460 archive wave forecasts, issued for the two-year period 2017 to 2018, and used to characterise the past performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) in the form of Receiver Operating Characteristic (ROC) and Relative Economic Value (REV) analysis. These data were then combined with a bespoke parameterization of the impact of adverse weather on the planned operation, allowing the relevant go/no-go ensemble probability threshold (i.e. the number of individual/constituent forecast members that must predict favourable/unfavourable conditions) for the interpretation of future forecasts to be determined. Following the computation of the probability thresholds for the Fenja location, trials on an unseen nine-month period of data from the site (Spring to Autumn 2019) confirm these approaches facilitate a simple technique for processing/interpreting the ensemble forecast, able to be readily tailored to the particular decision being made. The use of these methods achieves a considerably greater value (benefit) than equivalent deterministic (single) forecasts or traditional climate-based options at all lead times up to 15 days ahead, promising a more robust basis for effective planning than typically considered by the offshore industry. This is particularly important for tasks requiring early identification of long weather windows (e.g. for the Fenja tie-ins), but similarly relevant for maximising the exploitation of any ensemble forecast, providing a practical approach for how such data are handled and used to promote safe, efficient and successful operations.

Precipitation Forecast Verification of the FFGS and SisPI Tools during the Impact of the Tropical Storm Isaias over the Dominican Republic

During 2020, the Dominican Republic received the impact of several tropical organisms, among those that generated the greatest losses in the country, Tropical Storm Isaias stands out because of the significant precipitation and flooding it caused. The study analyzes the ability of the products of Flash Flood Guidance System (FFGS) and the Nowcasting and Very Short Range Prediction System (Spanish acronym SisPI) for the quantitative precipitation forecast (QPF) of the rains generated by Isaias on 30 and 31 July 2020 over the Dominican Republic. Various traditional verification methods are used in the study. The results show that both numerical weather-based systems are powerful tools for the QPF, and also to contribute to the prevention and mitigation of disasters caused by the extreme hydro-meteorological event analyzed.

http://method.meteorf.ru/publ/tr/tr380/htm/02.htm

Forecasting the flow of Russian rivers by hydrograph extrapolation / Borsch S.V., Koliy V.M., Semenova N.K., Simonov Yu.A., Khristoforov A.V. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 77-94. An automated system has been developed based on the hydrograph extrapolation method, which allows the year-round daily forecasting of water level and streamflow for the Russian rivers with up to 10-day lead time. The forecast of discharges or water levels is expressed by a linear formula depending on their values on the date of the forecast issue and five previous days. The forecasting scheme limits the possible minimum and maximum values of the discharge or water level based on historical data. Forecast schemes were obtained for 2776 river gauges. The time period from 2010 to 2019 with daily observations of discharge and water level was used. The forecast verification shows that this method can be successfully applied to large rivers with smooth hydrographs. Keywords: daily discharge and water levels, short- and medium-term forecasts, hydrograph extrapolation method, forecast verification, maximum lead time of satisfactory forecasts, self-learning of an automated system for preparing and issuing forecasts

Forecast verifications for the real COVID-19 pandemic dynamics in Qatar

The COVID-19 pandemic dynamics in Qatar in the second half of May and the first half of June 2021 was compared with the published results of SIR-simulations based on the data from the period April 25 - May 8, 2021. Forecast verification showed very good agreement with the real number of cases (which can exceed the laboratory-confirmed one more than 5 times). The positive effect of mass vaccination became visible in June 2021.