Crowdsourcing of weather observations at national meteorological and hydrological services in Europe

National Meteorological and Hydrological Services (NMHSs) increase their efforts to deliver impact-based weather forecasts and warnings. At the same time, a desired increase in cost-efficiency prompts these services to automatize their weather station networks and to reduce the number of human observers, which leads to a lack of ground truth information about weather phenomena and their impact. A possible alternative is to encourage the general public to submit weather observations, which may include crucial information especially in high-impact situations. We wish to provide an overview of the state and properties of existing collaborations between NMHSs and voluntary weather observers or storm spotters across Europe. For that purpose, we performed a survey among 30 European NMHSs, from which 22 NMHSs returned our questionnaire. This study summarizes the most important findings and evaluates the use of crowdsourced information. 86 % of the surveyed NMHSs utilize information provided by the general public, 50 % have established official collaborations with spotter groups, and 18 % have formalized them. The observations are most commonly used for a real-time improvement of severe weather warnings, their verification, and an establishment of a climatology of severe weather events. The importance of these volunteered weather and impact observations has strongly risen over the past decade. We expect that this trend will continue and that storm spotters will become an essential part in severe weather warning, like they have been for decades in the United States of America. A rising number of incoming reports implies that quality management will become an increasing issue, and we finally discuss an idea how to handle this challenge

Trusted Spotter Network Austria – a new standard to utilize crowdsourced weather and impact observations

Information from voluntary storm spotters has been an increasingly important part for the severe weather warning process at the Zentralanstalt für Meteorologie and Geodynamik (ZAMG), Austria's National Weather Service, for almost 15 years. In 2010 a collaboration was formalized and an annual training was established to educate voluntary observers into Trusted Spotters. The return of this investment is a higher credibility of their observations after these spotters have undergone a basic meteorological training and have become aware of their responsibility. The European Severe Storms Laboratory (ESSL) was included to this collaboration to adopt their successful quality control system of severe weather reports, which is employed in the European Severe Weather Database ESWD. That way, reports from Trusted Spotters automatically obtain a higher quality flag, which enables a faster processing by forecasters on duty for severe weather warnings, when time is a critical issue. The concept of combining training for voluntary storm spotters and a thorough quality management was recognized as a Best Practice Model by the European Meteorological Society. We propose to apply this concept also in other European countries and present its advancement into an even broader, pan-European approach. The European Weather Observer app EWOB, recently released by ESSL, provides a novel and easy-to-handle tool to submit weather and respective impact observations. We promote its use to provide better data and information for a further real-time improvement of severe weather warnings

Cost733cat: a database of weather and circulation type classifications

A new database of weather and circulation type catalogs is presented comprising 17 automated classification methods and five subjective classifications. It was compiled within COST Action 733 "Harmonisation and Applications of Weather Type Classifications for European regions" in order to evaluate different methods for weather and circulation type classification. This paper gives a technical description of the included methods using a new conceptual categorization for classification methods reflecting the strategy for the definition of types. Methods using predefined types include manual and threshold based classifications while methods producing types derived from the input data include those based on eigenvector techniques, leader algorithms and optimization algorithms. In order to allow direct comparisons between the methods, the circulation input data and the methods' configuration were harmonized for producing a subset of standard catalogs of the automated methods. The harmonization includes the data source, the climatic parameters used, the classification period as well as the spatial domain and the number of types. Frequency based characteristics of the resulting catalogs are presented, including variation of class sizes, persistence, seasonal and inter-annual variability as well as trends of the annual frequency time series. The methodological concept of the classifications is partly reflected by these properties of the resulting catalogs. It is shown that the types of subjective classifications compared to automated methods show higher persistence, inter-annual variation and long-term trends. Among the automated classifications optimization methods show a tendency for longer persistence and higher seasonal variation. However, it is also concluded that the distance metric used and the data preprocessing play at least an equally important role for the properties of the resulting classification compared to the algorithm used for type definition and assignment