International Conference on Opportunistic Sensing of Precipitation - OpenSense

Session

Oral session #3

Jun 26, 2025, 10:15 AM

Conveners

Oral session #3: OS data merging

• Jonas Olsson (Swedish Meteorological and Hydrological Institute)

37. Disaggregating path-averaged rain rate estimates from commercial microwave links with a multiplicative random cascade model

Martin Fencl (Czech Technical University in Prague)

6/26/25, 10:15 AM

OS data merging

Oral

Commercial microwave links (CMLs) measure total attenuation along their path. Thus when used as opportunistic sensors, they provide path-averaged rainfall estimates. This poses a challenge for rainfall map reconstruction and potentially for rainfall estimation itself, as the conversion of attenuation to rain rate implicitly assumes uniform rainfall along the CML path.
We propose a new...

51. Temporal Super-Resolution, Ground Adjustment and Advection Correction of Radar Rainfall using 3D-Convolutional Neural Networks

julius polz (KIT/IMK-IFU)

6/26/25, 10:30 AM

OS data merging

Oral

Ground-adjustment of weather-radar derived precipitation information is a common practice to correct for a variety of errors related to for example advection, size sorting or melting processes. Historically, this is mainly achieved by using point-like rain gauge observations which have a high temporal resolution and accuracy, but lack spatial representativeness and observation density, e.g....

3. Weather radar adjustment with commercial microwave links at DWD

Maximilian Graf

6/26/25, 10:45 AM

OS data merging

Oral

Weather radars provide high-resolution precipitation data but are subject to uncertainties due to their indirect measurement high above ground. To improve data quality, national meteorological services calibrate radar observations using ground-based station measurements for both operational and climatological applications. The emergence of opportunistic sensors (OS), data sources not...

49. Merging weather radar fields with data from commercial microwave links using mergeplg

Erlend Øydvin (NMBU)

6/26/25, 11:00 AM

OS data merging

Oral

Quantitative precipitation estimates are important for monitoring the water balance. Consequently, there exists a wide range of rainfall measurement methods. Weather radar has good spatial coverage, but the estimates can be biased. Ground observations, like rain gauges and commercial microwave links (CMLs), provide more accurate estimates, but have less good spatial coverage. Adjusting the...

8. The EURADCLIM gauge-adjusted radar precipitation dataset

Dr Aart Overeem (Royal Netherlands Meteorological Institute)

6/26/25, 11:15 AM

OS data merging

Oral

EURADCLIM is a publicly available climatological dataset of 1-h and 24-h precipitation accumulations covering Europe at a 2-km grid over the period 2013 – 2022. It is based on the surface rain rate composites from the EUMETNET programme OPERA. Algorithms are applied to remove remaining non-meteorological echoes as much as possible. The 1-h accumulations are merged with rain gauge accumulations...