Abstract: KETALE is a database and web application intended to improve the collaborative decision support of the Finnish Radiation and Nuclear Safety Authority (STUK) and of the Finnish Meteorological Institute (FMI). It integrates distributed modeling (weather forecasts and dispersion predictions by FMI, source term and dose assessments by STUK) and facilitates collaboration and sharing of information. It does so by providing functionalities for data acquisition, data management, data visualization, and data analysis. The report outlines the software development from requirement analysis to system design and implementation. Operational aspects and user experiences are presented in a separate report.

Abstract: Crowd sensing data (also known as crowdsourcing) are of great significance to support flood risk management. With the growing volume of available data in the past few years, researchers have used in situ sensor data to filter and prioritize volunteers' information. Nevertheless, stationary, in situ sensors are only capable of monitoring a limited region, and this could hamper proper decision-making. This study investigates the use of weather radar precipitation to support the processing of crowd sensing data with the goal of improving situation awareness in a disaster and early warnings (e.g., floods). Results from a case study carried out in the city of São Paulo, Brazil, demonstrate that weather radar data are able to validate flooded areas identified from clusters of crowd sensing data. In this manner, crowd sensing and weather radar data together can not only help engage citizens, but also generate high-quality data at finer spatial and temporal resolutions to improve the decision-making related to weather-related disaster events.

Abstract: Accurate predictions of severe weather events are extremely important for society, economy, and environment. Due to the fact that weather forecasts are inherently uncertain, it is required to give information about forecast uncertainty to all users providing weather forecasts in probabilistic terms utilizing ensemble forecasts. Since ensemble forecasts tend to be under dispersive and biased, they need to be calibrated with statistical methods. This paper presents a method for the calibration of temperature forecasts using Gaussian regression, and the calibration of wind gust forecasts with a box-cox t-distribution method. Statistical calibration was made for the operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (ENS) forecasts for lead times from 3 to 360 hours. The verification results showed that calibration improved both temperature and wind gust ensemble forecasts. The probabilistic temperature forecasts were better after calibration over whole lead time scale, but the probabilistic wind gust forecasts up to 240 hours.

Abstract: Emergency management organisations across the world routinely use social media to reach out populations for preparedness and response to extreme weather events. In this paper we present a preliminary analysis of social media strategies towards vulnerable populations in the State of Victoria (Australia). Using the notion of vulnerability in an emergency management context (e.g. older persons, socially/geographically isolated persons, people with disabilities, refugee/recent migrant communities) we explore whether and how organisations address vulnerable groups with targeted messages. Our initial findings suggest that organisations do not tend to interact directly with these groups. Rather, reliance on 'information brokers' (intermediary organisations and individuals with an expected duty of care) seems to be a preferred strategy.

Abstract: In this paper we leverage the power of citizen supplied data. We examined how both physical weather sensor data (obtained from the weather underground API) and social media data (obtained from Twitter) can serve to improve local community awareness during a severe weather event. A local tornado warning was selected due to its small scale and isolated geographic area, and only Twitter data found from within this geo-locational area was used. Our results indicate that during a severe weather event, an increase in weather activity obtained from the local weather sensors does correlate with an increase in local social media usage. The data found on social media also contains additional information from, and about the community of interest during the event. While this study focuses on a small scale event, it provides the groundwork for use during a much larger weather event.

Abstract: This paper discusses the impact of different dimensions of risk perception on people?s decision to take protective measures against natural hazards. Initial basis of the analysis was the winter storm XAVER which affected huge parts of Northern Europe including Berlin, Germany on 5 December 2013. Preliminary results of a representative online survey within the Berlin population show that affective variables such as fear of severe weather and confidence in weather forecasts showed a significant effect on people?s decision to take protective action. Contrary, high experience of natural hazards did not necessarily lead to action.

Abstract: This paper explores opportunities for information systems to support emergency response to multiple natural hazards. Interviews were conducted with 12 representatives from actors of the Swedish emergency response system about response to multiple natural hazards. Challenges and needs connected to five themes influencing the response effort were identified: Cooperation, Resource management, Command and control, Common operational picture, and Risk management. The results illuminate a lack of technology to support decisions and analyses during emergency response to both single and multiple natural hazards. Based on this, the paper suggests and discusses information systems and decision support tools to assist in satisfying the identified needs. The findings can inform policy makers in emergency response of where to concentrate the development of collaborative preparedness and response work, and the scientific community of future research directions.

Abstract: In the Netherlands, Royal Dutch Meteorological Institute warns water boards for extreme rainfall if per-specified thresholds are (expected to be) exceeded. When a water board receives a warning, certain response measures can be taken. In general, the thresholds are based on experience and intuition. Clear procedures, which describe decision-making under uncertainty in available information (e.g., forecasted rainfall), do not exist. In this document, first results of the project “Extreme weather for water boards” are briefly described. The aim of this project is to study integration of the uncertainty into emergency procedures of the water boards. The current emergency procedures of two water boards are analyzed. Recommended adjustments to the procedures allow including the uncertainty by estimation of a probability of overload and cost-benefit analysis of response measures (benefit as avoided damage). A simple scheme that supports estimation of the probability is introduced. © 2012 ISCRAM.