Gauge-independent, multi-source precipitation merging methods are well-established approach for improving precipitation estimates. These methods predominantly aim to minimise uncertainty in precipitation magnitude, yet they frequently neglect errors in distinguishing between rain and no-rain events. This oversight often leads to biased merging weights and suboptimal precipitation estimates. In this study, we introduce an innovative two-stage framework called the Generalised Signal-to-Noise Ratio Optimisation (G-SNR) framework, specifically designed to address these limitations. The first stage employs the Categorical Triple Collocation-Merging (CTC-M) method for binary merging, effectively mitigating errors in rain/no-rain classification. The second stage applies Signal-to-Noise Ratio Optimisation (SNR-opt) to enhance precipitation magnitude estimates, leveraging the improved classification outcomes. Evaluation results demonstrate that G-SNR consistently surpasses both input data and existing methods in terms of binary classification and magnitude estimation. Importantly, it achieves error reductions across all percentiles, delivering robust performance for both low and extreme precipitation events. This framework provides a comprehensive and reliable solution to longstanding challenges in precipitation merging, significantly enhancing both accuracy and dependability.
How to cite: Kim, S., Shah, S., Liu, Y., and Sharma, A.: Improving Precipitation Merging: A Generalized Two-Stage Framework Using the Signal-to-Noise Ratio Optimization (SNR-opt), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-530, https://doi.org/10.5194/egusphere-egu25-530, 2025.
Excess runoff from heavy precipitation events (HPEs) in urban environments often leads to urban flooding, a severe hazard with significant implications for human life, property, and infrastructure. Modeling runoff response in complex and heterogeneous urban areas, while accounting for rainstorm and surface characteristics, remains a significant challenge. Climate change and urbanization are key drivers of increased future urban runoff intensity. Research on the interaction between these factors and urban runoff in the eastern Mediterranean region is particularly limited. Previous studies using high-resolution models have projected an increase in short-duration rainfall intensities, alongside a decrease in long-duration intensities, rainfall coverage area, and total event rainfall during HPEs in the eastern Mediterranean under the RCP8.5 scenario. The current study examines the implications of these changes on peak discharge and volume of urban runoff by the end of the 21st century and evaluates the influence of varying urbanization scenarios, providing insights into the interplay between climate change and urban development. Using high-resolution radar-rainfall and surface data, we developed and calibrated a SWMM-based urban rainfall-runoff model for the Nahal Ra'anana basin (13 km²) on Israel's coastal plane. This Mediterranean-climate region encompasses most of the city of Ra'anana and has approximately 40% impervious surfaces. The model was developed using 23 runoff events utilizing leave-one-out cross-validation and a multi-objective optimization approach, and demonstrated robust performance with KGE values of 0.80 for runoff peak discharge and 0.83 for total runoff volume. A variance-based sensitivity analysis identified three primary factors influencing urban runoff: rainstorm intensity distribution, impervious surface coverage, and basin water storage. Analysis of HPEs under historical and future climatic conditions revealed that, at the current urbanization level of the city, climate change alone is unlikely to alter peak or total runoff discharge significantly. This is attributed to the decrease in total event rainfall and coverage area, alongside an increase in short-duration rainfall intensities. However, with substantial urbanization (e.g., increasing impervious surface to 52% or more), future climate HPEs are expected to exhibit a noticeable shift in the trend, leading to increased peak discharge. Further analysis indicates the elevated importance of rainfall intensities in determining runoff peaks in future climate conditions. In historical HPEs the maximum rainfall intensities over a 60-minute duration strongly correlate with peak runoff discharge (R2=0.75), where in future climate HPEs, correlations of shorter and longer rainfall durations are improved compared to historical HPEs with the maximum obtained for 60–120-minute durations (R2=0.81). The non-linear discharge response to climate change underscores the importance of integrating climate projections into urban planning to mitigate future flooding risks and highlight the potential for short-term peak discharge forecasting under both current and future climatic conditions.
How to cite: Nussbaum, R., Armon, M., and Morin, E.: Urban runoff response to climate-change-driven heavy precipitation and urbanization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-841, https://doi.org/10.5194/egusphere-egu25-841, 2025.
We investigate intensity-duration-frequency (IDF) relations. They describe the major statistical characteristics of extreme precipitation events (return level, return period, time scale) and provide information on the probability of exceedance of certain precipitation intensities. IDF relations help to visualize either how extreme (in terms of probability/frequency/return period) a specific event is or which intensity is expected for a given probability. We model the distribution of extreme precipitation in an extreme-value statistics setting. To increase model efficiency, we include the duration and model a duration-dependent GEV. The durations range from minutes to days and are modeled in one single model in order to prevent quantile-crossing and to assure that estimated quantiles are consistent. This way, we are capable of considering large-scale influences by using covariates for the GEV parameters.
The influence of climate change is included by letting the GEV parameters (covariates) depend on the covariates NAO, temperature, humidity, blocking and year (as a proxy for climate change). We found an increase in probability of extreme precipitation with year and temperature, while the effect of the other variables depends on the season. We present a downscaling approach under the perfect-prognosis assumption as a proof-of-concept, where we use future values of large-scale covariates from climate projections to derive future GEV distributions. This poses some challenges because the polynomial dependencies of the past might not hold for an extrapolation into the future. Right now, our model is based on measurement stations, but we will give an outlook how we plan to include gridded datasets of precipitation observations or reanalyses.
How to cite: Fauer, F. and Rust, H.: How IDF Relations Changed in the Past and How They Will Change in the Future, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3262, https://doi.org/10.5194/egusphere-egu25-3262, 2025.
This study explores the feasibility of using the information contained in observed streamflow discharge measurements to inversely correct catchment-average precipitation time series provided by reanalysis products. We explore this possibility by training LSTM models to predict precipitation. The first model uses discharge as an input feature along with other meteorological factors, while the second model uses only the meteorological factors. Although the model provided with discharge information showed better mean performance, a detailed analysis of various time series measures across the continental scale revealed underestimation biases when compared with the original reanalysis product used for training. However, an out-of-sample test showed that the inversely estimated precipitation is better able to reproduce small-scale, high-impact events that are poorly represented in the original reanalysis product. Further, using the inversely generated precipitation time series for classical hydrological “forward” modeling resulted in improved estimates for streamflow and soil moisture. Given the notable disconnect between reanalysis products and extreme events, particularly in data-scarce regions worldwide, our findings have implications for achieving better estimates of precipitation associated with high-impact events.
How to cite: Manoj J, A., Loritz, R., Gupta, H., and Zehe, E.: Can discharge be used to inversely correct precipitation?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4086, https://doi.org/10.5194/egusphere-egu25-4086, 2025.
The separation of storms into physically meaningful classes, including the key distinction between convective and non-convective events, is crucial for advancing precipitation science. Indeed, each of these classes may necessitate different modelling strategies, or distinct bias adjustment procedures for climate model simulations. Here, we present a large-scale study that aimed at achieving this separation only based on information from precipitation timeseries. We focused on a vast set of sub-hourly rain gauge records collected from four countries across the Alpine region and extracted hundreds of thousands of storms. We used an unsupervised clustering algorithm based on a small set of features to organize the storms into storm types. Despite the simplicity of the clustering approach, we successfully distinguished convective storms from other types, as validated using independent features that were not involved in the clustering, such as lightning counts. We analyzed the climatology of the storm types, including investigations of their spatial coherence and temporal changes in their occurrence. Overall, we believe that the storm clusters we provide can be used for several purposes, ranging from developing stochastic models tailored on the storm types of interests to improving bias adjustment methods for climate simulations. Given its simplicity and versatility, the framework can be transferred to other regions globally, with marginal adjustments based on the prior knowledge of the regional climatology and on the research objectives.
Our study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).
How to cite: Papacharalampous, G., Dallan, E., Armon, M., Saha, J., Price, C., Borga, M., and Marra, F.: Precipitation-driven storm types and their climatology across the Alpine range, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4684, https://doi.org/10.5194/egusphere-egu25-4684, 2025.
To properly adapt to climate change, we need to estimate extreme precipitation probability in future climate scenarios. The task is particularly challenging for sub-daily and sub-hourly extremes, as they are hardly represented by most of the available climate models. As an alternative to explicit model simulations, one can use stochastic models trained on physical covariates. For example, it was recently shown that we can predict changes in sub-daily and sub-hourly extreme precipitation only based on shifts in wet-day daily temperatures. With the aim of extending the applicability of such stochastic models, we examine here the use of covariates representing both thermodynamic and dynamic processes.
We focus on a set of ~300 stations in the Alps (from France, Switzerland, Austria, Italy) for which we have sub-daily precipitation and temperature observations. First, we assess the importance of statistical independence of the events on the identification of the scaling relationships between extreme precipitation and temperature that are commonly used to quantify the thermodynamic component. Then, we evaluate the relative importance of the thermodynamic and dynamic components for durations ranging between 10 minutes and 24 hours using as covariates dew point, vertical velocity at 500 hPa, and divergence at 300 hPa from ERA5 reanalysis simulations.
Our results show that (1) evaluating extreme precipitation-temperature scaling relations using all the wet time intervals (as done in several studies) leads to biased estimates of the scaling rates relevant for extreme sub-daily precipitation projections. (2) The scaling rates between extreme precipitation and dew point tend to decrease logarithmically with duration, an information that can be used to extract the scaling rate at sub-hourly durations from hourly observations. (3) The importance of the thermodynamic component decreases with duration (rank correlation decreases from ~0.55 at 10 minutes to ~0.2 at 24 hours), while the importance of the dynamic component that can be appreciated at the ERA5 resolution (~30 km) tends to increase with duration (rank correlation increases from ~0.2 at 10 minutes to ~0.45 at 24 hours). (4) From a stochastic simulation perspective, temperatures and dew point during precipitation events in the Alps can be simulated using generalized normal distributions (or normal distributions in case of seasonal data), while vertical velocities and divergence need to be simulated using skewed models such as a generalized extreme value distribution.
How to cite: Marra, F., Ciceri, R., Stante, S., and Sada, C.: Toward the stochastic modelling of extreme precipitation probability with thermodynamic and dynamic covariates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4866, https://doi.org/10.5194/egusphere-egu25-4866, 2025.
This study proposes a method utilizing Rainfall Transition Probability (RTP) to create rainfall scenarios that consider the temporal distribution of heavy rainstorm events. RTP refers to the probability of rainfall amount at time t occurring after a specific rainfall amount at time t+1. The method consists of a temporal distribution that builds region-specific RTPs using rainfall data observed at 1-minute interval, a function that users define the desired conditions for the rainfall scenario, and a processing module that generates scenarios based on the RTP. To develop the RTP, the rainfall data about 1-minute interval used for separating Independent Rainstorm Events (IREs) according to each region. Among the identified IREs, those exhibiting high-intensity rainfall (above 15 mm/hour) are used to calculate and establish the RTP. Afterward, users define the conditions for the rainfall scenario in the function with conditions such as region, total rainfall, and rainfall duration. The generator then utilizes the RTP for the selected region to generate various rainfall scenarios with different temporal distributions and presents them to the user. By extracting the temporal distribution from regional IREs, the generator reflects local rainfall patterns and can be applied to regional hydrological modelling.
Keywords: Rainfall Generator, Rainfall Transition Probability, 1-minute Rainfall Data, Temporal Distribution, Heavy Rainstorm Events
Acknowledgment
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00334564).
How to cite: Cha, H., Baik, J., Lee, J., Na, W., and Jun, C.: Development of Rainfall Scenario with Transition Probability Reflecting on Temporal Distribution of Heavy Rainstorm Events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5084, https://doi.org/10.5194/egusphere-egu25-5084, 2025.
Abstract
In this study was investigated three different microphysics schemes on the rainfall patterns over Kuwait on 02 January 2022. The primary objective is to improve precipitation predictions using the Weather Research and Forecasting (WRF) high resolution 4 km model, which has been dynamically downscaled from the Community Climate Model version 4 (CCM4). The performance of three selected microphysics schemes—Lin, WSM6, and Thompson was evaluated using the ERA5 reanalysis dataset. ERA5 has been previously validated in this region and has consistently provided reliable results, making it a suitable dataset for such studies. Three numerical simulations were conducted using the WRF model, each incorporating one of the three microphysics schemes. The simulations were assessed by comparing the model outputs against the ERA5 data to determine the accuracy of the rainfall forecasts. The results revealed that the WRF Single-Moment 6-class microphysics scheme (WSM6) outperformed the other microphysics schemes, including Lin and Thompson, in forecasting rainfall patterns during the storm. The Lin scheme was found to be the least reliable, providing less accurate rainfall predictions compared to the Thompson and WSM6 schemes. This study highlights the critical role of selecting appropriate microphysics schemes for accurate precipitation prediction, particularly in extreme weather events like the 2022 storm in Kuwait. The findings suggest that the WSM6 scheme is a more effective choice for rainfall forecasting in this region, whereas the Lin scheme may not be as suitable for this particular type of storm event. Further research is recommended to extend this analysis to different regions and storms for more comprehensive results.
How to cite: Alsarraf, H.: Evaluation of WRF Microphysics Schemes for Precipitation Forecasting in an Arid Region: A Case Study Over Kuwait, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5123, https://doi.org/10.5194/egusphere-egu25-5123, 2025.
The availability of accurate long-term gap-free precipitation data at high spatiotemporal resolutions is crucial for hydroclimatic extremes assessment, water resources management, infrastructure design, hydrological modeling, and evaluation of climate change impacts. However, many ground precipitation data contain gaps, which can hinder accurate assessments and analyses. Therefore, different gridded precipitation products (PPs) are promising alternatives to overcome this deficiency, especially in heterogeneous regions with different terrains where ground observations are sparse or non-existent. This study evaluates four daily precipitation products, i.e., SPARTACUS, IMERG-V07, CHIRPS-V2.0, and ERA5-land, to determine their performance in representing observed patterns, the intensity, and frequency of extreme precipitation events in Kamp Catchment in Austria for the period 1998-2020 at different temporal scales. The Kamp River is the longest in the “Waldviertel” region and has key ecological, societal, and economic functions, with many popular leisure and excursion destinations for tourism. The catchment also frequently experiences severe floods, causing adverse socioeconomic impacts. Ground-based precipitation data from 33 stations distributed within and around the catchment are used to conduct point-to-pixel evaluation for the four products. To measure the disparity between the products and the ground observations, six performance metrics were used: the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Nash-Sutcliffe Efficiency (NSE), Correlation coefficient (r), and Willmott index of agreement (d). The intensity and frequency of extreme precipitation reflected by the four evaluated PPs are assessed using selected extreme climate indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The PPs were ranked to select the best-performing product in the study area. The ranking results of the performance metrics revealed that SPARTACUS is the top-performing product on a daily and monthly scale and in capturing the frequency and intensity of precipitation extremes, followed by IMERG-V07 and ERA5-land, whereas CHIRPS-V2.0 ranked the lowest. SPARTACUS showed superior performance across the catchment with the highest correlation with the observed data and lowest bias (on daily and monthly scales, mean r values are 0.92 and 0.96 and mean MBE values are -0.02 and -0.81, respectively). Other products exhibit systematic precipitation underestimation. Regarding capturing precipitation extremes, all products show low skills and overestimate the daily extreme precipitation events, with the highest NSE of -0.32 shown in SPARTACUS. CHIRPS-V2.0 and ERA5-land presented different performances for detecting the longest wet and dry spells in the catchment. CHIRPS-V2.0 overestimated the consecutive dry days (CDD) and underestimated the consecutive wet days (CWD), whereas ERA5-land shows the opposite pattern. SPARTACUS shows minor overestimation of CDD and underestimation of CWD (MBE = -0.09 and 0.13 mm, respectively). Accordingly, a simple drought assessment was performed in the catchment using SPARTACUS data and the Standardized Precipitation Index (SPI). Our results highlight the importance of site-specific validation before using any precipitation products.
This study is conducted within the frame of the DISTENDER project (EU Horizon-ID 101056836), where climate extremes and climate change impacts upon several European catchments are analyzed and robust adaptation strategies are developed.
Keywords: Precipitation extremes, Precipitation products, Point-to-pixel evaluation, SPI, Kamp catchment, Austria
How to cite: Babker, Z., Zagar, M., G. Reichenau, T., Basheer, M., and Schneider, K.: Comparison and evaluation of different precipitation products in capturing climate extremes in Kamp Catchment, Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6812, https://doi.org/10.5194/egusphere-egu25-6812, 2025.
Precipitation downscaling, particularly at convection-permitting scales (less than 4 km), is highly uncertain. This is especially pronounced in mountainous regions due to the interplay of complex topography and atmospheric dynamics. It impedes reliable estimation of variability and risks in localized extreme rainstorms. Deep learning-based downscaling methods have gained increasing attention but have primarily focused on deterministic prediction, which fails to capture uncertainty. Here we developed a novel Probabilistic High-resolution Precipitation Downscaling Network (P-HRDNet) with prior knowledge of key precipitation characteristics to design its loss function and model architecture. This knowledge includes data imbalance, skewed distribution, heteroscedasticity, and spatial and temporal dependencies of precipitation. P-HRDNet was tested in the southeastern Tibetan Plateau, a mountainous region lacking high-resolution precipitation data. Ten-year WRF simulations with nested domains provided coarse (9 km) and fine resolution (1 km) daily precipitation to train P-HRDNet. Compared with a baseline model SRCNN, P-HRDNet achieved greater accuracy in terms of root mean square error, mean absolute error, and Pearson correlation coefficient. Besides, it offers better uncertainty coverage and narrower uncertainty widths. This superiority is particularly evident in the extreme values. Our study highlights the importance of incorporating prior knowledge of precipitation characteristics into deep learning, and has a potential to physically constrain Artitifical-Intelligience (AI) based weather forecasting models. Furthermore, our WRF-AI framework offers an efficient solution for obtaining reliable high-resolution precipitation estimates in poorly gauged regions.
How to cite: Li, D., Yang, L., Pan, B., Liu, Y., and Zhou, Y.: Prior knowledge-constrained deep learning for probabilistic precipitation downscaling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7599, https://doi.org/10.5194/egusphere-egu25-7599, 2025.
Unlike regional climate models, convection-permitting models (CPMs) are able to resolve convection-scale processes and therefore better estimate short-duration, extreme precipitation events, particularly useful for the urban drainage community. Despite their state-of-the-art capabilities, bias correction of CPMs is still required to ensure their output is representative of the station scale, a resolution needed by many urban drainage models. Due to its simplicity, quantile-mapping is commonly used for bias-correction and downscaling, but does come with limitations that have not yet been evaluated for CPMs. This study tests five variations of empirical quantile-mapping to bias-correct and downscale the 2.2 km simulations of COSMO-CLM (a CPM) for over 70 weather stations in Switzerland. Ten years of simulation data are corrected using ten years of observations at the 30-minute interval. Traditional QM and several advanced versions are evaluated, including: using a 91-day moving window to account for temporal variability, spatial pooling of surrounding grid cells for spatial variability, and extending the observational record (to 30 years) for data variability. These techniques are validated using cross-validation and through evaluation of historical rainfall indices (e.g., consecutive dry days) and the climate change signal. Findings show that wet biases in raw CPM output remain (up to 30-35 mm/hour above the 98th quantile) and only the moving window technique (and its combination with spatial pooling) is able to reduce biases in quantiles above the 98th. All QM methods do reduce remaining biases, but can distort the climate change signal, particularly in indices related to frequency of rainfall. Despite the additional computational burden, the moving window technique is highly recommended to the urban drainage community as a robust technique for CPM downscaling. As more CPM simulations become available, future work will reexamine these aspects for a range of CPMs, time periods, and simulation domains.
How to cite: Cook, L., Nguyen, T., Dietzel, A., and Velasquez, P.: Correction of Precipitation Bias from Convection-Permitting Models at the Station Scale in Switzerland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8629, https://doi.org/10.5194/egusphere-egu25-8629, 2025.
Understanding changes in global oceanic precipitation remains challenging due to limitations in current observational datasets and model deficiencies, particularly in the representation of cloud and precipitation properties within oceanic regions. To address this, we examined climatologies and trends in oceanic precipitation between 2001 and 2020 using a collection of 27 state-of-the-art satellite and reanalysis datasets available on a uniform daily 1°×1° resolution from the Frequent Rainfall Observations on Grids (FROGS) database. The results showed that reanalysis datasets generally report higher annual mean daily precipitation than satellite datasets. The tropical region exhibits the greatest absolute discrepancies in precipitation rates, while arid regions such as the southeast Pacific and Atlantic show significant relative differences among products. An increasing trend is primarily observed in satellite products, whereas reanalyses suggest strong declines. Taken together, reanalyses show pronounced decreases over the Intertropical Convergence Zone (ITCZ) and North Atlantic, contradicting the “wet gets wetter, dry gets drier” (WWDD) pattern. In contrast, the satellites better align with the WWDD pattern, with over half of oceanic regions meeting this expectation. The precipitation trend in the combined reanalysis products also exhibits the weakest consistency with sea surface temperature (SST) trends in wet regions (34.2%), compared with dry regions in the reanalysis cluster (53.4%) and both wet (59.6%) and dry (58.5%) regions in the satellite cluster. We recommend using an ensemble of satellite products for investigating global oceanic precipitation while exercising greater caution when utilizing reanalysis datasets.
How to cite: Cheng, S., Alexander, L., and Sherwood, S.: Decadal climatology and trends in global oceanic precipitation from 27 satellite and reanalysis datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9341, https://doi.org/10.5194/egusphere-egu25-9341, 2025.
Localized Constructed Analogs (LOCA) is a statistical downscaling technique that uses a multiple scale approach to determine appropriate local analogs from historical data. It was developed with a particular focus on the preservation of extreme events that were dampened or lost altogether when employing earlier analog methods. The LOCA method has been used to produce relatively high-resolution projections of precipitation over the coterminous United States for use in hydrologic applications but has never been applied over Europe. In this presentation we will describe the method in detail and show how it is being utilized to downscale CMIP6 precipitation to 1 arcmin x 1 arcmin horizontal resolution over the continent using the European Meteorological Observations (EMO-1) gridded dataset as the analog pool. Lastly, we will compare the LOCA output to that from other downscaled products.
How to cite: Thrasher, B.: Using LOCA to downscale precipitation over Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12783, https://doi.org/10.5194/egusphere-egu25-12783, 2025.
Despite significant advancements in the Poisson cluster-based Bartlett-Lewis model for effectively reproducing rainfall extremes, there is still room for further refinement. This study proposes a refined model, referred to as RBL7, introducing module k with a modified equation for rainfall disaggregation. This adjustment allows the power of the sine function to vary inversely with rain cell duration, thereby capturing the realistic characteristics of rainfall extremes, which often come with high intensity over short durations. Furthermore, an improved calibration approach is also proposed for the first module of the RBL7 model. This involves a hybrid optimization technique combining Particle Swarm Optimization (PSO) and fmincon methods, iterately executed until the objective function reaches the pre-assigned threshold. While the calibration of the RBL7 model relies solely on observed rainfall aggregated at hourly and longer timescales, it effectively reproduces rainfall extremes from uncalibrated sub-hourly to supra-hourly aggregation intervals, outperforming existing models using sine-2 and rectangular pulse shapes. Additionally, this refined model maintains its capability to capture rainfall standard statistics, i.e., mean, variance, covariance, skewness, and proportion of wet period, at various timescales ranging from 5 minutes to a month. These findings highlight the robustness of the RBL7 model in simulating rainfall characteristics, especially extreme values at sub-hourly aggregation intervals.
Acknowledgement
This study was supported by Korea Environment Industry & Technology Institute (KEITI) through R&D Program for Innovative Flood Protection Technologies against Climate Crisis Program (or Project), funded by Korea Ministry of Environment(MOE)(RS-2023-00218873).
How to cite: Vuong Tai, C., Park, J., Wang, L.-P., and Kim, D.: Bartlett-Lewis based stochastic rainfall model: An improvement to effectively reproduce sub-hourly rainfall extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14679, https://doi.org/10.5194/egusphere-egu25-14679, 2025.
Climate change is an essential part of sustainable development challenges in developing countries. Climate change represents one of the greatest environmental, social, and economic threats facing the world today. Accurate meteorological and hydrological projections are vital for effective climate adaptation and resource management, particularly under changing climate scenarios. However, the coarse spatial resolution of General Circulation Models (GCMs) limits their applicability for localized impact assessments. This study proposes a deep learning-based super-resolution approach combined with an advanced hydrological model to downscale and enhance the spatial resolution of three GCM datasets—GFDL-CM4, GISS-E2-1-G, and IPSL-CM6A-LR—to approximately 0.01°. The performance of the method is evaluated based on mean square error (RMSE), mean absolute error (MAE), Peak signal-to-noise ratio (PSNR), and Pearson correlation coefficient (R). This study hypothesizes to have more precise and accurate meteorological and hydrological predictions and projections under this framework. The model is conducted on historical climate data and compared with high-resolution observational datasets, showcasing its ability to capture fine-scale climatic and hydrological variability. This approach bridges the resolution gap in climate projections and provides a robust framework for better-informed decision-making in climate change adaptation and mitigation strategies.
Funding
This research was supported by Disaster-Safety Platform Technology Development Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT. (No. 2022M3D7A1090338).
How to cite: Huong, O. S. and Lee, G.: Improving Climate Change Data through Deep Learning Super-Resolution Downscaling of GCMs for Precise Hydrological Projections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15576, https://doi.org/10.5194/egusphere-egu25-15576, 2025.
Precipitation time series are used as input for hydrological modeling. As the main driver of the hydrological cycle, they directly influence soil moisture, runoff, river flows, and groundwater recharge. High-resolution precipitation data is required to obtain accurate hydrological models. In addition, data should be available from different locations to reflect spatial dependencies in these models. As precipitation is measured only at selected locations, the simulated series can be used for design purposes.
In recent years, various models have been developed based on the Fourier Transform because of its ability to preserve desirable statistical properties. The concept is to transform the time series from the time domain to the frequency domain and calculate the two main components of the transformed series: the power spectrum (the square of the absolute values of the Fourier frequencies) and the phase spectrum (phase angle of the frequencies). The main idea behind all the Fourier-based models is to preserve the power spectrum because it relates to the autocorrelation function and overall structure.
This study compares the most common Fourier-based time series generators using different measures. As most spectral methods are iterative, this can be challenging for the precipitation time series, especially for the hourly resolution. In this regard, a non-iterative method is introduced. This method takes advantage of the Wiener–Khinchin theorem for the transformation between the autocorrelation function and the power spectrum. Another method, the Phase Annealing method, is introduced for precipitation time series generation and keeping the spatial and temporal properties. The results have been compared for the developed models and the most common Fourier-based methods.
How to cite: Mehrvand, M. and Bárdossy, A.: Comparative study of spectral methods for precipitation time series generators based on the conserving observed spatial and temporal properties, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16085, https://doi.org/10.5194/egusphere-egu25-16085, 2025.
With the increasing frequency and intensity of extreme rainfall events, the importance of nowcasting to minimize damage from disasters such as flash floods is becoming ever more prominent. However, most nowcasting models use loss functions aimed at minimizing the average prediction error. As a result, they tend to underestimate extreme rainfall—which has relatively low occurrence frequency but significant impact. In this study, we applied various types of weighted loss functions to a ConvLSTM-based nowcasting model to more accurately predict extreme rainfall. In particular, we varied parameters within these weighted loss functions and conducted sensitivity analyses to identify the optimal weighting strategies. We also categorized extreme rainfall types and evaluated the models’ predictive performance with weighted loss functions, thereby examining both the accuracy and stability of the model’s forecasts under extreme conditions from multiple perspectives. The results showed that the model employing a weighted loss function achieved significantly improved accuracy in predicting extreme rainfall, compared to an unweighted model. Furthermore, depending on the type of weighted loss function and parameter settings, the model demonstrated notably strong performance for specific types of extreme rainfall. This finding suggests that, in a rainfall environment characterized by high variability, dynamically selecting weighted loss functions according to forecasting objectives and conditions can enhance both the efficiency and reliability of extreme rainfall prediction. The approach presented in this study can be applied to flood forecasting and is anticipated to contribute to the advancement of deep learning–based disaster response systems, reducing the potential damage caused by natural disasters.
Acknowledgements
This work was supported by Korea Environment Industry & Technology Institute(KEITI) through R&D Program for Innovative Flood Protection Technologies against Climate Crisis Program(or Project), funded by Korea Ministry of Environment(MOE)(RS-2023-00218873).
How to cite: Choi, H., Kim, Y., and Kim, D.: Enhancing Extreme Rainfall Nowcasting with Weighted Loss Functions in Deep Learning Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19416, https://doi.org/10.5194/egusphere-egu25-19416, 2025.
We test methods of postprocessing rainfall forecasts out to 7 days over East Africa.
Using the physical forecast models, IFS from ECMWF and GFS from NCEP, we apply several combinations of post-processing techniques to empirically correct the predicted rainfall towards IMERG blended satellite rainfall data. The techniques we apply include a generative adversarial neural network (GAN) model (Harris et al. 2022), isotonic distributional regression (EasyUQ, Walz et al. 2024), EMOS (Gneiting et al. 2005), linear regression, and the kernel density estimate. Other approaches are also considered, however for the purposes of practical operational forecasts, we mainly focus on computationally cheap methods. Because we are comparing against IMERG, our results compare favourably against fully empirical models, such as FuXi and Graphcast, that have been trained to predict ERA5.
Being computationally cheap, in an operational forecast cycle on a standard desktop computer, the GAN model can produce spatially correlated 1000 member ensembles from the input forecast data. from which we can display the distribution using a histogram. The other techniques also cheaply produce rainfall distributions. We compare the quality of these distributions using the CRPS, variogram score and reliability diagrams. Biases in the raw rainfall forecasts are most notably reduced over the large lakes, for example Lake Victoria, over mountains, Indian ocean, and other places of high rainfall. We find it difficult to reduce biases in dry regions and over the Congo rainforest.
Different empirical modelling methods are optimal for different physical phenomena, and there is no theory for the most accurate model without physical insight. We also observe that it is often possible to improve each of the models with various tweaks. Each of the tested approaches might improve in the future, and the question we are trying to answer is “what is the best practical model available today?”
How to cite: Cooper, F., Nath, S., Gudoshava, M., Kalladath, N., Amdihun, A., Kinyua, J., Kimani, H., Koros, D., Mwai, Z., Maswi, C., Teshome, A., Abebe, S., Obai, I., Mason, J., Pappenberger, F., Chantry, M., Weisheimer, A., and Palmer, T.: Postprocessing of rainfall forecasts over East Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19913, https://doi.org/10.5194/egusphere-egu25-19913, 2025.
Regional climate models (RCMs) are crucial tools for understanding and predicting climate change and its impacts, such as precipitation extremes. We investigate the characteristics of hourly precipitation and the associated extremes in RCM ensembles with two resolutions: km-scale (the CORDEX-FPS Convection ensemble with ~3 km grid spacing, where deep convection is represented explicitly), and coarser-scale (~12 km grid spacing, with parameterized convection). The km-scale ensemble is downscaled from the coarser one, and both cover three time periods: evaluation, historical, and end-of-the-century period under the RCP8.5 warming scenario (2000-2009, 1996-2005, and 2090-2099, respectively). Evaluating the model ensembles against data from 179 weather stations in Austria, we study how the intensity, duration, and the time of onset of precipitation depend on mean daily temperature. We then examine how these characteristics change under warming conditions.
It is well established that over the Alps the coarser RCMs produce too much light and persistent precipitation which is triggered too early in the day. We find that these shortcomings in models with parameterized convection become more pronounced with rising temperatures. We show that the km-scale ensemble closely matches observations and greatly outperforms the coarser ensemble in capturing the investigated hourly precipitation characteristics, especially at higher temperatures and on days with heavy rainfall. As high temperatures are expected to become more common in future climates, our results imply that coarser RCMs suffer from more severe biases in hourly precipitation in the future than under present climate conditions, especially for short-duration extremes.
In this light, we also assess the ability of both km-scale and coarser RCM ensembles to capture the Clausius-Clapeyron scaling of extreme precipitation with temperature, and discuss how model deficiencies in the coarser ensemble affect this relationship.
In summary, our findings highlight the importance of km-scale RCMs for accurate simulations of hourly precipitation and its extremes, particularly in the warming climate.
How to cite: Medvedova, A., Kohlhauser, I., Maraun, D., Rotach, M. W., and Ban, N.: Hourly Precipitation Biases and Clausius-Clapeyron Scaling in Convection-Resolving and Convection-Parameterizing Regional Climate Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15818, https://doi.org/10.5194/egusphere-egu25-15818, 2025.
Reliable precipitation data from in-situ stations is often limited by inconsistent quality, resolution, and spatial coverage. This is particularly true in regions like the West Bank, where ground-based observations are scarce. This hampers hydrological and environmental studies where accurate precipitation estimates are vital. Therefore, satellite-based rainfall products are an appealing alternative due to their broad spatial and consistent temporal coverage. However, the accuracy of these products in complex terrain is questionable due to sensor and retrieval errors, necessitating adjustment to improve their reliability. This study evaluates various adjustment methods for four satellite precipitation products (IMERG Final Run, PDIR-Now, CCS-CDR, and CMORPH) across the study area of Historical Palestine (West Bank and Israel). Daily satellite precipitation estimates were compared to observations from 316 in-situ stations (256 in Israel and 58 in the Palestinian territories). Adjustment methods included traditional bias correction techniques (Linear Scaling, Daily Translation, and Annual Sums), more advanced approaches (Empirical Quantile Mapping, Robust Quantile Mapping, Gaussian Distribution Mapping, and Local Intensity Scaling), and machine learning models (Random Forest and Artificial Neural Networks). Results show that, among the non-machine learning approaches, Daily Translation (DT) achieved the greatest improvement in accuracy followed by Power Bias adjustment. DT applied to IMERG resulted in an improvement of 24% and 17% in R2 and Mean Absolute Error (MAE) respectively. All machine learning approaches outperformed non-machine learning methods, with a two-step Random Forest (RF2) method delivering the best results. RF2, which leverages data from multiple satellites, had a 109% improvement in R2 and a 54% improvement in MAE. Additionally, the global RFG model showcased excellent results in producing a unified model that can be generalized for the entirety of the study area. The findings are globally applicable and evaluate multiple adjustment methods which opens the opportunity for easily accessible remotely sensed precipitation products to be used in many hydrological applications.
How to cite: Jayousi, F. and O'Loughlin, F.: Precision in Precipitation: Bias Corrections and Machine Learning for Reliable Satellite Precipitation in The Levant, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-722, https://doi.org/10.5194/egusphere-egu25-722, 2025.
The Sudano-Sahelian Region of Cameroon (SSRC) faces significant challenges due to high rainfall variability and rapid population growth. Despite long-standing adaptation strategies, the region's vulnerability to climate variability and change remains a critical concern, prompting extensive research and calls for greater adaptation funding. In Sahelian West Africa, the decline in rainfall stations has significantly reduced data availability, hindering the calibration and evaluation of climate models and limiting their ability to accurately represent the region's climate. However, there are notable discrepancies between global and regional models regarding projected changes in precipitation patterns across specific regions and seasons, particularly in areas like the Eastern Sahel region, which includes the SSRC. Bias correction (BC) and downscaling (DS) are crucial, as these bias can be propagated into impact models. This study aims to fill the gap of localized and reliable information for climate services in the Sudano Sahelian region.
Using high-resolution rainfall data from NoCORA—daily interpolated rainfall maps for Northern Cameroon based on 418 stations (1948–2022) at 0.01° resolution (Jérémy et al., 2023)—the 25-km resolution regional climate models derived from a CMIP5 model are employed to better capture the climatology of extreme precipitation events, with kilometer-scale bias correction applied to outputs over the study area. Additionally, a subset of CMIP6 simulations is statistically downscaled to evaluate local-scale model uncertainties and compare the effectiveness of statistical and dynamical downscaling methods.
This study evaluates the performance of four state-of-the-art statistical downscaling techniques namely Linear Scaling, CDF-t, Quantile Mapping and Quantile DeltaMapping using different metrics and compares extreme precipitation changes under climate change scenarios to identify a suitable method for correcting bias in climate models projections for the period 2005-2100. The findings of this study will help impact modelers by enhancing the application of bias adjustment methods, thereby supporting the development of robust local climate change impact assessments in agriculture and hydrology domains.
Keywords : extreme precipitation, biais correction, Statistical downscaling, climate models
How to cite: Njouenwet, I. and Lavarenne, J.: Statistical Downscaling Techniques and Projection of Future Climate Extremes in the Sudano Sahelian Environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1018, https://doi.org/10.5194/egusphere-egu25-1018, 2025.
Rainfall erosivity is a fundamental parameter in estimating soil erosion as it quantifies the potential of raindrops to detach soil particles and make them available for subsequent transport by surface runoff. Erosivity depends mainly on the intensity, duration, and energy of precipitation events, which directly affect the impact of raindrops on the soil surfaces and runoff. The most common methods for identifying erosive events emphasize short-duration, high-intensity rainfall events, while introducing critical thresholds for characterizing erosive events, such as the 30-minute maximum rainfall intensity (I30) and storm separation criteria (see e.g. Wischmeier and Smith, 1978, Foster et al., 1981 and Renard et al., 1997).
Nevertheless, both historical and recently proposed frameworks occasionally consolidate rainfall events that should be disaggregated according to the established six-hour dry period threshold, leading to overestimation of rainfall event durations and erosivity factors. The present study aims at refining the identification and analysis of erosive rainfall events, a key component of soil erosion prediction, by introducing an alternative approach that strictly adheres to the original criteria introduced by Wischmeier and Smith (1978) and Renard et al. (1997), ensuring precise segmentation of rainfall events when rainfall accumulation is below the 1.27 mm threshold over a six-hour period.
The proposed method classifies rainfall events as erosive when total rainfall exceeds 12.7 mm or meets intensity thresholds of 6.4 mm in 15 minutes or 12.7 mm in 30 minutes. Comparative analysis with existing approaches demonstrates improved alignment with fundamental criteria while addressing modern computational challenges, contributing to the advancement of soil erosion prediction by bridging historical methodologies with contemporary analytical precision.
References
Wischmeier, W.H., Smith, D. D. (1978) Predicting rainfall erosion losses: A guide to conservation planning. Agric. Handb. 537. US Gov. Print. Office, Washington, DC.
Foster, G.R., McCool, D.K., Renard, K.G., Moldenhauer, W.C. (1981) Conversion of the universal soil loss equation to SI metric units. J. Soil Water Conserv. 36, 355–359.
Renard, K., Foster, G., Weesies, G., McCool, D. and Yoder, D. (1997) Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). US Department of Agriculture, Agriculture Handbook No.703USDA, USDA, Washington DC.
How to cite: Serafeim, A., Deidda, R., Nasta, P., Romano, N., Pumo, D., and Langousis, A.: Refining Rainfall Erosivity Estimation: Methodological improvements towards more accurate soil erosion assessments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2058, https://doi.org/10.5194/egusphere-egu25-2058, 2025.
Previous research shows that for limited sample sizes applying the simplified metastatistical extreme value (sMEV) distribution instead of the more commonly used general extreme value (GEV) distribution can significantly reduce the associated uncertainty in rainfall return levels. Recent literature has also highlighted the possibility to analyze the effects of climate change using the non-stationary version of the sMEV distribution. Thus, the objective of this study is to test the performance of the sMEV and GEV for hourly precipitation using a convection-permitting regional climate model. The global climate model MIROC5 is employed to drive the regional climate model COSMO over the greater Germany area for the past, near future, and distant future. It is set up at a high temporal and spatial resolution allowing it to explicitly resolve deep convection, which is important when assessing extreme hourly precipitation. No comparable time series from a convection-permitting model has previously been analyzed using the sMEV distribution. The results show that the sMEV performs much better than the GEV in terms of the uncertainty for almost all return periods regardless of the observational years available. In addition, there is a north-south gradient in the return level difference, the uncertainty difference and the adequacy of the left-censoring threshold chosen for the sMEV. Investigating non-stationary versions of the sMEV and GEV shows that the non-stationary sMEV is more suitable to describing the change in return levels. However, both implemented versions of the non-stationary distributions are limited by the complexity of the temperature dependency. Therefore, we recommend a careful application for the prediction of return levels under higher temperatures.
How to cite: Lennartz, M. and Poschlod, B.: Exploring Hourly Rainfall Extremes in a Changing Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3254, https://doi.org/10.5194/egusphere-egu25-3254, 2025.
This study aims at integrating global precipitation data into hydrological models at the catchment scale, a common practice in hydrological research. Specifically, the study investigates how biased spatial and temporal patterns in precipitation data affect model performance and uncertainty. The European Meteorological Observations (EMO) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) global datasets are utilized as inputs for the GEOframe-NewAGE hydrological model to simulate the hydrological processes of the mountainous Aosta Valley catchment in northwestern Italy. Subsequently, the uncertainty of the hydrological model forced with global precipitation data is assessed using a proposed method called Empirical Conditional Probability (EcoProb). The results show that, although traditional performance metrics suggest similar outcomes for the model forced with EMO and CHIRPS, the proposed uncertainty analysis reveals higher uncertainty when CHIRPS is used as the precipitation input. To leverage all useful information in the global precipitation data, the spatial correlation of CHIRPS is combined with a subset of raingauges using the EcoProb method to modify the EMO precipitation data. This approach enables the integration of the advantages of EMO and CHIRPS, which offer higher temporal and spatial correlation with ground observation, respectively, into a unified precipitation product. The combined dataset, referred to as the EcoProbSet product in this study, outperforms both the CHIRPS and EMO products, reducing the uncertainty introduced into hydrological models compared to the original global datasets.
How to cite: Azimi, S., Massari, C., Roati, G., Barbetta, S., and Rigon, R.: A new tool for correcting the spatial and temporal pattern of global precipitation products across mountainous catchments: EcoProbSet Product, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2770, https://doi.org/10.5194/egusphere-egu25-2770, 2025.
Summer precipitation over High Mountain Asia (HMA) has exhibited a dipolar trend over the past 50 years. Understanding its future changes and underlying mechanisms relies heavily on climate models. However, the impact and mechanisms of model resolution on the simulation of long-term precipitation trends over the HMA remain underexplored. In this study, we use six pairs of models with high- and low-resolution comparisons from the CMIP6 all-forcing experiments to investigate the resolution-dependent differences in the long-term trends of summer precipitation from 1951 to 2024. The results show that compared to low-resolution models, the simulations from high-resolution models are closer to observations, with the largest improvement in the southern margin of the HMA and surrounding areas (STP), where the wet bias is reduced by approximately 65%. The moisture budget, moist static energy budget, and mixed-layer heat budget are used to explore the mechanism behind this reduction in wet bias. High-resolution models, with their enhanced ability to simulate oceanic advection and mixing, can capture the central-warm and eastern-cool tropical Indian Ocean SST pattern better. This SST pattern suppresses precipitation over Malaysia and the South China Sea, triggering Rossby waves that generate an anomalous anticyclone over the northern Bay of Bengal. The anticyclone then transports dry air to the STP, suppressing local convection and reducing wet bias. Our study emphasizes the importance of simulating Indian Ocean warming for accurately representing long-term precipitation trends over HMA.
How to cite: li, L.: Precipitation Trends over southern High Mountain Asia affected by Indian Ocean warming: Insights from high- and low-resolution versions of CMIP6 models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7737, https://doi.org/10.5194/egusphere-egu25-7737, 2025.
In this study, we developed a grid climate dataset with a horizontal resolution of 500m × 500m for South Korea, utilizing observational station data from the Korea Meteorological Administration (KMA). The high-resolution 500m data were calculated using a newly developed Multi-Step (MS) PRISM (Parameter-elevation Regressions on Independent Slopes Model) method, which enhances the Modified Korean (MK) PRISM—a statistical downscaling technique for estimating high-resolution gridded data from observational data. First, to produce high-resolution hourly precipitation data, we performed quality control on the hourly precipitation observation data to select valid entries. Next, we created geographic information data, including Digital Elevation Model (DEM), aspect, and coastal proximity, all at a resolution of 500m. This geographic data was then applied to the MS-PRISM method to calculate hourly precipitation data at the same resolution. To confirm the reliability of the 500m resolution hourly precipitation produced, we conducted a verification of the final estimated data. We compared and analyzed the daily precipitation estimation errors as well as the hourly precipitation estimation errors at the same spatial resolution. Additionally, we evaluated the estimation results based on changes in spatiotemporal resolution by comparing the estimation errors associated with different spatial resolutions while maintaining the same temporal resolution.
How to cite: Kim, M.-K., Jeong, S., and Lee, Y.: Statistical downscaling of hourly precipitation in South Korea using the MS-PRISM method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7792, https://doi.org/10.5194/egusphere-egu25-7792, 2025.
Precipitation is a key variable for assessing the impacts of climate change across diverse sectors, from hydrology to ecology. However, climate models frequently overestimate the occurrence of light precipitation events—days or hours that should be dry are instead assigned a low rainfall rate. This pervasive issue, known as the “drizzle bias” or “drizzle problem” in climate science, undermines the reliability of climate impact assessments.
Traditional bias correction methods, such as linear scaling or empirical quantile mapping, address overall precipitation distributions but often fail to properly account for the frequency and duration of wet and dry periods. As a result, these methods may improve precipitation totals but fail to correct the skewed distribution of rainy events.
In this study, we propose a simple yet effective two-step statistical downscaling approach to address the drizzle bias. The first step aligns the frequency of wet and dry periods by assuming equivalence between observed and simulated rain frequencies. The second step corrects the precipitation distribution exclusively for wet samples.
We apply this methodology to a range of climate data products, including ERA5 Land reanalyses, as well as simulations from global circulation models (GCMs), regional circulation models (RCMs), and convection-permitting models (CPMs). Our analysis focuses on multiple measurement stations in Northern Italy, encompassing urban contexts such as Milan and mountainous contexts in the Italian Alps. Results reveal that drizzle bias is a widespread issue across these datasets, regardless of the modeling framework.
The findings demonstrate that our two-step downscaling approach effectively adjusts for drizzle bias, significantly improving the statistical fidelity of precipitation projections. This approach offers a straightforward and practical solution for enhancing the reliability of climate model outputs, enabling more robust assessments of climate change impacts across sectors sensitive to precipitation variability.
How to cite: Sangiorgio, M., Caspani, R., Scarpellini, L., Giuliani, M., and Castelletti, A.: Drizzle Bias adjustment in climate models: A simple two-step downscaling approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9859, https://doi.org/10.5194/egusphere-egu25-9859, 2025.
The algorithm of removing interfering RLAN signals (so called spikes) in weather radar data is implemented in the Institute of Meteorology and Water Management – National Research Institute (IMGW) as a component of the RADVOL-QC system for the radar data quality control. Eliminating the interfering signals in C-band (5 GHz) radars is important for accurate weather monitoring. The main difficulty in spike removal are their unique shapes, and the task is especially challenging while they overlap the precipitation.
The process of detecting interference caused by signals from the RLAN network is carried out by evaluating the variability of echoes along and across the beam for each bin at various elevation angles. Such echoes are considered potential spikes. For each azimuth, the number of bins containing potential spike echoes is determined. If this count exceeds the established threshold for a given azimuth, the echoes are treated as real spikes.
The spike correction process consists of analyzing each bin with detected real spike and its surroundings. The analysis extends to bins in adjacent and further azimuths on left and right until bins without detected spikes are encountered. Depending on the specific case, these echoes may be replaced with an arithmetic mean if classified as precipitation or removed entirely. While removing spikes, the analysis extends to adjacent azimuths within a range of 3 to 4 bins on either side to ensure accurate identification and removal of false echoes. This extended analysis considers potential anomalies in adjacent data that may have been overlooked during the detection process.
Examples of applied techniques are presented using the weather radar product maximum reflectivity (CMAX). The examples illustrate the enhancement of the radar data, where the extended analysis effectively eliminates RLAN interference that was not identified by the detection algorithm but falls within the analysis area. This improvement is crucial from a meteorological perspective, as high-quality radar data significantly impacts meteorological and hydrological models, leading to more accurate forecasts.
How to cite: Specht, K., Ośródka, K., Szturc, J., and Freda, W.: Removal of interfering RLAN signals from C-band weather radar data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10165, https://doi.org/10.5194/egusphere-egu25-10165, 2025.
The increasing frequency and intensity of extreme events due to global warming, such as heavy rainfall and consequent floods, underline the need for research on the driving factors of these extremes. Accurate simulations of meteorological extremes at convection-permitting scale are crucial for understanding their spatial and temporal characteristics. Recently, various studies have demonstrated the added value of using convection-permitting regional climate models to simulate extreme precipitation. Further improvements of these regional models can therefore lay the foundation for better impact assessment, as well as for developing adaptation measures to tackle climate change.
In this study, we investigate the optimal model configuration for the regional climate model REMO2020-iMOVE to capture extreme precipitation events, using the heavy rainfall that led to the devastating Ahr valley flood of July 2021 as a case study. Our simulations are performed with the non-hydrostatic version of REMO with ERA5 reanalysis data as forcing at a horizontal resolution of 3 km. By including the vegetation module iMOVE, we aim to improve the representation of vegetation-atmosphere interactions and, in a future step, investigate the effects of land use and land cover changes on extreme events. Here, we explore the impact of different model setups such as different domain sizes and initialization times on the simulation results. Furthermore, we validate our findings against observations and assess uncertainty within the model. This research provides insight into optimizing regional climate models to improve our understanding of extreme weather events.
How to cite: Detjen, L., Rechid, D., and Böhner, J.: Optimizing convection-permitting model configurations for accurate simulation of extreme precipitation events with the regional climate model REMO-iMOVE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11604, https://doi.org/10.5194/egusphere-egu25-11604, 2025.
Reanalysis data have proven to be a valuable support for hydrologic modeling and calculation of standardized climate indices, useful tools for characterizing local climate regimes and improving water resource management in areas with limited availability of observational data.
This study examines the use of ERA5 dataset emphasizing bias correction techniques to enhance their applicability and understanding their limits in a case study in Georgia. The work assesses the effectiveness of five bias correction techniques - Linear Scaling (LS), Empirical Quantile Mapping (QM-EMP), Quantile Mapping Spline Bias Correction (QM-SBC), Mean Bias Subtraction (MBS), and Simple Linear Regression (SLR) - each examined through two different bias correction approaches: classical and sliding window, applied to daily and monthly reanalysis time series. Observational climate data are scarce in Georgia, therefore the opportunity of using reanalysis data for hydrological studies is of great interest for engineering applications.
In this study, performed in collaboration with Idrostudi S.r.l., one of the foremost European engineering professional services consulting firms, the extraction of ERA5 data for the entire nation of Georgia was performed automatically by developed algorithms that also allowed to do bias correction. The algorithms, developed using the open-source programming language R, employ observed data collected by five meteorological stations across diverse climatic zones of Georgia to test and compare different bias correction methodologies. The aim is to validate the performance of bias correction methods to improve the accuracy of rainfall data generated by ERA5 reanalysis model at daily and monthly scales. The techniques were evaluated carrying out two experiments, i.e. using (i) the complete datasets and (ii) the series that were split into a calibration and validation subset; metrics such as Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were used to assess the performance. The dataset undergoes a calibration phase using 70% of the data to tune the bias correction methods, followed by a validation phase with the remaining 30% to test their effectiveness.
Results demonstrate that bias correction improves the quality of reanalysis data, dealing to enhanced reliability for hydrological modelling and climate index computation. The LS method has emerged as the most effective among classical techniques for bias correction in daily-scale reanalysis data when all data are available. The introduction of the Sliding Window approach has further enhanced the performance of all techniques, adapting the correction to local variations and improving accuracy for daily precipitation events. It is important to note, however, that at a monthly scale, the classic approach to bias correction already proves to be sufficiently reliable. Therefore, further enhancements through the sliding window approach are not deemed necessary for monthly corrections. In the experiment (ii), techniques such as QM-EMP, QM-SBC, and SLR proved to be more suitable for applications in climatic contexts with high variability and fragmentation. This underlines the importance of selecting the appropriate bias correction technique based on the quality and availability of data, as well as the specific objectives of the analysis. Further studies are needed for a further optimization of bias correction approaches.
How to cite: Nobile, A., Zanello, F., Lubrano, F., Nicolini, M., and Arnone, E.: Reanalysis Data in Hydrological Applications: A Case Study from Georgia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12929, https://doi.org/10.5194/egusphere-egu25-12929, 2025.
Disaggregation of rainfall time series focuses on preserving the statistical properties of those small-scale intensities, which are being downscaled from measured large-scale values. Multifractal scaling properties have offered, for a few decades already, a parsimonious framework for simulating the joint statistics observed in the small-scale values, and recent work emphasizes the use of more sophisticated cascading processes, in order to better capture all statistical requirements imposed (Cappelli et al., Stoch Environ Res Risk Assess 2024, https://doi.org/10.1007/s00477-024-02827-8). Comparisons between downscaling models based on canonical vs. microcanonical cascades have been presented already more than two decades ago (see e.g. Molnar and Burlando, Atmos Res 77, 2005, https://doi.org/10.1016/j.atmosres.2004.10.024), but recent theoretical results (Aguilar-Flores and Carsteanu, Fractals 32, 2024, https://doi.org/10.1142/S0218348X24500725) have prompted us to consider the importance of taking into account the asymptotic properties of the measures generated by canonical and microcanonical cascades, respectively, for downscaling purposes. The reflection of such properties in real-life rainfall data is being analyzed in the work communicated herein.
How to cite: Carsteanu, A. A., Emmanouil, S., Langousis, A., and Deidda, R.: Considerations in multifractal downscaling of rainfall: canonical vs. microcanonical cascades, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13830, https://doi.org/10.5194/egusphere-egu25-13830, 2025.
Probabilistic radar-based precipitation nowcasting has become increasingly crucial for real-time hydrological applications due to its high accuracy at short lead time. However, its reliability for hydrological usage is limited by two major sources of error and uncertainty, both of which tend to exacerbate quickly with lead time. The first source lies in the limitations of nowcasting algorithms, including inaccuracies in rainfield advection and inadequate modeling of rain cell evolution. The second arises from discrepancies in precipitation measurements, referring to the differences between radar-derived estimates and rain gauge observations. Aligning these estimates with actual ground-level precipitation is vital for practical hydrological applications.
This study focuses on addressing the errors and uncertainties inherent in precipitation 'measurements', aiming to enhance the reliability of original nowcasts. Here, uncertainty refers to the range within which the true value is expected to fall at a given confidence level, while error denotes to the systematic bias between estimated and true values. The proposed methodologies utilise rain gauge data as the ground truth and employs the Short-Term Ensemble Prediction System (STEPS) to generate radar-based ensemble nowcasts. To deal with these issues, an initial attempt was conducted with the Censored Shifted Gamma Distribution (CSGD) model. However, the model faces challenges in selecting an appropriate metric as the adjusted value, limiting the potential reduction in RMSE to approximately 5–10%. To overcome this limitation, a random forest (RF) regression model is proposed, as it can avoid predefined assumptions about rainfall intensity distribution. This model incorporates variables such as nowcasted rainfall intensity, orographic features, and meteorological parameters such as wind speed, wind direction, humidity, cloud type, and cloud base height (CBH), to estimate corresponding rain gauge measurements. At each rain gauge location, the error distribution is parametrised by comparing the original and adjusted rainfall intensities and fitting them to various probability functions. These parameters are then spatially interpolated using geostatistical techniques to generate an error map. The resulting error map is applied to correct the original nowcasts across the study area, enhancing their overall accuracy and reliability.
The United Kingdom, benefiting from its comprehensive and high-quality meteorological data, was selected as the study area. The 1-km UK C-band radar composite, derived from the Met Office Nimrod System, serve as the radar rainfall dataset for generating ensemble nowcasts. Rain gauge data and additional meteorological variables are sourced from the Met Office Integrated Data Archive System (MIDAS) and the Met Office LIDARNET ceilometer network. Rainfall events from 2016 to 2022 are analysed, with events from 2016 to 2020 designated as the training period for developing random forest models and error maps. For validation, 20 events from 2021 to 2022 are selected to assess the performance of both the original and adjusted nowcasts. Preliminary results indicate that the adjusted ensemble nowcasts exhibit significantly improved alignment with rain gauge measurements compared to the original nowcasts. These findings highlight the potential of the proposed methodology to reduce both error and uncertainty in radar-based precipitation nowcasting, particularly for hydrological applications such as flood and landslide forecasting.
How to cite: Lin, H.-M. and Wang, L.-P.: Enhancing the applicability of radar-based precipitation nowcasting to hydrological applications with a machine-learning based error modelling method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14377, https://doi.org/10.5194/egusphere-egu25-14377, 2025.
Intensity-Duration-Frequency (IDF) curves are critical for urban drainage design and flood risk mitigation, particularly in highly urbanized regions like Singapore, where short-duration extreme rainfall events pose significant challenges. This study quantifies future changes in IDF curves and their associated uncertainties under two representative emission scenarios: SSP 2-4.5 and SSP 5-8.5. To construct future IDF curves, we compare two methods. First, we use a stochastic downscaling methodology that makes use of the AWE-GEN weather generator, to downscale precipitation projections from 25 Global Climate Models (GCMs) to the local point scale. The results show that the magnitude of future extreme precipitation quantiles is expected to get higher toward the end of the 21st century under both future scenarios. Higher-emission scenarios lead to substantial intensification of rare precipitation events, accompanied by a large uncertainty. However, internal climate variability is the dominant source of uncertainty, with climate model and emission scenario uncertainties being less relevant. Second, the results are compared with outputs of the TENAX (Temperature dependent Non-Asymptotic statistical model for eXtreme return levels) model, a novel framework that incorporates temperature as a covariate in a physically consistent manner to project rainfall return levels in a warmer climate using fewer inputs. This study compares state-of-the-art methodologies for computing IDF representative of future climates and provides actionable insights for engineers and policymakers to update urban stormwater design guidelines and enhance resilience against future rainfall extremes.
How to cite: Chen, M., Peleg, N., and Fatichi, S.: Quantifying Future Shifts in Intensity–Duration–Frequency (IDF) in Singapore: A comparison of methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14931, https://doi.org/10.5194/egusphere-egu25-14931, 2025.
Projecting climate change impact in southern Italy is particularly challenging because this region is located in the center of the Mediterranean basin, which is a recognized climate change hotspot, and is characterized by steep and complex orography requiring analysis at high spatial resolution. Therefore, climate models at the convection-permitting scale considerably improve the ability to simulate water cycle trends in that region, especially severe events.
This note introduces the modeling framework on which climate simulations are being carried out for southern Italy using CMIP6 projections and presents the first results related to the comparison of the historical simulation with observational datasets. A preliminary analysis revealed that the best CMIP6 global climate model (GCM) for reproducing the interannual cycle of precipitation and temperature over the study area is the High-Resolution MPI-ESM-1-2 model (1°x1° as horizontal resolution). Such a GCM was chosen to provide 6-hour boundary conditions for dynamic downscaling with the WRF (Weather Research and Forecasting) limited-area model with two domains one-way nested: the external one D01, with a horizontal resolution of about 20km, covering the entire Mediterranean area (209x214 grid points), and the internal one D02, with a horizontal resolution of about 4km, centered on southern Italy (285x265 grid points). The historical simulation extends from 1995 to 2014. The future simulations cover the period 2025 to 2045. The first future simulation employs the SSP 5-8.5 scenario.
Total precipitation and near-surface air temperature resulting from the historical simulation are compared with both observational datasets (namely, the spatially distributed products BigBang, SCIA, E-OBS, and validated weather station time series) and reliable downscaled reanalyses (e.g., ERA5-Land, MERIDA, MERIDA HRES, SPHERA, CERRA, VHREA_IT), which are increasingly available for the Italian peninsula. The results highlight that the evaluation of the performance of the historical simulation is partially affected by the selection of the reference dataset.
Acknowledgments: This study was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.3, project WaterWISE - Water Management Strategies and Climate Change Adaptation in Southern Italy, n. PE00000005, CUP D43C22003030002; and by the Next Generation EU - Italian NRRP, Mission 4 ‘Education and Research’ - Component C2, Investment 1.1, Research Project of National Interest (PRIN 2022 PNRR) - An integrated modeling approach for mitigating climate CHANge effects through enhanCEd weathering in Southern Italy (CHANCES, CUP H53D23011260001), Italian Ministry of University and Research.
How to cite: Mendicino, G., Furnari, L., Hatami Bahman Beygloo, E., Rummler, T., Kunstmann, H., and Senatore, A.: A Framework for Convection-Permitting Climate Downscaling over Southern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15936, https://doi.org/10.5194/egusphere-egu25-15936, 2025.
The Po river basin, in the north of Italy, is the lifeline of the economic and ecology of the North of Italy. The 661 km long river covers an area of 71327 km2 and replenishes the water demands of agriculture, industry and domestic consumers. The topography is diverse with alps mountains in the north and fertile plains in the south. The annual precipitation is 1200 mm which varies between ~2000 mm in the Alps to ~700 mm in the downstream. This study presents the estimates the precipitation on daily resolution over a grid of 1 km across the Po river basin for the period from 1991 to 2021, thus providing a consistent datasets for analyses of the recent climatology of the area. Total 1511 number of observed precipitation stations were included in the study along with topographic information. The statistical technique of kriging was employed to produce the grid data cube. The workflow of the study is summarized in the following steps:
- obtain the meteorological data from the data providers
- estimate the empirical semivariogram
- fit theoretical models to the empirical semivariogram and analyses of the statistical correlation
- use the theoretical model for solving the kriging system
- produce continuous surface maps or time series of the quantity desired in any gridded point of the domain
- calculate estimation errors.
For the estimation of errors Leave-one-out (LOO) is adopted which consists of removing a single station at a time and performing the interpolation for the location of the removed point by using the remaining stations. The approach is repeated until every station has been, in turn, removed and estimates are calculated for each station.
The results have shown that the average precipitation in the basin is 1131 mm with significant spatial patterns, some of which are reported for example. The northern subbasins have shown annual precipitation up to 2500 mm while the downstream planes receives up to 550 mm. The results show clear spatial and temporal patterns across the basin which are reported in the study.
How to cite: Baig, S., Roatti, G., Brian, M., Tornatore, F., Formetta, G., and Rigon, R.: Daily precipitation dataset (1991-2021) at 1 km resolution over the Po river basin area using Kriging, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17581, https://doi.org/10.5194/egusphere-egu25-17581, 2025.
High-quality input data is the foundation for good model performance, including catchment level hydrological models. The resolution and quality of meteorological data has a direct impact on modelling results and as such strongly influences the outcomes of scenario analyses of different types. Nowadays one can choose between different meteorological products when setting up a mathematical model, including direct measurements and reanalyses. The goal of this study was to test the ability of MET Nordic data, a reanalysis product from Met Norway, on improving the simulations of hydrological models. The MET Nordic Reanalysis Dataset consists of post-processed products that (a) describe the current and past weather (reanalysis), and (b) gives a best estimate of the weather in the short-term future (forecasts). The products integrate output from MetCoOp Ensemble Prediction System (MEPS) as well as measurements from various observational sources, including crowdsourced weather stations.
Two different catchment models were set up and calibrated against measured discharge data. The SWAT+ model was applied in two Norwegian and one Danish catchment, while the CWatM model was tested in one Norwegian catchment. The model’s performance was compared when using input datasets from measuring stations and MET Nordic reanalysis data. We concluded that applying reanalysis data can significantly improve the performance of the tested models, therefore the use of these data in hydrological modelling is highly recommended.
How to cite: Farkas, C., Shore, M., Fennell, J., and Shafiei, M.: MET Nordic Reanalysis data improves the performance of catchment-level hydrological models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13869, https://doi.org/10.5194/egusphere-egu25-13869, 2025.
Precipitation variability at small space-time scales significantly influences hydrological processes, particularly in heterogeneous environments such as urban areas. Building on established methodologies for generating universal multifractal cascade fields, we propose an alternative approach that optimizes memory efficiency while maintaining the fidelity and flexibility of high-resolution simulations. Our method generates cascade fields dynamically, we call it Cascade Tree, which reduces memory usage by over 100 times compared to precomputing and storing full datasets. This improvement complements existing techniques by offering a scalable option for real-time applications.
To further enhance the realism of the simulated fields, we integrate the blunt extension of universal multifractals, which smooths transitions between far branches in Cascade Tree and addresses non-conservativeness in a computationally efficient manner. By leveraging GPU acceleration, we achieve rapid computation of cascade fields, enabling their use in simulating complex phenomena such as rainfall dynamics in turbulent wind fields.
The method is applied to simulate 3D trajectories and velocities of raindrops in a high-resolution multifractal turbulent wind field, using real wind field data to improve the applicability of the results. Our simulations capture the spatial and temporal variability of rainfall and demonstrate the dispersion of over 100,000 raindrops across scales relevant to radar pixels and urban catchment hydrology.
This work provides new tools for exploring rainfall-driven processes, with applications ranging from downscaling radar precipitation data to refining hydrological response models. By complementing established methods with a memory-efficient and GPU-accelerated framework, our approach bridges the gap between drop-scale dynamics and catchment-scale impacts.
How to cite: Wei, C.-L., Gires, A., and Wang, L.-P.: Blunt Extension and Dynamic Generation of Multifractal Cascade Fields Tree for Rainfall Drop Trajectories Simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14564, https://doi.org/10.5194/egusphere-egu25-14564, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot A
EGU25-2576 | ECS | Posters virtual | VPS10
Rainfall Estimation in West Africa: A Performance Comparison of Satellite and Soil Moisture-Derived ProductsThu, 01 May, 14:00–15:45 (CEST) | vPA.9
Accurately capturing rainfall patterns is crucial for hydrometeorological applications, particularly in regions like Burkina Faso, West Africa, where rainfall variability significantly impacts water resources and agricultural productivity. However, challenges remain in identifying the most reliable rainfall products for such purposes. This research evaluates the effectiveness of satellite precipitation products (SPPs) and soil moisture-derived rainfall products (SM2RPPs) in representing rainfall patterns in Burkina Faso. Results show that SPPs generally perform better than SM2RPPs across daily to annual timescales. An analysis of total bias components highlights that hit biases dominate but are more pronounced in SM2RPPs. Systematic errors contribute significantly to these hit biases, indicating the potential for improvement through bias correction. Wavelet analysis reveals that both SPPs and SM2RPPs capture seasonal and annual rainfall variability effectively. However, all products exhibit limitations in accurately representing extreme rainfall indices, although SPPs demonstrate superior performance compared to SM2RPPs. While SM2RPPs currently underperform relative to SPPs in Burkina Faso, they show promise for hydrometeorological applications and could achieve comparable or improved results with enhanced bias correction techniques.
How to cite: Yonaba, R., Belemtougri, A., Fowé, T., Mounirou, L. A., Nkiaka, E., Dembele, M., Akpoti, K., Coly, S. M., Koïta, M., and Karambiri, H.: Rainfall Estimation in West Africa: A Performance Comparison of Satellite and Soil Moisture-Derived Products, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2576, https://doi.org/10.5194/egusphere-egu25-2576, 2025.
