The Community Collaborative Rain, Hail, and Snow Network: Informal Education for Scientists and Citizens

Abstract

he Community Collaborative Rain, Hail, and Snow Network (CoCoRaHS) originated in the aft ermath of a fl ash-fl ood storm that dropped more than 12-in. of rain over a small portion of Fort Collins, Colorado, on 28 July 1997, and a similar storm the following evening over the grasslands of northeastern Colorado. Th ese fl oods were responsible for several fatalities and at least $200 million in property damage. Neither event would have been accurately recorded by existing networks of offi cial weather stations. National Weather Service (NWS) radar also failed to assess the severity of these storms (Kelsch 1998a,b; Petersen et al. 1999). However, when the citizens of these areas were asked to help, they enthusiastically provided scientists with a wealth of information from their own backyard observations. Th ese data eventually resulted in the very accurate mapping of precipitation from the Fort Collins fl ash fl ood that continues to be used by engineers, hydrologists, weather forecasters, city planners, emergency managers, attorneys, teachers, historians, and many others for such applications as flood forecasting, drought monitoring, verifi cation of radar-estimated precipitation, and climate trends. Encouraged by the enthusiastic public response, the Colorado Climate Center at Colorado State Uni

Dual-polarized quantitative precipitation estimation as a function of range

“…Furthermore, results demonstrate the issues with analyzing QPE from a single gauge, explaining why the Community Collaborative Rain, Hail, and Snow Network (Kelsch, 1998;Cifelli et al, 2005;Reges et al, 2016) or other densely gauged networks (e.g., the Hydrometeorological Automated Data System, HADS; and the Meteorological Assimilation Data Ingest System, MADIS) tend to be more utilized since results have shown that measurements or qualitycontrolled techniques made by these organizations, especially CoCoRaHS (Community Collaborative Rain, Hail and Snow Network), are significantly more accurate than rain gauges (Simpson et al, 2017), especially for convective events (Moon et al, 2009). …”

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

“…For instance, in the project CrowdHydrology (Lowry and Fienen, 2013), a method to monitor stream stage at designated gauging staffs using crowd sourced text messages of water levels is developed using untrained observers. Cifelli et al (2005) described a community-based network of volunteers (CoCoRaHS), engaged in collecting precipitation measurements of rain, hail and snow. ISPUW, 2015).…”

ICT approaches to integrating institutional and non-institutional data services for better understanding of hydro-meteorological phenomena

“…), along with new software engineering paradigms like SOA (Service-Oriented Architecture), gave impulse to the development of a huge amount of applications (services) delivered through and accessed via the Web. The use of Web technologies to explore geo-scientific data is now well-established (Amagasa et al, 2007) as well as it is often more frequent to find HM datasets, originated by public or private sources freely exposed, via Web services Application Programming Interfaces (APIs) (Foster et al, Cifelli et al, 2005). What often lacks is the ability to manipulate, aggregate and re-arrange this heterogeneous information in some flexible way according to continuously changing HM scientists' need.…”

“…Such official observational data are not freely accessible: data utilisation requires an authorised access. The number of the official stations and sensors continues to grow, but despite of this fact, often also such dense network cannot provide sufficient coverage of an area affected by extreme hydrometeorological event, which can be very intense and highly localised in space and time, as it is observed for the Mediterranean storms (Ferraris et al, 2002). Therefore, additional sources of reliable, proven near-real-time weather observational data are often desired especially in highly-impacting hydro-meteorological scenarios: such supplementary source can be represented by non-institutional open-use networks.…”