Enhancing Precipitation Estimates using Opportunistically Sensed Data

Accurate quantitative precipitation estimates remain a significant challenge for any hydrological application. National rain gauge networks are known to be too sparse to capture precipitation accurately due to their high spatial and temporal variability. To address this limitation, there is growing interest in incorporating opportunistically sensed (OS) precipitation data, such as crowd-sourced observations from private weather stations (PWS). The potential of these data has been shown, and the number of available PWS has increased significantly in recent years. In Germany, for example, the number of PWS from a single provider already exceeds that of national rain gauge stations by a factor of 10 to 15. For efficient integration and application of such data, thorough quality control (QC) is essential, because these stations are neither professionally placed nor maintained.

Within the EU COST Action CA20136 “OpenSense”, substantial effort has been made to promote the uptake of OS precipitation observation methods, e.g., by developing open-source tools for processing of OS data. Additionally, data standards were defined, and several studies have been published showing the potential of OS data.

Building on this previous work, this project aims to develop methods that provide reliable, operational precipitation observations from OS data, and to facilitate their use in precipitation nowcasting and operational hydrological forecasting. The project involves the following three objectives:

1. Benchmarking of Quality Control
   Existing QC algorithms for OS precipitation data will be systematically benchmarked to assess their effectiveness. Based on this evaluation, an efficient QC pipeline will be developed to enable fast and user-friendly application in operational settings.
   
   
2. Added Value on Spatial Interpolation
   The additional value of quality-controlled OS data for spatial precipitation interpolation will be investigated using conditional simulation techniques, with a focus on improving spatial rainfall fields and a better representation of extreme events.
   
   
3. Hydrological Impact Assessment
   Finally, the impact of incorporating OS data as a high-quality precipitation product in real-time hydrological modeling or operational forecasting systems will be evaluated.