Optical Disdrometer Research Articles

Characteristics of Rain and Sea Spray Droplet Size Distribution at a Marine Tower

The effects of sea spray on open-ocean rainfall measurements-the drop size distribution (DSD) and rainfall intensities-were studied using a state-of-the-art optical disdrometer. The number of rain droplets less than 1 mm in diameter is affected by several factors, including the type of rainfall and seasonality. Over the ocean, small rain and large sea spray droplets co-exist in the same diameter size class (0.072 to 1000 mm); hence, sea spray creates uncertainty when seeking to characterize the drop size distribution (DSD) of rain droplets over the ocean. We measured droplet sizes at a marine tower using a state-of-the-art optical disdrometer, a tipping-bucket rain gauge, a wind anemometer, and a time-lapse camera, over a period that included typhoon Krosa of 2019. The number of rain droplets of diameter less than 1 mm increased monotonically as the horizontal wind speed became stronger. Thus, the shape parameter μ of the Ulbrich distribution decreased. This decreasing trend can be recognized as an increase in sea spray. During no-rainfall hours (indicated by rain gauges on the ocean tower and nearby land), sea spray DSDs were obtained at various horizontal wind speeds. Furthermore, the proportions of sea spray to rainfall at different rainfall intensities and horizontal wind speeds were determined; at a horizontal wind speed of 16 to 20 m s−1, the average sea spray proportions were 82.7%, 19.1%, and 5.3% during total rainfall periods of 2.1 mm h−1, 8.9 mm h−1, and 32.1 mm h−1, respectively. Representation of sea spray DSDs, as well as rainfall DSDs, is a key element of calculating real rainfall intensities over the open ocean.

A Critical Evaluation of the Adequacy of the Gamma Model for Representing Raindrop Size Distributions

AbstractThe adequacy of the gamma model to describe the variability of raindrop size distributions (DSD) is studied using observations from an optical disdrometer. Model adequacy is checked using a combination of Kolmogorov–Smirnov goodness-of-fit test and Kullback–Leibler divergence and the sensitivity of the results to the sampling resolution is investigated. A new adaptive DSD sampling technique capable of determining the highest possible temporal sampling resolution at which the gamma model provides an adequate representation of sampled DSDs is proposed. The results show that most DSDs at 30 s are not strictly distributed according to a gamma model, while at the same time they are not far away from it either. According to the adaptive DSD sampling algorithm, the gamma model proves to be an adequate choice for the majority (85.81%) of the DSD spectra at resolutions up to 300 s. At the same time, it also reveals a considerable number of DSD spectra (5.55%) that do not follow a gamma distribution at any resolution (up to 1800 s). These are attributed to transitional periods during which the DSD is not stationary and exhibits a bimodal shape that cannot be modeled by a gamma distribution. The proposed resampling procedure is capable of automatically identifying and flagging these periods, providing new valuable quality control mechanisms for DSD retrievals in disdrometers and weather radars.

Model sensitivities in the case of high-resolution Eulerian simulations of local tephra transport and deposition

Tephra transport modelling has predominantly been carried out over synoptic-scale resolutions, resolving only fundamental atmospheric circulations. Consequently, model sensitivity studies have focused on these resolutions. In recent years, improved eruption source parameter (ESP) estimation methods and computational efficiency have progressively led to the use of higher resolutions to increase the fidelity of tephra transport simulations. This is a new computational regime whose model sensitivities remain largely unexplored. Here, high-resolution simulations using a 300 m horizontal grid spacing were carried out using WRF and FALL3D to assess sensitivity to: meteorological input data (using both reanalysis and forecast data), boundary layer parametrisation, plume model, settling velocity, total grain-size distribution (TGSD), and aggregation, leading to a total of 1280 possible configurations. An eruption that occurred on 28 July 2019 at Sakurajima volcano, Japan, is used as a case study, as the northwards dispersal of a 3.8 km-high plume led to the temporary closure of Kagoshima airport. Sedimentation was observed at three optical disdrometers on the volcano, providing insight on the deposition timing and particle size distribution. Results reveal that meteorological input data can have a significant impact over the fidelity of the simulations, with errors changing over one order of magnitude depending on the initialisation dataset. This was found to be the result of local bias in the simulated wind fields over the region of dispersal, which was not represented in the domain-wide assessment of the WRF simulations. Ensemble simulations using different meteorological input data were found to mitigate this, significantly increasing the fidelity of the final ensemble product. Use of a plume model and the correct estimation of the TGSD were seen to significantly improve results. Finally, even with detailed data to inform the choice, use of an empirical aggregation scheme was seen to significantly degrade the accuracy of the simulations.

Characteristics of Orographic Rain Drop-Size Distribution at Cherrapunji, Northeast India

The rain drop size distribution (DSD) at Cherrapunji, Northeast India was observed by a laser optical disdrometer Parsivel 2 from May to October 2017; this town is known for the world’s heaviest orographic rainfall recorded. The disdrometer showed a 30% underestimation of the rainfall amount, compared with a collocated rain gauge. The observed DSD had a number of drops with a mean normalized intercept log 10 N w > 4.0 for all rain rate categories, ranging from <5 to >80 mm h − 1 , comparable to tropical oceanic DSDs. These results differ from those of tropical oceanic DSDs, in that data with a larger N w were confined to the stratiform side of a stratiform/convective separation line proposed by Bringi et al. (2009). A large number of small drops is important for quantitative precipitation estimates by in-situ radar and satellites, because it tends to miss or underestimate precipitation amounts. The large number of small drops, as defined by the second principal component (>+1.5) while using the principal component analysis approach of Dolan et al. (2018), was rare for the pre-monsoon season, but was prevalent during the monsoon season, accounting for 16% (19%) of the accumulated rainfall (precipitation period); it tended to appear over weak active spells or the beginning of active spells of intraseasonal variation during the monsoon season.

Preprocessing and Assessment of Rain Drop Size Distributions Measured With a K-Band Doppler Radar and an Optical Disdrometer

Rain attenuation in millimeter-wave links depends on the drop size distributions (DSDs) of the raindrops. Empirical models disregard this dependence and estimate the specific attenuation using only the integrated rainfall rate [ $R$ (mm/h)]. This approach is valid for lower frequencies but it progressively losses accuracy as the frequency of interest becomes higher within the millimeter-wave range. Both the characterization of rainfall phenomena and the prediction of rain attenuation can be improved with the knowledge of DSD, which, in turn, depend on the type of rain event (stratiform or convective) and the $R$ . In this article, long-term DSD measurements from a vertical Doppler radar [Micro Rain Radar (MRR-2)] and a laser optical disdrometer (Thies laser disdrometer) are used to obtain, classify, and compare the statistics of DSD in Madrid in a period of more than ten years. The process to obtain the DSD from these advanced instruments is analyzed in detail, providing recommendations about the calibration of the radar data and the most appropriate particle filtering to apply on the laser disdrometer data.

Diurnal Variation in the Vertical Profile of the Raindrop Size Distribution for Stratiform Rain as Inferred from Micro Rain Radar Observations in Sumatra

The diurnal variation in the vertical structure of the raindrop size distribution (RSD) associated with stratiform rain at Kototabang, West Sumatra (0.20°S, 100.32°E), was investigated using micro rain radar (MRR) observations from January 2012 to August 2016. Along with the MRR data, the RSD from an optical disdrometer and vertical profile of precipitation from the Tropical Rainfall Measuring Mission were used to establish the microphysical characteristics of diurnal rainfall. Rainfall during 0000–0600 LST and 1800–2400 LST had a lower concentration of small drops and a higher concentration of large drops when compared to rainfall during the daytime (0600–1800 LST). The RSD stratified on the basis of rain rate (R) showed a lower total concentration of drops and higher mass-weighted mean diameter in 0000–0600 LST and 1800–2400 LST than in the daytime. During the daytime, the RSD is likely governed by a riming process that can be seen from a weak bright band (BB). On the other hand, during 0000–0600 LST and 1800–2400 LST, the BB was stronger and the rainfall was associated with a higher concentration of midsize and large drops, which could be attributed to more active aggregation right above the melting layer with minimal breakup. Diurnal variation in the vertical profile of RSD led to a different radar reflectivity (Z)-R relationship in the rain column, in which Z during the periods 0000–0600 LST and 1800–2400 LST was larger than at the other times, for the same R.

On the association between lightning and precipitation microphysics

Lightning is one of the most important extreme weather conditions and is associated with heavy convective rain and thunderstorms. However, the present understanding of such systems as well their effect on precipitation characteristics are insufficient for proper modelling. In this paper, the rain microphysics is studied at Kolkata using an optical disdrometer in relation to the lightning activities. Lightning information is obtained from ground based World Wide Lightning Location Network and Lightning Imaging Sensor onboard International Space Station. The associated cloud features are studied with Doppler weather radar and MODIS satellite measurements. Results indicate significant change in rain microphysics between events with lightning and without lightning. The cloud microphysics indicates that the cloud top temperature is lower and cloud effective droplet size is smaller in case of lightning in comparison to non-lightning situation. On the other hand, large drops are abundant while small drops are less in numbers in rain with lightning than rain without lightning. The a value in Z-R relation is found to be significantly higher for rain with lightning than other type. The rain attenuation is also found to have significant variation under these two conditions. The implication of such varied rain microphysics on GPM Ku and Ka band radar are also reported.

Shapes and Fall Speeds of Freezing and Frozen Raindrops

AbstractThis study investigates the shapes and fall speeds of freezing and frozen raindrops through field observations using an instrument called the high-speed optical disdrometer (HOD) that we developed recently. Our field observations showed that while the shapes of all of the observed freezing raindrops and a portion of the frozen raindrops (39% of the frozen raindrops that are larger than 1.0 mm in volume equivalent diameter D) resemble the shapes of warm raindrops, majority of frozen raindrops (61% of the frozen raindrops with D > 1.0 mm) exhibited a distinct feature such as a spicule, bulge, cavity, or aggregation. Field observations of axis ratios (i.e., ratio of the vertical to horizontal chord) and fall speeds were compared with the predictions of available models. Separate empirical axis ratio parameterizations were developed for the freezing and frozen raindrops using the HOD field observations and extensions to an available shape model were also incorporated. For the fall speeds of freezing and frozen raindrops, field observations demonstrated a good agreement with the predictions of the available parameterizations. Frozen raindrops showed a larger scatter of fall speeds around the mean fall speed of a given drop size than those of the freezing raindrops due to the shape variety among the frozen raindrops with the aforementioned distinct features. The drag coefficients for the observed hydrometeors were compared with the predictions of the available drag coefficient models. Separate “drag coefficient–Reynolds number” relationships for freezing and frozen raindrops were developed.

Characteristics of Precipitation Particles Measured by PARSIVEL Disdrometer at a Mountain and a Coastal Site in Korea

This study analyzed HC (Hydrometeor Classification), rain rate, and DSD (Drop Size Distribution) observed using an optical disdrometer, parsivel, at a mountain (Daegwallyeong, DG) and a coastal site (Mokpo, MP) over 2 years (2010–2011). The HC accuracies of the drizzle, rain, and snow were about 95%, 87%, and 80% respectively. The R2 (coefficient of determination) of the parsivel with TB (Tipping Bucket) rain gauge was 0.91 at MP and 0.96 at DG. The shapes of drizzle and rain DSD observed at DG and MP were similar while the variation in snow DSD was easily influenced by weather condition such as temperature and wind speed. The relatively warm temperature (–5 °C ∼ -0 °C) increased the snow particle number concentration at around 0.6~1 mm diameter and the relatively cold temperature (–15 °C ∼ -10 °C) decreased it above 2 mm diameter. Although wind speed was not a strong factor in snow DSD, larger particles were apt to form in relatively strong wind conditions. Due to different wind directions for the maritime and continental regions, snow particle number density (N(D)) at MP and DG exhibited large differences in terms of snow DSD shape and the number concentration. For instance, in the maritime precipitation, snow DSD shape at MP was broader than that at DG and small-size snow particles were observed at DG more frequently than at MP. In addition, camera-observed snow particle type measurement was carried out at DG in January to March 2010. During the measurement at DG, a mixed type of plate and column was the most frequent and an aggregation of plate type frequently occurred at lower temperatures.

Disdrometer measurements under Sense-City rainfall simulator

Abstract. The Hydrology, Meteorology and Complexity Laboratory of École des Ponts ParisTech (http://hmco.enpc.fr, last access: 24 March 2020) and the Sense-City consortium (http://sense-city.ifsttar.fr/, last access: 24 March 2020) made available a dataset of optical disdrometer measurements stemming from a campaign that took place in September 2017 under the rainfall simulator of the Sense-City climatic chamber, which is located near Paris. Two OTT Parsivel2 disdrometers were used. The size and velocity of drops falling through the sampling area of the devices of roughly a few tens of square centimetres are computed by disdrometers. This enables the estimation of the drop size distribution and the further study of rainfall microphysics or kinetic energy for example. Raw data – basically a matrix containing a number of drops according to classes of size and velocity, along with more aggregated ones such as rain rate and drop size distribution with filtering – are available. The dataset is publicly available at https://doi.org/10.5281/zenodo.3347051(Gires et al., 2019).

Impact of Disdrometer Types on Rainfall Erosivity Estimation

Soil erosion by water is affected by the rainfall erosivity, which controls the initial detachment and mobilization of soil particles. Rainfall erosivity is expressed through the rainfall intensity (I) and the rainfall kinetic energy (KE). KE–I relationships are an important tool for rainfall erosivity estimation, when direct measurement of KE is not possible. However, the rainfall erosivity estimation varies depending on the chosen KE–I relationship, as the development of KE–I relationships is affected by the measurement method, geographical rainfall patterns and data handling. This study investigated how the development of KE–I relationships and rainfall erosivity estimation is affected by the use of different disdrometer types. Rainfall data were collected in 1-min intervals from six optical disdrometers at three measurement sites in Austria, one site in Czech Republic and one site in New Zealand. The disdrometers included two disdrometers of each of the following types: the PWS100 Present Weather Sensor from Campbell Scientific, the Laser Precipitation Monitor from Thies Clima and the first generation Parsivel from OTT Hydromet. The fit of KE–I relationships from the literature varied among disdrometers and sites. Drop size and velocity distributions and developed KE–I relationships were device-specific and showed similarities for disdrometers of the same type across measurement sites. This hindered direct comparison of results from different types of disdrometers, even when placed at the same site. Thus, to discern spatial differences in rainfall characteristics the same type of measurement instrument should be used.

The change of rainfall kinetic energy content with altitude

Erosion is the process of land degradation due to rainfall and other environmental factors, and it can be described by indices such as the kinetic energy of rainfall (KE). Microphysical data from disdrometers have been used to determine the relationship between kinetic energy content (KEmm, in J m−2 mm−1) and rainfall intensity (R, in mm h−1). Optical disdrometer (PWS100) measurements were gathered between 2014 and 2018 at five sampling sites across Mexico, and the data analyzed to study the characteristics of KEmm–R relationships with altitude. A PWS100 unit at each of the sampling sites was collocated a few meters from a tipping-bucket rain gauge to record R simultaneously. Individual raindrops fall speeds measured with the disdrometers were found to agree well with theoretical estimations using the environmental conditions at each sampling site. The mean fall velocities indicated that raindrops detected at the continental sampling sites (medium and high altitudes) have greater speeds than those near coastal areas (low altitudes). Comparisons of the estimated KEmm for rainy periods with comparable R show greater values as the altitude increases. This result is attributed to the increase of raindrop fall speed due to the air density decrement with altitude. Exponential relations between KEmm and R based on one-minute sampling periods were fitted for each sampling site, producing high values of coefficients of determination and suggesting a good response. Finally, the maximum kinetic energy parameter, KEmax, in the empirical KEmm–R equations was found to increase with altitude. These findings indicate that the detachment of soil particles by individual drops are magnified at higher altitude locations.

Disdrometer Performance Optimization for Use in Urban Settings Based on the Parameters that Affect the Measurements

There are currently different types of commercial optical disdrometers to measure the rainfall intensity, of which many are commonly used for monitoring road conditions. Having information about the amount of rain, the composition of the precipitation particles and visibility are essential to avoid accidents, which requires intelligent systems that warn drivers and redirect traffic. However, few studies related to Intelligent Transport Systems (ITS) have been performed regarding why these devices are not optimized for this type of applications. Therefore, this paper analyzes and evaluates the operating mode of these equipment through their theoretical model, which will allow the design of prototypes of disdrometers with different characteristics. In addition, this model will be implemented in a simulation program, through which an exhaustive study analyzing how the type of precipitation and its intensity affect the measures provided by the model will be conducted. In this way, the results will help optimize its operation to be thus used in urban settings, which will allow obtaining more accurate real-time information, better traffic management, and a reduction in the number of accidents.

Ash sedimentation by fingering and sediment thermals from wind-affected volcanic plumes

Ash fallout and volcanic plume dispersion represent critical hazards for local and global human populations for minutes to years after the onset of an eruption. Understanding the key processes governing the sedimentation of ash particles is a major challenge in modern volcanology from modeling and risk management perspectives. Recent experiments predict that sedimentation from eruption clouds rich in fine-grained ash can be driven by convective phenomena in the form of ∼100 m to km-scale ash fingers related to the intermittent formation and detaching of ash-rich particle boundary layer. Remote sensing observations of mammatus and cloud veils from numerous eruptions over recent years as well as field observations of spatially discontinuous ash deposits are consistent with this prediction. Indeed, this mode of ash sedimentation is predicted to be the predominant mechanism of fine ash removal for a significant fraction of eruptions in the geological records. Here, we use a novel combination of 3 millimeter-wavelength Doppler radar observations and time series of optical disdrometer data to characterize for the first time in real-time the time-varying structure and sedimentation properties of volcanic ash plumes under steady wind conditions. As a case study, we apply this new method to weak short-lived plumes from Stromboli Volcano. 96% of the disdrometer proximal sedimentation data highlight pulsatory phases of increased sedimentation rate that are 20–60 s apart and characterized by particle size distribution variation with bulk concentrations up to 681 mg/m3. Radar data also record intermittent periods of higher reflectivity (i.e. a factor of 3 in mass concentration) inside the ash sedimentation interspersed by 30 to 50 s and interpreted as ash fingers crossing the radar beam. From time series of radar signals and ground-based disdrometer measurements, together with simple analog experiments, we develop a conceptual model for intermittent sedimentation from wind-affected ash plumes. In particular, we show that when wind speeds are comparable to or greater than ash settling velocities, the dynamics of wind-driven rolls in ash clouds can govern the production of gravitational instabilities and the occurrence and timing of ash fingers that form descending sediment thermals in turn. This study suggests that ambient wind should affect the maximum size and minimum concentration of ash particles needed to form fingers but also control where and when fingers form at the base of wind-drifted ash plumes. These novel predictions highlight the need for future work aimed at refining our understanding of ash finger formation under windy conditions from the perspectives of in-situ characterization, numerical modeling and risk assessment of fine ash dispersal from the most frequent style of eruptions on Earth.

Comparison of three types of laser optical disdrometers under natural rainfall conditions

ABSTRACTOptical disdrometers can be used to estimate rainfall erosivity; however, the relative accuracy of different disdrometers is unclear. This study compared three types of optical laser-based disdrometers to quantify differences in measured rainfall characteristics and to develop correction factors for kinetic energy (KE). Two identical PWS100 (Campbell Scientific), one Laser Precipitation Monitor (Thies Clima) and a first-generation Parsivel (OTT) were collocated with a weighing rain gauge (OTT Pluvio2) at a site in Austria. All disdrometers underestimated total rainfall compared to the rain gauge with relative biases from 2% to 29%. Differences in drop size distribution and velocity resulted in different KE estimates. By applying a linear regression to the KE–intensity relationship of each disdrometer, a correction factor for KE between the disdrometers was developed. This factor ranged from 1.15 to 1.36 and allowed comparison of KE between different disdrometer types despite differences in measured drop size and velocity.

Assessment of an Optical Disdrometer by Laboratory Tests Using Spherical Bodies and All-particle Logging

Assessment of an Optical Disdrometer by Laboratory Tests Using Spherical Bodies and All-particle Logging

Diurnal Cycle of Raindrops Size Distribution in a Valley of the Peruvian Central Andes

In the Central Andes of Peru, convective and stratiform rainfall occurs, frequently associated with convective storms. The raindrop size distributions (RSD), measured by a Parsivel-2 optical disdrometer, were characterized by the variation of their normalized parameters. The RSD dataset includes measurements corresponding to 18 months between 2017 and 2019. As a result, it was found that the mass-weighted mean diameter Dm and the Nw parameter present respectively high and low values, in the interval of 15–20 LST (local standard time), wherein deeper and more active clouds appear. The events including convective rainfall contribute 67.5% of the accumulated total, wherein 92% corresponds to the 15–20 LST interval. It is concluded that the spectral variability of the RSD is strongly controlled by the cloudiness configuration field developing over the west (convection over highlands) and east (convection over Amazon) sides of the valley. In the afternoon, clouds develop and drift to the east, over the Andean valleys and towards the Amazon, intensified by local orographic circulation. The opposite happens at night, when the stratiform rainfall is dominant and it is controlled by clouds, located in the Inter-Andean valley, generated by the convection fields formed over the Amazon forest.

Microphysical Features of Rain and Rain events during different Seasons over a Tropical Mountain location using an Optical Disdrometer

In the present study, seven-year-long observations of rain microphysical properties are presented using a ground-based disdrometer located at Braemore; a site on the windward slope of the Western Ghats (WG) over the Indian Peninsula. The annual cycle of rainfall shows a bimodal distribution with a primary peak during summer monsoon and secondary peak during pre-monsoon. Pre-monsoon rain events are less in number but are with high intensity and characterize large raindrops and low number concentration. During summer monsoon, short and less intense rain events with small drops are noticed. Post-monsoon rain is having a long duration less intense events with lower concentration of large raindrops compared to the summer monsoon. In the seasonal variation of mean diameter (Dm) and raindrop concentration (NT) with Rain Intensity (RI), winter and pre-monsoon rains exhibit higher values of Dm and lower values of NT compared to the summer and post-monsoon seasons for all the RI ranges. The mean features of the rain microphysical parameters are also supported by the case studies of rain events. RI, Dm and NT are categorized into different range bins for all the seasons to identify their variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/Light rain has maximum rain duration, and the relative contribution to the rainfall is high from heavy rain type. Winter and pre-monsoon rains are mostly contributed from the larger raindrops (>Dm3), and during summer and post-monsoons it is from Dm2 onwards. The distribution of occurrence frequency of NT and rainfall are similar during all four seasons. NT2 recorded rainfall percentage nearly the same as NT1 in summer monsoon and this also supports large number of raindrops in this season. In RI-Duration analysis, all seasons showed similar distribution, and 90% of total duration is contributed from RI with less than 20 mm h−1.

Assessment of Rainfall Kinetic-Energy–Intensity Relationships

Raindrop-impact-induced erosion starts when detachment of soil particles from the surface results from an expenditure of raindrop energy. Hence, rain kinetic energy is a widely used indicator of the potential ability of rain to detach soil. Although it is widely recognized that knowledge of rain kinetic energy plays a fundamental role in soil erosion studies, its direct evaluation is not straightforward. Commonly, this issue is overcome through indirect estimation using another widely measured hydrological variable, namely, rainfall intensity. However, it has been challenging to establish the best expression to relate kinetic energy to rainfall intensity. In this study, first, kinetic energy values were determined from measurements of an optical disdrometer. Measured kinetic energy values were then used to assess the applicability of the rainfall intensity relationship proposed for central Italy and those used in the major equations employed to estimate the mean annual soil loss, that is, the Universal Soil Loss Equation (USLE) and its two revised versions (RUSLE and RUSLE2). Then, a new theoretical relationship was developed and its performance was compared with equations found in the literature.

Quantitative precipitation estimation with weather radar using a data- and information-based approach

Abstract. In this study we propose and demonstrate a data-driven approach in an “information-theoretic” framework to quantitatively estimate precipitation. In this context, predictive relations are expressed by empirical discrete probability distributions directly derived from data instead of fitting and applying deterministic functions, as is standard operational practice. Applying a probabilistic relation has the benefit of providing joint statements about rain rate and the related estimation uncertainty. The information-theoretic framework furthermore allows for the integration of any kind of data considered useful and explicitly considers the uncertain nature of quantitative precipitation estimation (QPE). With this framework we investigate the information gains and losses associated with various data and practices typically applied in QPE. To this end, we conduct six experiments using 4 years of data from six laser optical disdrometers, two micro rain radars (MRRs), regular rain gauges, weather radar reflectivity and other operationally available meteorological data from existing stations. Each experiment addresses a typical question related to QPE. First, we measure the information about ground rainfall contained in various operationally available predictors. Here weather radar proves to be the single most important source of information, which can be further improved when distinguishing radar reflectivity–ground rainfall relationships (Z–R relations) by season and prevailing synoptic circulation pattern. Second, we investigate the effect of data sample size on QPE uncertainty using different data-based predictive models. This shows that the combination of reflectivity and month of the year as a two-predictor model is the best trade-off between robustness of the model and information gain. Third, we investigate the information content in spatial position by learning and applying site-specific Z–R relations. The related information gains are only moderate; specifically, they are lower than when distinguishing Z–R relations according to time of the year or synoptic circulation pattern. Fourth, we measure the information loss when fitting and using a deterministic Z–R relation, as is standard practice in operational radar-based QPE applying, e.g., the standard Marshall–Palmer relation, instead of using the empirical relation derived directly from the data. It shows that while the deterministic function captures the overall shape of the empirical relation quite well, it introduces an additional 60 % uncertainty when estimating rain rate. Fifth, we investigate how much information is gained along the radar observation path, starting with reflectivity measured by radar at height, continuing with the reflectivity measured by a MRR along a vertical profile in the atmosphere and ending with the reflectivity observed by a disdrometer directly at the ground. The results reveal that considerable additional information is gained by using observations from lower elevations due to the avoidance of information losses caused by ongoing microphysical precipitation processes from cloud height to ground. This emphasizes both the importance of vertical corrections for accurate QPE and of the required MRR observations. In the sixth experiment we evaluate the information content of radar data only, rain gauge data only and a combination of both as a function of the distance between the target and predictor rain gauge. The results show that station-only QPE outperforms radar-only QPE up to a distance of 7 to 8 km from the nearest station and that radar–gauge QPE performs best, even compared with radar-based models applying season or circulation pattern.