Crowd sourcing rainfall data: A new twist on cloud computing
Crowd sourced data is a topic we have covered a number of times on Spatial Reserves, from recording environmental data to providing geographic base data for areas affected by natural disasters and other emergency situations. Attention turns this week to the notoriously difficult task of accurately predicting the weather. While recent advances in forecasting have improved the reliability of many 5-day weather reports, predicting more extreme weather events such as flooding and longer terms weather patterns remains a complex and challenging task.
One possible additional source of data to help provide on-the-spot updates to support real-time monitoring of meteorological phenomena is crowd sourced weather reporting. While there are an increasing number of mobile apps available that allow people to post updates on current local weather conditions, such as Weddar and Wezzoo, Rolf Hut, a scientist from the Delft University of Technology, has proposed a novel solution for the problem of collecting rainfall data – the humble umbrella.
In a recent report by the BBC, Hut argues that the information collected by smart umbrellas could help offset the rainfall data deficit that has resulted from the declining numbers of maintained weather gauge stations. With an in-built sensor (an acoustic rain gauge) connected to a mobile phone via Bluetooth, once the umbrella was opened it would start to transmit real-time rainfall and location data. There’s a rather cyclical dimension to the whole process: cloud > rain > umbrella >sensor> data > phone > cloud.
Although still at the prototype stage, the early results are promising and the data could potentially be used to augment the data collected by existing rainfall radar and satellite measurement systems. However, as with most crowd sourced data initiatives, simply having access to more data doesn’t necessarily improve the situation, and in some cases can even hinder the analysis. The quality of the data has to assured for that data to add value to the process.