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HS7.1

Precipitation modelling: uncertainty, variability, assimilation, ensemble simulation and downscaling

The assessment of precipitation variability and uncertainty is crucial in a variety of applications, such as flood risk forecasting, water resource assessments, evaluation of the hydrological impacts of climate change, determination of design floods, and hydrological modelling in general. This session aims to gather contributions on research, advanced applications, and future needs in the understanding and modelling of precipitation variability, and its sources of uncertainty.
Contributions focusing on one or more of the following issues are particularly welcome:
- Novel studies aimed at the assessment and representation of different sources of uncertainty versus natural variability of precipitation.
- Methods to account for accuracy in precipitation time series due to, e.g., change and improvement of observation networks.
- Uncertainty and variability in spatially and temporally heterogeneous multi-source precipitation products.
- Estimation of precipitation variability and uncertainty at ungauged sites.
- Precipitation data assimilation.
- Process conceptualization and approaches to modelling of precipitation at different spatial and temporal scales, including model parameter identification and calibration, and sensitivity analyses to parameterization and scales of process representation.
- Modelling approaches based on ensemble simulations and methods for synthetic representation of precipitation variability and uncertainty.
- Scaling and scale invariance properties of precipitation fields in space and/or in time.
- Physically and statistically based approaches to downscale information from meteorological and climate models to spatial and temporal scales useful for hydrological modelling and applications.

Co-organized by CL5.3/NH1/NP3
Convener: Giuseppe Mascaro | Co-conveners: Alin Andrei Carsteanu, Simone Fatichi, Roberto Deidda, Chris Onof
Presentations
| Thu, 26 May, 08:30–11:32 (CEST)
 
Room 2.44

Thu, 26 May, 08:30–10:00

Chairpersons: Giuseppe Mascaro, Roberto Deidda

08:30–08:40
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EGU22-3074
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solicited
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Highlight
Athanasios Paschalis et al.

Intensification of precipitation extremes under a changing climate is expected to severely impact societies due to increased flooding, and its impacts on infrastructure, agriculture, and ecosystems. Extensive research in the last decades has identified multiple facets of precipitation changes, from super Clausius – Clapeyron scaling of precipitation extremes with temperature increase, to the change of the intensity and spatial extent of mesoscale convective systems.

In this study we attempt to compile state of the art data and simulations to understand the multiple facets of the changes in precipitation extremes across the world. To do that we combined data from thousands of weather stations globally, reanalysis datasets, and general circulation and convection permitting model simulations. Our results show that:

  • Hourly precipitation extremes scale with temperature at a rate of ~7%/K globally, albeit very large spatial heterogeneities were found, linked to topography, large-scale weather dynamics and local features of atmospheric convection
  • Precipitation extremes change beyond this thermodynamic basis, with increases in the heaviness of the tails of precipitation distribution at fine scales
  • The spatial extent of convective systems is expected to increase
  • Precipitation extremes with shorter spell duration that are distributed more uniformly throughout the year are expected

How to cite: Paschalis, A., Moustakis, Y., and Chen, Y.: A global scale assessment of the intensification of rainfall extremes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3074, https://doi.org/10.5194/egusphere-egu22-3074, 2022.

08:40–08:46
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EGU22-10931
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ECS
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Virtual presentation
Stergios Emmanouil et al.

The quantification of future flood risk, as well as the assessment of impacts attributed to the evolution of extreme rainfall events under rapidly changing climatic conditions, require multi-year information at adequately high spatiotemporal scales. The spatial and temporal evolution of regional extreme rainfall patterns, however, is quite challenging to describe due to natural climate variability and local topography. Hence, the use of conventional climate model outputs to evaluate the frequency of extreme events may not be conclusive due to significant epistemic uncertainties.  To date, there is limited knowledge on how extreme precipitation patterns will evolve under the influence of climate change, at spatiotemporal resolutions suitable for hydrological modeling, and considering the non-stationarity of rainfall as a process. In this study, we evaluate future trends related to extreme rainfall using hourly estimates acquired through the North American (NA) CORDEX Program (see Mearns et al., 2017), spanning from 1979 to 2100, over a 25-km CONUS-wide grid. In view of the practical importance of high spatial and temporal resolutions in hydrological modeling, we first simultaneously bias-correct and statistically downscale the NA-CORDEX model outputs, by using the two-component theoretical distribution framework described in Emmanouil et al. (2021), as well as the Stage IV weather radar-based gridded precipitation data (4-km spatial resolution) as a high-resolution reference. To investigate the validity of the yielded rainfall intensity quantiles, we use as benchmark the hourly rainfall measurements offered by NOAA’s rain gauge network (National Centers for Environmental Information, 2017). Finally, to evaluate the effects of climate change on the spatial and temporal evolution of rare precipitation events while taking into consideration the nonstationary nature of rainfall, we apply a robust (Emmanouil et al., 2020) parametric approach founded on multifractal scaling arguments (Langousis et al., 2009) to sequential 10-year segments of the data, where conditions can be fairly assumed stationary. In view of revealing future infrastructure vulnerabilities over a wide range of characteristic temporal scales and exceedance probability levels, our analysis is founded on Intensity-Duration-Frequency (IDF) curves, which are derived using the previously acquired CORDEX-based, gridded (4-km), hourly precipitation estimates, and cover the entire CONUS for a period of 120 years.

References

Emmanouil, S., Langousis, A., Nikolopoulos, E. I., & Anagnostou, E. N. (2020). Quantitative assessment of annual maxima, peaks-over-threshold and multifractal parametric approaches in estimating intensity-duration-frequency curves from short rainfall records. Journal of Hydrology, 589, 125151. https://doi.org/10.1016/j.jhydrol.2020.125151

Emmanouil, S., Langousis, A., Nikolopoulos, E. I., & Anagnostou, E. N. (2021). An ERA-5 Derived CONUS-Wide High-Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework. Water Resources Research, 57(6), 1–17. https://doi.org/10.1029/2020WR029548

Langousis, A., Veneziano, D., Furcolo, P., & Lepore, C. (2009). Multifractal rainfall extremes: Theoretical analysis and practical estimation. Chaos, Solitons and Fractals, 39(3), 1182–1194. https://doi.org/10.1016/j.chaos.2007.06.004

Mearns, L. O., McGinnis, S., Korytina, D., Arritt, R., Biner, S., Bukovsky, M., et al. (2017). The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway. Boulder (CO): The North American CORDEX Program, 10.

National Centers for Environmental Information. (2017). Cooperative Observers Program Hourly Precipitation Dataset (C-HPD), Version 2.0 Beta. NOAA National Centers for Environmental Information, [accessed July 17, 2020].

How to cite: Emmanouil, S., Langousis, A., Nikolopoulos, E. I., and Anagnostou, E. N.: Assessing future extreme rainfall trends through multifractal scaling arguments: A CONUS-wide analysis based on NA-CORDEX model outputs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10931, https://doi.org/10.5194/egusphere-egu22-10931, 2022.

08:46–08:52
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EGU22-11629
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ECS
Stefano Farris et al.

Rainfall extremes are expected to intensify in a warmer environment according to theoretical arguments and climate model projections. Inferential analysis involving statistical trend testing procedures are frequently used to validate this scenario by investigating whether significant changes in precipitation measurements can be detected. Recent studies have shown that statistical trend tests applied to hydrological data might be misinterpreted if (1) the analyzed time series exhibit autocorrelation, and (2) field significance is not considered when tests are applied multiple times. In this study, these aspects have been investigated using time series of frequencies (or counts) of rainfall extremes derived from long-term (100 years) daily rainfall records of 1087 gauges of the Global Historical Climate Network (GHCN) database. Monte Carlo experiments are carried out by generating random synthetic count time series with the Poisson first-order Integer-valued AutoRegressive model (Poisson-INAR(1)) characterized by different sample size, level of autocorrelation, and trend magnitude. The main results are as follows. (1) Empirical autocorrelations are highly consistent with those exhibited by uncorrelated and non-stationary count time series, while empirical trends cannot be explained as the exclusive effect of autocorrelation; moreover, accounting for the impact of serial correlation has a limited impact on tests’ performance. (2) Accounting for field significance prevents wrong interpretations of results of multiple tests by limiting type-I errors, but it may reduce test power; a careful use of local test outcomes could help identify regions with potentially significant changes where clusters of multiple trends with coherent signs are detected. (3) Statistical trend tests based on linear and Poisson regressions are more powerful than nonparametric tests (e.g., Mann-Kendall) when applied to count time series. Finally, using these methodological insights, spatial patterns of statistically significant increasing (decreasing) trends emerge in central and eastern North America, northern Europe, part of northern Asia, and central regions of Australia (southwestern North America, part of southern Europe, and southwestern and southeastern regions of Australia).

How to cite: Farris, S., Deidda, R., Viola, F., and Mascaro, G.: Clarifying the importance of serial correlation and field significance in detection of trends in extreme rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11629, https://doi.org/10.5194/egusphere-egu22-11629, 2022.

08:52–08:58
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EGU22-1996
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ECS
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On-site presentation
Moshe Armon et al.

Heavy precipitation events (HPEs) can lead to deadly and costly natural disasters and, especially in regions where rainfall variability is high, such as the eastern Mediterranean, they are critical to the hydrological budget. Reliable projections of future HPEs are needed, but global climate models are too coarse to explicitly represent rainfall processes during HPEs. In this study we used pseudo global warming high-resolution (1 km2) weather research and forecasting (WRF) model simulations to provide rainfall patterns projections based on simulations of 41 pairs of historic and “future” (end of 21st century) HPEs under global warming conditions (RCP8.5 scenario). Changes in rainfall patterns were analyzed through different properties: storm mean conditional rain rate, storm duration, and rain area. A major decrease in rainfall accumulation occurs in future HPEs (−30% averaged across events). This decrease results from a substantial reduction of the storms rain area (−40%) and duration (−9%), and occurs despite an increase in the mean conditional rain intensity (+15%). The consistency of results across events, driven by varying synoptic conditions, suggests that these changes have low sensitivity to the specific synoptic evolution during the events. Future HPEs in the eastern Mediterranean will therefore likely be drier and more spatiotemporally concentrated, with substantial implications on hydrological outcomes of storms. (For hydrological results see: abstract #EGU22-4777)

  • Armon, M., Marra, F., Enzel, Y., Rostkier‐Edelstein, D., Garfinkel, C. I., Adam, O., et al. (2022). Reduced Rainfall in Future Heavy Precipitation Events Related to Contracted Rain Area Despite Increased Rain Rate. Earth’s Future, 10(1), 1–19. https://doi.org/10.1029/2021ef002397

How to cite: Armon, M., Marra, F., Garfinkel, C., Rostkier-Edelstein, D., Adam, O., Dayan, U., Enzel, Y., and Morin, E.: Reduced rainfall in future heavy precipitation events tied to decreased rain area and takes place despite increased rain rate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1996, https://doi.org/10.5194/egusphere-egu22-1996, 2022.

08:58–09:04
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EGU22-3117
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Highlight
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On-site presentation
Ida Bülow Gregersen et al.

Establishing a regional model for intensity-duration-frequency (IDF) curves remain a vital task for design of urban infrastructures such as sewer systems and storm water detention ponds. However, identifying a suitable model remains tricky as subjective decisions and assumptions, that easily can be challenged, is needed. The talk will focus on recognizing and overcoming these shortcomings to develop a framework that is trusted by the users, i.e., the engineering professionals.

Since 1999 a regional model for IDF-curves has been developed and employed in Denmark. The model consists of a Partial Duration Series (PDS) framework using covariates to explain the regional variation supplemented with a regression across different durations. The first model was based on 41 series with a total of 650 station-years. Currently a fourth model based on a total of 132 series with almost 3000 station-years is being developed. The underlying data for all models come from a network of tipping bucket gauges initiated in 1979.

While the PDS modelling framework to describe extreme rainfall data has been applied and validated every time, the model setup has changed during each of the three updates. The second model, released in 2006, focussed on describing a significant increase in the design intensities and identifying a new regionalization, reducing the number of regions in the country from three to two. The third model, released in 2014, further increased the design intensities substantially, but more importantly, a cycle of precipitation extremes in Denmark with a frequency of around 35 years was acknowledged, and new co-variates were identified, enabling a description of Denmark as one region with variations that could be explained by two spatially continuous covariates.

Presently a new model is being developed. Most parts of the model are unchanged. However, inclusion of many recent relatively short series (10-20 years) both increase the sampling uncertainty and bias the model towards the very peak of the cyclic variation of the precipitation extremes, whereby the mean intensities will increase, as well as the overall uncertainty of the model. Hence the short series have been excluded.  As a result hereof, the engineering community expresses a concern that such an update will not, in general, increase design intensities in a current climate that is regarded as non-stationary with increasing extreme rainfall. For the scientists it could be an indication that the model may have reached a mature state, where the changes are small and random over a 5-year horizon. For the practitioners there is a concern that this may lead to infrastructure design that over time proves inadequate and fails to meet the service levels set to protect the citizens and important assets.  

As indicated above having much data at hand for a regional model does not hinder large structural uncertainties. What are reasonable assumptions and how can they be communicated to the users? When looking across Europe the structural differences in the model setups are even larger, not only reflecting variations in climate, but also choices made by different groups of scientists.

How to cite: Gregersen, I. B., Arnbjerg-Nielsen, K., Danielsen Sørup, H. J., and Madsen, H.: Identifying a regional model for extreme rainfall in current climates – quo vadis?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3117, https://doi.org/10.5194/egusphere-egu22-3117, 2022.

09:04–09:10
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EGU22-12030
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ECS
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Nischal Sharma

Evaluation of winter mean precipitation over North India in CMIP6 models

Nischal Sharma1, Raju Attada1*, A. R. Dandi2, R. K. Kunchala3, Anant Parekh2, J. S. Chowdary2

1Department of Earth and Environmental Sciences - Indian Institute of Science Education and Research Mohali, Punjab – 140306

2 Indian Institute of Tropical Meteorology, Pune, India

3Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, India

*E-mail of corresponding author: rajuattada@iisermohali.ac.in

 

Abstract

North India receives a significant proportion of annual precipitation during winter (December to February) through mid-latitudinal cyclonic perturbations (Western Disturbances) embedded in subtropical westerly jet stream. This region accounts for a paucity of available in-situ observations owing to complex topography which underpins the necessity of other non-conventional tools for precipitation estimation. Global Climate Models are an effective tool to investigate global monsoon systems and are being extensively used to better understand spatio-temporal characteristics of precipitation. In the present study, north Indian winter precipitation (NIWP) and its variability has been characterized in 30 CMIP6 historical simulations (1979-2014) and compared with IMD gridded data observations. Normalized biases in different models relative to observations have been used to categorize models as wet (11), dry (8) and normal (11) models and further composite analysis has been conducted for these model categories. Our findings suggest that all the models show highest precipitation orientation along the western Himalayan belt, with the normal model category showcasing quite similar results to observations. Wet models show highest variability, errors and positive bias over the region while dry models exhibit least variability and negative bias. Majority of the models show an overall good correlation with observations. The representation of winter mean dynamical and circulation patterns has been carried out using composite analysis of three model categories relative to observations. The composite analysis reveals an intensified jet in both wet and dry model categories, with a southward shift of the jet position in wet models.  Detailed results will be discussed.

Keywords: Global climate models, CMIP6, winter precipitation

How to cite: Sharma, N.: Evaluation of winter mean precipitation over North India in CMIP6 models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12030, https://doi.org/10.5194/egusphere-egu22-12030, 2022.

09:10–09:16
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EGU22-323
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ECS
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On-site presentation
Minduri Uma Maheswar Rao et al.

Climate change is emerging as one of the most pressing issues facing our environment since it will have severe consequences for both natural and human systems. The ability to estimate future climate is required to investigate the influence of climate change on a river basin. The most reliable instruments for simulating climate change are Global Climate Models (GCMs), also known as General Circulation Models. The performance of a precipitation simulation for the Brahmani river basin spanning 94 locations (with a grid resolution of 0.25° X 0.25°) is evaluated in the present study. The observed and model historical temperature datasets cover the period from 2000-2019. Twelve Coupled Model Intercomparison Project – Phase 6 (CMIP6) GCMs (ACCESS- CM2, CESM2, CIESM, FGOALS- g3, HadGEM3, GFDL- ESM4, INM- CM5-0, MIROC- ES2L, NESM3, UKESM1, MPI- ESM1, NorESM2) are used for the climate variable (Pr) using five indicators of performance. Indicators used are Average Absolute Relative Deviation (AARD), Skill Score (SS), Absolute Normalized Mean Bias Deviation (ANMBD), Correlation Coefficient (CC), Normalized Root Mean Square Deviation (NRMSD). GCMs are downscaled to finer spatial resolution before ranking them. The statistical downscaling technique is applied to eliminate the systematic biases in GCM simulations. Weights are determined using the Entropy technique for each performance metric. Cooperative Game Theory (CGT), Compromise programming (CP), Weighted Average Technique, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE-2) methods are utilized to rank the GCMs for the study area. GDM is an approach utilized to integrate the ranking techniques of GCMs to get a collective single rank. The results obtained for precipitation suggest that MIROC-ES2L, HadGEM3, GFDL-ESM4, UKESM1, FGOALS-g3 are the top five models that are preferred for the prediction of precipitation in the Brahmani River Basin.

How to cite: Rao, M. U. M., Patra, K. C., and Jahan, A.: Study of Downscaling Techniques and Standings of Bias Corrected Global Climate Models for Brahmani Basin at Odisha, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-323, https://doi.org/10.5194/egusphere-egu22-323, 2022.

09:16–09:22
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EGU22-4405
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ECS
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Virtual presentation
Hannes Müller-Thomy et al.

Precipitation reanalysis products (PRP) are a promising data source for ungauged regions. Since observed time series are often i) too short, ii) their temporal resolution is not sufficient or iii) the network density is too low, they cannot be used as e.g. input for rainfall-runoff (r-r) modelling and derived flood frequency analysis. Reanalysis products as global simulation of the atmosphere over the last decades solve the aforementioned issues.

From the latest PRP three are most promising due to their spatial and temporal resolution for r-r modelling of small to mesoscale catchments: ERA5-Land (raster with approx. 9 km width), REA6 (6 km) and CFSv2 (22 km). These three PRP are able to cope with the dynamics of the r-r process due to their hourly resolution. The PRP are evaluated for Slovenia (Europe) with both, precipitation characteristics in space and time, and runoff characteristics. For areal precipitation, continuous and event-based characteristics are evaluated as well as precipitation extreme values. Simple correction methods for identified biases are suggested and applied. It can be seen that although the PRP clearly differ from each other, there is no clear ‘favourite’ to use as input for the r-r modelling.

To conclude about the suitability of the PRP for r-r modelling, continuous simulations are carried out with GR4H for 20 catchments in Slovenia (55 km²-480 km²). Models are re-calibrated for each PRP input based on KGE. Simulation results of calibration and validation period are evaluated by runoff extreme values, KGE, flow duration curve and intra-annual cycle. Interestingly, first results show that the deviations of some rainfall characteristics do not necessarily transfer to deviations in runoff characteristics, which can be explained by the high nonlinearity of the r-r process. PRP lead to better, at least similar results for runoff characteristics for catchments without rain gauges in their centre.

How to cite: Müller-Thomy, H., Nistahl, P., Bezak, N., and Alexopoulos, M.: Evaluation of precipitation reanalysis products in space and time for ungauged sites in Slovenia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4405, https://doi.org/10.5194/egusphere-egu22-4405, 2022.

09:22–09:28
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EGU22-11273
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ECS
Stephanie Gleixner et al.

Informed decision-making on adaptation strategies for future climate change need reliable climate information. In particular, vulnerable economies like Tanzania, which is strongly reliant on rain-fed agriculture, struggle with the lack of agreement on precipitation changes between the climate models. In order to find robustness in these projections, we compare precipitation simulations from the CORDEX Africa Ensemble under three emission scenarios (RCP 2.6, RCP 4.5, RCP 8.5) within different precipitation categories defined by the Standardized Precipitation Index (SPI). We find that despite the disagreement on the sign of the total precipitation trend, there is strong agreement among on a decrease in normal conditions and an increase in both extreme wet and extreme dry conditions throughout the 21st century. The differences between the projections in terms of total precipitation are related to shifts of (near) normal conditions to wetter conditions in the case of ‘wetter’ projections and to drier conditions for ’drier’ projections. These results indicate an overall broadening of the rainfall distribution especially toward extremely wet conditions.

How to cite: Gleixner, S., Lehmann, J., and Gornott, C.: A new perspective on projected precipitation changes in Tanzania, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11273, https://doi.org/10.5194/egusphere-egu22-11273, 2022.

09:28–09:34
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EGU22-4453
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Virtual presentation
Bhadran Deepthi and Bellie Sivakumar

Global climate change has become one of the major environmental issues today. Climate change impacts rainfall (and other hydroclimatic processes) in many ways, including its temporal and spatial variability. Hence, understanding the impact of climate change on rainfall is important to devise and undertake more effective and efficient adaptation and management strategies. The present study attempts to determine the temporal dynamic complexity of monthly historical and future rainfall in India at a spatial resolution of 1º × 1º. The historical and future rainfall data are simulated from 27 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The historical rainfall consists of the rainfall data simulated by GCMs for the period 1961–2014, and the rainfall simulated by the GCMs under shared socio-economic pathway scenarios (SSPs) constitutes the future rainfall. Four scenarios (SSP126, SSP245, SSP370, and SSP585) and two different timeframes (near future (2015–2060) and far future (2061–2099)) are considered to determine how the rainfall and its dynamic complexity vary across the scenarios and timescales. The false nearest neighbor (FNN) algorithm is employed to determine the dimensionality and, hence, the complexity of the rainfall dynamics. The algorithm involves two major steps: (i) reconstruction of the single-variable rainfall time series in a multi-dimensional phase space; and (ii) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the rainfall time series. The results suggest that the FNN dimensions of both the historical rainfall and future rainfall simulated by the 27 GCMs across India under all scenarios range from 3 to 20, indicating low to high-level complexity of the rainfall dynamics. However, only less than 1% of the study area shows high-level complexity in historical and future rainfall dynamics. Moreover, around 20 GCMs exhibit low to medium-level complexity of rainfall dynamics in 80% of the study area, with the dimensionality in the range from 3 to 10. Therefore, considering both the historical rainfall and future rainfall under all the four scenarios and the two timeframes considered in this study, the number of GCMs simulating rainfall that exhibits dimensionality in the range 11 to 20 are few. This may be an indication that the complexity of rainfall dynamics in India in the future will be low-to-medium dimensional.

How to cite: Deepthi, B. and Sivakumar, B.: Complexity of rainfall dynamics in India in the context of climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4453, https://doi.org/10.5194/egusphere-egu22-4453, 2022.

09:34–09:40
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EGU22-2531
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On-site presentation
Bodo Ahrens and Nora Leps

Limited-area convection-permitting climate models (CPMs) with horizontal grid-spacing less than 4km and not relying on deep convection parameterisations (CPs) are being used more and more frequently. CPMs represent small-scale features such as deep convection more realistically than coarser regional climate models (RCMs) with deep CPs. Because of computational costs CPMs tend to use smaller horizontal domains than RCMs. As all limited-area models (LAMs), CPMs suffer issues with lateral boundary conditions (LBCs) and nesting. We investigated these issues using idealised Big-Brother (BB) experiments with the LAM COSMO-CLM. Grid-spacing of the reference BB simulation was 2.4 km. Deep convection was triggered by idealised hills with driving data from simulations with different spatial resolutions, with/without deep CP, and with different nesting frequencies and LBC formulations. All our nested idealised 2.4-km Little-Brother (LB) experiments performed worse than a coarser CPM simulation (4.9km) which used a four times larger computational domain and yet spent only half the computational cost. A boundary zone of >100 grid-points of the LBs could not be interpreted meteorologically because of spin-up of convection and boundary inconsistencies. Hosts with grid-spacing in the so-called grey zone of convection (ca. 4 - 20km) were not advantageous to the LB performance. The LB's performance was insensitive to the applied LBC formulation and updating (if smaller or equal 3-hourly). Therefore, our idealised experiments suggested to opt for a larger domain instead of a higher resolution even if coarser than usual (~5km) as a compromise between the harmful boundary problems, computational cost and improved representation of processes by CPMs.

How to cite: Ahrens, B. and Leps, N.: On the Challenge of Convection Permitting Precipitation Simulations: Results from Idealised Experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2531, https://doi.org/10.5194/egusphere-egu22-2531, 2022.

Thu, 26 May, 10:20–11:50

Chairpersons: Simone Fatichi, Alin Andrei Carsteanu, Chris Onof

10:20–10:26
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EGU22-4004
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On-site presentation
Richard Chandler et al.

Users of climate data must often confront the problem that information is not available at the precise spatial locations of interest; or the related problem that multiple sources of information provide data at different collections of locations. An example of the first situation is the use of weather station data to calibrate a hydrological or land surface model requiring inputs on a regular grid; an example of the second is the use of information from an ensemble of climate models to sample structural uncertainty, but where each model produces output on its own grid. Dealing with this spatial misalignment is a common first step in any analysis, and is usually done by some form of interpolation. In this poster, we use standard approaches to convert regional climate model (RCM) outputs from the EuroCORDEX ensemble to the common grid used in the UK national Climate Projections (UKCP). We find that although these standard approaches perform acceptably in some situations, in others they can induce surprisingly large biases and inconsistencies in the statistical properties of the resulting fields – particularly those relating to variability and extremes. For example, although the resolutions of the UKCP grid and the EuroCORDEX RCMs are all similar, it is not hard to find locations where the maximum daily precipitation within a month is systematically underestimated by 5-10% in the regridded data; and where the maximum daily precipitation over a 20-year period is systematically underestimated by 25%. These effects could have major implications for impacts studies carried out using interpolated or regridded data, if they are not recognised and dealt with appropriately. We offer some suggestions, varying in ease of implementation, for dealing with the problem.

How to cite: Chandler, R., Barnes, C., Brierley, C., and Alegre, R.: Regridding and interpolation of climate data for impacts modelling – some cautionary notes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4004, https://doi.org/10.5194/egusphere-egu22-4004, 2022.

10:26–10:32
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EGU22-10437
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Virtual presentation
Angelica Caseri and Carlos Frederico Angelis

Extreme rainfall events can cause flash floods and are responsible for socioeconomic damage worldwide. In Campinas, southeastern Brazil, countless events take place throughout the year. In order to monitor and predict these events, with the support of Fapesp's SOS-Chuva project, a mobile rainfall radar was installed in the region. With the purpose to identify the accuracy of this data, the radar data were compared with rain gauge data. Through this study, it is noted that, at some points, the difference between the rain gauges measurements and the radar data is significant, which may hinder the calibration and performance of a rainfall-runoff hydrological model. To improve the rainfall measurement considering both data source, this study proposes to combine both information and generate rainfall probabilistic maps, derived from geostatistical methods, thus making possible to quantify the uncertainty of these data.

How to cite: Caseri, A. and Angelis, C. F.: Uncertainty Quantification of Precipitation Measurement with Weather Radar, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10437, https://doi.org/10.5194/egusphere-egu22-10437, 2022.

10:32–10:38
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EGU22-10355
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ECS
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On-site presentation
Nico Blettner et al.

Precipitation is characterized by large spatial variability. For hydrological applications it is crucial to estimate precipitation such that spatial correlation lengths and precipitation patterns are represented accurately.

We derive countrywide precipitation estimates using approx. 4000 commercial microwave links (CMLs) obtained from Ericsson and approx. 1000 rain gauges operated by the German Weather Service. CML and gauge observations are regarded as non-linear and linear constraints on the spatial estimate, respectively.

We apply the Random-Mixing-Whittaker-Shannon method in a Python based environment (RMWSPy) to reconstruct ensembles of precipitation fields. With RMWSPy, linear combinations of unconditional random spatial fields are conditioned to the observational data. This involves the exact local representation of rain gauge observations as well as the consideration of the path-averaged precipitation along the CMLs. Additionally, the method ensures that resulting estimates are similar to the data with respect to spatial correlations and marginal distributions. The stochastic process allows for variability at unobserved locations and thereby the calculation of ensembles.

We evaluate the spatial pattern of our results by performance characteristics such as ensemble Structure-, Amplitude-, and Location-error (eSAL). This approach considers precipitation objects as connected areas that exceed a certain precipitation value, and involves the analysis of the objects’ shapes and locations. Thereby, it is possible to quantify aspects of precipitation patterns in a way that is not possible with standard performance metrics which are based on pixel-by-pixel comparisons.

We find that our precipitation estimates are in good agreement with the gauge-adjusted weather radar product RADOLAN-RW of the German Weather Service which we use as a reference. In particular, we see advantages in reproducing the pattern of precipitation objects, in terms of smaller structure- and location-errors, when comparing our ensemble-based Random-Mixing approach to an Ordinary Kriging interpolation.

How to cite: Blettner, N., Chwala, C., Haese, B., Hörning, S., and Kunstmann, H.: Combining commercial microwave link and rain gauge observations to estimate countrywide precipitation: a stochastic reconstruction and pattern analysis approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10355, https://doi.org/10.5194/egusphere-egu22-10355, 2022.

10:38–10:44
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EGU22-6677
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ECS
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Virtual presentation
Nehal Ansh Srivastava and Giuseppe Mascaro

In this study, we assess the ability of 4-km, 1-h Quantitative Precipitation Estimates (QPEs) from the Stage IV analysis of the NEXRAD radar network to reproduce the statistics of extreme precipitation (P) in central Arizona, USA. As reference, we use 19 years of records from a dense network of 257 rain gages. For each radar pixel and gage record, we fit the generalized extreme value (GEV) distribution to the series of annual maximum P at durations, τ, from 1 to 24 hours. We found that the GEV scale and shape parameters estimated from the radar QPEs are slightly negatively biased when compared to estimates from gage records at τ = 1 h; this bias tends to 0 for τ ≥ 6 h. As a result, the radar GEV quantiles for return period, TR, from 2 to 50 years exhibit negative bias at τ = 1 h (median between -23% and -12% for different TR’s), but the bias is gradually reduced as τ increases (average of +4% for τ = 24 h). The relative root-mean-square-error (RRMSE) ranges from 17% to 44% across all τ’s and TR’s and these values are similar to those computed between gages and operational design storms from NOAA Atlas 14. Lastly, we found that radar QPEs reproduce fairly well observed scaling relationships between the GEV location and scale parameters and P duration, τ. Results of our work provide confidence in the utility of Stage IV QPEs to characterize the spatiotemporal statistical properties of extreme P and, in turn, to improve the generation of design storm values.

How to cite: Srivastava, N. A. and Mascaro, G.: Can Radar Quantitative Precipitation Estimates Reproduce Extreme Precipitation Statistics in Central Arizona?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6677, https://doi.org/10.5194/egusphere-egu22-6677, 2022.

10:44–10:50
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EGU22-5071
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ECS
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Virtual presentation
Ankita Pradhan and Indu Jayaluxmi

Precipitation-measuring satellites constitute a constellation of microwave and infrared sensors in geosynchronous earth orbit. The limited sampling of passive microwave constellations continues to be a problem, affecting applications such as hydrological modeling. Recent constellations have contributed in the construction of the next generation of earth and space science missions by allowing measurement settings to be customized to meet changing scientific understanding. Our study focuses on examining the Global Precipitation Measurement (GPM) constellation mission. The aim of the study is to examine the impact of different uncertainties carried by the GPM constellation on hydrological applications. Firstly we investigated the evaluation and comparison of spatial sampling error for the Global Precipitation Measurement (GPM) mission orbital data products. The region over India with high seasonal rainfall appears to have lower sampling uncertainty, and vice versa, with some exceptions due to differences in precipitation variability and space-time correlation length.  Second, we investigated how intermittency produced by low temporal sampling propagates through a hydrological model and contributes to stream flow uncertainty. We also examined the effect of grid resolution and how it relates to Clausius-Clapeyron scaling. This paper proposes and discusses techniques for quantifying the influence of grid resolution as a function of spatial–temporal characteristics of heavy precipitation based on these findings. Thirdly, we have quantified the influence of two different algorithms i.e top down and bottom up approach utilizing precipitation products that includes the Global Precipitation Measurement mission's (GPM) integrated Multi-satellite Retrievals (IMERG) late run, the SM2RAIN-Climate Change Initiative (SM2RAIN-CCI), and the SM2RAIN-Advanced SCATerometer (SM2RAIN-ASCAT) on hydrological simulations. The results from our study indicate that precipitation forcing at 6-hourly integration outperforms the stream flow simulations as compared to 3-hourly and 12-hourly forcing integration times. IMERG based precipitation also contains significant bias which is propagated into hydrological models when used as precipitation forcing.

How to cite: Pradhan, A. and Jayaluxmi, I.: Impact of GPM Precipitation Error Characteristics on Hydrological Applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5071, https://doi.org/10.5194/egusphere-egu22-5071, 2022.

10:50–10:56
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EGU22-6342
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ECS
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Highlight
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On-site presentation
Paola Mazzoglio et al.

The investigation of the influence of terrain morphology on rainfall extremes has never been conducted over the entire Italy, where some studies have been carried out over limited areas. We then present the first systematic investigation of the role of elevation and other morphological attributes on rainfall extremes over Italy, that is made possible by using the Improved Italian – Rainfall Extreme Dataset (I2-RED). I2-RED is a database of short duration (1 to 24 hours) annual maximum rainfall depths collected from 1916 until 2019 by more than 5200 rain gauges.

The analyses involved the relations between morphology and the mean annual rainfall extremes (index rainfall) using univariate and multivariate regressions. These relations, built countrywide, demonstrated that the elevation alone can explain only a part of the spatial variance. The inclusion of regression covariates as longitude, latitude, distance from the coastline, indexes of obstructions and the mean annual rainfall depth demonstrated to be significant in relations built at the national scale.

However, high local bias with notable spatial correlation derives from the national-scale analysis. This led us to focus on smaller areas. We started dividing Italy into 4 main regions: the Alps, the Apennines, and the two main islands (i.e. Sicily and Sardinia). A dedicated multiple linear regression analysis was conducted over each of these areas. Evident improvements were obtained through this approach; nevertheless, clusters of high residuals persisted, especially in orographically-complex areas. A different approach was then undertaken, based on a preemptive subdivision of Italy in morphologically similar regions, to both reduce the clustering of errors and better define the role of elevation. Using four morphological classifications of Italy from the literature, we applied simple regression models to the rain gauges available inside each region. Among all, the classification that embeds hydrological information turned out to produce the best results in terms of local bias, MAE and RMSE, outperforming the multivariate relations obtained at the national scale. This approach proved to better reproduce the effects of geography and morphology on the spatial variability of rainfall extremes.

Our analysis confirmed a general increase of 24-hour rainfall depths with elevation, as already pointed out by studies conducted over smaller areas. For 1-hour rainfall depths, in flat or in pre-hill zones a modest increase with elevation is visible, while over the Alps and in most of the Apennines a reverse orographic effect (i.e., a reduction of rainfall depth with increasing elevation) is clearly detected, confirming previous outcomes in those areas.

How to cite: Mazzoglio, P., Butera, I., Alvioli, M., and Claps, P.: Influence of morphology on the spatial variability of rainstorms over Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6342, https://doi.org/10.5194/egusphere-egu22-6342, 2022.

10:56–11:02
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EGU22-10253
András Bárdossy

Rainfall is highly variable in space and time. The knowledge of precipitation variability is very important for design or for uncertainty assessment of models. In this contribution two different aspects of variability are investigated – the treatment of zero observations for spatial interpolation and the problem of high order dependence. The finer the temporal resolution of precipitation observations the more zeros have to be considered. Should one include all zeros for the description of the spatial variability (for example variograms)? Examples corresponding to different time aggregations are show that zeros need a specific treatment. High order dependence is investigated using time series observed at multiple sites. Results are compared to a meta-Gaussian approach. A large high-resolution dataset from South-West Germany is used to demonstrate the problems and the different approaches.

How to cite: Bárdossy, A.: Spatial and temporal variability of rainfall on different time scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10253, https://doi.org/10.5194/egusphere-egu22-10253, 2022.

11:02–11:08
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EGU22-6217
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ECS
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On-site presentation
Emilie Tarouilly et al.

We present an analysis of uncertainty in model-based Probable Maximum Precipitation (PMP) estimates. The focus of the study is on “model-based” PMP derived from WRF (Weather Research and Forecasting) model reconstructions of severe historical storms and amplified by the addition of moisture in the boundary conditions (so-called Relative Humidity Maximization technique). Model-based PMP offers numerous advantages over the currently-used approach that is described in NOAA Hydrometeorological Reports. By scaling moisture and producing the resulting precipitation according to model formulation, the model-based approach circumvents the need for linearly scaling precipitation. Despite the significant improvement this represents, model-based PMP retains some degree of uncertainty that precludes its use in operational settings until the uncertainty is rigorously evaluated. This paper presents an ensemble of PMP simulations that samples recognized sources of uncertainty: (1) initial/boundary condition error, (2) choice of physics parametrizations and (3) model error due to unresolved subgrid processes. To our knowledge, this is the first uncertainty analysis conducted for model-based PMP. We applied this ensemble approach to the Feather River watershed (Oroville dam) in California. We first carried out in-depth evaluation of model reconstructions and found that the performance of some storm reconstructions that underlie the PMP estimate is not ideal, though the lack of uncertainty information about observations currently prevents us from identifying “well-reconstructed” storms or performing bias correction. That being said, our ensemble indicates that the 72-hour maximized precipitation totals used for PMP estimation do not differ greatly (110% at most) from the single-value estimate when model uncertainty is considered. We emphasize that model-based PMP estimates should always be presented as a range of values that reflects the uncertainties that exist, but concerns about model uncertainty should not hinder the development of model-based PMP.

How to cite: Tarouilly, E., Cannon, F., and Lettenmaier, D.: Improving confidence in model-based Probable Maximum Precipitation : Assessing sources of model uncertainty in storm reconstruction and maximization , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6217, https://doi.org/10.5194/egusphere-egu22-6217, 2022.

11:08–11:14
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EGU22-8792
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Virtual presentation
Simon Michael Papalexiou et al.

Simulating storms, or hydro-environmental fluxes in general, in space and time is challenging and crucial to inform environmental risk analysis and decision making under variability and uncertainty. Here, we advance space-time modelling by enabling simulation of random fields (RF) described by general velocity fields and anisotropy. This advances the skills of the Complete Stochastic Modeling Solution (CoSMoS) framework in space and time and enables RF's simulations that reproduce desired: (a) non-Gaussian marginal distribution, (b) spatiotemporal correlation structure (STCS), (c) velocity fields with locally varying speed and direction that describe advection, and (d) locally varying anisotropy. We demonstrate applications of CoSMoS by simulating storms at fine spatiotemporal scales that move across an area, spiraling fields such weather cyclones, air masses converging to (or diverging from) a point and more. The methods are implemented in the CoSMoS R package freely available in CRAN.

Reference: Papalexiou, S. M., Serinaldi, F., & Porcu, E. (2021). Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond. Water Resources Research, 57(8), e2020WR029466. https://doi.org/10.1029/2020WR029466

How to cite: Papalexiou, S. M., Serinaldi, F., and Porcu, E.: Space-time simulation of storms and beyond!, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8792, https://doi.org/10.5194/egusphere-egu22-8792, 2022.

11:14–11:20
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EGU22-11055
Alin-Andrei Carsteanu et al.

Mass scaling of atmospheric precipitation has been successfully characterized by multifractal frameworks in the literature dedicated to this subject. However, the dependence of the statistics of interarrival times and run lengths on the employed detection threshold, as theoretically predicted by multiplicative cascade models with different degrees of multifractality, is yet another aspect of interest when such models are being used for the purpose of rainfall modelling. It must be noted that interarrival times and run lengths are complementary variables, by representing uninterrupted time intervals above and below the detection threshold, respectively. The present communication deals with the intricacies of parametrizing and validating those aspects of multifractal rainfall models.

How to cite: Carsteanu, A.-A., Langousis, A., and Deidda, R.: Intensity-dependence of interarrival times and run lengths in multifractal rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11055, https://doi.org/10.5194/egusphere-egu22-11055, 2022.

11:20–11:26
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EGU22-10477
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ECS
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On-site presentation
Multi-site precipitation time series generation using Fourier Transform
(withdrawn)
Masoud Mehrvand and András Bárdossy
11:26–11:32
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EGU22-11126
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On-site presentation
Auguste Gires et al.

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

 

While being a well-known feature of rainfall fields, anisotropy is not yet addressed with blunt extensions of discrete Universal Multifractal cascades. In this paper, we suggest to extend this framework to account for anisotropy. It basically consists in using different sizes according to the direction for the moving window over which the interpolation is carried out. In a first step Multifractal expected behaviour is theoretically established. Then it is numerically confirmed with the help of ensembles of stochastic simulations. Finally, the features of simulated fields are compared with actual rainfall data ones. Data collected with help of a dual polarisation X-band radar operated by HM&Co-ENPC is used (radx.enpc.fr/).

 

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

How to cite: Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Accounting for anisotropy in the simulation of rainfall fields with blunt extension of discrete Universal Multifractal cascades, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11126, https://doi.org/10.5194/egusphere-egu22-11126, 2022.