Internship - Impact of rain gauge biases on multiscale rainfall analysis

Présentation

Durée : 5 to 6 months

Scientific context
Rainfall and wind are geophysical fields that exhibit an extreme variability across a wide range of spatial and
temporal scales, from a few seconds to a few years in time and a few meters to planetary scale in space. This
variability makes rainfall complex to measure, analyse and model. Quantifying rainfall variability across
scales, which gives access to the corresponding extremes, is much needed for numerous practical challenges
such as design of rainfall management infrastructures, or real time storm water management. Universal
Multifractals are a good candidate to cope with these features. It is a physically based framework that relies on
the underlying concept that rainfall is generated through a multiplicative cascade process, inherited from the
governing Navier-Stokes equations. See Schertzer and Lovejoy (1987) for early developments and Seppe et
al. (2025) for a recent application to comparison of rainfall measurement devices.
The most commonly used rainfall measurement device is tipping bucket rain gauge. It is affected by several
known biases. This first one is discretization, i.e. that it does not measure rainfall continuously but only the
cumulative depth with steps of 0.1 or 0.2 mm. It also suffers some instrumental and wind induced biases.
Rainfall intensity measurements from tipping bucket rain gauges are subject to a systematic underestimation
due to the amount of water that is not measured by either of the two compartments of the bucket during the
tilting movement (see e.g., Cauteruccio et al., 2021a). This bias can be quantified using a dynamic calibration
procedure as described in the European Standard EN 17277:2019 (see CEN, 2019), and the associated
calibration curve can be used to adjust the raw data. Wind is the main environmental factor that causes bias in
precipitation measurements. The aerodynamic behaviour of the gauge geometry, when impacted by wind,
deflects the trajectories of hydrometeors away from the collector (see Cauteruccio et al., 2021b). This results
in a significant reduction in collection performance. When contemporary wind speed and rainfall intensity
measurements are available, numerically derived collection efficiency curves (see e.g., Cauteruccio and
Lanza, 2020) can be used to adjust raw data.

Objectives
The aim of the project is to quantify the impact of those biases on the outcome of multifractal analysis of rain
gauge data.

Tasks
In order to achieve this, the student will first use disdrometer data (Gires et al. 2018, 2022) collected during
various measurement campaigns over about 10 years. Such device provides continuous data at drop scale,
which enables to mimic discrete rain gauge data, and then compare outcome of UM analysis on raw
disdrometer data and corresponding “virtual” rain gauge. In a second step, the student will use a thirty-year
rainfall time series from the DICCA rain gauge station (Genoa, Italy), available at one-minute resolution as
both a raw dataset and after the application of a dynamic calibration curve. For the most recent years, colocated wind measurements are also available and the correction for the wind-induced bias has been applied.
He / she will perform UM analysis on raw series, and the corresponding series corrected for instrumental and /
or wind induced biases. Impact on quantification of extremes will then be assessed.

Wanted background
- Currently in M2 (or MSc) or last year of engineering school in environmental science or physics
- Some basic knowledge in Python

References
- Cauteruccio A., Colli M., Stagnaro M., Lanza L.G. and E. Vuerich (2021a). In situ precipitation measurements, p. 359 –
400. In: Foken T. (ed.), Springer Handbook of Atmospheric Measurements. Springer Nature, Switzerland, ISBN 978-3-
030-52170-7, e-ISBN 978-3-030-52171-4, pp. 1740. https://doi.org/10.1007/978-3-030-52171-4_12
- Cauteruccio, A., Brambilla, E., Stagnaro, M., Lanza, L.G. and D. Rocchi (2021b). Experimental evidence of the windinduced bias of precipitation gauges using Particle Image Velocimetry and particle tracking in the wind tunnel. J. of
Hydrol., 600, 126690. https://doi.org/10.1016/j.jhydrol.2021.126690
- Cauteruccio, A. and L.G. Lanza (2020). Parameterization of the collection efficiency of a cylindrical catching-type rain
gauge based on rainfall intensity. Water, 12(12), 3431. https://doi.org/10.3390/w12123431
- CEN: Hydrometry - Measurement Requirements and Classification of Rainfall Intensity Measuring Instruments, EN
17277:2019 (Comité Européen de Normalisation, Brussels 2019).
- Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Two months of disdrometer data in the Paris area, Earth Syst. Sci. Data,
10, 941–950, https://doi.org/10.5194/essd-10-941-2018, 2018.
- Gires, A., Jose, J., Tchiguirinskaia, I., and Schertzer, D.: Combined high-resolution rainfall and wind data collected for 3
months on a wind farm 110 km southeast of Paris (France), Earth Syst. Sci. Data, 14, 3807–3819, https://doi.org/10.5194/
essd-14-3807-2022, 2022.
- Schertzer, D. and Lovejoy, S.: Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative
processes, J. Geophys. Res.-Atmos., 92, 9693–9714, https://doi.org/10.1029/JD092iD08p09693, 1987
- Seppe Silva, M., Vieira Casanova Monteiro, R., Jose, J., Gires, A., Paz, I., Tchiguirinskaia, I., & Schertzer, D.: Multifractal
comparison of rainfall measurement with the help of a disdrometer and a mini vertically pointing Doppler radar.
Hydrological Sciences Journal, 70(7), 1072–1090. https://doi.org/10.1080/02626667.2025.2464948, 2025