Chairpersons: Tanja Winterrath, Miloslav Müller
Characteristics of short-term rainfalls, especially their intensity and design values, are very important for the technical (e.g., sewage system) and hydrological practice. However, measurement and data processing of these characteristics are rather complicated. The rainfall intensity has been regularly measured in the Czech Republic since 1898 with a few manual pluviographs. Automatic rain gauges started to replace them in the late 1990s. Nowadays, a large part of historical pluviographs records has been digitalized. Both the digitalized pluviographs records and the automatic rain gauge measurement provide rainfall series in temporal resolution of 1 minute. Concerning the joined series of these two types of measurement, the relatively long series of rainfall intensity are available. The series longer than 40 years are available for almost 50 stations of the Czech Hydrometeorological Institute. In addition, the indirect rainfall data of high temporal resolution can be also derived from radar measurement. The radar measurement has been in operation for a relatively short period (since 2002) in the Czech Republic but it provides detailed indirect information on a spatial distribution of rainfall.
In our contribution, characteristics of short-term rainfalls (duration 10 – 360 min) for selected stations with the longest series of measurement are studied with regard to different extent of processed period. Especially, return levels of short-term rainfalls estimated for time series of different lengths and beginning are compared. The sensitivity of short-term rainfalls characteristics to the choice of processed period is analyzed in order to assess a possibility to use relatively short rainfall series (e.g., 20-year or shorter) for estimation of return levels with regard to the return period. Thus, a possibility to use return levels estimated from radar data combined with the estimates from longer station series in order to improve information about their spatial distribution is discussed.
How to cite: Crhova, L., Kliegrová, S., and Valeriánová, A.: Short-term rainfall characteristics dependence on the length of processed period, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-351, https://doi.org/10.5194/ems2022-351, 2022.
Titanlib is a library of functions for the automatic quality control of meteorological observations and it is publicly available on github:
https://github.com/metno/titanlib
Titanlib builds upon the experience of running TITAN (Båserud et al., 2020) for the quality control of temperature and precipitation serving operational applications, such as the production of automatic weather forecasts (Yr.no) and observational gridded datasets (seNorge.no).
We aim to manage titanlib as an open project that will eventually incorporate contributions from different working groups.
The distinctive feature of titanlib is the use of spatial quality control methods. A wide range of spatial checks are made available, from buddy-checks among neighbouring observations to spatial consistency tests (SCTs) based on more sophisticated statistical interpolation.
This presentation focuses on tuning the parameters of a generic titanlib function. In particular, a general approach to this problem will be presented. A set of reference observations is identified among all available observations. The reference observations are considered good observations. For the sake of simplicity, we call good observations those that are not affected by gross measurement errors, and bad observations those with gross errors. Synop stations constitute the natural reference set; however, other choices are possible. The quality control routine is always applied to the whole set of observations and the statistics are collected only over the reference observations. First, the routine is applied in the “unperturbed” mode and we count the number of: correct negatives (i.e. reference observations flagged as good ones), and false alarms (i.e. reference observations flagged as bad ones). Secondly the routine is applied on “perturbed” reference observations, by introducing known errors, and we count: misses (i.e. perturbed observations flagged as good ones), and hits (i.e. perturbed observations flagged as bad ones).
The combination of “perturbed” and “unperturbed” experiments for different settings of the QC routine allows us to obtain several contingency tables that we use to determine the optimal combination of parameters in an original way. In order to define the cost function, the user is required to specify the prior knowledge on: the expected probability of having a bad observation in the network, and the relative cost of a false alarm for the application considered.
The method is described by Alerskans et al. (2022), where it has been applied to QC of hourly temperature measured from crowdsourced observations. Furthermore, we will present an application for QC of hourly precipitation.
References:
How to cite: Abraham, I. R., Alerskans, E., Lussana, C., Nipen, T. N., Oram, L., and Seierstad, I. A.: A strategy for the optimization of quality control checks available in the titanlib open library, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-178, https://doi.org/10.5194/ems2022-178, 2022.
Low-Earth Orbit (LEO) satellites provide passive microwave (PMW) information about the hydrometeors. PMW data, which can be obtained from Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS), has a direct interaction with precipitation. The methods developed using PMW data are sensitive to the concentration of ice particles or water droplets associated with precipitation. Therefore, this data is more appropriate to estimate the rain rate.
Artificial Neural Network (ANN) is one of the techniques of Machine Learning (ML). It has been developed by imitating the stimulation and information received by the sense organs through the neurons in the computer environment. Today it is one of the technics of being used in almost all computational science fields. ANN can also be thought of as a black box that processes given inputs and generates outputs. This system process data in parallel, and it also learn coefficients among neurons with the principle of minimalizing and renewing mistake. In other words, ANN uses the method of trial and error.
The aim of this study is to develop an estimation model based on ANN by using brightness temperature data from five different channels (89, 150, 184, 186, and 190 GHz) of the AMSU-B and MHS mounted on NOAA 15-18 and METOP-A satellites. Brightness temperatures obtained by different channel frequencies are used as an input for the ANN model, and the rain rate values are tried to be estimated. In addition, the model results are compared with the rain gauge data and rain rate values of the 183 WSL Fast Rain Rate Retrieval Algorithm.
Keywords: Artificial Neural Networks, Precipitation, Rain rate, Turkey.
How to cite: Akın, M. and Öztopal, A.: Artificial Neural Network Approach in Rain Rate Estimation by Using AMSU-B and MHS Data, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-625, https://doi.org/10.5194/ems2022-625, 2022.
Precipitation remains as one of the most difficult meteorological variables to measure. In the Basque country case there are two main ways of performing point measurements; with the pluviometers network that has been developed during more than thirty years, and with the new disdrometer network that is being implemented during these last years.
Here we present the main characteristics of the Basque disdrometer network. This network is based on OTT Parsivel disdrometer, a laser-based optical instrument for simultaneous measurement of PARticle SIze and VELocity of all liquid and solid precipitation. This new instrumentation isincluded in the Basque multipurpose network, providing information for rain-drop distribution, velocities and other derived quantities on a 1-minute basis for different locations all around the territory.
In this work, we also present the visualization and analysis tool developed for an easy and intuitive exploitation of disdrometer derived data in real time together with information from the network of rain gauges and remote sensing systems (mainly radar). Such tool allows us to analyze in real and in deferred time all the information available in the territory for precipitation characterization at each observation point.
Finally, some aspects of interest are introduced in relation to different measurements obtained with the OTT Parsivel disdrometers at different points of the network during some interesting events. These disdrometers are equipped with two heads, one of which is an infrared laser emitter and the other a receiver that measures the attenuation produced in the light beam by the particles that pass through it. From this information, size, velocity and typology of the meteors, based on a diameter-velocity relationship are derived. Intensity of precipitation, the volume of water per volume of air, visibility or radar reflectivity, among others, are also available.
The effort to increase the understanding of the precipitation characteristics, that affects our territory, and its quantification through the new network of disdrometers, will allow an improvement in impact weather capabilities with a better surface precipitation fields characterization, integrating new data with that coming from radars or satellites. Many others areas of interest such as edafology, hydrology or climate change at local level, would also be positively affected.
How to cite: Gaztelumendi, S., Otxoa de Alda, K., Ruiz, R., Orue, J., Egaña, J., Maruri, M., and Aranda, J. A.: High temporal resolution monitoring of precipitation in the Basque Country, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-584, https://doi.org/10.5194/ems2022-584, 2022.
In the Intergovernmental Panel on Climate Change report (IPCC), “Climate Change 2022: Impacts, Adaptation and Vulnerability” it is stated that more frequent and intense extreme events due to climate change have a significant impact on the loss and damage of nature and people, which particularly holds for precipitation.
In the Galápagos archipelago, the primary source of water supply is rainfall, hence rainfall plays an important role for biodiversity and people in this iconic but remote region. The main assumption for Galapagos is that water supply is dominated by the cool season’s light Garúa rainfall originating from the Pacific stratus, which will significantly decrease under global warming conditions. At the same time, rainfall in the warm season shows large variability, particularly during extreme ENSO (El Niño-Southern Oscillation) events. While in the current decade, a decrease of strong El Niño rainfall events was observed in the eastern tropical Pacific, most (but not all) climate model projections of the CMIP6 ensemble reveal stronger El Niño rainfall under future warming.
To date, short and long-term rainfall dynamics in the Galápagos are not well understood, largely due to a lack of consistent spatially-time series of meteorological in-situ observations. The research project DARWIN ("Dynamics of precipitation in transition: The water source for the Galápagos Archipelago under climate change") has recently established 11 Automatic weather stations (AWS) covering a W-E and luff-lee transects over three islands (Isabela, S. Cruz, S. Cristóbal). The location of the stations is to consider different local and regional precipitation formation mechanisms. We seek to resolve influences of the Equatorial Counter Current and the Humboldt Current, as well as the topographic exposition towards the main airstream. Furthermore, the altitudinal gradients concerning vertical dynamics of the trade inversion are considered. One main goal of the DARWIN project is to produce area-wide rainfall information by satellite retrievals and WRF dynamical downscaling.
While warm-season rainfall is mainly driven by intense convection events, cool-season Garúa is assumed to be more in the drizzle intensity range. The area-wide techniques must thus properly model the very different types of occurring rain intensities in the cool and warm seasons. Hence, the observations from the AWS used as test and training data must be as accurate as possible. Beyond standard meteorology, we focus on different advanced observation principles (light, optical, radar, gauge) and their intercomparison, and warrant high-resolution measurements (up to one minute) including a vertical profiling of rainfall. The AWS stations in the Garúa zone are additionally equipped by a harp-type fog collector.
The poster will present the overall structure of the project and some first results of the AWS network, with a focus on temporally high-resolution rainfall dynamics during different weather situations and precipitation types along the transects.
How to cite: Turini, N., Achilles, S., Delgado Maldonado, B., Dobbermann, M., Schmidt, B., Scherer, D., and Bendix, J.: Understanding high-resolution rainfall dynamics and variability in the Galápagos archipelago: Installation of an automatic weather stations network., EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-599, https://doi.org/10.5194/ems2022-599, 2022.
Precipitation is the key input variable to hydrological models and its monitoring plays a significant role in water resources planning and improving flood and drought forecasting, also under climate change impacts. In recent years, many precipitation satellite products have been developed and released to the public; among these, the Integrated Multi-satellitE Retrievals from Global Precipitation Measurement (IMERG) is designed to address limitations and uncertainties related to traditional methods.
The primary purpose of this study is to provide a comprehensive assessment of precipitation estimates retrieved from the IMERG v6 Final Run over the Mediterranean island of Sicily (Italy) at daily and half-hourly temporal and at 0.1° spatial resolution for the first time using a quality-controlled sub-hourly gauge dataset. Sicily, which is characterized by a Mediterranean climate and a complex orography, experiences rather frequent short duration and high intensity precipitation originating from the interaction of steep orography on the coasts with winds carrying humid air masses from the Mediterranean Sea.
Previous studies have highlighted that most of the available satellite-based precipitation products show poor performance for capturing rainfall events at high temporal resolution particularly in coastal areas. Based on these findings, there is a critical need to put much effort to improve retrieval algorithms to account for coastal and morphological effects, thus enhancing satellite-based precipitation estimations for those areas. With this regard, this work also aims to show that a combination of multiple products may result in more accurate estimations especially for short duration events. This merging technique, which has been carried out exploiting artificial intelligence (AI) techniques, is shown to successfully reduce the error based on the comparison with data from a local rain gauge network. The results of the study will demonstrate the proficiency of the AI based approaches for improving remote-sensed daily and sub-hourly rainfall products even in coastal areas with complex orography.
How to cite: Noto, L., Beikahmadi, N., Pumo, D., and Francipane, A.: An Artificial Intelligence–Based Blending of Satellite products across Mediterranean Island of Sicily, Italy using GPM-IMERG V06 Final Run, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-503, https://doi.org/10.5194/ems2022-503, 2022.
Taking place at the Royal Meteorological Institute of Belgium (RMIB), the ongoing 2-year PRECIP-TYPE project aims at improving the hydrometeor classification in Belgium through the use of dual-pol radar observations and Numerical Weather Prediction (NWP) model output. Combining these two sources of information in a fuzzy logic scheme yields an estimate of the precipitation type (rain, hail, snow,...) at the height of the measurement of the radar. This information is then further processed by using vertical temperature, humidity and pressure profiles from NWP output in order to account for the melting process that can occur before precipitation reaches the ground. The output of this precipitation type product is compared to the crowdsourced weather reports sent by the users of the RMIB smartphone app. Such user reports can include the precipitation type as well as the size of the hydrometeors. These reports are ingested in a database of the RMIB and undergo reliability checks that allow an easier discrimination between realistic and unrealistic observations.
We present the step-by-step methodology followed to obtain this first product version, including an extended clutter filtering, and we demonstrate its capabilities for a few challenging cases. We discuss the identified uncertainties and the remaining open questions. Regarding the perspectives, several possibilities are considered to further enhance this preliminary precipitation type product, such as the merging of dual-pol observations obtained from several radars, the comparison with alternative schemes of hydrometeor classification or the inclusion of the citizen observations (RMIB app) within the classification scheme. An overview of these lines of research will be presented.
How to cite: Watelet, S., Delobbe, L., and Reyniers, M.: Improving the hydrometeor classification at ground level in Belgium, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-641, https://doi.org/10.5194/ems2022-641, 2022.
Accurate and homogeneous high resolution precipitation datasets are needed for many advanced weather and climate applications. A new automatic system for generating such a climatological product has been developed for Belgium. It is based on radar volumetric data from Belgium, France and Germany. This means that the data are quite heterogeneous with different scanning strategy, sampling resolution, signal filtering and post processing. The precipitation estimation is obtained after a complex processing of the volumetric data. This begins with a careful quality control of the radar data. Each radar is dynamically recalibrated based on the average of the daily median bias over 2 months. The reflectivity level with systematic contamination by non-meteorological signals is identified for each measurement bin. The remaining clutter is identified by comparing with satellite images and looking at the reflectivity field texture or vertical gradient of reflectivity. Ground rainfall estimation is obtained by a dynamic model of vertical profile of reflectivity including the identification of melting snow. Single radar rain rates are combined into a composite by taking the maximum value of the three closest radars in the convective season (May–August). In the other months, the composite is based on all values weighted based on the distance to the radar. In a last step the radar estimation is combined with measurements from dense automatic rain gauge networks. The radar-gauge merging is obtained by a single bias correction or by kriging with external drift. The method is based on the operational realtime product, which benefits from many years of quality control and continuous improvement. An independent verification is performed for the years 2013-2021 with the computation of various scores for different applications. The product is also analysed for the July 2021 extreme flood event.
How to cite: Goudenhoofdt, E. and Delobbe, L.: Generation of a high resolution climatological precipitation product for Belgium, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-580, https://doi.org/10.5194/ems2022-580, 2022.
Extreme rainfall events, due to its dynamic and still not always predictable character, continue to be a subject of social concern even in a highly-developed country such as Germany. They can lead to numerous hazards, like flash-floods, landslides and soil erosion, causing damages and huge economic costs, as well as direct risk to human life.
Thus, not only the real-time monitoring but also a deep analysis of past extreme precipitation events has a crucial meaning in understanding their possible impact. The investigation of past losses and risk factors can help to estimate the potential danger and shape human sensitivity to weather extremes.
With this objective, the Deutscher Wetterdienst (DWD) performs a radar-based object-oriented identification and classification of extreme rainfall. For the past events that occurred over the area of Germany in 2001-2021, the Catalogue of Radar-based Heavy Rainfall Events (CatRaRE) is available. It is derived from high-resolution climatological data (1h, 1km - RADKLIM). For the current cases of heavy rainfall, quasi real-time monitoring based on hourly operational hourly RADOLAN data is performed.
For all detected events a wide range of indices and extremity attributes (e.g. return period, heavy precipitation and weather extremity indices) were determined. The geospatial character of the dataset has allowed also for an enrichment with e.g. socioeconomic and geophysical variables, building the substantial basis for a risk analysis. In addition, combining meteorological and non-meteorological characteristics of each event with available data on corresponding insurance losses, fire-brigades’ operations or crowdsourcing reports served for an impact quantification. Through those steps, we obtained a rich basis that can be also used operationally as a lookup table when estimating a possible impact of current or forecasted events that show similar characteristics.
We will present results of a heavy rainfall hazard mapping for the area of Germany together with an impact analysis of all classified heavy precipitation events. In addition, we will give examples for using the extended version of the CatRaRE Catalogue as a basis for assessing the impact of operationally detected and/or predicted precipitation events.
How to cite: Walawender, E., Lengfeld, K., Winterrath, T., and Weigl, E.: From radar-based heavy rainfall event monitoring to impact assessment (an example from Germany), EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-658, https://doi.org/10.5194/ems2022-658, 2022.
Increasing concentration of water vapour in the Earth’s atmosphere being a result of continued global warming has a pronounced impact on precipitation patterns. Particularly affected are precipitation extremes, which are expected to intensify at a rate of 6-7% per each 1°C of air temperature warming following the Clausius-Clapeyron (CC) relation. However, many regional studies have shown that the CC rate is appropriate for long-duration precipitation extremes (LDPEs; multi-daily to daily extremes) rather than for short-duration precipitation extremes (SDPEs; sub-daily to hourly extremes). SDPEs are projected to intensify even twice as fast as expected from the CC relation posing substantial risk on human and natural systems. Yet over the years, SDPEs have received much less scientific attention than LDPEs, mainly due to the limitations of measurement systems. Our aim to provide insight into behaviour of SDPEs detected by the radar network of Deutscher Wetterdienst (DWD) from 2001 to 2020 by exploring their temporal and spatial variability as well as links to circulation patterns.
The study is based on the Catalogues of Radar-based heavy Rainfall Events (CatRaRE), which have been generated using reprocessed gauge-adjusted data of the DWD radar network known as RADKLIM (Lengfeld et al. 2021). The links between the precipitation extremes and circulation patterns have been quantified by applying two circulation type classifications developed by James (2019). Both of them are related to the Hess-Brezowsky Grosswetterlagen.
The results have demonstrated that SDPEs are common phenomena occurring most frequently in the afternoon hours of the summer season. They are, however, characterised by relatively small size occurring on a local rather than a regional scale – the median area of SDPEs reaches up to 24 km2, while the median area of LDPEs reaches up to 184 km2, as a results, SDPEs often cannot be captured by rain gauge station network. The circulation patterns favouring SDPEs can be divided into two groups. The first group constitute circulation patterns commonly known as those favorable for precipitation, such as cut-off lows and cyclonic circulation patterns. The second group constitute, in turn, anticyclonic meridional or mixed circulation patterns, frequently accompanied by southerly airflow. In the summer, these circulation patterns are capable of inducing high thermal instability and development of small-scale, isolated convective cells. As the convective structures are difficult to detect by rain gauge stations, the role of some circulation patterns in shaping the precipitation extremes seems to be underestimated in the previous studies.
James P, 2019, Extended Grosswetterlagen: A new synoptic type classification for Central Europe accounting for both circulation and air mass characteristics, EMS Annual Meeting Abstracts, 16.
Lengfeld K, Walawender E, Winterrath T, Weigl E, Becker A, 2021, Heavy precipitation events Version 2021.01 exceeding return period of 5 years from RADKLIM-RW Version 2017.002. doi:10.5676/DWD/CatRaRE_T5_Eta_v2021.01.
How to cite: Palarz, A., Junghänel, T., and Ostermöller, J.: Short-duration precipitation extremes detected by the DWD radar network and associated circulation patterns, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-379, https://doi.org/10.5194/ems2022-379, 2022.
With the effects of the advancing climate change the intensity and frequency of extreme rainfall events in many regions of the world is likely to increase. Heavy rainfall has a significant impact on the propagation of electromagnetic waves during wireless data transmission, especially for higher frequency ranges above 10 GHz. For the development of future wireless communication technologies like Terahertz (THz) links, precise information about the development and characteristics of extreme rainfall events is essential, especially in urban regions where these new technologies are likely to be implemented. In this study we investigate the dynamic of heavy rainfall events in the city of Berlin and surrounding areas for the timeframe between 2011 and 2020. Stationary measurements and crowdsourcing data of the urban climate observatory network as well as the measurement network of the German Meteorological Service (Deutscher Wetterdienst, DWD) is statistically evaluated to characterize the intensity, duration, and spatiotemporal variability of extreme rainfall events. Furthermore, possible connections between rainfall and factors like land cover and topography are analyzed. The influence of the distance between individual measurement stations on these results is examined using auto correlations. The rainfall data will also be used for attenuation models based on recommendations of the International Telecommunications Union to evaluate the rain specific impact on wireless signal propagation. For this investigation several common frequency ranges for the 5th wireless communication standard (5G), as well as frequencies in the THz ranges (100 GHz to 10 THz), are used to allow for an assessment of the vulnerability of wireless communication networks in the area of Berlin. Furthermore, these results are used as a reference for similar evaluations with spatially and temporally resolved meteorological datasets including the Central Europe refined analysis generated with the weather research and forecasting model and the radar-based precipitation climatology data set by the DWD.
How to cite: Bart, F., Meier, F., Scherer, D., and Hellmann, D.: Analysis of the dynamic of extreme rainfall events based on rain gauge measurement networks in Berlin, Germany in the context of wireless communication performance, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-282, https://doi.org/10.5194/ems2022-282, 2022.
Commercial microwave links (CMLs) have become established opportunistic rainfall sensors used to estimate rainfall fields of various temporal resolutions and spatial scales. A major potential of CMLs lies in their great abundance even in sparsely gauged regions. Moreover, they provide close-to-ground measurements and can be particularly valuable in mountainous terrain where the reliability of weather radars is limited. These advantages theoretically allow for valuable rainfall products of continental scale in near real-time availability.
However, in practice, limitations to such large scale CML-based products exist due to legal and administrative burdens which hinder data exchange and lead to processing algorithms that are customized to specific data sets. Not being dedicated rainfall sensors, CMLs require careful processing and filtering algorithms to retrieve rain rates from raw signal data. These algorithms have so far been developed by individual research groups for rather homogeneous data sets stemming from single acquisition systems. Up to now rainfall products have not been based on two independent national CML data sets.
In this study we merge data sets of 3900 CMLs in Germany and 2500 CMLs in the Czech Republic that are obtained from different network operators and have distinct lengths and frequencies distributions. We develop and adjust universal processing algorithms, and calculate transboundary rainfall fields. Our focus lies on producing and analyzing maps for the mountainous border region where radar observations particularly suffer from the large measuring height above ground. We analyze a period of one month in summer 2021 which contains several rainfall events. For evaluation we compare our results with German and Czech national radar and rain gauge observations.
We find that independent CML data sets can be merged successfully. We are able to produce coherent transboundary rainfall maps, which is an important step towards rainfall products at continental scale. This study demonstrates the interoperability using independent CML data sets and identifies limitations of current custom-made preprocessing algorithms.
How to cite: Blettner, N., Fencl, M., Špačková, A., Bareš, V., Chwala, C., and Kunstmann, H.: Merging independent networks of commercial microwave links from the Czech Republic and Germany to generate transboundary rainfall fields, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-186, https://doi.org/10.5194/ems2022-186, 2022.
For water management purposes, the design rainfall intensities with different durations are an essential data basis. However, the actual runoff response of such a rainfall event is substantially influenced by other factors, including the time structure of the rainfall event and the previous saturation of the landscape. In our paper, we focus on the latter of these factors because the design of small-scale water management structures is usually based on the assumption that the saturation of the catchment at the beginning of a rainfall event of a given duration is on average. The aim of our research is to test the validity of this assumption in different parts of the country and to trace any general patterns of the relation between precipitation extremes and antecedent precipitation totals in relation to relief.
We use data from 60 Czech rain-gauge stations for which short-term rainfall intensity data for several decades are at our disposal. At each station, we select annual maximum rainfall totals for time windows ranging from half an hour to one day. We estimate the magnitude of antecedent saturation using the antecedent precipitation index over a 30-day period (API30); for sub-daily extreme totals, we also consider any precipitation on that day from the morning to the start of the precipitation episode. Because the index exhibits almost the same seasonal distribution as the daily precipitation totals, we express the magnitude of API30 relative to the normal for a given calendar day. For each station, we calculate relative saturation values before each annual maximum rainfall episode of a given length. Considering the episodes with decreasing weight according to rainfall magnitude, we evaluate average relative saturation values. We find that already before episodes with a length of 150 minutes we have to take into account slightly increased saturation, especially in mountainous regions in the east of the Czech Republic.
How to cite: Müller, M., Crhová, L., Kašpar, M., and Laco, M.: Estimation of precipitation preceding precipitation extremes of different lengths, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-639, https://doi.org/10.5194/ems2022-639, 2022.
The necessity of reliable rainfall statistics for the planning and design of infrastructure is growing as climate change leads to more frequent heavy rainfall events. These rainfall statistics are often displayed as Intensity-Duration-Frequency (IDF) curves, which depict the rainfall intensity (return level) that can be expected at a certain location over a given duration and with a certain frequency or recurrence interval (return period).
The former Norwegian computational approach for IDF curves, in use until recently, was established more than 45 years ago and has never been updated. We show that this outdated method of fitting a Gumbel distribution to the heaviest precipitation events fails to reflect the return values associated with long return periods. Instead, we introduce a method based on Bayesian inference for fitting a Generalized Extreme Value (GEV) distribution to observed annual maximum precipitation for several target durations. Generally, the proposed method performs well. However, the resulting IDF curves for some stations may be inconsistent across durations and return periods due to the estimation being carried out independently across durations. To avoid this, we subject the IDF curves to a post-processing where a quantile selection algorithm searches for consistent return levels within the posterior quantiles of the Bayesian inference. The post-processing produces consistent estimates that are at least as accurate as the unadjusted, inconsistent estimates.
Unlike before, the resulting new IDF curves feature uncertainty intervals that are derived directly from the Bayesian approach. The curves are presented to the users on the homepage of the Norwegian Centre for Climate Services (https://klimaservicesenter.no).
How to cite: Lutz, J., Grinde, L., Dyrrdal, A. V., Roksvåg, T., and Thorarinsdottir, T. L.: Estimating consistent rainfall design values for Norway using Bayesian inference and post-processing of posterior quantiles, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-85, https://doi.org/10.5194/ems2022-85, 2022.
Extreme precipitation events that lead to excess surface water and flood are becoming an amplifying societal cost as a result of both the increasing precipitation amounts in recent years and urbanization. Knowledge about extreme precipitation events is important for the ability to predict them, but also to know how often they occur with various intensities in order to estimate design values for constructions and critical infrastructure.
To study extreme precipitation events by applying statistical analysis requires long timesteries, which often is a challenge when using conventional or new observational data records.
In the present study, a data set constructed from the numerical seasonal prediction system at ECMWF, SEAS5, has been applied in order to increase the event sample size compared to conventional observational or re-analysis data sets. The data are analyzed by fitting them to a GEV-distribution. This distribution is compared to an equivalent GEV-distribution for the gridded observational data set SeNorge. While this data set has a smaller sample size, the fine scale horizontal resolution allows for more spatial heterogeneity in the data set. In this study we propose a method to estimate return values by combining the two datasets, and in that way exploiting the advantages of both data sources: sample size from SEAS5 and spatial distribution from SeNorge. The combination is done by using a normalized “growth curve” from both data sets. The large sample size is important for the shape and scale parameters of the fit to the GEV and is thus used from the SEAS5 data set. These parameters will define the shape of the curve. Location from SeNorge is then used to get the correct level of the curve.
How to cite: Ødemark, K. and Ellingsbø Tveito, O. E.: Estimating return values for precipitation in Norway by combining a large ensemble data set with gridded observations to re-parameterize the extreme value distribution, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-538, https://doi.org/10.5194/ems2022-538, 2022.
A significant increase in the intensity of extreme precipitation of short duration is expected due to the global warming, and is already reported in recent literature. Estimating the changes in hourly extremes in climate models is of fundamental importance for improving risk management and adaptation to changing climate, because they cause numerous hydrological and geomorphological hazard. Convection-permitting models (CPMs) can resolve the scales at which convective processes occur, and thus provide higher confidence in the future estimates of hourly precipitation than coarser resolution models. However, the existing CPM simulations cover relatively short time periods (10–20 years), and this prevents the use of conventional extreme value methods for the estimation of extremes. Novel methods based on the concept of ordinary event have shown the potential of deriving accurate frequency analyses from short data records, and they can be successfully applied to CPMs.
Recent studies reported distinct orographic impacts on precipitation extremes. In particular, a “reverse orographic effect” was reported for hourly durations, meaning that the intensity of hourly extremes tends to decrease with elevation, as opposed to the orographic enhancement of precipitation for long durations. This reverse orographic effect was tentatively associated to orography-induced turbulence. As these processes could be sub-grid even for CPMs, it is crucial to understand whether and how CPMs are able to represent these effects before using these simulations to project future extremes in mountainous areas.
Here, we focus on a complex-orography area in North-eastern Italy and we use hourly precipitation records from ~150 5-min resolution rain gauges (our benchmark) and CPM simulations from COSMO model, run at 2.2 km resolution. The model is driven with ERA Interim reanalyses for the period 2000-2009. By applying a storm-based statistical method to both observed and simulated time series, we model the ordinary events tails using a Weibull distribution. We compute the distribution parameters and the extreme quantiles up to 20-year return period for 9 durations between 1 and 24 hours, and we evaluate: their dependence on elevation, the bias between the observation and the CPM, the dependence of the biases with elevation.
We find evident spatial patterns in the CPM biases on the annual maxima and the modelled quantiles, especially for short durations. The bias significantly depends on elevation, with increasing overestimation of the 1-hour quantiles with elevation. This seems to confirm our hypothesis that CPMs cannot well represent the “reversed orographic effect”. These findings can improve our understanding of the changes in the meteorological processes underlying the changes in the precipitation extremes, and could help us develop adjustment approaches that can account for the role of orography at multiple durations.
How to cite: Dallan, E., Marra, F., Fosser, G., Formetta, G., Marani, M., Schaer, C., and Borga, M.: Is the reverse orographic effect on hourly extreme precipitation well represented by a convection-permitting climate model?, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-487, https://doi.org/10.5194/ems2022-487, 2022.
Precipitation is an essential meteorological variable affecting the biosphere and human societies. At the same time, the precipitation is notoriously difficult to predict and verify. Two new spatial distance metrics for verification of precipitation are presented. The aim was to develop measures that would provide a good and meaningful approximation of the displacement of precipitation events in the two fields. An estimate of spatial displacement is very appealing for forecast interpretation because it is easy to understand and mimics how humans tend to judge fields by eye. Contrary to most other distance metrics, the new metrics do not require thresholding and can thus be used to analyze binary and non-binary (e.g., continuous or multi-level) fields. The analysis of comparisons with idealized geometric fields showed that the new metrics provide a good and meaningful approximation of the displacement in situations with a single displaced event. Typically, the estimate of displacement was better than the results provided by most other metrics that were also analyzed. For situations with multiple events, the measure’s behavior was not inconsistent with a subjective evaluation. The analysis also showed that the measures are not overly sensitive to noise, their results are directly related to the actual displacements of events, and that the events with a larger magnitude have a bigger influence on the resulting value. The analysis of ECMWF precipitation forecasts over Europe and North Africa confirmed that the new metrics provide a meaningful approximation of the displacement in real-world situations. The uncertainty of the mean value of the metric can be estimated via statistical parameters linked to the width of the distribution. The trend and its statistical significance can be determined using an appropriate variant of the Mann-Kendall test, which properly accounts for the autocorrelation present in the time series. The source codes for efficient calculation of the metrics were published in a freely accessible repository.
How to cite: Skok, G.: Spatial Distance Metrics for Verification of Precipitation, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-79, https://doi.org/10.5194/ems2022-79, 2022.
