PICO
This session brings together scientists and practitioners who aim to measure and understand precipitation variability from drop scale to catchment scale as well as its hydrological consequences. Contributions addressing one or several of the following topics are encouraged:
- Novel techniques for measuring liquid and solid precipitation variability at hydrologically relevant space and time scales (from drop to catchment scale), from in-situ measurements to remote sensing techniques, and from ground-based devices to spaceborne platforms. Innovative comparison metrics are welcomed;
- Drop (or particle) size distributions, small scale variability of precipitation, and their consequences for precipitation rate retrieval algorithms for radars, commercial microwave links and other remote sensors;
- Novel modelling or characterization tools of precipitation variability from drop scale to catchment scale from various approaches (e.g. scaling, (multi-)fractal, statistic, deterministic, numerical modelling);
- Novel approaches to better identify, understand and simulate the dominant microphysical processes at work in liquid and solid precipitation.
- Applications of measured and/or modelled precipitation fields in catchment hydrological models for the purpose of process understanding or predicting hydrological response.
- Rainfall simulators developed to investigate the accuracy of disdrometer measurements in assessing drop size and fall velocity.
To investigate the microphysical characteristics of summer precipitation in the Northern Yellow River Irrigation Area, the Central Arid Zone, and the Southern Mountainous Area of Ningxia, this study analyzed disdrometer data collected from Yinchuan, Yanchi, and Liupanshan stations from 2022 to 2024. A comparative analysis of Raindrop Size Distribution (RSD) was conducted from the perspectives of the overall dataset, different rainfall rates, and precipitation types. The results indicate that the average RSD at Liupanshan station is broader with a higher number concentration of small raindrops, whereas the average RSD at Yinchuan station is narrower with a higher concentration of mid-size raindrops. Under different rainfall rates and precipitation types, the number concentrations of both small and large raindrops increase with rising altitude. Specifically, when the rainfall rate is less than 2mm·h-1, the mass-weighted mean diameter (Dm) gradually decreases while the normalized intercept parameter (log10NW) increases with altitude. When the rainfall rate exceeds , the log10NW at Yanchi and Liupanshan stations surpasses that of Yinchuan station, whereas the Dm is smaller than that of Yinchuan. Furthermore, for a given shape parameter (µ), the slope parameter (⋀) increases with altitude. In convective precipitation events, the empirical relationships tend to overestimate the rainfall intensity at all three stations when the rainfall rate exceeds 20mm·h-1.
How to cite: Xue, Z.: Characteristics of Raindrop Spectrum in different areas of Ningxia during Summer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2128, https://doi.org/10.5194/egusphere-egu26-2128, 2026.
Errors in the representation of the drop size distribution are a major source of uncertainty in rainfall estimation, since both radar reflectivity and microwave attenuation depend nonlinearly on precipitation microphysics. These uncertainties propagate directly into the specific attenuation–rain rate (k–R) relationship through the interaction between electromagnetic waves and hydrometeors, leading to systematic biases when globally fixed coefficients are used. In standard practice, the k–R relationship is expressed as a power law of the form k=aRb, where the coefficients a and b are typically taken from the International Telecommunication Union (ITU) recommendations and assumed to be globally applicable. The use of the ITU coefficients implicitly assumes stationary rainfall microphysics, which is physically inconsistent under varying cloud and rain regimes. This highlights the need for stratified parameterizations in which the coefficients are optimized for different microphysical conditions. In this context, cloud phase information from geostationary satellites provides a physically meaningful basis for clustering the k–R relationship, as different cloud phases are associated with distinct precipitation formation processes and drop size distributions.
The objective of this study is to derive cloud phase dependent k–R parameterizations and to assess their performance across a large disdrometer network. A global disdrometer dataset (Ghiggi et al., 2021, DISDRODB) covering multiple climatic regions is used to simulate k–R relationships across a wide frequency range from 5 to 100 GHz using the T-matrix scattering method. SEVIRI MSG observations are used as input to the Cloud Physical Properties (CPP) product provided by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF), from which cloud phase is classified into water, supercooled water, mixed phase, deep convective, cirrus, and opaque ice categories. Frequency dependent k–R coefficients are derived separately for each cloud type. The framework is evaluated across more than 100 independent disdrometer sites, primarily concentrated in Europe.
Relative to the ITU recommended model (ITU-R P.838-3), the cloud phase adaptive parameterization substantially reduces root mean square error (RMSE), with the strongest improvements observed at 5 to 8 GHz. At these frequencies, more than 90 percent of sites show lower RMSE, with average reductions reaching up to 1.5 mm.h-1. More moderate improvements are found at higher frequencies from 60 to 100 GHz, where around 60 percent of sites show RMSE reductions, with average improvements below 0.5 mm.h-1.
These results show that cloud phase informed k–R parameterizations can significantly improve rainfall estimation from commercial microwave links and indicate potential applicability to radar systems.
Reference:
Ghiggi, G., Billault-Roux, A. C., Candolfi, K., Pillac-Mage, L., Unal, C., Schleiss, M., Uijlenhoet, R., Raupach, T., and Berne, A.: DISDRODB – A global disdrometer archive of raindrop size distribution observations, PrePEP 2025, Karlsruhe, Germany, 10–12 March 2025, https://indico.kit.edu/event/4015/contributions/18545/, 2025.
This work was supported by the Czech Science Foundation (GACR), Czech Republic, under Grant No. 24-13677L (MERGOSAT).
How to cite: Shit, T., Fencl, M., and Bareš, V.: Adaptive K–R relationships based on cloud phase classification using SEVIRI observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5139, https://doi.org/10.5194/egusphere-egu26-5139, 2026.
Cloudbursts and mini-cloudbursts are on the rise over India, frequently triggering flash floods. According to the India Meteorological Department (IMD), a cloudburst is defined as rainfall exceeding 100 mm in an hour over a spatial extent of 20-30 km², while mini-cloudbursts are characterized by rainfall of about 50 mm in an hour. Although IMD issues cloudburst reports within 24 hours of occurrence, accurate identification and categorization of these events remain challenging in several regions due to the sparse distribution of meteorological stations, particularly in complex terrain. Satellite-based observations provide high spatial coverage and can detect intense clouding or heavy rainfall events. However, satellites often infer rainfall or cloud properties from radiance, which can introduce uncertainties compared to direct ground measurements. Here, we assess how effectively satellite-based precipitation datasets capture cloudburst events over India by comparing satellite-based rainfall estimates with station-based hourly observations. We evaluate the performance of IMERG and ERA5-Land datasets to identify regions where satellites successfully detect cloudburst events and regions where their performance is limited across India. The results aim to improve understanding of the regional strengths and limitations of satellite datasets for monitoring extreme rainfall and enhancing flash flood preparedness in data-sparse regions of India.
How to cite: Dilip K, N. and Mishra, V.: Do Satellite-Based Precipitation Datasets Capture Flash Flood-Producing Cloudburst Events?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6612, https://doi.org/10.5194/egusphere-egu26-6612, 2026.
Precipitation falling onto vegetation is partly intercepted by the canopy and subsequently evaporates, while the remainder reaches the ground as throughfall or stemflow. Throughfall refers to precipitation that reaches the ground after crossing the canopy. It comprises free throughfall (raindrops not intercepted), drips, and splash droplets. Different rainfalls and foliage yield different number, size and velocity of each throughfall droplet type [1]. The resulting drop size distribution significantly affects infiltration and surface runoff processes [2]. Moreover, drips may induce the erosion and compaction of bare soil [3] while splash droplets may transport pathogenic spores [4]. Finally, the part of leaves that remains wet may experience significant leaching or water/nutrient uptake [5].
Predicting throughfall drop size distribution with physical models is complex because the physically relevant scale is that of a raindrop impacting a leaf, while the scale of interest is at least that of a tree. Previous studies (e.g., [6-8]) provided measurements at either scale but never at both. A few numerical models [4, 9-10] were proposed to estimate throughfall statistics and rain-induced transport by modelling interception at raindrop scale, but these models relied on strong and unverified assumptions on drop-scale dynamics.
In this original study, we first provide a detailed experimental characterization of interception at leaf scale. Hundreds of raindrop surrogates impacted single birch leaves. The leaf was weighed and imaged over time, and water storage variations were resolved at the scale of individual impacts. The storage capacity, the wetting-up time, the drip diameter and the splash fraction were measured as functions of the leaf area, the leaf inclination and the raindrop size. The results are extensively compared to previous studies at leaf scale.
Then rain interception is quantified at tree scale, with the same birch species and leaves in the same phenophase. Rain amount, intensity and drop size distribution in both open rainfall and throughfall were measured using two disdrometers positioned respectively above and below the canopy of a birch tree. Free throughfall, splash droplets and drips were separated for selected rainfall events with different intensities. The storage capacity and the wetting-up time were also estimated for each event. We relate these tree-scale measurements to the mechanisms observed at the leaf scale.
[1] D. F. Levia et al., Hydrol. Process. 33, 1698-1708 (2019)
[2] K. Nanko et al., Hydrol. Process. 24, 567-575 (2010)
[3] M. Beczek et al., Geoderma 347, 40-48 (2019)
[4] T. Vidal et al., Ann. Bot. 121, 1299-1308 (2018)
[5] T. E. Dawson and G. R. Goldsmith, New Phytol. 219, 1156-1169 (2018)
[6] C. Bassette and F. Bussière, Agric. For. Meteorol. 148, 991-1004 (2008)
[7] X. Li et al., Agric. For. Meteorol. 218, 65-73 (2016)
[8] C. D. Holder, Ecohydrol. 6(3), 483-490 (2012)
[9] Q. Xiao et al., J. Geophys. Res. 105 (D23), 29173-29188 (2000)
[10] R. P. de Moraes Frasson and W. F. Krajewski, J. Hydrol. 489, 246-255 (2013)
How to cite: Gilet, T. and Zabret, K.: Bridging the scales of rainfall interception, from raindrop impacts on leaves to throughfall under a tree., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6681, https://doi.org/10.5194/egusphere-egu26-6681, 2026.
Accurate rainfall measurement remains challenging, even for in-situ point observations commonly considered the “ground truth”, owing to precipitation undercatch primarily caused by wind effects and instrument design. These biases limit reliable rainfall estimation, especially at very high and low intensities, and hinder the robust characterisation of precipitation variability. This study first used disdrometer data from multiple sites across the UK to develop a new rainfall classification system based on observed drop size distributions rather than intensity thresholds alone. The proposed classification distinguished periods of rainfall with similar bulk intensities but different microphysical structures, providing a more physically meaningful framework for precipitation characterisation and supporting the development of more targeted undercatch correction strategies. Second, a custom-built rainfall simulator was developed to replicate the identified rainfall types under controlled laboratory conditions. The simulator enables independent control of rainfall rate and drop size distribution, allowing the reproduction of a wide range of precipitation regimes representative of natural UK rainfall. Controlled experiments were used to systematically quantify the response of rain gauges to different drop populations and intensities, providing new insights into the mechanisms driving undercatch and its dependence on rainfall microstructure. By explicitly linking drop-scale processes, controlled experimentation, and population-level rainfall classification, this work contributes to the improved accuracy of precipitation measurements and the representation of rainfall at hydrologically relevant scales, with direct implications for rainfall monitoring, model input uncertainty, and flood risk assessment.
How to cite: Dunn, R., Fowler, H., Green, A., and Lewis, E.: Understanding Rain Gauge Undercatch Through Drop Size Distribution–Based Rainfall Classification and Artificial Rainfall Generation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7894, https://doi.org/10.5194/egusphere-egu26-7894, 2026.
Accurate flood simulations necessitate rainfall inputs with fine spatiotemporal resolution, especially if semi- or fully-distributed hydrological models are used. Rainfall data are commonly obtained from rain gauges and/or weather radars, each with their associated uncertainties and challenges, especially with capturing heavy, localised events, and with high implementation- and maintenance costs [1]. This further translates into high costs of hydrological modelling of flood events [2].
An interesting alternative to rain gauges and radars are the rainfall data gathered from opportunistic sensors, such as Commercial Microwave Links (CMLs). CML data come at no infrastructure cost as they are generated by the network management system of mobile networks to monitor link performance. Furthermore, CMLs cover a large part of the world. Their strong potential to providing near-surface, fine-resolution rainfall fields has been demonstrated in many studies [3]. However, their usage for hydrological modelling has been little investigated so far. CML data have been mostly used for fully-distributed models in small catchments with an area of few square kilometres [1], with isolated examples of application in large catchments and/or with semi-distributed models [1],[4].
In this study, we analyse the impact of various modelling decisions about application of CML rainfall data on simulated flood hydrographs. Specifically, selection of (i) the approach to pre-processing CML signals to obtain hyetographs [3], (ii) CML data usage as a standalone input or in a combination with conventional datasets, and (iii) the way to calculate sub-catchment-averaged rainfall, are analysed. Different rainfall inputs are created accordingly, and used to force a semi-distributed model of the pre-alpine, peri-urban Lambro catchment in northern Italy notorious for intensive, tightly-localised events that trigger floods [4]. The simulated hydrographs of twelve flood events are compared to the observed ones in terms of the Nash-Sutcliffe coefficient, relative errors in peak magnitudes and runoff volumes, and timing of peak occurrence. Based on our analyses, specific recommendations are provided, with the ultimate goal to promote a wider application of CML data for hydrological modelling.
Acknowledgments
The authors would like to thank the “OpenSense” COST Action (CA20136) for supporting their collaboration through the STSM program.
References
[1] J. Olsson et al., ‘How close are opportunistic rainfall observations to providing societal benefit?’, Journal of Hydrometeorology, Aug. 2025, doi: 10.1175/JHM-D-25-0043.1.
[2] J. Seibert, F. M. Clerc‐Schwarzenbach, and H. J. (Ilja) Van Meerveld, ‘Getting your money’s worth: Testing the value of data for hydrological model calibration’, Hydrological Processes, vol. 38, no. 2, p. e15094, Feb. 2024, doi: 10.1002/hyp.15094.
[3] S. C. Doshi, C. De Michele, G. Cazzaniga, and R. Nebuloni, ‘A Framework for Minimizing the Impact of Wet Antenna Attenuation on Rainfall Estimates Provided by Commercial Microwave Links’, IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, vol. 19, pp. 421–437, 2026, doi: 10.1109/JSTARS.2025.3632933.
[4] G. Cazzaniga, C. De Michele, M. D’Amico, C. Deidda, A. Ghezzi, and R. Nebuloni, ‘Hydrological response of a peri-urban catchment exploiting conventional and unconventional rainfall observations: the case study of Lambro Catchment’, Hydrol. Earth Syst. Sci., vol. 26, no. 8, pp. 2093–2111, Apr. 2022, doi: 10.5194/hess-26-2093-2022.
How to cite: Todorović, A., Roberto, N., Carlo, D. M., Greta, C., Cristina, D., Ranka, K., and Alessandro, C.: Leveraging opportunistic rainfall sensors to improve hydrological flood modelling in a peri-urban catchment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10315, https://doi.org/10.5194/egusphere-egu26-10315, 2026.
Rainfall retrieval algorithms for weather radars are linked to assumptions about drop size distributions (DSDs), but DSD properties vary strongly across rainfall regimes. To reduce regime-dependent biases in radar-based quantitative rainfall estimation, we use high-temporal-resolution disdrometer observations to quantify microphysical differences between strong convection, embedded convection, and stratiform rainfall with a bright-band, and to test how well these regimes can be separated in the (Dm, log10Nw) phase space, where Dm is the mass-weighted mean diameter and Nw the normalized intercept parameter.
Our analysis shows a systematic convective–stratiform contrast. Strong convection has larger characteristic drop sizes and higher normalized concentrations (mean Dm ≈ 1.07 mm; mean Nw ≈ 2.93 × 104 m−3 mm−1). Embedded convection has slightly smaller Dm but Nw remains comparably high (mean Dm ≈ 1.02 mm; mean Nw ≈ 2.00 × 104 m−3 mm−1). Stratiform rainfall with a bright-band has smaller Dm and markedly lower Nw (mean Dm ≈ 0.92 mm; mean Nw ≈ 6.38 × 103 m−3 mm−1).
Cumulative DSD curves indicate that regime separation is driven primarily by the large-drop tail: strong convection shows the highest contribution of drops above ~2–3 mm, embedded convection is intermediate, and stratiform rainfall declines steeply at large diameters. To translate these findings into an objective regime indicator, we train a linear SVM (Support Vector Machine) on canonical samples (strong convection vs stratiform rainfall with a bright-band) and apply it to all events. Convective and stratiform rainfall are largely separable, while embedded convection occurs on both sides of the boundary, supporting a probabilistic classification with a transition band. These results provide microphysical insights that can be used to refine regime-dependent radar retrieval parameterizations and improve radar-based rainfall estimates at hydrologically relevant scales.
How to cite: Rulfova, Z. and Potuznikova, K.: Disdrometer-based microphysical contrasts between convective and stratiform rainfall to improve radar rainfall retrievals, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10645, https://doi.org/10.5194/egusphere-egu26-10645, 2026.
Analyzing the transition probability of disdrometer data revealed a sigmoid relation between precipitation intensity of the current and next minute. The sigmoid changes in it's parameters slope, location and asymmetry based on the intensity of the current value. In particular the evolution of the parameters shows some distinct bends that mark transition points. Replicating how the sigmoid morphs with intensity we build a Markov chain model that generates realistic precipitation data. In particular it can generate the power law relation in the high intensity range of the distribution and also correctly includes a transition to exponential distribution at low intensities. To complete the algorithm we included a threshold based transition to dry periods. This introduces realistic intermittency into the data. What makes our findings compelling is that we strictly replicated the micro structures we found in the data and ended up with a random walk that generates the large scale structure of the data set. No optimizing was involved. We still have to fully validate the performance of our algorithm and understand the essential components that generate key characteristics as for example the transition between exponential and power law. With that we hope to find a universal mechanism that is able to generate very different precipitation distributions based on how we shape the morphing of the sigmoid function.
How to cite: Frechen, T. N. and Hinz, C.: Replicating the micro structure of disdrometer data leads to a rainfall generator that correctly reproduces the large scale structure of the data set, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11452, https://doi.org/10.5194/egusphere-egu26-11452, 2026.
Rainfall exhibits extreme spatial and temporal variability observable across wide range of scales. This variability is not limited to precipitation totals but also concerns the microphysical structure of the rain characterized with the help of the drop size distribution (DSD). It is defined as the number of raindrops per unit volume of air with a given equivolumic diameter. The DSD can be described through its statistical parameters (basically its moments) such as the rain rate (RR), the liquid water content (LWC), the mass-weighted mean diameter (Dm) and the total number concentration (Nt). The vertical variability of DSD remains an active field of research, particularly due to the challenges associated with observing and generalizing microphysical profiles which are used to improve rainfall ground estimates from radar measurements.
Vertically-oriented radar measurements are a valuable tool for studying the vertical variability of DSD along the precipitation column with small spatial and short temporal observation scales. In this study, nine months of a Micro Rain Radar PRO (MRR-PRO) measurements were gathered in Ecole nationale des ponts et chaussées (ENPC), Institut Polytechnique de Paris (IPP), which is located in the eastern part of the Paris region, France. The MRR-PRO is a K-band weather radar that provides high-resolution vertical profiles of precipitation features that reach more than 4 kilometers of altitude above its position with a 35 meters spatial resolution and a 10 seconds time step. Based on the collected data and simple assumptions, several parameters related to the raindrop size distribution can be estimated empirically, such as RR, LWC, Dm and Nt. The spatial and temporal variability of the DSD was studied using the Universal Multifractal (UM) framework, a physically based framework designed to characterize geophysical fields across wide range of scales through a limited set of physically interpretable parameters.
Two types of UM analysis were conducted in this study. First, the time series of DSD statistical moments is explored at each altitude. Then, vertical profiles of these moments are examined to extract UM parameters that characterize the variability along the vertical column. The results and their interpretation within a spatiotemporal framework will be presented.
Authors acknowledge the France-Taiwan Ra2DW project for financial support (grant number by the French National Research Agency – ANR-23-CE01-0019-01).
How to cite: Chikhaoui, E. and Gires, A.: Multifractal analysis of Drop Size Distribution parameters vertical and temporal variability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11781, https://doi.org/10.5194/egusphere-egu26-11781, 2026.
Z-R relationships are a fundamental component of rainfall estimation and are widely applied in radar meteorology and hydrology supporting operational applications such as flood forecasting. Despite their extensive use, the procedures adopted to derive Z-R coefficients are often not described in sufficient detail, and key methodological choices, such as the selection of the dependent variable in the regression analyses, are frequently left implicit.
In this study, we analyze the determination of Z-R relationships using rain gauge, disdrometer, and X-band radar observations with solid-state transmitters collected over the Seveso-Olona-Lambro river basin and the Milan metropolitan area (northern Italy). A set of rainfall events recorded in 2023 is examined, including both stratiform and convective events. Z-R coefficients are determined using a regression-based approach following a leave-one-out methodology across events and multiple instrument pairings, to account for differences in sampling volumes and measurement characteristics.
The resulting relationships are evaluated by comparing radar-based rainfall estimates against rain gauge observations and estimates obtained using standard Z-R formulations. The analysis focuses on the performance of rainfall estimates for different methodological choices in the regression process and for stratiform and convective events, and includes an assessment of mean areal accumulated rainfall to emphasize the hydrological relevance of properly defining Z-R relationships. The study highlights the sensitivity of rainfall estimation to methodological choices in Z-R coefficient determination and underscores the importance of clearly documenting regression setups.
How to cite: Chaves González, N. A., Ceppi, A., De Michele, C., Ravazzani, G., and Cazzuli, O.: Determination of Z-R Relationships for Rainfall Estimation from Weather Radar, Rain Gauges, and Disdrometers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12024, https://doi.org/10.5194/egusphere-egu26-12024, 2026.
Reconciling rainfall records from different sources, even from co-located instruments, is often difficult unless proper adjustment for instrumental and environmental sources of bias is applied. Comparisons between disdrometer and rain gauge measurements may show deviations that are usually attributed to their very different measurement principles. In this work, we show that rainfall intensity measurements from the 2D Video Disdrometer (2DVD) and a co-located tipping-bucket rain gauge can be largely reconciled once the relevant sources of bias are quantified and raw measurements are consequently adjusted.
The instrumental bias of the co-located tipping-bucket rain gauge is obtained from laboratory calibration performed at the Hong Kong Observatory (HKO). Meanwhile we rely on factory calibration for the instrumental bias of the 2DVD. Wind is assumed as the primary source of environmental bias for both instruments. Adjustment curves for the wind-induced bias of cylindrical rain gauges are here derived from existing literature (see Cauteruccio et al. 2024).
For the 2DVD, the wind-induced bias is obtained by means of numerical simulation. Using the OpenFOAM software, Computational Fluid Dynamics (CFD) and Lagrangian particle tracking simulations have been performed. CFD simulations provide the wind velocity field around the instrument body for different combinations of wind speed and direction. A k-ω SST turbulence model and a local time-stepping approach are used. Hydrometeor trajectories are modelled by numerically releasing drops ranging from 0.25 mm to 8 mm in diameter into the computational domain. The wind-induced bias is then expressed in terms of the Catch Ratio (CR), representing the ratio between the number of drops crossing both the 2DVD’s light beams in the presence of wind and their number considering undisturbed conditions.
The simulations shows that wind direction is a relevant factor since the instrument is not radially symmetric. A significant geometric shielding effect is also present and CRs may reach zero for medium to high wind speeds and small raindrop size, meaning that no drops are sensed by the 2DVD in certain conditions.
After adjustment, measurements from the 2DVD installed at the HKO’s field test site at the Hong Kong International Airport are compared against co-located rain gauge measurements. Results show an average reduction of the deviation between measurements to less than about 1 mm/h. Adjusted measurements from both instruments also report about 10% higher RI values, indicating that the raw data significantly underestimate precipitation. The adjustment procedure presented in this work is quite general and can be applied to raw measurements obtained from any 2DVD sensor if measurements from a co-located anemometer are available at the site.
Measurements obtained from the 2DVD in windy conditions should be therefore treated with caution, especially when the measured DSD is used to inform research studies on the microphysical properties of the rain process or for any comparison with other disdrometers or precipitation gauges.
References:
Cauteruccio, A., Chinchella, E., & Lanza, L. G. (2024). The overall collection efficiency of catching‐type precipitation gauges in windy conditions. Water Resources Research, 60(1), e2023WR035098. https://doi.org/10.1029/2023WR035098
How to cite: Chinchella, E., Cauteruccio, A., Chan, P.-W., and Lanza, L. G.: Numerical evaluation of the wind-induced bias for the 2D Video Disdrometer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12669, https://doi.org/10.5194/egusphere-egu26-12669, 2026.
The high spatial and temporal variability of precipitation, especially during short, high-intensity events, is typically not captured by rain gauge networks. Furthermore, the actual precipitation maxima do not necessarily occur at the locations of the rain gauges. This consequently leads to a systematic underestimation of interpolated precipitation amounts (Bárdossy and Anwar, 2023). Since this phenomenon depends on the sample size, i.e., the number of rain gauges, a way to increase the sample size is to use additional data of so-called opportunistic precipitation sensors. A suitable data source is provided by personal weather stations (PWS) equipped with rain gauges, which have exceeded the number of stations operated by national weather services and other authorities. They therefore offer the potential to improve quantitative precipitation estimates (Bárdossy et al. 2021, Graf et al. 2021).
In this study, we investigate the behaviour of precipitation extremes from interpolations in the Lazio region in Italy using different rainfall data sets. The Lazio region is characterized by a dense network of approximately 230 professionally maintained rain gauges and more than 300 Netatmo Personal Weather Stations, both providing data in high temporal resolution Although these stations offer a valuable opportunity to enhance the spatial coverage of rainfall observations, they do not generally comply with professional standards in terms of installation, maintenance, and data reliability, and therefore require a rigorous quality control (QC) procedure. In this study, the most recent QC filters and bias correction methodologies are applied to the PWS dataset. Following the QC process, the performance of the corrected PWS observations is assessed through comparison with co-located professional rain gauges. Furthermore, the potential added value of incorporating PWS data is investigated by analyzing their contribution to the representation of rainfall spatial variability, with particular emphasis on extreme precipitation events, as well as their impact on precipitation interpolation results. The outcomes of this study aim to provide insights into the effective integration of crowdsourced weather observations into operational and research-oriented hydrometeorological applications.
References:
Bárdossy, A., Seidel, J., El Hachem, A.: The use of personal weather station observations to improve precipitation estimation and interpolation, Hydrology and Earth System Sciences, 25, 583-601, 2021. https://doi.org/10.5194/hess-25-583-2021
Bárdossy, A., Anwar, F.: Why do our rainfall–runoff models keep underestimating the peak flows? Hydrology and Earth System Sciences, 27, 1987–2000, 2023. https://doi.org/10.5194/hess-27-1987-2023
Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H., Bárdossy, A.: Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales. Journal of Hydrology: Regional Studies, 37. https://doi.org/10.1016/j.ejrh.2021.100883
How to cite: Seidel, J., Zulkarnaen, D., Moccia, B., Ridolfi, E., Napolitano, F., Russo, F., and Bárdossy, A.: Enhancing Rainfall Spatial Representation through Quality-Controlled Personal Weather Stations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13237, https://doi.org/10.5194/egusphere-egu26-13237, 2026.
High-quality gridded precipitation datasets are essential for climate analysis and flood-risk assessment in Great Britain (GB); however, such datasets remain limited, and existing products suffer from important limitations. Rain gauge measurements provide highly accurate point-scale observations, but sparse gauge networks limit their applicability. Radar quantitative precipitation estimates (QPEs) offer useful spatial information on rainfall fields at national scale, but suffer from multiple artefacts and errors. Blended rainfall datasets therefore represent a promising approach, as they capitalise on the complementary strengths of radar and gauge observations. Accordingly, this study aims to develop a high-resolution blended precipitation dataset for GB, focusing on two key components: quality control (QC) of radar QPEs and the merging of radar and gauge rainfall.
First, radar QPEs are shown to contain substantial and spatially variable errors even after standard reflectivity-based QC. We assess the Met Office composite radar QPE for GB (hourly, 1 km resolution; 2006–2018) against approximately 1300 hourly rain gauges, demonstrating that errors increase with elevation, distance from radar, and rainfall intensity. Radar QPEs frequently underestimate high-intensity hourly rainfall and fail to detect many extreme events (≥40 mm h⁻¹), with underestimation occurring approximately 1.7 times more often than overestimation (for rainfall ≥0.2 mm h⁻¹). To address these issues, we develop a holistic, rule-based QC framework that exploits spatial–temporal continuity and rainfall-field uniqueness to further quality-control radar QPEs already processed by the Met Office. The framework (i) detects and recovers beam-blocked regions, (ii) classifies normal versus suspect rainfall fields, and (iii) identifies and replaces bad rainfall pixels associated with radar malfunction, ground clutter, and electronic noise. Application of this framework reduces the Root Mean Squared Error (RMSE) relative to gauges from 0.546 to 0.386 (−29%) and increases the correlation coefficient from 0.552 to 0.725 (+31%), while preserving genuine extreme rainfall.
Second, building on the quality-controlled radar product, we introduce a Gauss Blending Method (GBM), adapting the Gauss–Seidel method to merge radar rainfall with gauge constraints (970 gauges) and generate a spatially complete, structure-preserving hourly precipitation field at 1-km resolution. Independent evaluation using 194 gauges (2006–2018) shows that the blended product improves RMSE and mean absolute error by ~14.5% and reduces mean relative error by ~22% compared with radar-only data. The GBM also enhances rainfall detectability and outperforms commonly used adjustment approaches, including the Additive Adjustment, Multiplicative Adjustment, Mixed Adjustment, and Mean Field Bias Adjustment methods. Its overall performance is comparable to Kriging with External Drift; however, GBM shows superior performance for higher rainfall intensities (≥10 mm h⁻¹), provides substantially greater spatial data coverage, better preserves local rainfall variability, and is easier to implement in practice.
Together, the proposed QC framework and GBM enable the production of GRaD-GB (1H1K), an hourly 1-km gauge–radar merged precipitation dataset for Great Britain covering the period 2006–2023. The dataset combines hourly quality-controlled radar QPEs with hourly rainfall observations from approximately 1500 quality-controlled rain gauges. GRaD-GB (1H1K) is well suited for analysing precipitation variability, storm life cycles, and extreme rainfall, thereby providing a robust basis for hydrological applications, flood risk estimation, and extreme rainfall analysis.
How to cite: Qiu, X., C. Green, A., Blenkinsop, S., and J. Fowler, H.: Developing a Gauge–Radar Merged Precipitation Dataset (1 hour and 1 km) for Great Britain: GRaD-GB (1H1K), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15175, https://doi.org/10.5194/egusphere-egu26-15175, 2026.
Urbanization and air pollution are increasingly recognized as important modifiers of precipitation microphysics, yet their combined influence on raindrop size distributions (DSDs) remains uncertain. This study investigates how urban land cover and particulate air pollution affect rainfall microphysical properties using multi-year disdrometer observations at three urban-edge sites near Brussels, Liège, and Ghent. Measurements from two optical laser disdrometers and one forward-scattering disdrometer are combined with ERA5 reanalysis data, Local Climate Zone (LCZ) classifications, and gridded air-quality datasets. Disdrometer data are subjected to quality control, including filtering for liquid precipitation, internal consistency checks based on rainfall rate, and comparison with nearby rain-gauge measurements. Raindrop size distributions are characterised using integral microphysical parameters, including volume mean diameter (VMD), area mean diameter (AMD), rainfall rate, reflectivity, and kinetic energy. Convective and stratiform precipitation are distinguished using reflectivity-based thresholds and variability in rainfall rate. Urban effects are quantified by relating wind-direction-dependent urban fraction to disdrometer-derived DSD parameters. Preliminary results indicate a site-dependent response of raindrop diameter to upwind urban fraction, with statistically significant positive relationships at two locations and a negative relationship at one location, highlighting the complexity and heterogeneity of urban–precipitation interactions. Seasonal stratification and wind-speed filtering do not reveal a consistent pattern across all instruments. The influence of air pollution is assessed using daily mean PM2.5 and PM10 concentrations, with initial analyses suggesting that elevated pollution levels are associated with more extreme DSD behaviour, characterised by an increased occurrence of significantly smaller and larger drop sizes compared to more narrowly distributed DSDs under cleaner conditions. Ongoing analyses further examine how these effects depend on precipitation type and how they translate into changes in rainfall kinetic energy. This work provides new observational insight into the nonlinear interactions between urban environments, aerosols, and precipitation microphysics with implications for urban hydrology, radar-based rainfall estimation, and the representation of aerosol-cloud-interactions in climate models.
How to cite: Passtoors, A., Van Weverberg, K., Reinoso-Rondinel, R., Reyniers, M., Poelman, D., and Ghilain, N.: Urbanization and Air Pollution Effects on Precipitation Microphysics: Evidence from Disdrometer Observations in Belgium, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18043, https://doi.org/10.5194/egusphere-egu26-18043, 2026.
An increasing number of personal weather stations (PWSs) is installed by citizens, resulting in a large amount of real-time available precipitation data. This study assesses the applicability of these data for flood forecasting. We focussed on 30 catchments (total area 2474 km2) located in the management area of Water Board Rijn and IJssel, a water authority in the Netherlands which uses PWS data as input for their operational flood forecasting system. We compared rainfall from a network of 869 Netatmo PWSs (after applying a quality filter) and the real-time radar product from the KNMI (Royal Netherlands Meteorological Institute). Next, we used both products as input for the rainfall-runoff model WALRUS and compared the simulated discharges. These two datasets with almost no latency were validated with the final reanalysis KNMI radar product and discharge observations, for a full year (2023).
For precipitation, the real-time radar was closer to the final reanalysis radar than the PWSs in terms of Kling-Gupta Efficiency, Pearson correlation coefficient and coefficient of variation, but had a stronger negative bias. However, discharge simulations based on PWSs were closer to observations and simulations with the final reanalysis radar than simulations based on the real-time radar. This contrasting result can be explained by the bias, which was stronger for the real-time radar than for the PWSs, and is amplified in the discharge simulations due to the memory in the hydrological system. We found no clear relation between catchment size, PWS density and PWS distribution and the performance of PWS rainfall product. Reducing the density of the PWS network only led to a small deterioration in performance. The results indicate the potential of these devices to be used in hydrological applications, especially when initial hydrological model conditions are improved with data assimilation in operational flood forecasting systems.
How to cite: Brauer, C., Schoonhoven, J., and Bogerd, L.: Flood forecasting based on personal weather station rainfall data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18161, https://doi.org/10.5194/egusphere-egu26-18161, 2026.
Disdrometers positioned at different elevations above the ground experience different wind conditions, with increasing wind velocity as the elevation increases and possibly changing wind direction. On the contrary, bulk properties of the rainfall process, such as the rainfall intensity, are not expected to change along the vertical within a limited elevation gain.
In this work, high resolution data collected over 2.5 years on a meteorological mast located at Pays d'Othe wind farm, 110 km South-East of Paris France is used. More precisely, data from an OTT Parsivel2 disdrometer, with 30 s observation time step, and a Thies Clima 3D sonic anemometer at 100 Hz, located at roughly 40 m, are used. The same setting is replicated at 80 m.
In previous research (Chinchella et al., 2025), the expected wind-induced bias of the OTT Parsivel2 disdrometer was numerically quantified using computational fluid dynamics simulation. Adjustments are here applied to raw disdrometer data depending on the measured wind speed and direction. Not only updated rain rate is provided but also the whole DSD enabling to study a few key features such as mean diameter or total concentration.
The disdrometer measurements (rain rate and DSD) at the two heights are compared before and after the correction. In a first step standard scores such as RMSE, normalized bias or Nash-Sutcliffe efficiency are used. In a second step, Universal Multifractal (UM) features are compared to get results valid, not only at a few selected scales, but across a wide range of scales. UM is a parsimonious mathematically robust framework, relying on the physically based notion of scale invariance inherited from the governing Navier-Stokes equations. It has been widely used to characterize and simulate geophysical fields extremely variable over wide range of scales such as rainfall, with the help of only 3 parameters.
This study enables to discuss the effect of the wind correction with increasing wind on the same location. It also enables to quantify the influence of wind on disdrometers measurements and retrieved UM features, an effect that has been neglected in previous investigations.
Authors acknowledge the ANR PRCI Ra2DW project supported by the French National Research Agency – ANR-23-CE01-0019-01 for partial financial support.
References
Chinchella, E.; Cauteruccio, A.; Lanza, L.G. Impact of Wind on Rainfall Measurements Obtained from the OTT Parsivel2 Disdrometer. Sensors 2025, 25, 6440. https://doi.org/10.3390/s25206440.
How to cite: Cauteruccio, A., Gires, A., Chinchella, E., and Lanza, L. G.: Wind effects on disdrometer measurements at different elevations along a meteorological mast, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18992, https://doi.org/10.5194/egusphere-egu26-18992, 2026.
The interactions between large collections of settling snowflakes and various turbulence intensity levels within the air column make snow precipitation difficult to forecast. Characterizing the multi-scale spatial distribution and transport of snowflakes is crucial for understanding the spatial modulations in the snow deposition process and for interpreting remote sensing signals. In this work, we perform large-scale three-dimensional tracking of snowflakes falling through the atmospheric surface layer in the Swiss Alps. We utilize a novel super-resolution field imaging system that combines 16 high-resolution cameras mounted on arrays and is flexibly deployed in ice-fishing tents at different instrumented field sites with collocated snow and wind characterization. Each camera array is fitted with shifted lenses to stitch an equivalent 100 Megapixel imaging over a 20x20 square Meter field of view at a 2-Millimeter diffraction-limited tracking resolution. Snowflakes are illuminated using white light of 5500 Kelvin at 250′000 Lumens from multiple powerful 1575 Watt stadium floodlight panels mounted on snowboards and retrofitted with lenticular lenses. Shooting data at a 150 Hertz, the system is capable of tracking millions of snowflakes over 10x10x10 cubic Meters simultaneously. We first present collective snow tracking data obtained in a mild wind vector of approximately 3 Kilometers per hour. Analyzing the fall velocity, our data suggests a multimodality for fast and slow falling snow particles, which we discuss in relation to recorded snow particle variability. Subsequently, analyzing the point-cloud data using a Voronoi tessellation, we find a predominance of clusters and voids compared to the clustering diagram for a random Poisson process. Secondly, we present field experiments being caught in a blizzard with windspeeds exceeding 30 Kilometers per hour. We first conduct a qualitative assessment of the observed patterning of snowfall in the atmosphere at high wind speeds, as well as the appearance of saltation and blowing snow layers during the field measurements. We then identify signatures of these field observations in the acquired tracking data and compare events of extreme clustering dynamics against those of the cluster diagram for the mild wind vector.
How to cite: Muller, K., Bölsterli, R., Gonzàlez-Herrero, S., Lehning, M., and Coletti, F.: Large-scale Clustering of Natural Snowfall: Collective Precipitation Dynamics in Three Dimensions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19729, https://doi.org/10.5194/egusphere-egu26-19729, 2026.
