UP1.6

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.

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.

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.