Exploring the use of underground gravity monitoring to evaluate radar estimates of heavy rainfall

Abstract

Abstract. The radar-based estimation of intense precipitation produced by convective storms is a challenging task and the verification through comparison with gauges is questionable due to the very high spatial variability of such types of precipitation. In this study, we explore the potential benefit of using a superconducting gravimeter as a new source of in situ observations for the evaluation of radar-based precipitation estimates. The superconducting gravimeter used in this study is installed in Membach (BE), 48 m underneath the surface, at 85 km distance from a C-band weather radar located in Wideumont (BE). The 15-year observation record 2003–2017 is available for both gravimeter and radar with 1 and 5 min time steps, respectively. Water mass increase at ground due to precipitation results in a decrease in underground measured gravity. The gravimeter integrates soil water in a radius of about 400 m around the instrument. This allows capture of rainfall at a larger spatial scale than traditional rain gauges. The precision of the gravimeter is a few tenths of nm s−2, 1 nm s−2 corresponding to 2.6 mm of water. The comparison of reflectivity and gravity time series shows that short-duration intense rainfall events produce a rapid decrease in the underground measured gravity. A remarkable correspondence between radar and gravimeter time series is found. The precipitation amounts derived from gravity measurements and from radar observations are further compared for 505 rainfall events. A correlation coefficient of 0.58, a mean bias (radar–gravimeter)/gravimeter of 0.24 and a mean absolute difference (MAD) of 3.19 mm are obtained. A better agreement is reached when applying a hail correction by truncating reflectivity values to a given threshold. No bias, a correlation coefficient of 0.64 and a MAD of 2.3 mm are reached using a 48 dBZ threshold. The added value of underground gravity measurements as a verification dataset is discussed. The two main benefits are the spatial scale at which precipitation is captured and the interesting property that gravity measurements are directly influenced by water mass at ground no matter the type of precipitation: hail or rain.

Technical note: Introduction of a superconducting gravimeter as novel hydrological sensor for the Alpine research catchment Zugspitze

“…4.2) results in a gravity increase of 0.9 nm s −2 . This shows that the gravity variations can be used as reference for the estimation of the total sum of precipitation (Delobbe et al, 2019) in this alpine terrain with large variability in precipitation instead of using point measurements with precipitation collectors. The higher precipitation admittance factor (factor of 3 compared to 0.298 nm s −2 mm −1 for the SWE) results from the large geographical heterogeneity of the SWE in the RCZ.…”

Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards 4D hydrogeology

“…Time-lapse gravimetry has been used to identify and constrain subsurface water storage changes, e.g. in artificial recharge facilities (Kennedy et al, 2016), to separate precipitation and groundwater mass signals (Delobbe et al, 2019), to locate karst storage dynamics (Pivetta et al, 2021) and identify evapotranspiration patterns (Carrière et al, 2021). Further data acquisition procedures and treatments that enhance sensitivity to local processes (e.g., gravity gradients and hydrological modelling coupled with gravity measurements (Cooke et al, 2020, table 1.C.5) are needed to provide more quantitative interpretations.…”

Exploring the Relationships of Atmospheric Water Vapor Contents and Different Land Surfaces in a Complex Terrain Area by Using Doppler Radar

“…The land surface data used in this study include a digital elevation model (DEM), spatial vector data of different land use types, and red and near-infrared image data from The radar reflectivity values were converted into rain rates using the Marshall-Palmer (MP) formula [27], Z = 200 R 1.6 , and the reflectivity (Z) data are converted into instantaneous rain rates (R) [28]. Radar data were transformed into water vapor content parameters.…”