HS7.1
PICO: Thu, 27 Apr | PICO spot 4
Local rainfall measurements can be done in a significant range of methods which rely on very different underlying measurement concepts and assumptions. As an illustration, mechanical rain gauges collect small rainfall amounts; optical disdrometers assess size and velocity of each drop passing through a sampling area, while Doppler sensors derive a rain rate from estimated average fall velocity. Hence, the quality of the measurements can vary a lot, depending on factors such as rain drop size, wind velocity, rain rate etc. Understanding the differences between various technologies enables us to determine the most reliable device depending on each raining condition. This research aims to compare the performance of two of those devices: the optical disdrometer Parsivel2 (manufactured by OTT) and a mini Doppler radar part of a mini Meteorological Station (manufactured by Thies). The comparison was done with two research focuses: by evaluating the scaling features of the fields measured by both instruments utilizing the framework of Universal Multifractals (UM) to have a performance assessment valid across scales and not only separated scales, and by analyzing the influence of physical parameters namely drop size, wind velocity and rainfall rate in the performance of the devices.
The data used was collected on a meteorological mast located in the Pays d’Othe wind farm, 110km southeast of Paris. This measurement campaign is part of the RW-Turb project (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency (ANR-19-CE05-0022). The mast is operated with two sets of devices, one around 75m in height and the other around 45m. The observation time step of the Parsivel2 is of 30 seconds, and it measures full binned drop size and velocity distribution, while the mini station provides data (rainfall, 2D wind, temperature, pressure, humidity) with 1 second time step. In general, the mini-doppler radar is found to measure a smaller amount of rain with regards to the Parsivel2. More precisely, we found that the mini doppler radar returned very low rain measurements when subjected to rain conditions with a bigger mean drop size (Dm), and that heavy wind was related to a non-detection of the field in situations with light rain. Scaling analysis enabled us to show that mini Doppler radar exhibited white noise from observation scale smaller than 4s. Hence, it was used only with large time steps. UM analysis also revealed different scaling behaviour for mini Doppler radar rain data at finer temporal resolution than that of Parsivel (30 s).
How to cite: Seppe Silva, M., Vieira Casanova Monteiro, R., Jose, J., Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Multi-scale comparison of rainfall measurement with the help of a disdrometer and a mini vertically pointing Doppler radar , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-706, https://doi.org/10.5194/egusphere-egu23-706, 2023.
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, 24–28 Apr 2023, EGU23-9256, https://doi.org/10.5194/egusphere-egu23-9256, 2023.
Rain is known to be highly variable from drop to catchment scales. Properly quantifying this variability is important in efforts to accurately measure rain, reliably model hydrological processes, and validate previous work in the QPE community.
On the individual drop level, this variability can be observed by measuring how droplet size, fall speed, shape, and composition differs for drops arriving in close spatial and temporal proximity at the surface. Here, we present a novel method of measuring some of these individual drop properties using a liquid nitrogen capture technique. This method allows for the capture and subsequent isolation of individual drops in a sterile environment, which permits more detailed study of the differences in composition between drops and how those changes in composition may be related to other properties of physical interest.
The prototype capture system was utilized to capture drops across multiple rain events occurring near Charleston, South Carolina. Drop size measurements are compared to disdrometer measurements, and relationships between captured drop size and composition are explored.
How to cite: Boss, L. and Larsen, M.: Exploration of Drop-to-Drop Variability with a with a Novel Measurement Technique, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9270, https://doi.org/10.5194/egusphere-egu23-9270, 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, 24–28 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, 24–28 Apr 2023, EGU23-14766, https://doi.org/10.5194/egusphere-egu23-14766, 2023.
Weather radar provides rainfall estimates at high resolution in both space and time, which is useful for many hydrological applications. Despite this, the radar rainfall estimation process introduces many sources of error, impacting the reliability of results obtained from the radar rainfall estimates. Key error sources include signal attenuation, radar calibration issues, ground clutter contamination, variability in the drop-size distribution and variation in the vertical profile of reflectivity. To gain an improved understanding of potential limitations, and the corresponding uncertainty of rainfall rates, the impact of these errors has been systematically investigated, developing a radar error model by inverting the rainfall estimation process.
To this end, an ensemble of realistic rainfall events is simulated, and working backwards in a stochastic manner gives an ensemble of weather radar images, corresponding to each rainfall event, at each time step. The radar error model includes random noise effects, drop-size distribution errors, sampling estimation variance and importantly, attenuation effects. To allow for direct comparisons, standard radar processing methods are applied to each radar image, to obtain corrected ‘best guess’ rainfall estimates which would be obtained from each weather radar ensemble member in real world applications. The difference between the simulated and corrected rainfall for each ensemble member is then treated as the uncertainty corresponding to the radar rainfall estimation process.
A simple measure is introduced, to help understand how often errors result in a rainfall signal completely irretrievable, referred to as ‘rainfall shadow’. Areas of rainfall that are ‘shadowed’ are defined as pixels where the simulated ‘true’ rainfall rate is significant, but the ensemble member has less than 10% of the original signal. This is equivalent to considering where a significant rainfall rate has been completely lost, and would therefore be irretrievable using standard correction methods, to quantify the frequency of occurrence in real-world radar rainfall applications. The impact of location of rainfall within images is considered, by introducing the second moment of area for radar images, in order to quantify the proximity of intense rainfall to the radar transmitter.
Results show relationships between rainfall shadows and high bias and uncertainty in rainfall estimates, related to the amount of rainfall (i.e. proportion and rates) in images. More central rainfall also results in higher errors and higher variability. The minimum likelihood of occurrence of rainfall shadows showed that 50% of event images have at least 3% of significant rainfall shadowed. In addition, 25% of images had a shadowed area of over 45km2, with the minimum largest shadow in one area for 5% of images exceeding an area of 50km2. This gap would result in an underestimation of the impact of potential floods, showing that weather radar has potential for important information to be lost. A model framework for representing this uncertainty in the radar rainfall estimation process provides methodology for assessing the impacts of radar rainfall errors on hydrological applications.
How to cite: Green, A., Kilsby, C., and Bardossy, A.: Quantifying the uncertainty corresponding to the radar rainfall estimation process: an inverse model for radar attenuation error, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14569, https://doi.org/10.5194/egusphere-egu23-14569, 2023.
The effectiveness of a hydrologic model is largely driven by the availability and nature of the input data. Among these, many studies proved precipitation to be the most important because it regulates the amount of water entering the system. Spatially continuous precipitation data can be obtained from radar technology. However, radar precipitation values are an indirect measure, and it is widely believed that their use in hydrologic modelling is complicated due to the presence of bias. The use of radar data is increasingly problematic in mountain regions where elevation plays a key role on precipitation, creating significant variations in few kilometers. Also, mountains can lead to a shadow effect of the radar beam.
The research objective is to integrate precipitation data derived from the radar into a partially distributed hydrologic model, running in an area with complex morphology. The study area is a portion of Upper Valtellina valley (about 2300 km2), located within the Alpine belt on the border between Italy and Switzerland, and characterized by an elevation range between 350 and 3400 m a.s.l. The hourly series of 22 rain-gauges (18 Italian and 4 Swiss stations) and hourly precipitation from a radar dataset (1km x 1km resolution, from MeteoSWISS) from 2010 to 2020 are used. The mean bias between the series extracted in the radar cells at the station locations and the series measured by rain-gauge is around -28%, indicating a general underestimation of the radar data. The targets of the correction techniques are the precipitation series at the centroids of the sub-basins defined by the hydrologic model.
For the correction, two approaches are tested: (i) the radar precipitation is corrected in every centroid of the hydrologic model subbasins (point-based correction); (ii) the radar precipitation is adjusted by spatializing the radar-station error (interpolation-based correction). The first approach is based on finding the statistical relations between the radar-station series of the three closet stations to the target centroid and applying the statistical correction (Copula or Cumulative Distribution Function (CDF) matching bias correction) to the precipitation series in the centroid cell. The result of the correction is a combination of the statistical relationships weighted according to a Triangular Irregular Network. The second technique focusses instead on the interpolation of the error (residuals) calculated as the difference between radar and rain-gauge values, which is subsequently added to the original radar raster. Two different interpolation techniques are used: Thin Plate Splines and Inverse Distance Weighting. All methods are evaluated through performance indices (KGE and RMSE) at the station locations by Leave One Out cross validation.
Point-based applications are cost-effective and require less computational effort than spatial interpolations. Preliminary results show that the point-based corrections through Copula and CDF have similar performances. In detail, the KGE increases from 0.18 to 0.52 and 0.55 for Copula and CDF, respectively. RMSE decreases from 0.78 mm to 0.53 mm (Copula) and 0.62 mm (CDF). Interpolation-based corrections are still ongoing, therefore there are no definite results regarding the comparative effectiveness of one type of correction over the other.
How to cite: Citrini, A., Lazoglou, G., Bruggeman, A., Zittis, G., Beretta, G. P., and Camera, C.: Correction of hourly radar precipitation data based on rain-gauges values: what is the most efficient method for hydrologic modeling purposes?, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15902, https://doi.org/10.5194/egusphere-egu23-15902, 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, 24–28 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, 24–28 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, 24–28 Apr 2023, EGU23-4992, https://doi.org/10.5194/egusphere-egu23-4992, 2023.
With the expected increase in flooding due to climate change, accurate estimation of precipitation and the resulting modelled hydrographs are an essential requirement for reliable flood forecasts. At present, most adjustment methods for remotely sensed precipitation require raingauge data to increase the accuracy of the precipitation estimates. One disadvantage is that raingauges only measure precipitation at a given point and usually there are a relatively small number of these points in a typical catchment (and some smaller catchments may not have any raingauges). River discharge from the catchment integrates the influence of catchment-wide precipitation and can often be more accurately measured than the areal rainfall, especially in areas with a sparse raingauge network. Here, we present a non-raingauge adjustment method for remotely sensed precipitation estimates that utilises discharge data only to adjust these estimates for input to hydrological models. This method allows a hydrological model to adjust its treatment of precipitation input, through an additional model parameter, by comparing the observed and modelled hydrographs. This proposed method is comparable to a traditional raingauge adjustment methods over a number of catchments and hydrological models, for both peak flows and for the entire hydrograph. Additionally, this method allows for the adjustment of catchment averaged raingauge precipitation measurements to correct for any possibly errors due to using point data i.e. spatial density or representative issues. This results in a substantial improvement in discharge estimation compared to the un-adjusted raingauge measurements.
How to cite: O'Loughlin, F. and Bruen, M.: Adjustment of Remotely Sensed Precipitation using Discharge, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12503, https://doi.org/10.5194/egusphere-egu23-12503, 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, 24–28 Apr 2023, EGU23-14407, https://doi.org/10.5194/egusphere-egu23-14407, 2023.
In a changing climate accurate measurements and near-real time rainfall monitoring are essential for sustainable societies. Commercial microwave links (CMLs) offer a great alternative, or addition, to traditional sensors, like rain gauges and radar. While CMLs are a great source of opportunistic sensors the data from CMLs are usually limited by their accessibility for both research and actual implementation. To help in gaining better access and research into CML-derived rainfall we present a dataset at 10 second resolution with true coordinates for 364 bi-directional CMLs gathered during a pilot study in Gothenburg, Sweden over a three-month period (June-August 2015). These data are complemented by additional data from 11 high-resolution rain gauges (ten 1 min and one 15 min) and radar data (5 min and 2 km resolution) from the Swedish operational weather radar composite over the Gothenburg area.
Analysis of the data show that data collection is very complete, with 99.99% of the CMLs, 100% rain gauges and 99.6% of the radar data available. The gauge data shows that around 260mm rainfall was measured during this period with 6% precipitation during 15-minute intervals. At the Torslanda gauge on 28 July 2015 one the of the most intense events was observed during the three-month period with a peak intensity of 1.1 mm min−1. The CML data reflect this event well and show a drop of around 27 dB during the peak intensity. Radar data also showed a good distribution of the reflectivity of the precipitation with some measurements above 40 dBZ, which is commonly taken as an indication of convective precipitation. Some low intensity clutter was also found, mostly around -15 dBZ.
The data are accessible at https://doi.org/10.5281/zenodo.7107689 (Andersson et al., 2022). The sharing of these Open high-resolution data of Microwave links, radar and gauges (OpenMRG) should enable further research in microwave-link based environmental monitoring. In the longer term we hope that this dataset will also contribute to easier access of CML data and help in the development of the merging of multi-sensor products.
How to cite: van de Beek, R. (C. Z. )., Andersson, J., Olsson, J., and Hansryd, J.: OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14295, https://doi.org/10.5194/egusphere-egu23-14295, 2023.
The use of Commercial microwave links (CMLs) to estimate rainfall has been under investigation for the past 15 years. CMLs still seem like a promising supplement to standard measurement methods. So far, CMLs have almost exclusively been applied for rainfall only situations. It is expected that different precipitation types affect the CML signal strength and error sources differently. For CML applications in high latitude countries with frequent and extended periods with snowfall and mixed precipitation, an extension of the classification methods for these precipitation types is needed.
In this presentation we study how the CML signal attenuation is affected by different precipitation types and how those can be used to classify the different events. We use nearby disdrometers as a ground truth reference and CML data from different climatological conditions in Norway.
How to cite: Øydvin, E., Hagland, R. F., Nilsen, V., Wolff, M. A., and Kitterød, N.-O.: Classification of snow and rainfall using commercial microwave links, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12061, https://doi.org/10.5194/egusphere-egu23-12061, 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, 24–28 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, 24–28 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, 24–28 Apr 2023, EGU23-13080, https://doi.org/10.5194/egusphere-egu23-13080, 2023.
Precipitation is one of the most important factors in hydrological cycle and climate change. Due to global climate change, the global and regional hydrological cycle has been changed significantly, and the precipitation pattern has changed, which made natural disasters happened more frequent. In this study, we taken the Pearl River Basin as a case study area and used APHRODITE dataset to investigate the spatiotemporal trend of precipitation during the period of 1951-2015 based on six extreme rainfall indices recommended by the WMO. Then, the MK test was used to verify their trend and analyze the temporal and spatial variability. The results indicated that: (1) The annual PRCPTOT in the Pearl River Basin displayed an increasing trend with an increasing rate of 0.019mm/yr. Although the number of annual rainy days was decreasing, the annual SDII exhibited an increasing trend. The annual R95P and RX1day exhibited an increasing trend, but the R95D and CWD showed a decreasing trend. The seasonal PRCPTOT increased in summer and winter, but decreased in spring and autumn. R95P and SDII displayed an increasing trend in four seasons. (2) The annual variation of PRCPTOT increased from west to east, the trend of SDII, R95P and RX1day were similar with PRCPTOT, but the high value of R95D happened in the middle and lower reaches of Xijiang River, and CWD increased from north to south. Except autumn, the seasonal spatial distribution of PRCPTOT, SDII and R95P were similar. In spring and winter, the spatial distribution of PRCPTOT, SDII and R95P increased from west to east, and from north to south in summer, indicating that the Beijiang River basin and Dongjiang River basin had a higher flood risk. (3) MK test of indices shown that the Yunnan-Guizhou Plateau was becoming drier, and the risk of extreme rainfall was increasing in the Beijiang River basin and Dongjiang River basin. The study results are valuable for future water resources management and ecological environment protection in the Pearl River Basin.
How to cite: Cai, S., Niu, K., Mu, X., and Yang, X.: Spatiotemporal pattern of precipitation in the Pearl River basin, China from 1951 to 2015, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7987, https://doi.org/10.5194/egusphere-egu23-7987, 2023.
In an attempt to get the best parameter estimations of the theoretically consistent IDF (Intensity Duration Frequency) models of rainfall intensity for the entire state of Baden Wuerttemberg, three well-defined optimization algorithms such as Differential Evolution (DE), Nelder Mead (NM), and TNC Truncated Newton (TNC) are taken into account for comparison.
Seven-parametric IDF model contains mean intensity µ, intensity scale parameters λ1, λ2 , time scale parameter α, fractal/smoothness parameter Μ, Hurst parameter Η, exponent of the expression of probability dry θ, and tail index ξ, which are obtained by minimizing the error between empirical k-moments and model quantiles. Error metric focusing on distribution quantiles x(k,T) is thus minimized for all available scales k and a series of return periods T . Non-linear solver is chosen to perform this step as these errors are non-linear functions of the parameters.
All results are demonstrated visually, and a final decision is made on the basis of precisely fitted parameter values to the model. This crucial step will also assist us in finding the optimum design values for stormwater and floods.
How to cite: Amin, B. and Bárdossy, A.: Comparative Analysis of Parameter Optimization of Theoretically Consistent IDF Models of Rainfall Intensity, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14215, https://doi.org/10.5194/egusphere-egu23-14215, 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, 24–28 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, 24–28 Apr 2023, EGU23-6808, https://doi.org/10.5194/egusphere-egu23-6808, 2023.
