CL5.1.5

In the context of the CORDEX project, an ensemble of regional climate simulations of high resolution on a 0.11º grid has been generated for Europe with the objective of improving the representation of regional to local-scale atmospheric phenomena. However, such simulations are computationally expensive and do not always reveal added value.

In this study, a recently proposed metric (the distribution added value, DAV) is used to determine the added value of the available EURO-CORDEX high-resolution simulations at 0.11º for daily mean wind speed compared to their coarser-gridded 0.44º counterparts and their driving global simulations, from hindcast and historical experiments. The analysis is performed using observations data as a reference. Furthermore, the use of a normalized metric allows for a spatial comparison among different regions and time periods.

In general, results show that RCMs add value to their forcing model or reanalysis, but the nature and magnitude of the improvement on the representation of wind speed vary depending on the model, the region or the season. We found that most RCMs at 0.11º outperform models at 0.44º resolution in terms of their quality to represent measured wind speed PDF. However, the benefits of downscaling are not as clear in the upper tail of the wind speed.
At regional scale, higher DAVs are obtained at 0.11º than 0.44º resolution for all subdomains studied, particularly over the Mediterranean, the Iberian Peninsula and the Alps. With altitude, the 0.11º models represent better the locations below 50 m and above 350 m, while the 0.44º models under-perform with increasing altitude. Overall, DAVs are higher at 0.11º than at 0.44º resolution, probably due to a better performance of local-scale feedbacks at high resolution.

How to cite: Molina, M. O., Careto, J., Gutiérrez, C., Sánchez, E., and Soares, P.: The added value of high-resolution EURO-CORDEX simulations to describe daily wind speed over Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1043, https://doi.org/10.5194/egusphere-egu22-1043, 2022.

One year after the eruption of the Tambora volcano, the “Year Without a Summer” of 1816 was characterised by extraordinarily cold periods in (Central) Europe, and it was associated with severe crop failures, food shortages, famine and socio-economic disruptions.

The summer of 1816, has been analysed based on a number of early meteorological measurements, as well as on ample documentary information. A statistical reconstruction of spatial fields with daily resolution has been conducted for Switzerland. However, this dataset encompasses only a limited set of variables. In turn, dynamical downscaling methods allow to reconstruct past weather on a higher temporal and spatial resolution. In our work, we simulate a particularly cold episode in June 1816 by downscaling data from the Twentieth Century Reanalysis version 3 (20CRv3). The simulation uses the Weather Research and Forecasting (WRF) model with three nested domains for the greater Alpine region and provides hourly output with a 3-km resolution. In addition, we include recently digitised station series of temperature and pressure for a Three-Dimensional Variational (3DVAR) data assimilation in the innermost domain. Results are then validated against independent station observations.

First results suggest that dynamical downscaling and data assimilation may become a promising approach to obtain physically consistent information on past weather on a local and subdaily scale. This may hold even for extreme events in an era with a scarce network of instrumental weather observations compared to today, although erroneous results may occur. A successful application of dynamical downscaling and data assimilation for the early 19^th century might open the door for a regional atmospheric reanalysis product that covers the last two centuries.

How to cite: Pfister, L., Stucki, P., Martynov, A., and Brönnimann, S.: Dynamical Downscaling and Data Assimilation: Insights from the Case Study of the "Year Without a Summer" 1816, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7049, https://doi.org/10.5194/egusphere-egu22-7049, 2022.

How to cite: Addison, H., Watson, P., Aitchison, L., and Kendon, E.: Downscaling UK rainfall using machine-learning emulation of a convection-permitting model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6150, https://doi.org/10.5194/egusphere-egu22-6150, 2022.

Statistical downscaling (SD) methods are extensively used to provide high-resolution climate information based on the coarse outputs from Global Climate Models (GCM). In the context of climate change and under the perfect prognosis approach, these methods learn the relationships that link several large-scale predictor variables coming from a reanalysis (e.g. humidity) with the local variables of interest (e.g. precipitation) over a reference historical period. Subsequently, the so-learnt relationships are applied to GCM predictors to obtain downscaled projections for a future period.

In a recent paper, Legasa et al. (2021) introduced a posteriori random forests (AP-RFs), a modification of classical random forests which make use of all the data in the leaves to estimate any probability distribution. Following the experimental framework proposed in Experiment 1 of VALUE (http://www.value-cost.eu, Gutiérrez et al. 2018), the study showed that AP-RFs obtained reliable stochastic time-series over several locations in Europe using reanalysis predictors. As compared to more classical techniques like generalized linear models (GLMs), this study concluded that AP-RFs are a competitive SD method in terms of different forecast aspects, with one of their key advantages being the ability to automatically perform predictor/feature selection. This avoids the task of manually selecting the most adequate large-scale variables and geographical domain of interest, something which, at present, relies on human expertise and constitutes a substantial source of uncertainty for downscaling climate change projections.

Nevertheless, an assessment of the suitability of AP-RFs for producing local climate change projections from GCM predictors is still lacking. This work aims to fill this gap by providing a fair comparison of AP-RFs with GLMs and state-of-the-art convolutional neural networks (CNNs), which were recently shown to provide satisfactory results for this task (Baño-Medina et al. 2021). We build on VALUE’s Experiment 2a and train the different methods considered using ERA-Interim “perfect” predictors. Afterwards, the EC-Earth model is used to generate downscaled projections for 86 locations distributed across Europe under a strong emission scenario, the RCP8.5. 

Our preliminary results suggest that AP-RFs generate plausible downscaled future projections of precipitation. In particular, differently to traditional GLMs, which are very sensitive to the predictor set considered and may produce implausible climate change projections (Manzanas et al. 2020), this technique yields delta changes consistent with those obtained from both the raw EC-EARTH outputs and the CNNs.

References
Baño-Medina, J., Manzanas, R. & Gutiérrez, J.M. On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections. Clim Dyn 57, 2941–2951 (2021). doi: https://doi.org/10.1007/s00382-021-05847-0

Gutiérrez, J.M., Maraun, D., Widmann, M. et al. An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment. Int. J. Climatol. 2019; 39: 3750– 3785. doi:  https://doi.org/10.1002/joc.5462

Legasa M.N., Manzanas R., Calviño, A. et al. A Posteriori Random Forests for Stochastic Downscaling of Precipitation by Reliably Predicting Probability Distributions. Submitted to Water Resources Research.

Manzanas, R., Fiwa, L., Vanya, C. et al. Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi. Climatic Change 162, 1437-1453 (2020). doi:  https://doi.org/10.1007/s10584-020-02867-3

How to cite: Legasa, M. N. and Manzanas, R.: Assessing the Suitability of A Posteriori Random Forests for Downscaling Climate Change Projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4717, https://doi.org/10.5194/egusphere-egu22-4717, 2022.

High-quality snow is critical for the Winter Olympic Games. Snow quality is very sensitive to the changes of meteorological elements, especially temperature and humidity. Forecasts on snow quality can provide information for snow maintenance and require high-resolution meteorological forecasts. However, the complex terrain of the mountainous areas where the winter sports are often carried out could result in complex local meteorological fields, which makes it difficult to forecast. Zhangjiakou Competition Zone is one of the three competition zones for the Beijing 2022 Winter Olympics and includes two districts: Genting snow park and Guyangshu Ski Resort. Taking Genting snow park as an example, there is a difference of about 350m in altitude in Genting snow park which covers an area of about 2×2km², and there is an average difference of about 3℃ in hourly temperature and about 10% in hourly relative humidity at noon.

Short-term forecasts in the past Winter Olympic Games were usually based on mesoscale NWP models with a horizontal resolution of up to 1×1km. Due to the limitation of boundary layer parameterization schemes, some small-scale air processes affected by local topography cannot be caught in mesoscale models. Some MOS methods can correct the systematic bias of the models but are unable to deal with the non-systematic errors caused by these small-scale processes.

The purposes of this study were to develop statistical downscaling methods for the temperature and humidity forecasts, which are required in the snow-quality risk classification for the Zhangjiakou Competition Zone of the Beijing 2022 Winter Olympics.  

Hourly data during 2018-2021 from 20 meteorological stations and ERA5-Land reanalysis in the study area were used for the calibration and validation of models. A decaying average method which is similar to the Kalman Filter method was applied to develop the downscaling models. To evaluate the efficiency of the models on snow-quality risk forecasts. A classification model of snow-quality risk developed by the Climate Centre of Hebei Province was applied. Snow-quality risk classification model was developed based on the four years’ meteorological and snow-quality observations in the study area, in which the risk of snow quality was classified into 4 levels: zero-risk, low-risk, medium-risk and high-risk based on the input temperature and humidity. The downscaled prediction fell into 3 cases: (1) the predicted risk level equal to the observed risk level (Accuracy); (2) the predicted risk level lower than the observed risk level (Miss); (3) the predicted risk level higher than the observed risk level (False-Alarm).

The results showed that: (1) the downscaling models can decrease the RMSE of the ERA5 by ~13% for the temperature and by ~14% for the dewpoint temperature; (2) the accuracy of the snow-quality risk classification increased from 72% to 76% on average comparing the inputs of ERA5 and the downscaled temperature and humidity. For the stations with high elevation, the ratio of False-Alarm decreased by ~13%. Further research will focus on improving the statistical model by calibrating the model for different locations and different circulation patterns.

How to cite: Yue, T., Yin, S., and Wang, H.: Statistical downscaling of temperature and humidity for snow-quality risk forecasts for Beijing 2022 Winter Olympics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11128, https://doi.org/10.5194/egusphere-egu22-11128, 2022.

Stochastic weather generators (WGs) are tools for producing weather series, mimicking statistical properties of their real-world counterparts. They are often used in climate change impact experiments as a source of the data representing the present and/or future climates (alternative to RCMs and GCMs). Development of our SPAGETTA generator started in 2016 (Dubrovsky et al 2020; https://doi.org/10.1007/s00704-019-03027-z). The presentation will focus on (A) Basic details. (B) Functionalities of the generator. (C) Results obtained with the generator by now. (D) Most critical problems, which were met (and not yet satisfactorily solved) while making the generator fully operational.

A. SPAGETTA is a multivariate multisite parametric generator, which is based on autoregressive modeling (following the D. Wilks’ papers). It is designed mainly (but not solely) for use in agricultural and hydrological modeling. It may produce time series of up to 8 variables for as many as (approx.) 200 stations or grid points. Typically, it produces time series of temperature, precipitation, solar radiation, humidity and wind speed. It usually runs with a daily time step.

B. The main functionalities include: (1) It may produce arbitrarily long time series representing the climate defined by the data used for calibrating the generator (might be observational data or, for example, RCM outputs). (2) Having modified the WG parameters by the climate change scenario (typically derived from GCM or RCM simulations), SPAGETTA may produce weather series representing the future climate. In this case, one may study sensitivity of selected climatic indices to changes in various statistics (e.g. means and standard deviations of weather variables, and characteristics of temporal and spatial structure of the time series). (3) SPAGETTA may be interpolated so that it can produce weather series for sites with no observational data. (4) It can be linked with the circulation generator so that WG may better represent larger-scale (both in space and time) weather variability.

C. The results obtained with the generator by now include: (a) Validation of the generator in terms of WG parameters, various climatic indices, and outputs of hydrological model fed by the synthetic series produced by SPAGETTA. (b) Impacts of the forthcoming climate change on various climatic characteristics (RCM-based climate change scenarios were used here). Focus was put on spatial temperature-precipitation compound characteristics. (c) Validation of the interpolated generator. (d) Validation of the generator driven by the larger scale circulation generator.

D. Problems to be solved: (i) Under some circumstances (especially when a large number of the stations is used, or while interpolating the generator), matrices of the AR model imply unstable AR process which diverges to unrealistic values of weather variables. (ii) The generator underestimates the low frequency variability. Development of the larger scale circulation generator, which would eliminate this drawback, is still under development.

Only examples of the previous points will be shown in the presentation.

How to cite: Dubrovsky, M., Lhotka, O., Miksovsky, J., Stepanek, P., and Meitner, J.: The Spatial Weather Generator SPAGETTA: Hard Times of its Adolescence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7485, https://doi.org/10.5194/egusphere-egu22-7485, 2022.

Climate change and air pollution are two phenomena that can no longer be considered separately. Changes in climate also alter the effects of air pollution. For example, emissions of ammonia (NH₃) and non-methane organic compounds (NMVOC), which are precursors of ozone (O₃) and secondary particles (PM), are drastically sensitive to temperature and humidity changes. Moreover, the impacts of O₃ and secondary PMs on the climate were previously investigated. The first step for air quality modeling studies is the modeling of meteorological fields. In this study, important meteorological parameters in terms of air pollution were obtained from global climate models for a historical and future periods (SSP585). Selected parameters will be corrected and downscaled to a high resolution for Eastern Mediterranean. Then, the bias-corrected and downscaled meteorology outputs will be used in other studies related to air quality.

Countries in the Mediterranean Region are being affected significantly by the changing climate due to their location. Previously conducted studies evaluated the meteorological parameters of global climate models with low resolutions. Within the scope of this study, future estimates will be downscaled to a selected domain in Eastern Mediterranean with a spatial resolution of 4×4 km² However, recent studies have argued that a bias-correction method should be implemented to the selected meteorological parameters prior to downscaling. In previous studies, CMIP simulation outputs were evaluated for Turkey with or without downscaling. There are also studies that biases between observation/reanalysis and GCM model data are calculated. However, according to our knowledge, evaluation of downscaled climate change scenarios in the Mediterranean Region using a bias-correction method has not been conducted yet. Here, a bias correction methodology (Xu et al. (2021)) was used, and an ensemble was generated by choosing appropriate global climate models which are compatible with reanalysis data for the selected region.

Native global climate model simulation results and non-linear long-term global climate model simulation trends were evaluated as the preliminary investigation. The temperature means of the global climate models (GCMs) and ERA5 reanalysis data were compared globally and for the EMEP domain. Initial findings showed underestimation or overestimation for the same GCM depending on the selected study domain. This result highlights the importance of the selection of the model for the study domain for weather generation and the models to be chosen for the ensemble. After calculating the long-term non-linear trend, the standard deviations were calculated for the interannual variability for the GCM and ERA5. For the historical period (1979-2014), annual temperature means of BCC-CSM2-MR, CMCC-CM2-SR5, EC-EARTH3, EARTH3-Veg, FIO-ESM-2-0, and KIOST-ESM showed similarity between ERA5 (r² > 0.70). Summer and fall months show mostly higher correlations compared to other seasons. 22 model ensemble global domain (EMEP Domain) temperature mean, minimum and maximum values were found as 7.62 (6.09), 7.18 (5.28), and 8.12^oC (6.89^oC), respectively. The values for reanalysis data are 7.95 (6.92), 7.57 (5.94), and 8.27 ^oC (7.87^oC).

How to cite: Akyuz, E., Kaynak, B., and Unal, A.: Downscaling GCM data using a bias-correction method for the Eastern Mediterranean., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12499, https://doi.org/10.5194/egusphere-egu22-12499, 2022.

Bias adjustment is the practice of statistically transforming climate model data in order to reduce systematic deviations from a reference data set, typically some sort of observations. There are numerous proposed methodologies to perform the adjustments -- ranging from simple scaling approaches to advanced multi-variate distribution based mapping. In practice, the actual bias adjustment method is a small step in the application, and most of the processing handles reading, writing and linking different data sets. These practical processing steps become especially heavy with increasing model domain size and resolution in both time and space. Here, we present a new implementation platform for bias adjustment, which we call MIdAS (MultI-scale bias AdjuStment). MIdAS is a modern code implementation that supports features such as: modern Python libraries that are suitable for large computing clusters, state-of-the-art bias adjustment methods based on quantile mapping, "day-of-year" based adjustments to avoid artificial discontinuities, and also introduces cascade adjustment in time and space. The MIdAS platform has been set up such that it will support development of methods aimed at higher resolution climate model data, explicitly targeting cases where there is a scale mismatch between data sets. In this presentaton, we describe the MIdAS assumptions and features, and present results from the main evaluation of the method for different regions around the world. We also present the most recent development of MIdAS towards different parameters.

How to cite: Berg, P., Bosshard, T., Yang, W., and Zimmermann, K.: MIdAS---MultI-scale bias AdjuStment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-737, https://doi.org/10.5194/egusphere-egu22-737, 2022.

Climate projections downscaled with statistical methods often focus on precipitation (P) and temperature (T) variables, which is not sufficient to run offline land surface models (LSMs) and derive hydrological projections based on both water and energy budgets.

This work extends a proposition by Clemins et al. (2019) to build on a multivariate high-resolution reanalysis dataset to infer projected ancillary variables from P & T projections based on analogue resampling. The refined method is a multisite and multivariate resampling method based on analogy of spatially distributed P & T daily anomalies. Anomalies are derived with respect to a baseline monthly climatology. The method thus makes use of spatial and multivariate consistency available in the archive reanalysis to supplement projections for additional variables and for a possibly extended spatial domain. The baseline climatology is considered as linearly transient for temperature variables to deal with anomalies not experienced during the archive period, and large-scale additional transient changes in T are passed on ancillary variables based on present-day anomaly relationships.

The new proposed method is exemplified for the Greater Pyrenean Region (GPR) defined as all basins draining the Pyrenees mountain range and extending over France, Spain, and Andorra. The archive reanalysis used here is the 2.5 km gridded SAFRAN-PIRAGUA surface reanalysis for the Pyrenees over 1965-2005 derived from existing SAFRAN reanalyses over France (Vidal et al., 2010) and Spain (Quintana-Seguí et al., 2017).

The projections considered here are 6 CMIP5 GCMs run under both RCP4.5 and RCP8.5 previously downscaled and including only P, TN, and TX variables and not available north of the Pyrenees (Amblar Francés et al., 2020). Applying the resampling method over the GPR led to 2.5 km gridded projections of daily time series of all variables required by LSMs. Results show – on top of an increasing temperature and a decreasing precipitation over the 21^st century – a decrease in wind speed, relative humidity, and infrared radiation, and an increase in visible radiation and evapotranspiration. These projections come with a large inter-GCM dispersion and more pronounced changes under RCP8.5

This work was funded by the Interreg V-A POCTEFA 2014-2020 through the EFA210/16 PIRAGUA project.

Amblar-Francés, M. P., Ramos-Calzado, P., Sanchis-Lladó, J., Hernanz-Lázaro, A., Peral-García, M. C., Navascués, B., Dominguez-Alonso, M., Pastor-Saavedra, M. A. & Rodríguez-Camino, E. (2020) High resolution climate change projections for the Pyrenees region. Advances in Science and Research, 17, 191-208

Clemins, P. J., Bucini, G., Winter, J. M., Beckage, B., Towler, E., Betts, A., Cummings, R. & Chang Queiroz, H. (2019) An analog approach for weather estimation using climate projections and reanalysis data. Journal of Applied Meteorology and Climatology, 58, 1763-1777

Quintana-Seguí, P., Turco, M., Herrera, S. & Miguez-Macho, G. Validation of a new SAFRAN-based gridded precipitation product for Spain and comparisons to Spain02 and ERA-Interim (2017) Hydrology and Earth System Sciences, 21, 2187-2201

Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M. & Soubeyroux, J.-M. A 50-year high-resolution atmospheric reanalysis over France with the Safran system (2010) International Journal of Climatology, 30, 1627-1644

How to cite: Vidal, J.-P., Quintana Seguí, P., and Beguería, S.: Beyond the usual suspects P&T: deriving multivariate high-resolution transient forcings for land surface models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2720, https://doi.org/10.5194/egusphere-egu22-2720, 2022.

Gridded climatological data derived for Bhutan from internationally available data sets either come at very low spatial resolution, do not provide daily data or contain few in-situ measurements. We present a newly developed daily high-resolution (1 km) gridded data set for Bhutan covering the years 1996 to 2019 for precipitation and for maximum and minimum temperature (BhutanClim) and compare it with state-of-the-art global observation data sets. As input, we used quality controlled and homogenized data from up to 67 weather stations from the National Center for Hydrology and Meteorology of Bhutan (NCHM).

The spatial interpolation method of temperature is based on methods that have already been successfully implemented in Austria, Switzerland and Germany. It allows non-linear lapse rates and considers geographical obstacles in the interpolation. The new climatology benefits from the use of local measurements and shows plausible small scale spatial patterns. Compared to other available state-of-the-art data sets BhutanClim there are new features especially in the precipitation field: Some valleys in central Bhutan border a dry climate classification according to Köppen-Geiger.

How to cite: Lehner, F. and Formayer, H.: Daily 1 km gridded temperature and precipitation in Bhutan, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3769, https://doi.org/10.5194/egusphere-egu22-3769, 2022.

Over 608 million farms exist around the world, occupying 36.9% of global land and 72% of annual freshwater withdrawals. These farms are highly diverse and heterogeneous. More than 80% of them are smaller than 2 hectares and they only utilize around 20% of farmlands but support millions of livelihoods in the rural area. Many datasets are available describing the global crops, soil conditions, and water availability. A few of them are farm-size specific. There is a lack of a global overview on how the soil-water-agriculture system is different across farm sizes.
This study aims to explore how soil, water, and crop change along with farm sizes. Specifically, we used the current best available databases on cropland extent, farm size structure, crop-specific harvested area, and field size distribution to develop a gridded, farm-size specific, and crop-specific harvested area map for 56 countries, representing half global cropland, using a downscaling algorithm. The resulting maps were validated by empirical data and compared to previous similar studies. We then overlapped the farm-size specific, and crop-specific map with global soil and water scarcity maps to explore differences between small and large farms on planted crops, soil nutrient availability, and level of water scarcity.
Our results show, in comparison to larger farms, smaller farms plant more pulses, roots and tubers, vegetables, and fewer oilcrops, sugar crops, and fodder crops. The majority of small farms do not have severe limitations on soil nutrient availability, but they do face severe water scarcity. Large farms, on the other hand, do not confront severe water scarcity but do face severe limitations on soil nutrient availability. Small farms may also be less capable of adapting to water scarcity through irrigation. However, there is still spatial variation in our results. Our results can help to further differentiate the sustainable soil, water, and agriculture management for small and large farms. 

How to cite: Su, H., Willaarts, B., Luna Gonzalez, D., S. Krol, M., and J. Hogeboom, R.: How soil, water, and crop change along with farm sizes: insights from a new high-resolution farm-size specific and crop-specific map covering 56 countries, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-882, https://doi.org/10.5194/egusphere-egu22-882, 2022.

The Hellmann rainfall recorders have been one of the primary instruments of the rainfall intensity measurement, mainly in the countries of the central part of Europe, during the 20th century. These water level measurement-based rainfall recorders ensure the continuity of measurement by periodically emptying siphoned measurement cylinder. During the emptying, the measurement is paused, resulting in under-measurement. The duration and number of emptying can be known, and the correction of under-measurement can be adjusted if the registration ribbon is available. In the case of historical data, the registration ribbon is often unavailable, and only extracted values of the most intensive periods of rainfalls can be gained from databases. A procedure for correcting such kinds of data is shown, and also the influence of the correction of Hellmann-Fuess recorder’s data on the IDF curves.

The procedure is based on the method published in 2002 by Luyckx and Berlamont, which was compiled based on the physical process of the siphoning, and it assumed the existence of the original records (ribbons), with the exact time of siphoning. The extracted data tables nor time nor number of siphoning was registered, so the method of Luyckx and Berlamont for the data correction cannot be used. The proposed procedure’s principal is the estimation of the number and length of pauses in the extracted measurement interval. These estimated data make passible to fix the rainfall quantity and intensity for the given interval. The measurement cylinder of the device is generally not empty at the beginning of the most intensive period of rainfall. The water level is assumed as a uniformly distributed probability variable what can be estimated with its expected value. The raw data comprise underestimation in all cases, so the raw data represent only the possible minima of the plausible intensity values. The fixed data result in the expected value of the plausible intensities, which are sometimes higher and sometimes lower than the actual intensity values, with the same probability; so, if there are a high number of fixed data, the positive and negative deviations diminish the errors statistically. The use of the correction formula is presented with the parameters of a Hellmann-Fuess rainfall writer. The correction range has been the highest over ten years of average recurrence, and its measure was 10%. For 30 minutes and longer sampling intervals, the magnitude of the correction is not relevant. After that, the correction is based on statistical estimation, the exact value of the rainfall intensity cannot be retrieved, but the underestimation can be decreased significantly. The fixed data modify the IDF curves as well, and in this way, the effect of climate change can be investigated more appropriately.

How to cite: Racz, T.: Correction of siphoning error in processed historical rainfall intensity data, a case study of data measured by Hellmann-Fuess type rainfall recorder, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3709, https://doi.org/10.5194/egusphere-egu22-3709, 2022.