Rainfall is a “collective” phenomenon emerging from numerous drops. Understanding the relation between the physics of individual drops and that of a population of drops remains an open challenge, both scientifically and at the level of practical implications. This remains true also for solid precipitation. Hence, it is much needed to better understand small scale spatio-temporal precipitation variability, which is a key driving force of the hydrological response, especially in highly heterogeneous areas (mountains, cities). This hydrological response at the catchment scale is the result of the interplay between the space-time variability of precipitation, the catchment geomorphological / pedological / ecological characteristics and antecedent hydrological conditions. Therefore, (1) accurate measurement and prediction of the spatial and temporal distribution of precipitation over a catchment and (2) the efficient and appropriate description of the catchment properties are important issues in hydrology.
This session will bring together scientists and practitioners who aim to measure and understand precipitation variability from drop scale to catchment scale as well as its hydrological consequences. Contributions addressing one or several of the following topics are especially targeted:
- Novel techniques for measuring liquid and solid precipitation variability at hydrologically relevant space and time scales (from drop to catchment scale), from in situ measurements to remote sensing techniques, and from ground-based devices to spaceborne platforms. Innovative comparison metrics are welcomed;
- Precipitation drop (or particle) size distribution and its small scale variability, including its consequences for precipitation rate retrieval algorithms for radars, commercial microwave links and other remote sensors;
- Novel modelling or characterization tools of precipitation variability from drop scale to catchment scale from various approaches (e.g. scaling, (multi-)fractal, statistic, deterministic, numerical modelling);
- Novel approaches to better identify, understand and simulate the dominant microphysical processes at work in liquid and solid precipitation.
- Applications of measured and/or modelled precipitation fields in catchment hydrological models for the purpose of process understanding or predicting hydrological response.
vPICO presentations: Fri, 30 Apr
Precipitation variability from drop scale to regional scale is not fully understood, except we know there is variability at all scales. The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) Dallas-Fort Worth (DFW) urban demonstration network consists of a high-resolution, dual-polarized X-band radar network and a National Weather Service S-band radar system for areal coverage as well as a network of in-situ instruments including tipping bucket gauges, and disdrometers in the DFW international airport. Based on the CASA DFW monitoring platform, we have been exploring the rainfall variability across the airport scale of a large airport such as DFW. We study the variability of precipitation within the airport grounds and the corresponding impact on airport monitoring and regulatory compliance issues. We also extend this variability analysis across the DFW metro which is also considered a large metro region. The particle size distribution and its small-scale variability are analyzed on both heavy and light rainfall events. As for the catchment scale, the spatial heterogeneity of precipitation in the DFW international airport is specially explored. As for the regional scale, the DFW metropolis is used, and its precipitation variability and trends are demonstrated under the DFW urban radar network. Finally, hydrological response to precipitation variability during the rainstorm event in the DFW international airport is discussed. These observations provide an insight into the relation between space time variability of precipitation and practical response activities in an important region such as airport grounds.
How to cite: Chandra, C. V. and Ma, Y.: Precipitation variability from drop scale to urban scale in the Dallas-Fort Worth metro region, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14097, https://doi.org/10.5194/egusphere-egu21-14097, 2021.
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.
Commercial Microwave Links (CMLs) have demonstrated to be a valuable complementary measuring technique with regard to rainfall measuring. Their intrinsic characteristics give them an edge over traditional networks such as meteorological radars, satellites, and rain gauges. For instance, given their high density, especially in urban areas, they offer a higher spatial (and even temporal) resolution against rainfall observations from rain gauges. Moreover, they observe rainfall in a close proximity to the ground surface compared to radar and/or meteorological satellites. As their use in monitoring rainfall is in its “early stage”, there are still some challenges to overcome, e.g., a low accuracy when observing light rainfall.
In general, CMLs networks used to operate within the C, X, Ku, K, and Ka bands of the electromagnetic spectrum (i.e., ~4 - 40GHz) over distances varying from hundreds-of-meters to tens-of-kilometres. A big advantage offered by these bands is the linear relationship between rainfall intensity and power attenuation, which actually is the cornerstone of rainfall retrievals from CMLs. Nevertheless, as the continuously increasing demand for a larger throughput in such networks, mobile operators are gradually moving into the 71 - 86 GHz region, i.e., the E band. This fact alone brings more challenges in the retrieval of rainfall as the relationship between rainfall intensity and power attenuation not only starts departing from linearity in this band but also is more sensitive to the drop size distribution of rainfall. On the other hand, over such frequencies/band, it is possible now to reliably monitor rainfall intensities lower than 1 mm/h, which was practically impossible with lower-frequency CMLs.
Our work focuses on the performance of ~250 E-Band CMLs over a continuous period of ~7 months in 2020. These CMLs are part of a larger network located in the city of Prague (Czech Republic) and its surroundings. We evaluate their performance against a local network of ~50 rain gauges. We demonstrate the potential of E-band CMLs in retrieving accurate estimates for both light and heavy rainfall. Recently, there has been only few studies focused on E-band links. Our contribution to the field is in performing analyses over a larger spatio-temporal scale.
How to cite: Rios Gaona, M. F., Fencl, M., and Bares, V.: Citywide rainfall estimates from hundreds of E-band CMLs, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8454, https://doi.org/10.5194/egusphere-egu21-8454, 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.
The 2-Dimensional Video Disdrometer (manufactured by Joanneum Research) is an instrument widely used for ground validation and precipitation microphysics studies. This instrument is capable of reporting back multiple properties of each detected hydrometeor; fields in the data record include arrival time, fall velocity, oblateness, mass-weighted equivalent diameter, detection position, and estimated detector sample area for each detected drop.
The last of these variables is necessary for using the data record to reliably estimate the instantaneous rain rate and total accumulations; it varies from detected drop to detected drop because a detected hydrometer must be fully enclosed within a fixed sample area to be successfully characterized by the instrument; this means that larger droplets have a smaller region that their centers can fall through and still be accurately measured. Careful analysis reveals that improvements can be made to the manufacturer’s calculation of this drop-dependent effective sample area.
These improvements are related to four key observations. (1) Due to the optical geometry of the instrument, not every pixel comprising the detection area has the same size. (2) The manufacturer’s algorithm makes some sub-optimal corrections for accounting for the detection area boundary. (3) The assumed extent of the full detection area field-of-view has been found to be slightly inaccurate. (4) There is a recently found anomaly that intermittently renders part of the detection area insensitive to reliable drop detection.
Here, we present a review of these observations, outline the structure of a simple post-processing algorithm developed to adjust the effective sampling area for each drop, and present results quantifying the overall impact on precipitation accumulations for a data record incorporating over 200 million detected raindrops.
How to cite: Larsen, M. L. and Blouin, C. K.: Modifications to the Effective Sample Area in Data Acquired by 2-Dimensional Video Disdrometers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1622, https://doi.org/10.5194/egusphere-egu21-1622, 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”.
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.
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.
Representing microphysics within weather and climate models is challenging because we lack fundamental understanding of microphysical processes and are limited by the computational inability to track each hydrometeor within a cloud system. Microphysics schemes parameterize rates for specific processes such as drop evaporation, collision-coalescence, or collisional-breakup, but their inherent assumptions lead to uncertainty in model solutions which are often difficult to understand and quantify. Observations such as those from polarimetric radar provide insight into the microphysical evolution of clouds, but alone they are unable to provide quantitative information about the process rates that lead to this evolution. The Bayesian Observationally Constrained Statistical-Physical Scheme (BOSS) is a recently-developed bulk microphysics scheme designed to bridge the gap between observations and the processes acting on individual drops, such that process rate information can be directly learned from polarimetric radar observations. BOSS operates with no predefined drop size distribution (DSD) shape and makes few assumptions about the process rate formulations. Because there is no prescribed DSD shape, a new moment-based polarimetric forward operator is used to relate model prognostic moment output to polarimetric radar variables. Process rates are written as generalized power functions of the prognostic DSD moments (related to bulk quantities such as mass concentrations), with flexibility to choose the number and order of the prognostic DSD moments and number of power terms in the process rate formulations. The corresponding process rate parameters are constrained directly with observation using Markov chain Monte Carlo in a Bayesian inference framework, allowing BOSS to learn microphysical information directly from observations while simultaneously quantifying parametric uncertainty. The process rate formulations in BOSS can be made systematically more complex by adding more terms and/or more prognostic DSD moments, which allows us also to track down sources of structural uncertainty. In this study, we use a detailed bin microphysics scheme as “truth” to generate the constraining observations synthetically, which include profiles of polarimetric radar variables (ZH, ZDR, KDP) and vertical fluxes of prognostic DSD moments at the surface. An error analysis shows that BOSS produces process rate profiles similar to those of a bin scheme when only provided polarimetric rain profiles and surface prognostic moment fluxes. We also display initial results where BOSS is used to estimate microphysical process rate information from real polarimetric radar observations.
How to cite: Reimel, K., van Lier-Walqui, M., Kumjian, M., Morrison, H., and Prat, O.: Using BOSS to learn microphysical process rate information from polarimetric radar observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12963, https://doi.org/10.5194/egusphere-egu21-12963, 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.
The growing attention to modifications in climate in several societal sectors has led to an increasing number of studies and research on the topic of climate change and especially on changes in precipitation. The analysis presented here draws a “state of the art” of changes in the Italian precipitation regime through the review of the most relevant published studies, in peer-review journals. The aim of the study is to summarize a large quantity of information derived from specific studies, in a unique analysis and to highlight the main patterns of rainfall changes in Italy in the last decades. The results of 54 selected studies are discussed through the introduction of a weight factor, which considers the importance of each study according to its geographical area, stations density, and time series length, and provides a quantitative evaluation of the review. To offer a coherent climatic classification of the review findings, Italy is subdivided in three main macro areas and studies are also subdivided in 3 groups according to the Time-Series Length: Short TSL, less than 65 years; Long TSL, until 100 years; and centennial TSL, over 100 years. The analysis is focused on the Total Precipitation (TP) and the number of Wet Days (WDs) indices at the annual and seasonal scale. Looking at the overall results of the review, most of the studies agree about a decrease at the annual scale of the Wet Days index throughout the Italian territory for short and centennial TSL. The reduction of precipitation is confirmed by the Total Precipitation index that at the annual scale reflects this tendency except for the Northern Italy. This feature also emerges from the seasonal analysis, with some heterogeneity in the results due to difference in the number of studies used in the various areas, suggesting that there is an underlying climatic pattern driving trends toward a reduction in wet days and rainfall over the Italian territory.
How to cite: Caporali, E., Lompi, M., Pacetti, T., Chiarello, V., and Fatichi, S.: An evaluation of rainfall regime changes in Italy over the last decades from literature, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12786, https://doi.org/10.5194/egusphere-egu21-12786, 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.
It has been stated that "the study of drop-size distributions, with its roots in both land-surface processes [e.g. interception, erosion, infiltration and surface runoff] and atmospheric remote sensing [e.g. radar meteorology], provides an important element to an integrated program of hydrometeorological research" (Smith, 1993). Although raindrop size distributions have been studied from a scientific perspective since the early 20th century, it was not until the mid-1990s that researchers realized that all parameterizations for the drop size distribution published until then could be summarized in the form of a scaling law, which provided "a general phenomenological formulation for drop size distribution" (Sempere Torres et al., 1994). The main implication of the proposed expression is that the integral rainfall variables (such as rain rate and radar reflectivity) are related by power laws, in agreement with experimental evidence. The proposed formulation naturally leads to a general methodology for scaling all raindrop size data in a unique plot, which yields more robust fits of the drop size distribution. Here, we provide a statistical interpretation of the law’s scaling exponents in terms of different modes of control on the space-time variability of drop size distributions, namely size-control vs. number-control, inspired by the work of Smith and De Veaux (1994). Also, an attempt will be made toward interpreting the values of the scaling exponents and the shape of the scaled drop size distribution in terms of the underlying (micro)physical processes.
Smith, J. A., 1993: Precipitation. In Maidment, D. R., editor, Handbook of Hydrology, pages 3.1–3.47. McGraw-Hill, New York.
Sempere Torres, D., J.M. Porrà, and J.-D. Creutin, 1994: A general formulation for raindrop size distribution. J. Appl. Meteor., 33, 1494–1502.
Smith, J.A. and R.D. De Veaux, 1994: A stochastic model relating rainfall intensity to raindrop processes. Water Resour. Res., 30, 651–664.
How to cite: Uijlenhoet, R.: Controls on the space-time variability of raindrop size distributions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16392, https://doi.org/10.5194/egusphere-egu21-16392, 2021.
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