Binary raindrop collisions are crucial for the evolution of raindrop size distributions (DSDs), a fundamental quantity with significant impacts on hydrological, meteorological, and related modeling.  This study presents multi-year field observations of these collisions, acquired using the High-Speed Optical Disdrometer (HOD), an instrument we developed for the accurate measurement of raindrop microphysical properties.  The HOD's high-speed image capture system, operating at 1000 frames per second in this study, yields a rich dataset of sequential raindrop images.  These high-frequency observations enable the precise measurement of raindrops' geometric (e.g., diameter) and dynamic (e.g., fall speed) properties before, during, and after collision events.  The rainfall events considered in this study were observed at our outdoor rainfall laboratories located on the West campus of the University of Texas at San Antonio, Texas, and at Clemson University, South Carolina, USA, as well as during the IPHEx field campaign conducted near Asheville, North Carolina, USA.  Our observations captured a diverse range of collision outcomes, including coalescence and various breakup types (neck, sheet, and crown).  This unique field dataset was used to evaluate the theoretical collision outcome regime diagram (T09) developed by Testik (2009) [Outcome regimes of binary raindrop collisions. Atmospheric Research, 94(3), 389-399], which predicts raindrop collision outcomes based on the Weber number and drop diameter ratio.  The strong agreement found between our field observations and the T09 predictions supports the model's effectiveness and its potential integration into warm rainfall microphysics schemes.  In this presentation, we will detail our observations of raindrop collisions and provide comprehensive comparisons with the T09 predictions.  This material is based upon work supported by the U.S. National Science Foundation under Grants No. AGS-1741250 to the first author (FYT).

How to cite: Testik, F. and Rahman, K.: Unveiling Raindrop Collision Outcomes and T09 Model Comparison Using Multi-Year High-Speed Optical Disdrometer Observations, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-320, https://doi.org/10.5194/ems2025-320, 2025.

Understanding and accurately classifying precipitation types is essential for hydrological and climate-related applications. In our study, we employ advanced disdrometer measurements to derive detailed drop size distribution (DSD) parameters, which in turn facilitate a robust separation between convective and stratiform rainfall. The proposed methodology relies on both traditional and novel statistical approaches to analyze high-resolution disdrometer data. By deriving key DSD parameters and establishing an empirically optimized separation line, we aim to improve radar-based precipitation classification and quantitative precipitation estimation.

Data for this study were collected in the Czech Republic, representing a mid-latitude, temperate climate. Unlike tropical regions, where convective precipitation is often characterized by more intense and variable DSD features, precipitation in Central Europe exhibits distinct properties. These differences are critical since the evolution and microphysical processes governing precipitation vary with geographical and climatic conditions. By focusing on region-specific data, our work provides refined DSD parameter estimates tailored for moderate latitudes, ultimately enhancing the performance of radar algorithms in these environments.

Preliminary results indicate that spatial composite radar products generated over the Czech territory can effectively validate the DSD-based classification. The computed DSD parameters and the derived separation line clearly delineate convective from stratiform rainfall, with notable improvements in precipitation estimates when region-specific characteristics are accounted for. Furthermore, the integrated analysis of high-resolution disdrometer and radar data demonstrates the potential to significantly reduce uncertainties in radar-based quantitative precipitation estimation.

Our findings contribute to the growing body of literature that emphasizes the importance of adapting radar retrieval algorithms to the specific precipitation regimes of different climatic zones. This study underscores the necessity of incorporating local microphysical variability into precipitation monitoring systems, thereby providing more accurate inputs for hydrological models and climate studies.

How to cite: Rulfová, Z. and Potužníková, K.: High-Resolution Classification of Convective and Stratiform Precipitation Using DSD Parameters, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-100, https://doi.org/10.5194/ems2025-100, 2025.

Reliable extreme precipitation estimates are essential for understanding, predicting, and mitigating natural disasters. However, their inference at a global scale is limited by the sparse and uneven distribution of direct rainfall observations. Satellite-based estimates provide a promising source of information for extreme value analysis, but their high uncertainty and low spatial resolution hinder their applicability. Additionally, grid sizes ranging from 10 to 600 km² prevent direct comparisons with point-scale extreme value estimates, as point- and area-averaged statistics inherently differ in their construction.
This study addresses this limitation by assessing the sensitivity of a downscaling method for extreme value statistics, based on random field theory and the Metastatistical Extreme Value Distribution (MEVD). We use a comprehensive dataset of approximately 140 rain gauges in northeastern Italy, along with multiple satellite precipitation products, including IMERG, CMORPH, CHIRPS, SM2RAIN, MSWEP, and PERSIANN. The downscaling process, based on the autocorrelation structure of precipitation fields, is applied individually to each product at the grid cells corresponding to the available rain gauges.
To perform the downscaling process, two key variables must be obtained: the variation in the wet fraction (Beta) and the variation in the rainfall spatial correlation (lambda), both between satellite pixel and point scale. However, this requires defining a neighborhood centered on the point of interest, as well as a function that represents the decay of spatial correlation within this neighborhood. Therefore, we assess the method's sensitivity by considering three different neighborhood sizes (3, 5, and 7 pixels) and two functions to represent the spatial correlation (Exponential kernel with Power law tail and Stretched exponential).
Finally, downscaling results for extreme daily event magnitudes with a 50-year return period at the point scale are obtained from the above methodology and are validations against those derived from rain gauge time series.

How to cite: Sanchez Peña, C. A., Marra, F., and Marani, M.: Estimates of Point Rainfall Extremes from Satellite Precipitation Products: Testing in Northeastern Italy, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-509, https://doi.org/10.5194/ems2025-509, 2025.

The Japanese islands are highly vulnerable to heavy rainfall during warm periods due to the inflow of warm, moist air, which often results in disasters such as floods and landslides. In particular, the western region, Kyushu, experiences frequent heavy rainfall, including linear rainbands that can persist for several hours. This study aims to investigate the characteristics of heavy rainfall areas in Kyushu using a self-organizing map (SOM), a type of unsupervised neural network and pattern recognition method.

Initially, we applied the SOM algorithm to train average meteorological fields over three-hour intervals, consisting of 850 hPa winds and equivalent potential temperature from 1979 to 2023, including the Kyushu region. The patterns obtained were then compared to heavy rainfall areas from 2006 to 2023, defined as regions with 3-hour rainfall amounts exceeding 100 mm/3h over an area of at least 500 km², with a maximum value of at least 150 mm/3h within that region.

To extract these heavy rainfall areas, we identified the closed regions with precipitation exceeding 100 mm/3h from the actual rainfall distribution. Principal component analysis (PCA) was then applied to the coordinates of the heavy rainfall areas, and the first principal component, which maximizes the variance, was considered the axis of movement for the heavy rainfall area. The first and second principal components correspond to the major and minor axes of the heavy rainfall area, respectively. Finally, we placed a rectangle parallel to the major axis, adjusting its four sides to touch the edges of the heavy rainfall area, which helped determine its shape.

The analysis revealed that heavy rainfall areas are more frequent in meteorological fields associated with the inflow of warm, moist air from the southwest or west-southwest into the Kyushu region. Approximately 70% of these areas were identified as elongated precipitation bands with an axis ratio greater than 2.5. Additionally, heavy rainfall areas tend to occur most frequently between 3:00 and 9:00 AM. Furthermore, the occurrence of heavy rainfall areas was particularly high in meteorological fields associated with fronts. These areas were concentrated on the western side of Kyushu, with very little occurrence on the eastern side. The probability of occurrence was about 10% during the daytime but exceeded 20% between 6:00 and 9:00 AM.

Based on these findings, it is crucial to recognize that heavy rainfall areas in Kyushu are especially prominent at night. In particular, when the region is covered by meteorological fields associated with fronts and warm, moist air, with an equivalent potential temperature exceeding 340 K, heavy rainfall during nighttime hours should be closely monitored. These insights should be integrated into the disaster prevention plans of local governments.

How to cite: Nishiyama, K., Asai, K., and shirozu, H.: Statistical analysis on heavy rainfall areas causing serious disasters in Kyushu, Japan, using Self-Organizing Map, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-286, https://doi.org/10.5194/ems2025-286, 2025.

The September 2024 flood affected large areas of Central Europe, particularly Czechia, Poland, Slovakia, Austria, and Germany. The meteorological cause of the flood was the low-pressure system "Boris", which moved from the Mediterranean towards Central Europe, where its progression was stopped by a blocking anticyclone over Eastern Europe. The frontal boundary associated with this cyclone exhibited intense rainfall activity. This mechanism is consistent with the one observed during the major floods in Central Europe in 1997 and 2002. The most severely impacted area was the Jeseníky Mountains, where a Central European record 1-day precipitation of 385.6 mm was observed. The objective of this work is to evaluate the 2024 flood in Czechia, focusing on the exceedance of return periods of areal precipitation.

The return periods are analyzed for four selected catchment size classes, based on the official catchment classification system used in Czechia. For each catchment, areal averages of precipitation totals are calculated using 10-minute precipitation intensities during the flood event. These intensities are derived from radar reflectivity data at a height of 2 km above sea level (pseudo-CAPPI 2 km) with a spatial resolution of 1 km, and subsequently adjusted using 1-day precipitation totals from ground-based rain gauges.

The second input dataset consists of parameters of the Generalized Extreme Value (GEV) distribution of areal design precipitation. These data are also derived from adjusted radar data, covering the 20-year period from 2002 to 2021. From the resulting precipitation intensities, areal precipitation totals are computed for accumulation durations ranging from 30 minutes to 3 days. Based on annual maxima, GEV distribution parameters are estimated using L-moments. These parameters are then used to calculate the return periods of the areal precipitation totals observed during the flood event.

The spatial distribution of return periods across the catchment size classes is analyzed, with particular attention given to the influence of topographic factors on return period values. Special emphasis is placed on orographic effects, such as the orographic enhancement of precipitation. Our analysis confirms extreme values of return periods over the affected area for long accumulation durations and at particular catchments even for short accumulation durations. 

How to cite: Hulec, F., Bližňák, V., Kašpar, M., and Müller, M.: Return periods of areal precipitation during the Central European Floods 2024 in Czechia, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-436, https://doi.org/10.5194/ems2025-436, 2025.

Short-term rainfall characteristics, especially their intensity and design values, are of great importance in engineering (e.g. sewerage systems) and hydrological practice. However, the measurement and data processing of these characteristics is rather complicated. The design values of short-term rainfall that are still widely used in Czechia are based on studies that are more than 60 years old.

Therefore, new Intensity-Duration-Frequency (IDF) and Depth-Duration-Frequency (DDF) curves have been prepared and published in a public database within the project “Prediction, Evaluation and Research for Understanding National sensitivity and impacts of drought and climate change for Czechia” (PERUN). These IDF and DDF curves have been based on 2-year to 100-year design values of 5-min–3-day rainfall totals estimated from rainfall intensity measurement at selected stations of the Czech Hydrometeorological Institute. More than 170 stations with sufficient length (mostly more than 25 years) of joined digitalized pluviograph records and the automatic rain gauge measurement series from 1951–2022 were processed. The design values were estimated using the General Extreme Value (GEV) distribution fitted to annual maxima series. The parameters of the distribution were estimated using L-moments and adjusted by the region-of-influence (ROI) method. However, design values with an average frequency of occurrence greater than 2 years (e.g. twice or once a year) are also highly required in engineering practice and therefore our database should be extended.

In our contribution, we focus on estimation of rainfall design values with a frequency of occurrence higher than 2 years. As the previously published estimates of 2-year to 100-year design values are based only on annual maxima series, a different methodology is needed in this case. Due to the high frequency of occurrence of additionally estimated design values and the use of relatively long station measurement series (20-70 years), the method of quantiles calculated from sets of rainfall totals selected above the threshold was suggested. The results and the possibility of using them to extend the already published estimates of 5-years to 100-year design values are analyzed and discussed.

How to cite: Crhova, L.: Estimates of the rainfall design values with a high frequency of occurrence, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-463, https://doi.org/10.5194/ems2025-463, 2025.

Extreme precipitation events present significant threats to society, infrastructure, and the economy. To mitigate the potential impacts of such events, it is essential to design appropriate infrastructure, water management systems, and warnings. Guidelines for the hydrological and hydraulic resilience of infrastructure are typically based on statistically derived estimates of return periods and return levels of extreme hydrological events. Therefore, a reliable information on the estimated return periods and return levels is crucial. Furthermore, combining this statistical information with weather forecasts may allow to anticipate the extremity of predicted events and, consequently, to estimate the impact an expected event may have.

Extreme value theory offers a theoretical foundation for the statistical modelling of exceptional phenomena. A common approach for identifying extremes is the block maxima method. After dividing a time series into blocks (e.g., years), the maxima of these blocks are selected, and a distribution is fitted. Based on the extremal types theorem, when the maxima are appropriately renormalized, they can only follow one of three distributions: Fréchet, Gumbel, or Weibull. These are the only possible cases of the generalized extreme value (GEV) distribution. When analysing rainfall, the duration of events is of particular importance: the more rain falls per unit of time, the higher is the resulting runoff. In agreement with the currently used methodology at Deutscher Wetterdienst (DWD), we distinguish 22 duration levels ranging from 5 minutes to 7 days. After identifying annual maxima for each duration level a GEV distribution was fitted to each series of maxima, with the constraint that we fixed the shape parameter at 0.1 (Fréchet distribution). To account for the temporal interdependence of rainfall events across different durations, the framework developed by Koutsoyiannis et al. (1998) was implemented, allowing for the integration of multiple duration levels into a single statistic.

This methodology has been applied to various datasets, including interpolated rain-gauge station data (HYRAS), spatially and temporally homogenized climatological precipitation data derived from weather radars (RADKLIM), and reanalysis data (COSMO REA6 and R6G2). In this preliminary study, the results are compared and assessed with respect to agreements, discrepancies, and inconsistencies, considering the characteristics of each data source.  Comparing the statistical estimates across various data sources is a first step toward developing a methodology to determine the extremity of weather forecasts — an essential part of an impact-oriented warning system for extreme precipitation.

Koutsoyiannis et al., 1998, A mathematical framework for studying rainfall intensity-duration-frequency relationships. J. Hydrol. (206), 118-135, https://doi.org/10.1016/S0022-1694(98)00097-3.

How to cite: Linder, M., Walawender, E., Lengfeld, K., and Winterrath, T.: Statistical extreme value analysis of precipitation data as enriching information for warnings against severe weather – first steps of the approach for Germany, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-650, https://doi.org/10.5194/ems2025-650, 2025.

Design precipitation estimates are crucial for hydrological modeling, flood risk assessment, and infrastructure planning. Traditional methods often rely on relatively sparse rain-gauge measurements or on precipitation derived from weather radar data with varying degrees of accuracy.

The presented study introduces a methodological approach to improving the accuracy and spatial consistency of design precipitation estimates from weather radar data by incorporating rain-gauge measurements, surpassing standard adjustment methods. This approach is based on a geostatistical merging of two design precipitation fields: one derived from high-resolution precipitation intensity data calculated from radar reflectivity and adjusted using direct precipitation measurements, and the other from long-term ombrographic measurements. It preserves relatively reliable estimates at gauge locations while maintaining spatial gradients derived from radar data and mitigates the relatively high uncertainty in the computation of precipitation intensity from radar reflectivity as well as the limited spatial representation of rain-gauge data. It is thus particularly relevant when long station records are available and cover the entire area of interest.

The approach is validated using data from a Central European country of Czechia. Before merging, the robustness of design precipitation estimates is enhanced by applying the L-moment-based index storm procedure and the region-of-influence method. Final design precipitation fields are derived by interpolating the ratios between rain-gauge and adjusted radar design precipitation totals with Empirical Bayesian Kriging. The identified spatial variability of design precipitation adequately reflects heavy precipitation formation mechanisms, causal circulation patterns, and orographic effects. This paves the way for future research focused on local adjustments of extreme value distribution parameters and interpolation settings, which may further mitigate the uncertainty of the estimates in certain areas.

How to cite: Kašpar, M., Hulec, F., Müller, M., and Crhová, L.: An improved approach to design precipitation estimation using radar data and rain-gauge measurements, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-248, https://doi.org/10.5194/ems2025-248, 2025.