Short online conferences (max. 1 hour; every two months) should bring the OpenSense community the idea of the newest opportunistic sensor data achievements concerning opening data, data processing algorithms, and applications. During the conference, two speakers will be invited (20 min talk + 10 min discussion).
 
The third conference will take place on 2 April 2024, 9:00 CET.
 
Tess O’Hara will present on Overview and comparison of three quality control algorithms for rainfall data from personal weather stations. 
The presentation will introduce three algorithms designed for the quality control of low-cost sensor rain data, with examples from the Netatmo rain gauge network in Amsterdam. The algorithms have unique approaches for improving data quality (a Technical Note describing them is currently under review https://hess.copernicus.org/preprints/hess-2023-195/). The presentation will provide an overview into the algorithms in support of anyone wanting to try them as they are available in the OpenSense Sandbox and exploration is encouraged. 
 
Jochen Seidel will present on Using personal weather station data for improving precipitation estimates and gauge adjustment of radar data.
In the area of the water boards Emschergenossenschaft and Lippeverband (EGLV) in Northrhine-Westfalia (Germany), investigations were carried out to determine whether data from private weather stations (PWS) can improve the interpolation of rainfall fields and if PWS can be used for the gauge-based adjustment of radar data. The results show that the additional information from PWS can capture the spatial structures of precipitation better than a standard measurement network alone. Individual quality controlled PWS can be used for the gauge adjustment of radar data, there are however issues due to data gaps and the underestimation of hourly precipitation maxima. This requires further research in the quality control, especially with respect to filling data gaps and improving the bias correction.
 

Join Zoom Meeting
https://tuwien.zoom.us/j/64842384214

Meeting ID: 648 4238 4214
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