HS7.1

Radar measurements provide information on precipitation in space and time. They do not measure precipitation but reflectivity. The transformation to precipitation is not straightforward.  The result is that different, partly random, partly systematic errors may occur.  Radar precipitation pixels are usually considered to measure the mean over a large area of 500 x 500 m. However the measurement itself is represented in polar coordinates and is subsequently transformed to a Cartesian system. As the measurements in the polar coordinate system deliver areal averages corresponding to different block sizes this is likely to have an effect on the estimates of the true precipitation values. This particularly applies to extremes. In the outer circles of the radar scan the blocks are bigger, and thus the measurements deliver areal extremes where a kind of area reduction factor is the result of the resolution. In order to investigate the influence of this on the extremes two numerical simulation examples were considered. Results from a long high resolution simulation using the String of Beads model applied with data from South Africa, and of a copula based direct simulation, are analysed and presented. The results show that the extremes towards the outer ring of the radar observations may, under stationary conditions, be reduced by up to 20 %.

How to cite: Bárdossy, A. and Pegram, G.: Extreme precipitations measured by radar, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4729, https://doi.org/10.5194/egusphere-egu21-4729, 2021.

Uncertainty in the gauged network can lead to inaccuracies in dam operations. Entropy is a well-known measurement of uncertainty. Goesan Dam has a small basin area and is affected by a small amount of precipitation, and Hwacheon Dam is contained outside the territory of South Korea, making it difficult to observe the water flow. The observed gauged precipitation and radar data on rainy days were considered between 2018 and 2019. Choosing appropriate radar were performed based on the priority of the rainfall gauge network using conditional entropy. This is because the rainfall gauge network is the actual precipitation and it can only cover certain points. However, the radar is the cloud reflectivity of a large area. Therefore the location of additional rain spots was selected through conditional entropy of highly consistent radar data. Nevertheless, there might be difficulties in installing gauged equipment in reality. So the optimal rainfall network was designed in consideration of the road network. As a result, the uncertainty of precipitation in Goesan Dam and Hwachoen Dam could be decreased by 63.3% and 67.9% respectively when three additional potential rain points were operated without any restriction. The uncertainty in the Goesan Dam basin and Hwachoen Dam would be reduced up to 55.3% and 65.0% when three additional potential rain points were installed nearby the road network. Therefore, through the proposed method, an optimal rainfall network can be designed by balancing cost and uncertainty.

This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 2018-Tech-20)

How to cite: Kwon, T. and Yoon, S.: Design of rain gauge network using radar and road network, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4355, https://doi.org/10.5194/egusphere-egu21-4355, 2021.

In-situ liquid precipitation measurements are the essential source of information about the rainfall process, its spatiotemporal variability, and the expected frequency of intense events. Other sources are remote sensors or the measurement of hydrologically connected variables, such as the water flow in rivers or evaporation, but all these only provide indirect estimates of precipitation. Notwithstanding the advantage of allowing areal estimates, they still require accompanying in-situ measurements for calibration or validation purposes.

The accuracy of in-situ precipitation measurements, though understated in most research studies and hydrological applications, is imperative to substantiate both scientific achievements and decision making. Unfortunately, due to budgetary shortages and other priorities, the managers of monitoring networks rarely address accuracy and traceability issues to a significant extent, and measurements are performed at a much lower level of accuracy than the current scientific knowledge and technological development would actually permit.

The neglected precipitation measurement biases propagate through the applications or the modelling chain and their awareness is often rapidly lost, together with the reliability of the obtained results. The comparability and homogeneity of precipitation estimates and their hydrological consequences between different studies is also questionable.

High-resolution measurements, even down to the scale of the single drop, are the way to achieve better knowledge of the precipitation process and to raise the confidence of users in the accuracy of their basic source of information. In this work, based on the most recent results in precipitation measurement studies, we aim at demonstrating that the accuracy of catchment scale rainfall and snowfall estimates rely on the interpretation of high-resolution raw data from traditional sensors and on the knowledge of the drop size distribution and other microphysical parameters of the rainfall process. Drop scale measurements must be accurate as well, and this is still an open issue for the currently available disdrometers.

How to cite: Lanza, L. G. and Cauteruccio, A.: The link between the drop scale, high resolution measurements and precipitation estimates at the catchment scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12771, https://doi.org/10.5194/egusphere-egu21-12771, 2021.

Rain gauges and weather radars are the default sources of rainfall information. Rainfall estimates from these sensors improve our understanding of the hydrological cycle and are vital for water-resource management, agriculture, urban planning, as well as for weather, climate, and hydrological modelling. Still, due to the high spatio-temporal variability of rainfall and the specific drawbacks of the individual rainfall sensors, the rainfall variability cannot be captured completely. In the last decade, the number and availability of opportunistic rainfall sensors increased rapidly. These sensors are initially not meant to measure rainfall for scientific or operational purposes, but, if processed carefully, can be used for these cases . Here we present an analysis of two years of data from two opportunistic rainfall sensors, namely personal weather stations (PWS) and commercial microwave links (CMLs). We evaluate the performance of rainfall maps derived from these sensors on different spatial and temporal scales in Germany.

The data from around 15000 PWS tipping bucket-style rain gauges from the Netatmo network were accessed via Netatmos API. The data from around 4000 CMLs, which can be used to derive rainfall estimates from the rain-induced attenuation of the CMLs’ signal, were obtained from Ericsson. As both, PWS and CML data, can suffer from various error sources e.g. from unfavourable positioning and poor maintenance of PWS and from non-rain induced attenuation of the CMLs signal, we used a strict filtering routine. A total of seven gridded rainfall products were derived from different combinations of PWS, CML, and rain gauge data from the German Weather Service (DWD) with a geostatistical interpolation approach. This approach incorporates the uncertainty of the opportunistic sensors and the path-averaging characteristic of the CML observations.

To evaluate the resulting rainfall maps, we used three rain gauge data sets with different temporal and spatial scales covering the whole of Germany, the state of Rhineland-Palatinate and the city of Reutlingen, respectively. For all three reference data sets, rainfall maps from opportunistic sensors provided good agreement, with best results being derived from the combinations with PWS. Rainfall maps including CML data had the lowest bias. In a comparison with gauge adjusted radar products from the DWD, the radar products yielded better results than the rainfall maps from opportunistic sensors for the country-wide comparison of daily rainfall sums, which was carried out using the DWD’s independent network of manual rain gauges. But for the hourly references covering Rhineland-Palatinate and Reutlingen, the rainfall maps derived from opportunistic sensors outperformed the radar products. These results highlight the capabilities of opportunistic rainfall sensors which could be used in many hydrometeorological applications.

How to cite: Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H., and Bárdossy, A.: Combined rainfall estimates from personal weather station and commercial microwave link data in Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12445, https://doi.org/10.5194/egusphere-egu21-12445, 2021.

Many studies in literature have showed that hydrological models are highly sensitive to spatial variability of the rainfall field. Limited and inaccurate rainfall observations can negatively affect flood forecasting and the decision-making processes based on warning system. This problem becomes much more evident in urban catchments which usually covers huge areas and where the runoff process is faster, due to the highly impervious surfaces. Given this, it is a priority to develop always new operational instruments which can improve rainfall data availability and accurately quantify rainfall variability in space. To face this challenge, in the recent years, it has been investigated the use of commercial microwave links (CML) as opportunistic rainfall sensors which could be integrated with traditional rainfall observations in areas lacking sensors. The technique relies on the well-established relationship between CML's signal attenuation and rainfall intensity across the signal propagation path. Here, we assess the operational potential of a CML network, located in the northern area of Lambro river (Lombardia region, Italy). This urbanized region is of great hydrological interest, since it is often subjected to flash floods, hence it requires a robust and accurate warning system. We considered a set of about 80 CMLs distributed quite uniformly over the entire study area and we assessed if and how rainfall data collected by them can improve river discharge predictions. To this aim, we implemented a semi-distributed rainfall-runoff model, which reproduces the river flow at the outlet section in Lesmo (Monza e Brianza), and we fed the hydrological model with CML rainfall data. We tested the use of CML rainfall data as input to the hydrological model. In particular, we used path-averaged rainfall intensities, calculated from CML path attenuation, as point measurements with a weight inversely proportional to CML length. To check the suitability of CML data as input to our urban rainfall-runoff model, we compared the observed river discharge with the predicted one, obtained using different rainfall data layouts. Indeed, we tested CML data but also rain gauges measurements and a combination of CML and rain gauge observations.

How to cite: Cazzaniga, G., De Michele, C., Deidda, C., D'Amico, M., Ghezzi, A., and Nebuloni, R.: Commercial microwave links as rainfall input data in urban hydrological modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13247, https://doi.org/10.5194/egusphere-egu21-13247, 2021.

Environmental sources of measurement biases affect the accuracy of non-catching (mostly contact-less) precipitation gauges (Lanza et al., 2021). Wind is among the most significant influencing variables, since instruments exposed to the wind generate strong airflow velocity gradients and turbulence near their sensing volume. Hydrometeor trajectories are diverted by the induced updraft/downdraft and acceleration near the instrument, affecting the measured particle size distribution, and leading to an over- or underestimation of the precipitation intensity. This bias is common to all precipitation measurement instruments, including traditional catching-type gauges, but is amplified in non-catching gauges due to their complex shapes and measuring principles. Wind also changes the velocity of the falling hydrometeors, introducing further potential biases since velocity is explicitly used by disdrometers (in combination with the hydrometeors size) to determine the type of precipitation and to discard outliers.

The present work focuses on the Thies laser precipitation monitor, which employs a laser beam to detect hydrometeors in fight. It has a complex, non-axisymmetric shape, due to the physical constraints of its measuring principle. To evaluate the effect of wind on liquid precipitation measurements, Computational Fluid Dynamics simulations were run, using OpenFOAM, together with a Lagrangian particle tracking model. The drag coefficient formulation validated by Cauteruccio et al. (2021) was implemented in the OpenFOAM package. Various drop diameters were considered (0.25, 0.5, 0.75 and from 1 to 8 mm in 1 mm increments), and for each drop size, the vertical and horizontal velocity components were set equal to the terminal velocity and the free-stream velocity, respectively. Nine angles of attack were considered, from 0° to 180°, in 22.5° increments. For each angle, five different wind speed values (2, 5, 10, 15 and 20 m/s) were simulated. Each combination was run twice, first using a constant velocity field (as if the instrument were transparent to the wind) to evaluate the sole shielding effect of the instrument body on the measurement section, and then using the effective velocity fields.

The data were then processed, using a suitable drop size distribution and for each velocity/angle/rainfall intensity combination the collection efficiency of the instrument was calculated. This work is funded as part of the activities of the EURAMET project 18NRM03 – “INCIPIT – Calibration and Accuracy of Non-Catching Instruments to measure liquid/solid atmospheric precipitation”.

References:

Lanza L.G., Merlone A., Cauteruccio A., Chinchella E., Stagnaro M., Dobre M., Garcia Izquierdo M.C., Nielsen J., Kjeldsen H., Roulet Y.A., Coppa G., Musacchio C., Bordianu C., 2021: Calibration of non-catching precipitation measurement instruments: a review. J. Meteorological Applications (submitted).

Cauteruccio A, Brambilla E, Stagnaro M, Lanza LG, Rocchi D, 2021: Wind tunnel validation of a particle tracking model to evaluate the wind-induced bias of precipitation measurements. Water Resour. Res., (conditionally accepted).

How to cite: Chinchella, E., Cauteruccio, A., Stagnaro, M., and Lanza, L. G.: The wind-induced bias of the Thies Laser Precipitation Monitor obtained using CFD and a Lagrangian particle tracking model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10750, https://doi.org/10.5194/egusphere-egu21-10750, 2021.

Raindrop size distribution (DSD) is the key factor to derive reliable rainfall estimates. It is strongly related to a number of integral rainfall parameters, including rain intensity (R), rain water content (W) and radar echo (Z). Disdrometers are the senors commonly used to measure DSD based upon microwave or laser technologies; such as JWD (Joss-Waldvogel Disdrometer), Parsivel and 2DVD (Two-Dimensional Video Disdrometer). These sensors have different strengths and weakness, and they are relatively expensive. This hinders the possibility to have a large-scale and high-density observation of DSD. In this work, our goal is to explore the possibility to develop a lightweight and low-cost disdrometer with high accuracy.

We start with establish a model that can well simulate the signal reaction of a single drop falling on a cantilever piezo film. A series of experiments were conducted to test the reaction of drops with different sizes (diameters ranging from 2 - 4 mm) and as drops falling onto different locations of the film. We then modelled the collision by assuming the piezo film to be a damped cantilever beam and drop force to be a step force; and the drop force is derived based upon the measurement of the deflection of beam end, which is further used to fit the damp ratio. Preliminary results suggest that the signal reaction of a single drop hits can be well simulated based upon the proposed model under current experimental setting. More experiments and simulations are currently undergoing to explore the capacity of the proposed model with different drop falling velocity, size and position, as well as its reaction of multiple drops.  

How to cite: Wei, C.-L., Su, W.-J., Chang, S.-W., and Wang, L.-P.: Toward a low-cost disdrometer: simulating the collision of raindrops with a cantilever piezo film, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10785, https://doi.org/10.5194/egusphere-egu21-10785, 2021.

Wind-induced biases that affect catching-type precipitation gauges have been largely studied in the literature and dedicated experimental campaigns in the field were carried out to quantify this bias for both liquid and solid precipitation (including the recent WMO intercomparison on solid precipitation – SPICE). Experimental results show a large variability of the Collection Efficiency (CE) curves that depend on the precipitation type, intensity and the Particle Size Distribution (PSD) (see e.g. Colli et al. 2020). This was confirmed by recent studies using Computational Fluid Dynamic simulations to assess the airflow pattern around the gauge body and particle tracking models to simulate the particle trajectories when approaching the collector and calculating the Catch Ratio (CR) associated with various drop size - wind speed combinations (see e.g. Colli et al 2016, Cauteruccio and Lanza 2020).

In the present study, the CR values derived from the work of Cauteruccio and Lanza (2020) for a catching-type cylindrical gauge as a function of the drop size were fitted with an inverse second-order polynomial. The parameters of such curves were themselves expressed as a function of the wind speed. This formulation was adopted to calculate the CE of a catching-type cylindrical gauge based on contemporary wind and PSD measurements. These were obtained at the field test site of the Hong Kong International Airport using six co-located anemometers and a two-dimensional video disdrometer (2DVD), at one-minute resolution. The obtained CE was used to correct the rainfall intensity measured by three catching-type cylindrical gauges, located at the same site, and was compared with the ratio between the raw data measured by the three cylindrical gauges and the 2DVD rainfall intensity measurements. Results show the improvement due to the correction and suggest that the 2DVD is subject to some wind-induced bias as well.

References:

Cauteruccio, A. and L. G. Lanza, 2020. Parameterization of the Collection Efficiency of a Cylindrical Catching-Type Rain Gauge Based on Rainfall Intensity. Water, 12(12), 3431.

Colli, M., Lanza, L.G., Rasmussen, R. and J.M., Thériault, 2016. The Collection Efficiency of Shielded and Unshielded Precipitation Gauges. Part II: Modeling Particle Trajectories. Journal of Hydrometeorology, 17(1), 245-255.

Colli, M., Stagnaro, M., Lanza, L.G., Rasmussen, R. and J.M., Thériault, 2020. Adjustments for Wind-Induced Undercatch in Snowfall Measurements based on Precipitation Intensity. Journal of Hydrometeorology, 21, 1039-1050.

How to cite: Stagnaro, M., Cauteruccio, A., Lanza, L. G., and Chan, P.-W.: Using the measured Particle Size Distribution to assess the wind-induced bias of catching-type raingauges., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12651, https://doi.org/10.5194/egusphere-egu21-12651, 2021.

Recently the frequency of autumn typhoons has increased on the Korean Peninsula and their damage has also increased. The Korea Meteorological Administration (KMA) established a super-strong stage to raise awareness of such a powerful typhoon, Typhoon Haishen (2020). In usual the life cycle of the typhoon is divided into three stages: developing, mature, decaying. To analyze the impact of typhoon Haishen on the Korean Peninsula, this study focused on the landfall and decaying stage. To investigate the microphysical characteristics of the typhoon over time, the drop size distribution (DSD) at the azimuthal direction of the typhoon was studied. DSD variables were obtained by using PARSIVEL (PARticle SIze and VELocity) disdrometers at eleven observation sites from Geoje (34.88°N, 128.57°E) to Ulsan (35.58°N, 129.33°E) that located along the southern coast of Korea. As typhoon Haishen landed at the vicinity of Ulsan (35.3°N,129.3°E), the observation sites were included between the centre of the typhoon and the wind impact radius. Four days before typhoon Haishen landed, typhoon Maysak (2020) landed at the vicinity of Busan (35.4°N,128.9°E) and decayed. The intensity of typhoon Maysak was weakened and the form of convective cells became unclear after landing. Typhoon Haishen was also slightly weakened after landing, however, the form of convective cells and wind impact radius were continuously maintained.

How to cite: Kim, J. A. and Lee, D.-I.: Drop Size Distribution Characteristics of Typhoon Haishen (2020) in Korea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14150, https://doi.org/10.5194/egusphere-egu21-14150, 2021.

Several microphysical processes determine phase partitioning between ice and liquid water in a mixed-phase cloud. Here we investigate the collective growth of ice particles and liquid droplets affected by turbulent fluctuations in temperature and water vapor fields. All cloud particles, including inactivated nuclei (both CCN and IN), are described by Lagrangian super-particles. To account for local variability in the turbulent cloud environment we apply a Lagrangian microphysical scheme, where temperature and vapor mixing ratio are stochastic attributes attached to each super-particle. In addition, a simple linear relaxation scheme models turbulent mixing of the scalar fields probed by each super-particle. The limit of a locally homogeneous growth environment corresponds to an infinitely short turbulent mixing timescale. The impact of our Lagrangian microphysical scheme on phase partitioning is tested in adiabatic cloud parcel simulations. Results are confronted with idealized reference simulations that use bulk microphysics based on an assumed (temperature-dependent) phase partitioning function. Our study suggests that accounting for local variability in a turbulent cloud is important for reproducing steady-state mixed-phase conditions.

How to cite: Gomes Albuquerque, D., Coelho Abade, G., and Pawłowska, H.: Effects of turbulent fluctuations on phase partitioning in adiabatic mixed-phase cloud parcels, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8998, https://doi.org/10.5194/egusphere-egu21-8998, 2021.

Universal Multifractals have been widely used to characterize and simulate geophysical fields extremely variable over a wide range of scales such as rainfall. Despite strong limitations, notably its non-stationnarity, discrete cascades are often used to simulate such fields. Recently, blunt cascades have been introduced in 1D and 2D to cope with this issue while remaining in the simple framework of discrete cascades. It basically consists in geometrically interpolating over moving windows the multiplicative increments at each cascade steps.

In this paper, we first suggest an extension of this blunt cascades to space-time processes. Multifractal expected behaviour is theoretically established and numerically confirmed. In a second step, a methodology to address the common issue of guessing the missing half of a field is developed using this framework. It basically consists in reconstructing the increments of the known portion of the field, and then stochastically simulating the ones for the new portion, while ensuring the blunting the increments on the portion joining the two parts of the fields. The approach is tested with time series, maps and in a space-time framework. Initial tests with rainfall data are presented.

Authors acknowledge the RW-Turb project (supported by the French National Research Agency - ANR-19-CE05-0022), for partial financial support.

How to cite: Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Guessing the missing half of a geophysical field with blunt extension of discrete Universal Multifractal cascades, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10550, https://doi.org/10.5194/egusphere-egu21-10550, 2021.