Three Ways Forward to Improve Regional Information for Extreme Events: An Early Career Perspective

This paper provides an early career researchers (ECRs) perspective on major challenges and opportunities that arise in the study and understanding of, and the provision of regional information for Climate, Weather and Hydrological (CWH) extreme events. This perspective emerged from the discussions of the early career 3-day Young Earth System Scientists - Young Hydrologic Society (YESS-YHS) workshop, which was conjointly held with the Global Energy and Water Exchanges (GEWEX) Open Science Conference. In this paper we discuss three possible ways forward in the field: a stronger interaction between Earth system scientists and users, a collaborative modeling approach between the different modeling communities, and an increased use of unconventional data sources in scientific studies. This paper also demonstrates the important role of ECRs in embracing the above outlined pathways and addressing the long-standing challenges in the field. YESS and YHS networks encourage the global community to support and strengthen their involvement with ECR communities to advance the field of interdisciplinary Earth system science in the upcoming years to decades

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state‐of‐the‐art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water and natural hazard management are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined

Crowdsourcing User-Generated Mobile Sensor Weather Data for Densifying Static Geosensor Networks

Static geosensor networks are comprised of stations with sensor devices providing data relevant for monitoring environmental phenomena in their geographic perimeter. Although early warning systems for disaster management rely on data retrieved from these networks, some limitations exist, largely in terms of insufficient coverage and low density. Crowdsourcing user-generated data is emerging as a working methodology for retrieving real-time data in disaster situations, reducing the aforementioned limitations, and augmenting with real-time data generated voluntarily by nearby citizens. This paper explores the use of crowdsourced user-generated sensor weather data from mobile devices for the creation of a unified and densified geosensor network. Different scenario experiments are adapted, in which weather data are collected using smartphone sensors, integrated with the development of a stabilization algorithm, for determining the user-generated weather data reliability and usability. Showcasing this methodology on a large data volume, a spatiotemporal algorithm was developed for filtering on-line user-generated weather data retrieved from WeatherSignal, and used for simulation and assessment of densifying the static geosensor weather network of Israel. Geostatistical results obtained proved that, although user-generated weather data show small discrepancies when compared to authoritative data, with considerations they can be used alongside authoritative data, producing a densified and augmented weather map that is detailed and continuous