Use Of Forecasts Research Articles

The Jive Verification System and Its Transformative Impact on Weather Forecasting Operations

Abstract Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved to be a Bureau operational verification system in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and “Evidence Targeted Automation” dashboards for exploring the performance of competing forecast systems. Additionally, Jive includes a Jupyter Notebook server with the Jive Python library, which supports research experiments, case studies, and the development of new verification metrics and tools. This paper describes the Jive verification system and how it helped bring verification to the forefront at the Bureau of Meteorology, leading to more accurate, streamlined forecasts. Jive has provided evidence to support forecast automation decisions and has helped to understand the evolving role of meteorologists in the forecast process. It has given operational meteorologists tools for evaluating forecast processes, including identifying when and how manual interventions lead to superior predictions. Work on Jive led to new verification science, including novel metrics that are decision-focused, including diagnostics for extreme conditions. Jive also provided the Bureau with an enterprise-wide data analysis environment and has prompted a clarification of forecast definitions. These collective impacts have resulted in more accurate forecasts, ultimately benefiting society and building trust with forecast users. These positive outcomes highlight the importance of meteorological organizations investing in verification science and technology.

Evaluating USDA’s farm balance sheet forecasts

PurposeThe United States Department of Agriculture Farm Balance Sheet forecasts provide important, timely information on the financial assets and debt in the U.S. farm sector. Despite their prominent role in policy and decision making, the forecasts have not been rigorously evaluated. This research examines the degree to which the USDA’s Farm Balance Forecasts are optimal predictors of subsequent official estimates.Design/methodology/approachFollowing prior studies of USDA’s farm income forecasts, archived asset and debt forecasts from 1986 through 2021 are used in regression-based tests of bias and efficiency.FindingsForecasts from 1986–2021 are found to be unbiased but inefficient. The forecasts have a tendency to over-react to new information early in the revision process.Originality/valueThese findings can be helpful for forecast users in adjusting their expectations and for forecasters in adjusting the current forecasting methods.

Game Theoretic Approaches to Modelling Cooperative and Competitive Dynamics for Enhanced Routing and Resource Allocation

The goal of the field of game theory is to simulate scenarios in which decision-makers must choose between courses of action that may or may not conflict and in which the choices made by one element may affect the choices made by other elements. Techniques from game theory have been widely used to solve a variety of engineering design issues. Game theory may be applied to wireless networks to create cooperative strategies between nodes, terminals and network providers, among other organizations. The majority of the time, game theory has been used in networking to address routing and resource allocation issues in competitive settings. Because radio communication channels are typically shared in wireless networks, the actions of one wireless device may have an impact on the communication capabilities of a neighboring device. Game theory may be used to simulate situations such as these. A multi-layered viewpoint is presented in Applications of Game Theory in Wireless Networking, highlighting the domains in which game theory might find practical application. This explains how different wireless network interactions may be represented as games and how game-theoretical methods can accurately mimic or forecast genuine user behavior in cooperative or competitive settings. Because of these similarities, it is possible to grasp the intricate relationships between nodes in this highly dynamic and dispersed environment by using a strong mapping between classical game theory components and network parts

Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

FOREWARNS: development and multifaceted verification of enhanced regional-scale surface water flood forecasts

Abstract. Surface water flooding (SWF) is a severe hazard associated with extreme convective rainfall, whose spatial and temporal sparsity belie the significant impacts it has on populations and infrastructure. Forecasting the intense convective rainfall that causes most SWF on the temporal and spatial scales required for effective flood forecasting remains extremely challenging. National-scale flood forecasts are currently issued for the UK and are well regarded amongst flood responders, but there is a need for complementary enhanced regional information. Here we present a novel SWF-forecasting method, FOREWARNS (Flood fOREcasts for Surface WAter at a RegioNal Scale), that aims to fill this gap in forecast provision. FOREWARNS compares reasonable worst-case rainfall from a neighbourhood-processed, convection-permitting ensemble forecast system against pre-simulated flood scenarios, issuing a categorical forecast of SWF severity. We report findings from a workshop structured around three historical flood events in Northern England, in which forecast users indicated they found the forecasts helpful and would use FOREWARNS to complement national guidance for action planning in advance of anticipated events. We also present results from objective verification of forecasts for 82 recorded flood events in Northern England from 2013–2022, as well as 725 daily forecasts spanning 2019–2022, using a combination of flood records and precipitation proxies. We demonstrate that FOREWARNS offers good skill in forecasting SWF risk, with high spatial hit rates and low temporal false alarm rates, confirming that user confidence is justified and that FOREWARNS would be suitable for meeting the user requirements of an enhanced operational forecast.

Research on emotional analysis and integration of multimodal data report

In recent years, emotional analysis has remained a hot topic, permeating people's daily lives and playing an indispensable role in various environments. Researchers can improve products and services and forecast user behavior using sentiment analysis to understand consumers' emotional inclinations and reactions. This paper's comprehensive review of emotional analysis is conducted through various research data. This study integrates emotional analysis and multimodal data research, elaborating on emotional analysis's characteristics and pros and cons from aspects such as facial expressions, voice information, and textual data. This paper points out the current applications of emotional analysis and recent research advancements, selecting various features and methods for emotion tendency classification, and utilizing existing technological tools for analysis. Lastly, this work combines multimodal data with various studies to make emotional analysis more widely and aptly applied in everyday life. There are greater opportunities for sentiment analysis in various domains thanks to the advancements in multimodal sentiment analysis research and modal fusion technologies.

Visualizing the Shelf Life of Population Forecasts: A Simple Approach to Communicating Forecast Uncertainty

It is widely appreciated that population forecasts are inherently uncertain. Researchers have responded by quantifying uncertainty using probabilistic forecasting methods. Yet despite several decades of development, probabilistic forecasts have gained little traction outside the academic sector. Therefore, this article suggests an alternative and simpler approach to estimating and communicating uncertainty which might be helpful for population forecast practitioners and users. Drawing on the naïve forecasts idea of Alho, it suggests creating “synthetic historical forecast errors” by running a regular deterministic projection model many times over recent decades. Then, borrowing from perishable food terminology, the “shelf life” of forecast variables, the number of years into the future the forecast is likely to remain “safe for consumption” (within a specified error tolerance), is estimated from the “historical” errors. The shelf lives are then applied to a current set of forecasts and presented in a simple manner in graphs and tables of forecasts using color-coding. The approach is illustrated through a case study of 2021-based population forecasts for Australia. It´s concluded that the approach offers a relatively straightforward way of estimating and communicating population forecast uncertainty.

Map-Based Ensemble Forecasts for Maritime Operations: An Interactive Usability Assessment with Decision Scenarios

Abstract Previous research indicates that forecast uncertainty can, in certain formats and decision contexts, provide actionable insights that help users in their decision-making. However, how to best disseminate forecast uncertainty, which factors affect successful uptake, and how forecast uncertainty transforms into better decision-making remains an ongoing topic for discussion in both academic and operational contexts. Interpreting and using visualizations of forecast uncertainty are not straightforward, and choosing how to represent uncertainty in forecast products should be dependent on the specific audience in mind. We present findings from an interactive stakeholder workshop that aimed to advance context-based insights on the usability of graphical representations of forecast uncertainty in the field of maritime operations. The workshop involved participants from various maritime sectors, including cruise tourism, fisheries, government, private forecast service providers, and research/academia. Geographically situated in Norway, the workshop employed sea spray icing as a use case for various decision scenario exercises, using both fixed probability and fixed threshold formats, supplemented with temporal ensemble diagrams. Accumulated operational expertise and characteristics of the forecast information itself, such as color coding and different forms of forecast uncertainty visualizations, were found to affect perceptions of decision-making quality. Findings can inform codesign processes of translating ensemble forecasts into usable and useful public and commercial forecast information services. The collaborative nature of the workshop facilitated knowledge sharing and coproduction between forecast providers and users. Overall, the study highlights the importance of incorporating methodological approaches that consider the complex and dynamic operational contexts of ensemble-based forecast provision, communication, and use. Significance Statement We wanted to understand how maps showing uncertainty in weather forecasts can help maritime users in their operational decisions. We organized a workshop with Norwegian maritime stakeholders and forecasters, who interpreted maps that combined layers of maritime operational activities and the likelihood of sea spray icing (an important hazard for ships operating on higher latitudes). The results show that contextual knowledge, and the use visual formats such as traffic light colors may help users to understand the maps. The results will help to better communicate weather forecasts to maritime users and gives suggestions about how to involve users in codesigning forecast products. Follow-up research could use our approach to investigate other hazardous conditions, such as wind, waves and sea ice.

Business applications and state‐level stock market realized volatility: A forecasting experiment

AbstractWe analyze the predictive value of (the surprise component of) state‐level business applications, as a proxy of local investor sentiment, for the state‐level realized US stock‐market volatility. We use high‐frequency data for the period from September 2011 to October 2021 to compute realized volatility. Using an extended version of the popular heterogeneous autoregressive realized volatility model and accounting for the possibility that users of forecasts have an asymmetric loss function, we show that business applications tend to have predictive value for realized state‐level stock‐market volatility, as well as for upside (“good”) and downside (“bad”) realized volatility, for users of forecasts who suffer a larger loss from an underprediction of realized volatility than from an overprediction of the same (absolute) seize, after controlling for realized moments (realized skewness, realized kurtosis, realized jumps, and realized tail risks). We also highlight that the COVID‐19 period is a major driver of our empirical results.

Wholesale Food Price Index Forecasts with the Neural Network

Food price forecasts in the agricultural sector have always been a vital matter to a wide variety of market participants. In this work, we approach this forecast problem for the weekly wholesale food price index in the Chinese market during a 10-year period of January 1, 2010–January 3, 2020. To facilitate the analysis, we propose the use of the nonlinear auto-regressive neural network. Technically, we investigate forecast performance, based upon the relative root mean square error (RRMSE) as the evaluation metrics, corresponding to one hundred and twenty settings that cover different algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data. Our experimental result suggests the construction of the neural network with three delays and 10 hidden neurons, which is trained through the Levenberg–Marquardt algorithm, as the forecast model. It leads to high accuracy and stabilities with the RRMSEs of 1.93% for the training phase, 2.16% for the validation phase, and 1.95% for the testing phase. Comparisons of forecast accuracy between the proposed model and some other machine learning models, as well as traditional time-series econometric models, suggest that our proposed model leads to statistically significant better performance. Our results could benefit different forecast users, such as policymakers and various market participants, in policy analysis and market assessments.

Probabilistic vs deterministic forecasts – interpreting skill statistics for the benefit of users

Owing to probabilistic uncertainties associated with seasonal forecasts, especially over areas such as southern Africa where forecast skill is limited, non-climatologists and users of such forecasts frequently prefer them to be presented or distributed in terms of the likelihood (expressed as a probability) of certain categories occurring or thresholds being exceeded. Probabilistic forecast verification is needed to verify such forecasts. Whilst the resulting verification statistics can provide clear insights into forecast attributes, they are often difficult to understand, which might hinder forecast uptake and use. This problem can be addressed by issuing forecasts with some understandable evidence of skill, with the purpose of reflecting how similar forecasts may have performed in the past. In this paper, we present a range of different probabilistic forecast verification scores, and determine if these statistics can be readily compared to more commonly known and understood ‘ordinary’ correlations between forecasts and their associated observations – assuming that ordinary correlations are more intuitively understood and informative to seasonal forecast users. Of the range of scores considered, the relative operating characteristics (ROC) was found to be the most intrinsically similar to correlation.

Fully integrating probabilistic flood forecasts into the decision-making process across southern Quebec, Canada: some factors to consider

Flood forecasts can enable decision-makers to plan for mitigation measures, ensure the safety of people and reduce damages to private and public property. However, while flood forecasting systems are becoming increasingly sophisticated, those technical improvements do not always translate into a reduction of damages. In particular, ensemble and probabilistic streamflow forecasts provide more information regarding the uncertainty, but such forecasts are also more difficult to interpret. The connection between streamflow, water depth and flood extent is also not straightforward, as this connection requires extensive previous experience with a given river reach. Streamflow forecasts can be transformed in predictive flood maps through a hydraulic model, and those maps might have a more intuitive interpretation. However, if the system is designed to also represent the uncertainty of both water depth and extent, the interpretation becomes considerably more complex. Within this context of continuous improvement of flood forecasting systems, a broad consultation (90 participants) of forecast users was conducted across southern Quebec, Canada, with the aim of understanding their perception of flood forecasts and how they use those forecasts for decision-making. Participants responses emphasize the importance of contextualizing the forecasts according to potential consequences, providing an accurate representation of the time evolution of flood events, and facilitating the communication between forecasters and decision-makers. Following this, our main recommendation is to organize workshops to provide training for decision-makers on how to interpret hydrological forecasts correctly. Another recommendation is to implement additional communication channels between forecasters and end-users.

Impact-based seasonal rainfall forecasting to trigger early action for droughts

The Horn of Africa faces an ongoing multi-year drought due to five consecutive failed rainy seasons, a novel climatic event with unpreceded impacts. Beyond the starvation of millions of livestock, close to 23 million individuals in the region are currently facing high food insecurity in Kenya, Somalia and Ethiopia alone. The severity of these impacts calls for the urgent upscaling and optimisation of early action for droughts. However, drought research focuses mainly on meteorological and hydrological forecasting, while early action triggered by forecasts is seldom addressed.This study investigates the potential for early action for droughts by using seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 system for the March–April-May (MAM) and October–November-December (OND) rainy seasons. We show that these seasonal rainfall forecasts reflect major on-the-ground impacts, which we identify from drought surveillance data from 21 counties in Kenya. Subsequently, we show that the SEAS5 drought forecasts with short lead times have substantial potential economic value (PEV) when used to trigger action before the OND season across the region (PEVmax = 0.43). Increasing lead time to one or two months ahead of the season decreases PEV, but the benefits persist (PEVmax = 0.2). Outside of Kenya, MAM forecasts have limited value. The existence of opportunities for early action during the OND season in Kenya and Somalia is demonstrated by high PEV values, with some regions recording PEVmax values close to 0.8. To illustrate the practical value of this research, we point to a dilemma that a pastoralist in the Kenyan drylands faces when deciding whether to adopt early livestock destocking.This study underscores the importance to determine the value of early actions for forecast users with different action characteristics, and to disseminate this value alongside the standard forecasts themselves. This allows users to trigger effective actions before drought impacts develop.

Population Projections for Russia

The subject of the work is population forecasting against the background of changes in the demographic situation in Russia. The topic is an overview of the country’s population projections. Special attention is paid to the forecasts of the size and composition of the population of the Russian Federation in the period from 1991 to 2022. The source data are statistical collections and databases of Rosstat and the Population Division of the UN Department of Economic and Social Affairs. The purpose and objectives of the work are to overcome the attitude to previously developed forecasts as to the material that “does not matter” in comparison with current forecasts. The retrospective of forecasts not only provides grounds for reasoning about the demographic losses of the country in certain historical periods, but also allows us to anticipate possible prospects for the development of the population and to improve the conceptual basis of its forecasting. Verification of officially published forecasts, comparison of prognostic developments from different sources, allows us to develop their methodology and clarify the requirements for them on the part of users– government agencies, science, the expert community, the education system. The paper describes the process of compiling a “library” of predictive sources, suggests tabular and graphical methods of its formalization and subsequent analysis. The article provides conclusions that may be useful for developers and users of demographic forecasts.

Forecast Families: A New Method to Systematically Evaluate the Benefits of Improving the Skill of an Existing Forecast

A growing number of studies have investigated how forecast skill, i.e., predictive power, translates into forecast value, i.e., usefulness, for improving forecast-informed decisions. The relationship between skill and value is widely understood to be complex and case-specific, yet few methods enable its systematic exploration using realistic forecast errors. This paper addresses this gap by proposing a single-parameter linear scaling method to generate families of synthetic forecasts with the desired skill improvements on an existing hindcast (a retrospective forecast of already-observed events). The method is applicable to any quantity for which a deterministic or ensemble hindcast is available, and generates a set of forecasts with different skill but strictly proportional errors. This like-for-like comparison preserves the autocorrelation and cross-correlations of errors, and opens the door for thorough, yet easily interpretable, explorations of the relationship between skill and value of a realistic forecast. We apply this new method to seasonal precipitation hindcasts (produced by the fifth generation of the Seasonal forecasting System of the European Centre for Medium-range Weather Forecasts, ECMWF-SEAS5) in order to explore their value for improving the management of a water supply system in the UK. The application showed that although value generally increases with skill, the skill–value relationship is not necessarily linear, and it strongly depends on operational preferences and hydrological conditions (wet or dry years). It also suggests that the forecast families methodology can help water managers and forecast developers identify when a skill increase would be most valuable. This has the potential to foster productive two-way conversations between forecast producers and users.

Application of Machine Learning Algorithms in Online Marketing

Abstract: AI has been able to advance beyond simply carrying out the duties that were put into it thanks to machine learning algorithms. Prior to ML becoming widely used, AI applications were exclusively employed in corporate and enterprise settings to automate simple operations. This covered activities like simple rule-based classification or clever automation. This meant that the domain for which AI algorithms could be used was limited. With machine learning, however, computers were able to go beyond carrying out their programming and started growing with each repetition. Machine learning is used by marketers to identify trends in website user behavior. This enables them to immediately optimize advertising offers and forecast users' future behavior. This paper highlights the application of machine learning in online marketing.

A tale of two recession-derivative indicators.

Two recession-derivative indicators (RDIs) have been used extensively as forecast objects in business cycle prediction, viz. (1) the target variable takes value 1 if there is a recession starting exactly at a specific horizon in the future, and (2) the target variable takes value 1 if there is a recession starting any time over a specified period in the future. Using daily yield spread as an illustrative predictor, we formally and quantitatively compare the two RDIs using the receiver operating characteristics analysis. Over 1962-2021 covering eight NBER recessions, we find that generally the second RDI, ceteris paribus, will make the the predictor better performing. However, the first RDI can generate better-looking and more useful predictions under certain scenarios, depending on forecast horizon, recession duration and time profile of signals. We also consider a semiannual chronology proposed by Peláez (J Macroecon 45:384-393, 2015) and find that its performance is in the middle of the other two. Our analysis suggests that the choice of a particular RDI should be dictated by the needs of forecast user in a particular decision making context.

Application of Autoregressive Integrated Moving Average for Modeling and Forecasting of Crude Oil Production

Most of the crude oil production forecasting and modeling studies have focused on the traditional model of decline curve analysis as techniques used for generating forecast values of future oil and gas in place in conventional and unconventional wells. To many production reservoir economists, the importance of forecast estimations is critical because it allows forecast users to have a profound interest in monitoring and improving forecast performance. It also provides clear indications for the directions, strategies, and bottom line of both the National Oil Companies (NOCs) and International Oil Corporations (IOCs). While the traditional DCS model techniques have well-grounded mathematical underlining. This statistical design does necessarily assure that predicated function regardless of the values of the predictive variables. Moreover, the power traditional oil reservoir production forecasting technique is inefficient. This current research attempts to provide appropriate modeling and forecasting techniques for reservoirs utilizing a time series approach. It reveals how the historical oil production data can be used to project future oil reservoir production and how these projections influence future oil reservoir production decisions. Hence, the main objective of this research study to is practically explore the possibility of the autoregressive integrated-moving average model as a feasible function preference for predicting crude oil production. The historical oil reservoir production time series were used to establish respective autoregressive integrated moving average models through the time series technique by Box–Jenkins and the suitable models were designated with four performance criteria: maximum likelihood, standard error, Schwarz Bayesian criterion, and Akaike criterion for seven elected regions oil reservoir production and the established models conformed to the ARIMA ( p , d , q ). Once the process is identified, the adequacy of the forecasts will be determined and compared with the traditional decline curve analysis. Thus, as an accurate and effective oil production prediction for stretching a reservoir life cycle and enhancing reservoir productivity and recovery factors. These results provide production economists and reservoir engineers with quick, reliable, consistent, and real-time guidelines in budgeting, planning, and making decisions regarding field development. Finally, ARIMA forecasting models are more precise and deliver operational efficiency for dynamic forecasting of oil reservoir production. Keywords: Oil and gas production forecasting, decline curve analysis, time series, ARIMA modeling, DOI: 10.7176/JETP/13-1-05 Publication date: February 28 th 2023

Improved sub-seasonal forecasts to support preparedness action for meningitis outbreak in Africa

West African countries are hit annually by meningitis outbreaks which occur during the dry season and are linked to atmospheric variability. This paper describes an innovative co-production process between the African Centre of Meteorological Applications for Development (ACMAD; forecast producer) and the World Health Organisation Regional Office for Africa (WHO AFRO; forecast user) to support awareness, preparedness and response actions for meningitis outbreaks. Using sub-seasonal to seasonal (S2S) forecasts, this co-production enables ACMAD and WHO AFRO to build initiative that increases the production of useful climate services in the health sector. Temperature and relative humidity forecasts are combined with dust forecasts to operationalize a meningitis early warning system (MEWS) across the African meningitis belt with a two-week lead time. To prevent and control meningitis, the MEWS is produced from week 1 to 26 of the year. This study demonstrates that S2S forecasts have good skill at predicting dry and warm atmospheric conditions precede meningitis outbreaks. Vigilance levels objectively defined within the MEWS are consistent with reported cases of meningitis. Alongside developing a MEWS, the co-production process provided a framework for analysis of climate and environmental risks based on reanalysis data, meningitis burden, and health service assessment, to support the development of a qualitative roadmap of country prioritization for defeating meningitis by 2030 across the WHO African region. The roadmap has enabled the identification of countries most vulnerable to meningitis epidemics, and in the context of climate change, supports plans for preventing, preparing, and responding to meningitis outbreaks.

Mind the gap! A consensus analysis of users and producers on trust in new sea ice information products

In the ice-infested Arctic Ocean environment, the uptake of new sea ice services is an important factor in ensuring safe and efficient marine operations. Producers increasingly turn to co-productionfor user input, similar to the wider field of climate services. This paper asks how the uptake of sea ice information services can be optimized, by gauging the extent to which producers and users alreadyshare an understanding of how trust develops towardnew products. By adopting a consensus analysis approach, we gain insights about how to balance further investments in knowledge co-production versus change implementation. We chose cultural consensus analysis, a method that produces valid estimates even in small sample sizes. Our survey presented thirty-two propositions based on seven dimensions of trust in weather, water, ice and climate services. The survey was completed by fifty-sevenrespondents (n = 29 users, n = 28 producers) and revealed a strong consensus model among the two groups about the necessary improvements needed to increase users’ trust in new services. Our results suggest that forecast producers for the Arctic region, specifically in the field of specialized sea ice predictions and mapping/charting, share a substantial understanding with users about how trust develops toward new products. We discuss the importance of automation, peer endorsement and perceptions of cost-performance ratio for necessary strategic approaches to help experienced forecast users to trust and adapt products to their specific operational context, and reflect on the costs associated with the use of specialized sea ice services in closing the usability gap.