Opportunistic precipitation sensing network
|Project ID:||COST CA20136|
|Principal investigator:||Milan Onderka|
|Investigators from institution:|
Dr. Vojtěch BAREŠ (ČR) – koordinátor
Despite advances in remote sensing, precipitation observations remain one of the weakest links in the description of Earth’s water cycle. This is especially critical in the face of climate change, human-induced hydrologic changes e.g. due to rapid urbanisation, and consequent increase in frequency and magnitude of extreme events. Opportunistic sensing can greatly improve spatial and temporal resolution of standard precipitation monitoring networks on continental scale by complementing them with measurements from personal weather stations or devices primarily not intended for precipitation monitoring such as commercial microwave links or broadband satellite terminals. The number of opportunistic sensors has already now exceeded traditional in-situ observations by an order of magnitude, and it is increasing exponentially. Nevertheless, it is still unclear how to make this data operationally accessible, achieve robust quality control of these observations, and integrate them into standard observation systems.OPENSENSE brings together scientists investigating different opportunistic sensors, experts from national weather services, owners of sensor networks, and end-users of rainfall products to build a worldwide reference opportunistic sensing community. It will i) overcome key barriers preventing data exchange and acceptance as hydrometeorological observations ii) define standards to allow for large-scale benchmarking of OS precipitation products developing new methods for precipitation retrieval iii) coordinate integration of the opportunistic observations into traditional monitoring networks, and iv) identify potential new sources of precipitation observations. These coordinated activities will boost uptake of OS as precipitation observation methods and enable generation of high-quality precipitation products with unprecedented spatial and temporal resolution.