A disdrometer is an instrument designed to assess both the size and velocity of descending hydrometeors. The applications of rainfall measurements retrieved with the help of disdrometers are diverse, spanning areas such as traffic control, scientific research, airport observation systems, and hydrology. Modern disdrometers leverage microwave or laser technologies that have increased the accuracy of the measurements with each iteration. Still, the quality of measurements fluctuates depending on factors such as raindrop size, wind velocity, and rain rate. A comprehension of these variations is needed to better understand the level of reliability of each device depending on the specific rain conditions.

In this study, we compare the performance of two optical disdrometers : 3D Stereo disdrometer (manufactured by Thies Clima) and Parsivel2 (manufactured by OTT). Both devices provide size resolved measurement of rainfall along with velocity of falling drops. Parsivel is set to record data every 30 seconds over a sampling area of 54 cm² and arranges the information in 32 x 32 classes of drop size and velocity. Unlike the Parsivel, 3D Stereo does not discretize measurements, and directly provides the diameter and velocity of each falling drop in a sampling area of 100 cm² with a measuring resolution of 0.08 mm and 0.2 m/s respectively, and a temporal resolution of 1 millisecond. This finer resolution data enables us to study rainfall variability at very small scales which are not usually available.

Here, we used continuously and simultaneously measured data since 21/08/2023, from TARANIS observatory of ENPC (https://hmco.enpc.fr/portfolio-archive/taranis-observatory/). The initial comparison of the data was done using a time series of rain-rate for rainfall events in between a dry period of at least 15 minutes and total depth >0.7 mm. This revealed an unexpected disparity in the water volume collected between the devices. Parsivel collected more than 3D Stereo on every instance, and the disparity got bigger as the rain rate increased. With the purpose of studying the source of this disparity, the sampling area of the 3D Stereo was divided into 8 sections and compared with each other. This showed that the estimate of rainfall parameters such mean diameter, mean velocity of the drops (which were expected to be uniform over long periods regardless of the section where drops are measured) were not the same for the sections studied, and exhibited clear trends. To understand this discrepancy in a scale invariant way, and to evaluate the performance of devices across scales and not only at a single scale, the widely used framework for studying variability of geophysical fields – Universal Multifractals (UM) was employed for assessing the scaling behavior of fields. Rainfall from both devices showed previously reported average scaling behavior from 30 s to 30 min. The difference between rain events and also the behavior at finer scales, which can be accessed from 3D stereo disdrometer were also studied using the UM framework and will be discussed.

Authors acknowledge the Ra2DW project (supported by the French National Research Agency - ANR-23-CE01-0019), for partial financial support.

Keywords: rainfall; disdrometer; multifractals;

How to cite: Santos de Souza, M. M., Gires, A., and Jose, J.: Multi-scale comparison of rainfall measurement in Paris area between two optical disdrometers of different working principles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-651, https://doi.org/10.5194/egusphere-egu24-651, 2024.

Convective rainfall in West Africa is poorly monitored and understood. There are large gaps between remote sensing rainfall products and what is observed on the ground. There are several reasons for these gaps. First, satellites and rain gauges measure at very different scales so one would expect that remote sensing products contain more events at lower intensities than small gauges. Second, a lot happens between the clouds observed by satellites and the ground. Rainfall may evaporate and move with the wind, causing further disconnects between space and ground observations. There are also indications that clouds in West Africa contain many small drops due to the presence of many aerosols, thereby possibly “misleading” satellite products. Finally, it is likely that there are further factors that are not yet accounted for.

In order to tackle this disconnect between ground and space observations, we plan to build the TUD - UDS, or TUDS, rainfall observatory near Tamale and Nyankpala in northern Ghana. The following are initial ideas that we would like to discuss at the EGU. It will be a multi-scale observatory, starting at a grid of nine gauges on a 500m grid (1km x 1km total). This small grid should capture the inherent spatial variability of convective rainfall events with convective cells of 2km or less. The largest grid would also contain nine gauges and have an extent of 10km x 10km, or larger. This outer grid would capture the movement of convective cells, including those contained within so-called line squalls. An intermediate grid may complete this picture. The structure will look, more or less, like the one in the picture below.

Different instruments will be at our disposal, from simple totalling rain gauges to disdrometers. There will be five Thies disdrometers, one Ott Parsivel, and several TAHMO stations and/or tipping bucket rain gauges. Also experimental intervalometers will be placed in the grid to better understand rainfall structure over time and space. Several instruments will be co-located to examine strengths and weaknesses of the different methods.

We explicitly invite comments and contributions.  

How to cite: van de Giesen, N., Annor, F., Ayambila, S., Dogbey, R., Hoogelander, V., Kranjac-Berisavljevic, G., Kwabena, K., Mackenzie, R., Schleiss, M., and Uijlenhoet, R.: Designing the TUDS rainfall observatory in northern Ghana, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2655, https://doi.org/10.5194/egusphere-egu24-2655, 2024.

Precipitation detailed characteristics, namely spectrum of particles by their sizes, phase and precipitation intensity with high-resolution timestep, still need to be investigated due to the complexity of their direct instrumental measurements but necessity for improving forecast for different applications including hydrological and emergency service. Our study is focused on the stratiform precipitation associated with cloud system (Ns-As) of warm front during prolonged and intense precipitation event on the 25^th October 2023 in Kyiv, Ukraine. This warm front cloud system was connected with an occluded low over Poland which developed on the East periphery of a huge depression (970 hPa) over the Northern Atlantic.

We analyzed the OTT Parsivel² - Laser Weather Sensor measurement data with 10sec time steps. Parsivel² was installed nearby regular meteorological station, which is a part of the WMO network, and its measurements were used for verification. Precipitation intensity and raindrop distributions had wavy character, where we can distinguish a few waves of precipitation enhancement. The average intensity of the minimum wave was 0.02mm/min that corresponds to 30 raindrops with size varying from 0.5 to 1.5mm and maximum falling speed 4m/s for the largest raindrops. The average intensity of maximum precipitation enhancement wave was 0.15mm/min with around 100 raindrops per 10sec with sizes mainly from 0.5 to 2.5mm (with some raindrop sizes up to 3.5mm) and average falling speed 5-6m/s. Total amount of 26-hour precipitation event was 24.2mm according to OTT Parsivel² measurements and 26mm according to SYNOP data from Kyiv WMO station (ID 33345). We should note that in modern climate condition in Kyiv such prolonged frontal precipitation even in autumn is rather rare event in respect to previous decades.  

Gained results were compared with previous studies based on 20-year measurement by pluviograph at the same Kyiv WMO station. For stratiform precipitation, average maximum precipitation intensity within precipitation enhancement waves was around 0.11mm/min. Duration of main precipitation enhancement waves was around 21 minutes. Characteristics of precipitation enhancements waves are key for assessment of surface runoff value. The significant fraction of water on the ground that forms surface runoff goes mainly from such precipitation enhancement waves, when around 60 up to 90% of the maximum surface runoff can be formed.

In conclusion, OTT Parsivel² Laser Weather Sensor was used in Ukraine for the first time and demonstrated good performance versus the city station accumulation measurements and historical pluviograph data at the station. At the moment this instrument is under way to the Ukrainian Antarctic station Akademik Vernadsky where further exploitation will allow to test and obtain measurement data for different phase of precipitation, mostly mixed and solid and compare with data from Micro Rain Radar Pro. Obtained and future results will extend our understanding of precipitation formation, their microphysics and dynamics, interconnections between precipitation intensity and size/fall speed of raindrops and solid particles. Future studies could help to evaluate the transformation of cloud and precipitation formation processes under the climate change for better parameterization in numerical models, to study the microphysical structure and composition of precipitation.

How to cite: Krakovska, S., Palamarchuk, L., and Chyhareva, A.: Microphysical properties of the stratiform precipitation in Kyiv city based on OTT Parsivel2 and pluviograph data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6767, https://doi.org/10.5194/egusphere-egu24-6767, 2024.

The aim of the Fresnel platform of École des Ponts ParisTech is to foster research and innovation in multiscale urban resilience. Studying the hydrological response of such complex urban areas accounting also for small scale spatio-temporal precipitation variability requires adapted tools. For these reasons, RadX provides a user-friendly graphical interface to run simulations using a fully distributed and physically based model: Multi-Hydro. RadX is designed as a Software as a Service (SaaS) platform, allowing users to work with data across a wide range of space-time scales and the appropriate tools for analyzing and simulating this data.

The hydrological model, developed at École des Ponts ParisTech, integrates four open-source software applications previously used and validated independently by the scientific community as well as practitionners. Its modular structure includes a surface flow module, sewer flow module, a ground flow module and a precipitation module. It is able to simulate the quantity of runoff and rainwater infiltrated into unsaturated soil layers from any space-time varying rainfall event at any location of the studied peri-urban watersheds, as well as depth and flow in all the pipes and nodes of the sewer network.

Users can launch hydrological simulations using the Multi-Hydro model directly from their web browser, while they are run on dedicated servers. They can adjust two key input parameters: the land use of the studied catchment and the rainfall data. Dedicated tools have been developed to enable users to modify the land use of the catchment with the same ease as using a raster graphic editor. Users can either choose real rainfall events captured by the X-band weather radar located at École des Ponts ParisTech or utilize user-defined synthetic rainfall as input. Data from other radar can also easily be integrated. 

For the simulation output, the interface provides users with different tools to study in detail the impact of the chosen input parameters. For instance, by simply selecting two sewer junctions on an interactive map, users can generate a sewer path between these two points and display an interactive representation of the water level heights in sewer conduits and junctions along the user-defined sewer network path.

Additional components can be integrated into RadX to meet specific requirements using visual tools and forecasting systems, including those from third parties. Developments are still in progress, with a constant loop of requests and feedback from the scientific and professional world.

How to cite: Drouen, G., Schertzer, D., Gires, A., and Tchiguirinskaia, I.: Cloud based tool to enhance urban resilience with the Fresnel Platform using the Multi-Hydro Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7802, https://doi.org/10.5194/egusphere-egu24-7802, 2024.

The RY precipitation product of the German Weather Service (DWD) is severely affected by the presence of the low melting layer and frequently shows circular features of enhanced precipitation around the radar sites during the winter time. 
The radars tend to be installed at relatively high terrain and to scan at elevations at a minimum of 0.5° in order to avoid beam blockage and ground clutter. In doing so two problems arise:

1) the difference between the ground and the radar beams becomes a problem especially at large distances from the radar, and consequently precipitation processes in the lowest layers are not observed.
2) the radar beam often reaches the melting layer and may even cross it where it is sampling the snow above.As a result problems arise when deriving surface QPE from the radar: regions of enhanced QPE in ring shapes around the radar sites, and underestimation of the precipitation beyond the melting layer.

A new methodology (PVPR - Polarimetric Vertical Profile of Reflectivity) developed by Ryzhkov et al. 2022 is tested here for which the radar reflectivity (Z_H) is reconstructed to correct for the effect of the melting layer and snow beyond. In this methodology the melting layer is detected independently for each azimuth based on the values of Z_H and ρ_HV (cross-correlation coefficient between horizontal and vertically polarized radar waves). In particular the range bin at which the melting layer was reached is recorded (mlb_r). The strength of the melting layer (ML_S) is defined based on how much the value of ρ_HV  dropped within the melting layer. The values of ML_r and ML_S at a specific elevation are considered sufficient to characterize the melting layer, and are then compared with lookuptables which were generated by simulations of the melting layer effect on the radar beam. A correction factor is then applied based on the lookuptables to the Z_H profile within and beyond the melting layer. Visually the result shows a smoother field of reflectivity without the obvious bright band and decreased values associated with snow at farther ranges.

In this study the PVPR methodology was used to correct Z_H which in turn was used to calculate rain rates and rain accumulations in a few winter events in Germany.  The results show a strong improvement in the quality of the QPE when compared to rain gauges. The quality of the resulting QPE depends on the event and on the location of the radar. More specifically, the quality decreases when the melting layer is very low, at heights comparable to the radar height, and when the difference between the beam and the surface increases. These problems will be analyzed and potential solutions will be tested in order to improve the quality of the rainfall product.

Ryzhkov, Alexander, Pengfei Zhang, Petar Bukovčić, Jian Zhang, and Stephen Cocks. 2022. "Polarimetric Radar Quantitative Precipitation Estimation" Remote Sensing 14, no. 7: 1695. https://doi.org/10.3390/rs14071695 

How to cite: Evaristo, R., Chen, J., Ryzhkov, A., and Trömel, S.: Testing a new Radar QPE methodology for winter events with a low melting layer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12054, https://doi.org/10.5194/egusphere-egu24-12054, 2024.

In a mountainous environment, high-intensity and short-duration precipitation can generate sudden and abundant runoff at the base of rocky cliffs. This runoff, upon impacting the debris deposits present there, can trigger debris-flow phenomena. In the province of Belluno, in the Boite River valley, a network of rain gauges has been set up to monitor precipitation in the Rovina di Cancia site, where 12 debris-flow events have occurred in the last 10 years. The rain gauges are strategically placed both upstream and downstream of the debris-flow initiation area. In most cases, the precipitation showed significant spatial variability in both planimetric and altimetric aspects. This variability is crucial when simulating the runoff that triggers stony debris flows. The simulation of the peak runoff that triggered the 12 occurred events using a single rain gauge presented a high scatter compared to the simulation performed with the spatially recorded rainfall, except when the chosen rain gauge was close to the rocky cliffs. Furthermore, modelling using radar estimates as rainfall input also displayed significant variability based on the rain gauge used to correct the radar data. Essentially, accurate real-time simulation of runoff triggering debris flows requires the presence of rain gauges upstream of the initiation area, particularly in close proximity to the rocky cliffs.

How to cite: Boreggio, M., Barbini, M., Bernard, M., Berti, M., Schiavo, M., Simoni, A., Vesco Lopez, S., and Gregoretti, C.: Implications of the rainfall spatial variability for the real-time modeling of runoff triggering stony debris flows, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12374, https://doi.org/10.5194/egusphere-egu24-12374, 2024.

Commercial microwave links (CMLs) serve as point-to-point radio connections in cellular backhaul and offer a promising way to measure rainfall opportunistically. Raindrops along the CML path attenuate electromagnetic waves, allowing the conversion of this attenuation into path-averaged rain rates. Wide coverage of CML networks, high density in urban areas, and cost-effective operation present clear advantages over traditional rain gauges and radar networks. However, the integrated nature of CML data poses a challenge. When transforming this data into spatially representative rainfall estimates, such as 2D maps, path-integrated rain rates need to be converted into point data and interpolated to a regular two-dimensional Cartesian grid. The most direct method involves reducing each CML observation to a single-point measurement at the path's center, followed by interpolation using techniques like kriging or inverse distance weighted (IDW) interpolation. Yet, past studies indicate that for longer CMLs (several kilometers) and intense localized rain showers, this approach can introduce significant biases and unrealistic rainfall distributions due to the substantial spatial and temporal variability of rainfall.

In this contribution, we introduce a new disaggregation method employing random cascades. The method redistributes rainfall amounts along CML paths across progressively smaller scales using a discrete, conservative multiplicative random cascade. Inspired by the EVA (Equal-volume area) cascade developed by Schleiss (2020) for disaggregating spatially intermittent rainfall fields, our approach involves splitting each CML segment into two new segments with different path-lengths but identical path-integrated rainfall. We call this new method CLEAR (CML segments with equal amounts of rain). CLEAR is tested for CML network of 77 CMLs located in Prague, CZ. First, the disaggregation is evaluated using simulated CML observations and, second, CML rain rates derived from real attenuation data.

Our findings demonstrate that CLEAR surpasses reconstruction algorithms that reduce CML observations into a single point. It accurately replicates the highly diverse rainfall distributions observed along CMLs, including their intermittency. Moreover, the stochastic nature of the cascade enables the quantification of uncertainty associated with the spatial redistribution of rainfall rates along CMLs.

References

Schleiss, Marc. “A New Discrete Multiplicative Random Cascade Model for Downscaling Intermittent Rainfall Fields.” Hydrology and Earth System Sciences 24, no. 7 (July 23, 2020): 3699–3723. https://doi.org/10.5194/hess-24-3699-2020.

How to cite: Fencl, M. and Schleiss, M.: A new method for disaggregating path-averaged rain rates from commercial microwave links, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20231, https://doi.org/10.5194/egusphere-egu24-20231, 2024.

In natural environments, rainfall causes soil erosion, which has a significant impact on the agricultural production and the ecological conditions of the streams. Due to different types of vegetation, their unique characteristics and seasonality, there are still a lot of open scientific questions about how rainfall interception process influences the rainfall erosivity and soil erosion. With the aim of improving knowledge about rainfall interception by different vegetation and its impact on the rainfall erosivity, an interdisciplinary and international research team (Faculty of Civil and Geodetic Engineering at the University of Ljubljana, Slovenian Forestry Institute and Technical University of Vienna) work together in the research project entitled “Evaluation of the impact of rainfall interception on soil erosion”. In the scope of the project, drop size distribution measurements above and below selected plants will be conducted in combination with classical measurements of rainfall partitioning. The measurements are ongoing in the small urban park in Ljubljana, Slovenia and in the experimental catchment with mainly agricultural land use in Lower Austria (The Hydrological Open Air Laboratory HOAL in Petzenkirchen). To evaluate the differences in rainfall characteristics for the two research plots, a comparative analysis on rainfall event properties such as rainfall amount, duration and intensity, size and velocity distribution of raindrops is performed. The aim of the presentation is to introduce the project and presents the first comparison of the rainfall characteristics at research plots in Austria and Slovenia.

Acknowledgments: This contribution is part of the ongoing research project entitled “Evaluation of the impact of rainfall interception on soil erosion” supported by the Slovenian Research and Innovation Agency (project J2-4489) and the Austrian Science Fund (FWF) I 6254-N.

How to cite: Komma, J., Szeles, B., Zabret, K., Šraj, M., and Parajka, J.: Comparative analysis of rainfall characteristics for two distinct research plots, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20898, https://doi.org/10.5194/egusphere-egu24-20898, 2024.