Meteorological forecast models, notably snowfall predictions, require accurate knowledge of the properties of snow particles, such as their size, cross-sectional area, mass, shape, and fall speed. Therefore, measurements of individual snow particles’ fall speed and their cross-sectional area, from which a size parameter and area ratio can be derived, provide very useful datasets. We have compiled such a dataset from measurements with the Dual Ice Crystal Imager (D-ICI) in Kiruna during several winter seasons from 2014 to 2019. Using that data, we have previously studied shape-dependent relationships between fall speed and particle size, cross-sectional area, and particle mass. While we had used maximum dimension as the size parameter, we have found that it seems unsuitable for certain shapes like columnar particles. Here, we investigate which particle size parameter should be used depending on the shape or if one size parameter is suitable for all shapes. With a more suitable particle size parameter, we aim to improve the relationships between fall speed and particle size and mass.

How to cite: Kuhn, T., Eliasson, S., and Vázquez-Martín, S.: Improving shape-dependent snow fall speed relationships using different particle size parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9256, https://doi.org/10.5194/egusphere-egu23-9256, 2023.

Universal Multifractal (UM) has been a useful tool to model rainfall processes across a wide range of spatialtemporal scales. Double Trace Moment (DTM) is a technique that helps estimate parameters for the UM model. Based upon the estimated UM parameters, a discrete random cascade process can be used to generate samples with realistic rainfall properties. UM parameters are of physical meanings, representing the levels of mean intermittence (C1) and the changing rate of the mean intermittency deviating from the average field (α, know as the multifractality index), respectively. Therefore, these parameters are also widely used to characterise rainfall features across scales. UM has been tested in many countries under various weather conditions. However, its applications to extreme storm events, such as typhoons, are limited. In light of this, this study intends to analyse UM’s capacity of capturing and modelling extreme storm events recorded by a rainfall monitoring network in the South of Taipei City. On the roof of the Civil Engineering Research Building at National Taiwan University, an innovative extreme rainfall monitoring campaign has been set up and collecting high-quality rainfall measurements at fine timescales over the past two years. Rainfall data from several extreme rainfall events, including four typhoons and 10+ thunderstorms, has been collected. In this work, high-resolution rainfall time series from the laser disdrometer for typhoon Nalgae is used for analysis. Rainfall measurements are first aggregated from the native 10-second resolution to 80-second and coarser resolution and then downscaled back to 10-second to verify the downscaling results. The UM analysis is conducted in three different ways. The first way is to apply UM analysis to the entire time series. The resulting parameters are α = 1.32 and C1 = 0.108. Then, the time series is equally divided into 16 sections such that the temporal variations in rainfall features can be observed. Similarly to the first way, the second way applies the ’standard’ UM analysis but to each section. This leads to α ranging from 1.1 to 1.9 and C1 from 0.05 to 0.18. Finally, the third way applies ’ensemble’ UM analysis that concatenates divided sections into a single matrix. This results in α = 1.55 and C1 = 0.125. The derived parameters are then used to sample 10-second rainfall estimates with a discrete cascade process. The performance is quantified based upon the capacity of preserving observed extreme features. We first analyse the ranges of α and C1 resulting from the samples downscaled from the first and the third ways. We can see that the resulting α ranging from 1.2 to 1.8 and C1 from 0.06 to 0.16, which fails resembling the aforementioned variability of the UM parameters (i.e. 1.1−1.9 and 0.05−0.18). In fact, only the second way leads to satisfactory result. This preliminary study suggests that typhoon rainfall experiences drastic behaviour changes within a short period, which requires a more ’dynamic’ way to model these changes well. Similar analyses will be conducted over other collected typhoons and thunderstorm events to see if the findings can be generalised.

How to cite: Chou, C.-C., Gires, A., and Wang, L.-P.: Modelling Typhoon Rainfall with Universal Multifractal, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12880, https://doi.org/10.5194/egusphere-egu23-12880, 2023.

Since 2012 a joint Université Cheikh Anta Diop de Dakar and Universidad Complutense de Madrid meteorological observation network (UCadMet) has been in place in the city of Dakar (Senegal). During the last years, the observation and data storage systems have been considerably improved. Last summer of 2022, a laser disdrometer was installed providing  detailed information on the size and speed of precipitation with a time resolution of one minute. Observations from several tipping bucket rain gauges are available also at the same site. Summer 2022 has been anomalously rainy in West Africa, with large precipitation events during the African monsoon season, which seems to be enhanced by a La Niña situation in the Pacific. These events have proven to be particularly suitable for evaluating the performance of the installed observing systems and for drawing some conclusions about the characteristics of monsoon precipitation in this region not only at different time scales, but also across scales (from 1 min to season). Commonly used rain rate together with drop size distribution are used to access information on rainfall microphysics. This analysis allows the design of future lines of action considering climate change, for which large precipitation events are expected to become more frequent.

How to cite: Rodríguez de Fonseca, B., Durán Montejano, L., González Cervera, A., Gires, A., Fall, C. M. N., Dieng, A. L., Gaye, A. T., and Mohino, E.: Multiscale Characteristics of West African Summer Monsoon Precipitation Derived from UCadMet Network Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14766, https://doi.org/10.5194/egusphere-egu23-14766, 2023.

Satellite infrared (IR) data, with high temporal resolution and wide coverages, have been commonly used in precipitation measurement. However, existing IR-based precipitation retrieval algorithms suffer from various problems such as overestimation in dry regions, poor performance in extreme rainfall events, and reliance on an empirical cloud-top brightness-rain rate relationship. To solve these problems, a deep learning model using a spherical convolutional neural network was constructed to properly represent the Earth's spherical surface. With data inputted directly from IR band 3, 4, and 6 of the operational Geostationary Operational Environmental Satellite (GOES), the new model of Precipitation Estimation based on IR data with Spherical Convolutional Neural Network (PEISCNN) was first trained, tested and validated. Compared to the commonly used IR-based precipitation product PERSIANN CCS (the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Network, Cloud Classification System), PEISCNN showed significant improvement in the metrics of POD, CSI, RMSE and CC, especially in the dry region and for extreme rainfall events. The PEISCNN model may provide a promising way to produce an improved IR-based precipitation product to benefit a wide range of hydrological applications.

How to cite: Yi, L., Gao, Z., Shen, Z., Lin, H., Liu, Z., Ma, S., Li, S. Z., and Li, L.: Precipitation measurement based on satellite data and machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2689, https://doi.org/10.5194/egusphere-egu23-2689, 2023.

How to cite: Ignaccolo, M. and De Michele, C.: A novel methodology for remote sensing retrieval of rainfall rates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10190, https://doi.org/10.5194/egusphere-egu23-10190, 2023.

The aim of the Fresnel platform of École des Ponts ParisTech is to develop research and innovation on multiscale urban resilience. To achieve this goal, it is therefore conceived as a SaaS (Software as a Service) platform, providing data over a wide range of space-time scales and appropriate softwares to analyse and simulate them over this range.

To study the different technical solutions of the water cycle in an urban environment at different scales, RadX now provides a user-friendly graphical user interface to run simulation using a fully distributed and physically based model: Multi-Hydro.

This model that has been developed at École des Ponts ParisTech, from four open-source software applications already used separately by the scientific community. 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 the quantity of rainwater infiltrated into unsaturated soil layers from any temporally-spatially varied rainfall event at any point of the peri-urban watersheds. The spatial and temporal variation of meteorological, hydrological, geological and hydrogeological data across the model area is described in gridded form of the input as well as the output from the model.

The use of RadX as a graphical user interface gives users the ability to easily customize the input data for their simulation. They can, for instance, modify the land use to study the effect of urban climate mitigation strategies like green roofs. They can select real hydrological events measured by the ENPC X-Band radar as rainfall input, but also generate virtual rainfall events. To ease the interpretation of the simulation, RadX can render interactive 2D and 3D graphics directly in the users' web browser by the use of open source libraries that focus on performance using low level graphic API. For example, it gives the user an intuitive and efficient way to spot singular points of the infiltration output display. Users can also download the file outputs to use in their GIS software.

Other components can be integrated to RadX to satisfy the particular needs with the help of visual tools and forecasting systems, eventually 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.: The Fresnel Platform for Greater Paris: enhancing the urban resilience with the fully distributed and physically based model, Multi-Hydro, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4992, https://doi.org/10.5194/egusphere-egu23-4992, 2023.

The safety of autonomous vehicles will depend critically on the performance of sensors (such as 77GHz radar), which will degrade in the presence of propagation losses during severe weather events. Variations in the drop size distribution lead to significant uncertainty in attenuation estimates. As part of the UK government's commitment to the safe introduction of autonomous vehicles, and in collaboration with the National Physical Laboratory, we have set up a series of observing platforms at Met Office Cardington to measure a multitude of weather-related variables such as temperature, pressure, illumination, precipitation particles, fog, etc. In this contribution, I will cover our work on characterising the rain drop size distribution, using a network of 5 disdrometers located 125m apart, and returning a drop size distribution every minute. From the spectra, we derived an estimate of the attenuation, including an estimate of the uncertainty.

How to cite: Husnoo, N. and Jones, D.: The impact of drop size distribution variability and rainfall attenuation on autonomous vehicle sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14407, https://doi.org/10.5194/egusphere-egu23-14407, 2023.

Opportunistic sensors have great potential for rainfall monitoring, as the density of their networks can outperform standard rainfall monitoring networks. The commercial microwave link (CML) network enables indirect monitoring of path-averaged rainfall intensity. It is retrieved from signal attenuation caused by raindrops, which can be related to rainfall intensity by a simple power law. Quantitative precipitation estimates from CMLs are, however, affected by uncertainty, which is still challenging to estimate.

This study proposes, for the first time, to use information theory methods to quantify uncertainty in CML QPEs. This method enables measuring the firmness of relationships between different variables using discrete probability distributions and also estimates the uncertainty. The advantage resides also in the fact that it allows any type of data to be used. This approach was recently applied by Neuper and Ehret (2019) to evaluate quantitative precipitation estimates with weather radar.

Data from non-winter periods of 2014 – 2016 are used at a temporal resolution of 15 min. The target (reference) data are the rain gauge adjusted radar observation. The CML data (signal attenuation and its processing) from the Prague network and its hardware characteristics are used as predictors. Additionally, other predictors, e.g., temperature and synoptic types, are used as further predictors. First, the information content of individual predictors of the target rain gauge adjusted radar data is measured. Specifically, we tested how different combinations of predictors reduce uncertainty. Second, the effect of the sample size on uncertainty is investigated. Different sizes of random samples are selected from the dataset and their information content for the target is quantified.

Depending on the choice of the predictor(s), their abilities to estimate the target variable can be compared. Their predictive uncertainties are different, which results in a ranking of suitability of available predictors and their combinations.

References
Neuper, M. and Ehret, U. (2019) Quantitative precipitation estimation with weather radar using a data- and information-based approach, Hydrol. Earth Syst. Sci., 23, 3711–3733, https://doi.org/10.5194/hess-23-3711-2019.

This study is supported by the Student Grant Competition grant of Czech Technical University in Prague no. SGS22/045/OHK1/1T/11.

How to cite: Špačková, A., Fencl, M., and Bareš, V.: Information-based approach for quantifying uncertainty in precipitation estimates from commercial microwave links, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15924, https://doi.org/10.5194/egusphere-egu23-15924, 2023.

On 14 and 15 July 2021, heavy and prolonged precipitation caused flooding in large areas in western Germany and adjacent regions. The Ahr River valley in the Federal State of Rhineland-Palatinate was particularly affected, with numerous fatalities and large-scale damage. Due to the spatio-temporal variability of precipitation and failure of several gauging stations, the estimation of the flood triggering areal precipitation as well as determination of peak discharges is associated with high uncertainties.

In this study, we present results where data from opportunistic sensors (commercial microwave links (CML) and personal weather stations (PWS)) were used to interpolate hourly precipitation sums for the Ahr catchment. The data from the opportunistic sensors was quality controlled, filtered and interpolated using the methods from Graf et al. (2021). This precipitation data was compared to a gauge adjusted weather radar product from the German Weather Service DWD as well as interpolated rain gauge data. In order to determine the maximum discharges at the gauges in the Ahr, flood was simulated with the water balance model LARSIM (Large Area Runoff Simulation Model) using the aforementioned precipitation products as input data.

The results show that the areal precipitation obtained from opportunistic sensors yielded higher sums than the gauge adjusted radar products and the interpolated gauge data, especially in the northern part of the Ahr catchment where the station density of the conventional rain gauges was not sufficient to capture the spatial variability of this extreme event. Furthermore, the modelled run-offs using the precipitation input from opportunistic sensors yielded higher and more plausible peak discharges than the ones with the gauge adjusted weather radar product. This suggests that the radar underestimated precipitation due to attenuation. The difference in the resulting peak discharges point to the fact that due to the saturated soils any additional precipitation during the flood event in July 2021 lead to a direct run-off effect.

References:

Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H., & Bárdossy, A. (2021). Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales. Journal of Hydrology: Regional Studies, 37, 100883.

How to cite: Seidel, J., Bárdossy, A., Eisele, M., El Hachem, A., Chwala, C., Graf, M., Kunstmann, H., Demuth, N., and Gerlach, N.: Using Opportunistic Rainfall Sensing to improve Areal Precipitation Estimates and Run-off Modelling – The Case Study of the Ahr Flood in July 2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12265, https://doi.org/10.5194/egusphere-egu23-12265, 2023.

Attenuation data from commercial microwave links (CMLs) has proven useful for estimating rainfall. Their major benefits are a high abundance in most regions on earth, a high resolution in time, close to ground measurement, and the absence of installation costs and efforts. The spatial and temporal coverage of CMLs would theoretically enable the generation of continental rainfall maps for various aggregation times.

However, there exist limitations that have so far inhibited rainfall estimation on larger scales. The data is generally obtained on a national basis from different network providers and networks can vary significantly in characteristics such as frequency and length distributions. CML data requires careful processing that depends on these characteristics and which has so far been adjusted to independent data sets only.

In this study we investigate what kind of processing is required to use independent and heterogeneous CML data sets for the generation of transboundary rainfall maps. We use 3900 CMLs from Germany and 2500 CMLs from the Czech Republic. The German data set is rather evenly distributed with respect to spatial coverage, frequencies and lengths. The Czech data set, on the other hand, varies significantly more in all these regards: it is characterized by dense networks of short CMLs in the cities, a large share of CMLs with E-Band frequency, and hence a large range of sensitivities.

We find that quality control is important especially when dealing with independent data sets. We propose several algorithms and the consideration of network characteristics when combining two CML data sets, and show how adapted but straightforward processing allows the generation of transboundary rainfall maps.

How to cite: Blettner, N., Fencl, M., Bareš, V., Chwala, C., and Kunstmann, H.: Challenges in the usage of commercial microwave links for the generation of transboundary German-Czech rainfall maps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13080, https://doi.org/10.5194/egusphere-egu23-13080, 2023.

In previous work [Aguilar Flores et al., Stoch. Environ. Res. Risk Assess. (2021) 35: 1681-1687], distributional convergence of breakdown coefficients (BDCs) to symmetric probability distribution functions of weights in discrete-scale multiplicative cascades has been shown. Asymmetric weights distributions, however, cannot become the limiting functions of symmetric BDC distributions. A procedure has been devised and is presented herein for the computation of the limiting distributions in the aforementioned cases, involving a convolution that is identified with the first-level BDCs probability distribution, and thereby can be used for the purpose of model validation in otherwise non-ergodic single realizations of multiplicative cascade models.

How to cite: Aguilar Flores, C. and Carsteanu, A. A.: Breakdown coefficients of multiplicative cascades having asymmetrically distributed generators with bounded essential range, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10237, https://doi.org/10.5194/egusphere-egu23-10237, 2023.

The microphysical processes were found to be vital in facilitating the system evolution for a merger-formation bow echo (MFBE) in southeast China, where the reinforced precipitation enhanced the cold pool strength via evaporation cooling. However, current numerical model failed to accurately perform such processes, suggesting the large uncertainties for microphysical schemes in simulating MFBE events in southeast China. In this study, three microphysics schemes including Thompson (THOM), Morrison (MORR), and Weather Research and Forecasting Double-Moment 6-Class (WDM6) schemes were evaluated by comparing against polarimetric observations and Variational Doppler Radar Analysis System (VDRAS) analyses. The three schemes captured the basic kinematic structures for this MFBE event after assimilating radar radial velocities, but all underpredicted the cold pool strength by ∼25%. Particularly, THOM produced the best raindrop size distributions (DSDs) and precipitation pattern, and the larger raindrop size bias and the weak cold pool strength were owing to the relatively low rain breakup efficiency and inefficient rain evaporation, respectively. By decreasing the cutoff diameter of rain breakup parameterization from the default 1.6–1.2 mm (i.e., increasing breakup efficiency) and increasing evaporation efficiency by threefold in THOM, the simulated DSDs and precipitation were greatly improved, and the cold pool strength was significantly increased from 77% to 99% compared to that in VDRAS analyses. This study illustrated a plausible approach of combining polarimetric radar retrievals and VDRAS analyses as bases to adjust THOM default settings in simulating a MFBE event in southeast China with physical characteristics more consistent with observations. Since microphysical processes vary from convective organizations and climate regions, it is recognized more cases studies are needed in the future to examine the validity and approach in this study to improve simulations and predictions of MFBEs in southeast China.

How to cite: Zhao, K., Zhou, A., Lee, W.-C., and Huang, H.: Evaluation and Modification of Microphysics Schemes on the Cold Pool Evolution for a Simulated Bow Echo in Southeast China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6808, https://doi.org/10.5194/egusphere-egu23-6808, 2023.