Bibliometric Mapping for Geospatial Intelligence in Remote Sensing-Based Maritime Weather Warning Systems

Croatian Meteorological and Hydrological Service (DHMZ) is responsible for weather warnings in Croatia and issued it on official webpage meteo.hr, as well as on meteoalarm.org. Information about weather warnings are communicated with Civil Protection Directorate (CPD) using standard operation procedure on everyday basis. Additionally, DHMZ uses social networks and media for distribution of the weather warnings to the public. But, in 2023 a huge step was done in alerting the public. Ministry of Internal Affairs of the Republic of Croatia formed the procedure for CB/SMS warning system – Early Warning and Crises Management System (SRUUK). Although system was in the final phase in 2023, severe derecho storm in July that hit continental part of Croatia and cause huge damage, unfortunately, also four fatalities, was the big additional motivation for the earliest possible operational application, and it started in August 2023.DHMZ has a crucial role in decision to alert the public via CB/SMS message when the severe weather phenomena tend to develop into a major accident or disaster, while CPD coordinates the actions of all collaborators included in SRUUK. Since 2023 for few possible severe thunderstorm episodes the SRUUK was activated. In two cases forecasted extremely severe weather phenomena did not occur, although the environment was very conductive for severe organized convective thunderstorm. This reflects key issues in alerting the public via CB/SMS messages for severe thunderstorm. These are: definition of criteria in what kind of convective favorable environment the message should be sent, what information the message contain, when the message will be sent – for very likely forecast event (even more than 12 hours in advance) or for the severe thunderstorm that is already developed. It is necessary to have in mind that under certain conditions, such a warning can cause additional panic, further endangering human lives. Because of all of the above, extensive preparation is necessary, especially in cases of thunderstorms which are one of the most challenging to forecast, in order to maintain or build a high level of trust within the public.

SummaryThis study examined the relationship between hip fractures and weather warnings with the hypothesis higher rates of fractures occur during warnings. National hip fracture database and weather warning records were utilised. Higher rates of hip fractures were found with severe warnings. This has implications for planning in future severe warnings.BackgroundHip fractures represent a significant burden on the Irish Health system with 3666 in 2020. The Irish National Meteorological Service operates a colour coded warning system. Yellow being least severe, while orange represents weather with capacity to impact individuals in affected areas and red represents advice to protect themselves and property. Previous studies investigated the seasonality of hip fractures, which remains but none have investigated the relationship between weather warnings and rates of hip fractures. The hypothesis was that increasing weather warnings would be associated with increased hip fractures. The aim was to investigate the relationship between weather warnings and hip fractures in the Republic of Ireland.MethodsComparison of national weather warning archives from 2013 to 2019 to Fracture Database records. Reviews assessed whether fractures occurred on days a weather warning was in place in the individual’s local county. A statistical analysis of warning features and their relationship to hip fractures was then performed. Fractures and weather warnings were stratified by county with both a panel and case crossover analysis performed.ResultsThere was a tendency towards increased incidence of hip fractures with weather warnings in adjusted analysis (IRR 1.02; 95%CI 0.99–1.06; p-value 0.123). Orange warnings were associated with a statistically higher incidence of fractures (IRR 1.06; 1.01–1.12; p-value 0.026). In both panel and case crossover analysis, both orange and yellow warnings were associated with fractures. Red warnings were associated with a lower incidence of fracture on day of warning (adjusted incidence rate ratio 0.92; 0.70–1.22; p-value 0.569) but a higher incidence on the following day (adjusted incidence rate ratio 1.14; 0.88–1.46; p-value 0.313).ConclusionAn increased incidence of hip fractures appears to occur during weather warnings. Consideration should be given when preparing for periods of extreme weather, ensuring sufficiently resources.

Meteorological Station El Tari Kupang (El Tari Kupang Stamet) has a fundamental duty to implement functionsin the field of Meteorology BMKG in particular to support the safety of air transportation and provide public information such as weather forecasts of NTT province, weather forecasts cruise along the waters of the sea wave heightin NTT and early warning of bad weather. Problem that often encountered is the dissemination of information that is still less effective and efficient. To overcome these problems,a website is designed as a media to deliver information quickly and can be accessed anywhere. In this research,the website is designed and manufactured, where the process begins with the analysis of needs and roles of the users, and then create the design to realize those needs. The next process is to implement it by making an appropriate website design and the final process of this research is testing the system. This website is built using PHP and Mysql languange programes. The output soft his website are in the form of profile information, news, galleries, downloads, employee data, image data, maritime weather information, maritime weather maps, provinces weather information, early warning of bad weather, airline data, guest book and flight information document.

One of the most important tasks of the German Meteorological Service (DWD) is to issue weather warnings. In the current operational warning system, forecasters interpret a multitude of meteorological data with respect to a set of threshold-based warning criteria. Warning regions are then manually identified and sent to an automatic post-processing chain, which generates the final warning products to be distributed in relevant communication channels. Even though DWD’s weather warnings are mostly perceived well by its end-users, there are some drawbacks to the current system. They include a short lead time of warnings, a complex catalog of warning criteria, and missing flexibility towards specialized user requirements. To tackle these shortcomings, DWD launched a program called RainBoW (“Risikobasierte, anwendungsorientierte, indiviualisierbare Bereitstellung optimierter Warninformationen” or “Risk-based, application-oriented and individualizable delivery of optimized weather warnings” in English) to optimize its warning system. RainBoW focusses on three fields of action. First, the forecast horizon of warnings will be extended up to 7 days into the future to inform users early on, while also communicating the uncertainties resulting from larger lead times. Second, the comprehensibility of warnings will be enhanced by reducing the complexity of the warning criteria catalog and by taking weather impacts into account. Third, warnings will be made individualizable. This means, that users with specific requirements, e.g. in terms of warning thresholds and/or considered areas, will get the possibility to configure individual warnings matching their particular use case and their individual meteorological thresholds. These individualized warnings will be generated automatically based on user-created warning profiles.  The three fields of action serve RainBoW’s overarching goal to tailor warnings more strongly towards the needs of end-users, such that they are enabled to take appropriate action in case of significant and extreme weather. This contribution will describe the conceptual ideas behind RainBoW along with some first results.

The enhancement in user communication is one of the key aspects in the RainBoW (Risk-based, Application-oriented and INdividualizaBle delivery of Optimized Weather warnings) programme, which encompasses the development of the new weather warning system for the German Meteorological Service (Deutscher Wetterdienst). Within RainBoW, a probabilistic weather warning system is developed that supplies both information to users with special requirements but also generates standardised warnings for the general public. It addresses a number of weather elements, such as wind, precipitation, and thunderstorm. Hence, a good understanding of the issued weather warnings by the recipients plays a crucial role in order to enable both professional forces but also individuals to take appropriate action in affected regions. In RainBoW, one of the means to achieve a better perception is to move impact information into the focus for future weather warnings provided by the German Meteorological Service. In this contribution, we present a study on hazards of wind gusts, which is among the weather elements that show the largest impacts on infrastructure and society. In order to shed light on the severeness of impacting events, we discuss the generation of an impact proxy for Germany, which we model by evaluating historical wind gust data by means of extreme value statistics. Despite the available several years of detailed reanalysis data (e.g. from the COSMO REA6 dataset), one of the main challenges is the sparsely available extreme weather data. One reason for this lies in the very profound nature of extreme value statistics, but wind gusts are also not very well represented in the model data. This is further complicated by the rather short period of sufficiently detailed and consistent data, which is governed by few more or less local extreme weather situations that occured during roughly two decades. We discuss the potential of this approach and motivate its implementation into the future warning system (RainBoW) of the German Meteorological Service. Moreover, we complement this with user generated crowd sourcing data and discuss its suitability for an impact proxy of wind.

This paper appraises current usage and future weather service needs in Ireland. The data for this study were collected using a household preparedness questionnaire and focus groups with urban communities, rural dwellers, and marine users, farmers, students, and an island community. The questionnaire was used to collect data on weather warnings and preparedness following a category red severe weather warning. Data on participants’ need for weather forecasts and warnings, current and future weather service requirements, and the effectiveness of the National Meteorological and Hydrological Service (NMHS) were collected from the focus groups.Our analysis identifies the importance of accurate weather forecasts to the public and groups such as farmers as they plan their professional and private lives. Participants were aware of the weather warning system's color-coded structure, with the highest-level warnings considered most effective in capturing attention. Most participants spoke negatively about category yellow warnings, as they perceived them to be issued too frequently. Experience of warnings being issued and threats failing to materialize caused a minority of participants to ignore warnings and not take preparedness action. The professionalism of the NMHS was praised by focus group participants, and there was a high level of overall satisfaction with the quality of the national weather warning system (75.1% of survey participants were mostly or extremely satisfied).Opportunities to improve weather services included enhanced communication with service users, improved web and app interfaces, a move to probabilistic forecasting, and weather warnings that encompass calls to action.

<p>The  weather information value chain provides a framework for characterising the production, communication, and use of information by all stakeholders in an end-to-end warning system. Since the generation of weather warning and climate services has become more complex, both technically and organizationally, the  value chain concept has become a popular tool for describing and assessing the production, use and  benefits of such services.</p><p>The end-to-end warning system for high impact weather brings together hazard monitoring, modelling and forecasting, risk assessment, communication and preparedness activities and systems and processes which enable timely action to reduce risks. Weather and associated warning services are typically developed and provided through a multitude of complex and malleable value chains (networks), often established through co-design, co-creation and co-provision.  </p><p>A new international project under the WMO World Weather Research Programme is using value chain approaches to describe and evaluate the end-to-end warning system for high impact weather. Its aims are</p><p>(1) To review value chain approaches used to describe weather, warning and climate services to assess and provide guidance on how they can be best applied in a high impact weather warning context that involves multiple users and partnerships;</p><p>(2) To generate an easily accessible means (an End-to-End Warning Chain Database) for scientists and practitioners involved in researching, designing and evaluating weather-related warning systems to review previous experience of high impact weather events and assess their efficacy using value chain approaches.</p><p>We encourage the research and operational community to participate in this project by contributing case studies of high impact events and collaborating in their analysis. Integration of the physical and social sciences in this project will lead to new insights that we hope will ultimately improve the effectiveness of warnings for high impact weather.</p>

<p>The weather information value chain provides a framework for characterising the production, communication, and use of information by all stakeholders in an end-to-end warning system. Since the generation of weather warning and climate services has become more complex, both technically and organizationally, the value chain concept has become a popular tool for describing and assessing the production, use and benefits of such services.</p><p>The end-to-end warning system for high impact weather brings together hazard monitoring, modelling and forecasting, risk assessment, communication and preparedness activities and systems and processes which support people to take timely action to reduce risks. Weather and associated warning services are typically developed and provided through a multitude of complex and malleable value chains (networks), often established through co-design, co-creation and co-provision.  </p><p>A 4-year international project under the WMO World Weather Research Programme that started in November 2020 is using value chain approaches to describe and evaluate the end-to-end warning system for high impact weather. Its aims are</p><ul><li>To review value chain approaches used to describe weather, warning and climate services to assess and provide guidance on how they can be best applied in a high impact weather warning context that involves multiple users and partnerships;</li> <li>To generate an easily accessible means (an End-to-End Warning Chain Database) for scientists and practitioners involved in researching, designing and evaluating weather-related warning systems to review previous experience of high impact weather events and assess their efficacy using value chain approaches.</li> </ul><p>We encourage the research and operational community to participate in this project by contributing case studies of high impact events and collaborating in their analysis. Integration of the physical and social sciences in this project will lead to new insights that we hope will ultimately improve the effectiveness of warnings for high impact weather.</p>

Severe weather can cause considerable damage to nature and infrastructure and may endanger people. Timely and accurate warnings are crucial to protect the population. Considerable efforts are being taken at MeteoSwiss to improve weather warnings. The evaluation of the quality of weather warnings forms an important part of the ongoing developments. Currently, weather warnings at MeteoSwiss are verified manually by a team of forecasters. This method effectively leverages expert knowledge and has proven successful over the years. However, manual verification also comes with a number of limitations: it is time-consuming, subjective, and may lead to inconsistencies. Additionally, due to resource constraints the granularity of the results and the ability to produce long-term statistics is restricted. All of these limitations hamper the identification of systematic biases and opportunities for improvement. To overcome these limitations, we developed an objective and automated verification system aimed at enhancing the efficiency, consistency, and detail of warning evaluations. This approach provides a formalized framework for verification, reduces human bias, incorporates a broader range of observational data, and supports the generation of a comprehensive set of verification metrics. Moreover, automated verification facilitates retrospective analyses, making it easier to uncover long-term trends, recurring patterns, and potential weaknesses in the warning system. In this presentation, we share results from our ongoing work, including case study verifications and comparisons with traditional manual assessments. We show that the performance of weather warnings at MeteoSwiss has significantly improved over the past decade. This positive trend is in line with advances in weather forecasting capabilities. Furthermore, we illustrate how objective verification with detailed diagnostics can be used to further improve those warnings and minimize adverse effects of severe weather.

From the 21st century, accompanying global industrialization and rapid development of economy, the natural environment is getting steadily worse, factors affecting public security have increased, and unexpected incidents also occur from time to time. Through analyzing road surface temperature data gathered by a mobile road surface condition detecting equipment, and considering the effects of terrains and landforms as well as climate, this study taking Jingshen expressway as representation, studies optimizing methods of road weather sites based on thermal mapping technology. On these bases, a road weather monitoring and warning system was built. The system achieves to monitor adverse weather in time, warn and provide the information service on real time. At the same time, the system is also easy to operate for engineers and managerial personnel, and is playing a positive and exemplary role for highway network alarm emergency service that Ministry of Transport is carrying out.

Expert users of weather warnings often have specific and diverse needs. These needs depend on the type of application, as the relevant warning thresholds for weather conditions and forecast probability can vary significantly.To meet these individual requirements, the German Meteorological Service (Deutscher Wetterdienst, DWD) is developing a new tool called the warning portal (“DWD-Warnportal”). It is a web app that allows users to customize, receive, and visualize probabilistic weather warnings based on their specific needs. In its current version, users can define an area of interest, select weather elements and choose from a set of pre-configured thresholds for these elements. Based on these settings, the occurrence probabilities for each threshold are calculated and presented both spatially on a map and temporally in a bar chart.The warning portal is part of the broader RainBoW program (“Risk-based, Application-oriented and Individualizable Provision of Optimized Warning Information”), which aims to renew the weather warning system with a strong focus on the end users.One key user group of the warning portal includes experts from the hydrological sector. To ensure that the application meets their needs, we are following a co-design approach. This involves close collaboration with flood forecasting centers through joint workshops and meetings to gather feedback and ideas. These insights are integrated into our agile development process and directly shape the design of new features. Features inspired by the requirements of the hydrological sector include warnings based on aggregation measures over individual areas, such as river catchments. This enables rainfall warnings based on spatial averages, maximum, minimum, or specific percentiles, providing a measure for areal rainfall that can serve as a rainfall-based signal for flood risk. We are also working to include extreme value analysis of historical rainfall observations for user-specific catchments. This allows the determination of return periods for forecasted rainfall events, helping to assess and communicate their statistical extremity and thus enabling faster detection of potentially critical events.Here, we will give an overview of the current development of the warning portal, focussing on the relevant features for flood forecasting centers. We welcome feedback from hydrological experts to help us better understand their needs and improve the warning portal.

Verifying the quality of weather warnings is crucial to improving warnings, building trust, and enabling stakeholders to choose the right course of action in the face of a warning. In the context of the renewal of the warning system at Deutscher Wetterdienst (i.e., the RainBoW program: “Risk-based, Application-oriented and Individualizable Provision of Optimized Warning Information”) with the main goal to align weather warnings more strongly with the needs of their users, there is a need for verification approaches which are specifically user-oriented.The recipients of weather warnings, such as emergency response teams, commercial actors, or the general public, however, have different preferences as to which aspects of weather warnings are the most important to them. This could be valuing the correct intensity of an event over its correct timing in a forecast, or the correct location over the correct duration. It could also mean that the trade-off between over- and under-forecasted weather events could vary between stakeholders as some might prioritize minimizing missed events (i.e., increasing the hit rate, POD) while others prefer not having too many false alarms (i.e., reducing the False Alarm Rate, FAR). Incorporating these user-specific requirements provides the potential to build on common verification methods and expanding them, making them more applicable and useful to individual stakeholders.Additionally, communicating the results in a way that recipients understand and benefit from information on forecast quality is an important step in a user-oriented verification approach. This likely requires translating complex statistical information into real-world examples that are comprehensible for non-academic stakeholders as well.Therefore, this work showcases the efforts made at Deutscher Wetterdienst to provide user-oriented weather warning verification byconducting small surveys to find out which aspects of weather warnings are more important to a certain user group, testing verification methods that take these preferences into account when evaluating the accuracy of a warning, developing and testing comprehensible and user-focused ways of communicating the quality of warnings to user-groups.

Decisions with weather warnings when waiting is an option

Decision making with risk-based weather warnings

In October 2021, the Swedish Meteorological and Hydrological Institute launched a novel national system for impact-based weather warnings, moving from the traditional format for meteorological, hydrological, and oceanographic warnings towards an assessment process that includes collaboration and consultation with regional stakeholders on the impacts that certain weather events would have for a specific geographic area and time frame. As part of this new system, local and regional administrative efforts are made to create assessment-support documentation drawing on local knowledge and providing support ahead of and during extreme weather events. We present initial results from the ongoing research project ‘AI4ClimateAdaptation’ (https://liu.se/en/research/ai4climateadaptation), which explores the potential of employing AI-based image and text analysis to support the process and evaluate the precision of impact-based weather warnings. The project collects image and text data appropriate for subsequent use in AI-based analysis from citizen science campaigns and social media. The presentation focuses on the concept of integrating AI-based text and image analysis with the processes of the warning system, as well as the barriers and enablers that are identified by local, regional, and national stakeholders related to the role of AI in weather warning systems. We further discuss to what extent data and knowledge on historical extreme weather events can be integrated with local and regional climate adaptation efforts, and whether these efforts could bridge the divide between long-term adaptation strategies and short-term response measures related to extreme weather events. The results of this study are expected to contribute to the national system for impact-based weather warnings and to increase resilience to extreme climate-related weather events.