On the assessment of precipitation extremes in reanalysis and ensemble forecast datasets

Precipitation extremes can trigger natural hazards with large impacts. The accurate quantification of the probability and the prediction of the occurrence of heavy precipitation events is crucial for the mitigation of precipitation-related hazards. This PhD thesis provides methods for the assessment of precipitation extremes. The methods are applied to different gridded datasets. The framework of extreme value theory, and more precisely the extended generalized Pareto distribution (EGPD), is used to quantify precipitation distributions. Chapter 2 compares ERA-5 precipitation dataset with observation-based datasets and identifies the regions of low or high agreement of ERA-5 precipitation with observations. ERA-5 is a reanalysis dataset, i.e. a reconstruction of the past weather obtained by combining past observations with weather forecast models. The strengths of reanalysis precipitation fields are the regular spatio-temporal coverage and the consistence with the data on the atmospheric circulation from the reanalysis. However, precipitation in ERA-5 stem from short-term forecasts and the precipitation data calculation does not include observed precipitation. Therefore a comparison with observational datasets is needed to assess the quality of the precipitation data. We compare ERA-5 precipitation with two observation-based gridded datasets: EOBS (station-based) over Europe and CMORPH (satellite-based) globally. Both intensity and occurrence of precipitation extremes are compared. We measure the co-occurrence of extremes between ERA-5 and the observational datasets with a hit rate of binary extreme events. We find a decrease in the hit rate with increasing rarity of events. Over Europe, the hit rate is rather homogeneous except near arid regions where it has a larger variability. In the global comparison, the midlatitude oceans are the regions with the largest agreement for the occurrence of extremes between the satellite observations and the reanalysis dataset. The areas with the largest disagreement are the tropics, especially over Africa. We compare the precipitation intensity extremes between ERA-5 and the observational datasets using confidence intervals on the estimation of extreme quantiles and a test based on the Kullback-Leibler divergence. Both the confidence intervals and the Kullback-Leibler divergence calculations are based on the fitting of the precipitation distribution with the EGPD. The quantile comparison indicates an overlap of the confidence intervals on extreme quantiles (with a probability of non-exceedance of 0.9) for about 85% of the grid points over Europe and 72% globally. The regions with non-overlapping confidence intervals between ERA-5 and EOBS correspond to regions where the observation coverage is sparse and therefore where EOBS is more uncertain. The two datasets have a good agreement over countries with dense observational coverage. ERA-5 and CMORPH precipitation intensities agree well over the midlatitudes. The tropics are a region of disagreement: ERA-5 underestimates quantiles for heavy precipitation compared to CMORPH. In Chapter 3, we provide return levels of heavy precipitation events with regional fittings of the EGPD. The goal of this chapter is to develop a regional fitting method being a good trade-off between a robust estimation of the distribution and parsimony of the model, with a focus on precipitation extremes. We apply the method to ERA-5 precipitation data over Europe. This area of the dataset contains more than 20,000 grid points. A local fit of EGPD distributions for all grid points in Europe would therefore imply estimating a large number of parameters. To reduce the number of estimated parameters, we identify homogeneous regions in terms of extreme precipitation behaviors. Locations with a similar distribution of extremes (up to a normalizing factor) are first clustered with a partitioning-around-medoid (PAM) procedure. The distance used in the clustering procedure is based on a scale-invariant ratio of probability-weighted moments focusing on the upper tail of the distribution. We then fit an EGPD with a constraint: only one parameter (out of three) is allowed to vary within a homogeneous region. The outputs of Chapter 3 are 1) a step-by-step blueprint that leverages a recently developed and fast clustering algorithm to infer return level estimates over large spatial domains and 2) maps of return levels over Europe for different return periods and seasons. The relatively parsimonious model with only one spatially varying parameter can compete well against statistical models of higher complexity. The last part of this thesis (Chapter 4) evaluates the prediction skill of operational forecasts on a subseasonal (S2S) time scale. Good forecasts of extreme precipitation are crucial for warnings and subsequent mitigation of natural hazards impacts. The skill of extreme precipitation forecasts is assessed over Europe in the S2S forecast model produced by the European Centre for Medium-Range Weather Forecasts. ERA-5 precipitation is used as a reference. Extreme events are defined as daily precipitation exceeding the 95th seasonal percentile. The precipitation data is transformed into a binary dataset (threshold exceedance vs. no threshold exceedance). The percentiles are calculated independently for the forecast and the reference dataset: the direct comparison of dataset-specific quantiles removes potential biases in the data. The Brier score is computed as a reference metric to quantify the skill of the forecast model. In addition to the Brier score, a binary loss function is used to focus the verification on the occurrence of the extreme, discarding the days when the daily precipitation is not extreme, in both the forecast and the verification datasets. A daily and local verification of extremes is conducted; the analysis is extended further by aggregating the data in space and time. Results consistently show higher skill in winter compared to summer. Portugal, Norway and the South of the Alps are the regions with the highest skill in general. The Mediterranean region also presents a relatively good skill in winter. The spatial and temporal aggregation increases the skill. Each part of this thesis provides methods to model and evaluate precipitation extremes. The outcome of Chapter 2 is an evaluation of ERA-5 precipitation. Europe is found to be a region of good performance in this dataset. ERA-5 is therefore used to apply the regionalized estimation of return levels developed in Chapter 3. Furthermore, the reanalysis dataset is used as a reference for the estimation of the S2S forecast skill for precipitation extremes, in Chapter 4. The appendix contains the additional articles in which I was involved during my PhD project, as a lead author or as a coauthor

A Comparison of Moderate and Extreme ERA-5 Daily Precipitation With Two Observational Data Sets

A comparison of moderate to extreme daily precipitation from the ERA-5 reanalysis by the European Centre for Medium-Range Weather Forecasts against two observational gridded data sets, EOBS and CMORPH, is presented. We assess the co-occurrence of precipitation days and compare the full precipitation distributions. The co-occurrence is quantified by the hit rate. An extended generalized Pareto distribution (EGPD) is fitted to the positive precipitation distribution at every grid point and confidence intervals of quantiles compared. The Kullback–Leibler divergence is used to quantify the distance between the entire EGPDs obtained from ERA-5 and the observations. For days exceeding the local 90th percentile, the mean hit rate is 65% between ERA-5 and EOBS (over Europe) and 60% between ERA-5 and CMORPH (globally). Generally, we find a decrease of the co-occurrence with increasing precipitation intensity. The agreement between ERA-5 and EOBS is weaker over the southern Mediterranean region and Iceland compared to the rest of Europe. Differences between ERA-5 and CMORPH are smallest over the oceans. Differences are largest over NW America, Central Asia, and land areas between 15°S and 15°N. The confidence intervals on quantiles are overlapping between ERA-5 and the observational data sets for more than 80% of the grid points on average. The intensity comparisons indicate an excellent agreement between ERA-5 and EOBS over Germany, Ireland, Sweden, and Finland, and a disagreement over areas where EOBS uses sparse input stations. ERA-5 and CMORPH precipitation intensity agree well over the midlatitudes and disagree over the tropics

A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods

Temporal (serial) clustering of extreme precipitation events on sub-seasonal time scales is a type of compound event. It can cause large precipitation accumulations and lead to floods. We present a novel, count-based procedure to identify episodes of sub-seasonal clustering of extreme precipitation. We introduce two metrics to characterise the frequency of sub-seasonal clustering episodes and their relevance for large precipitation accumulations. The procedure does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this procedure to daily precipitation from the ERA5 reanalysis data set, we identify regions where sub-seasonal clustering occurs frequently and contributes substantially to large precipitation accumulations. The regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the sub-seasonal time window (here 2–4 weeks). This procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (sub-seasonal to decadal). The code is available at the listed GitHub repository

Identifying meteorological drivers of extreme impacts: an application to simulated crop yields

Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.DFG, 251036843, GRK 2043: Naturgefahren und Risiken in einer Welt im Wande

Guidelines for studying diverse types of compound weather and climate events

Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (1) preconditioned, (2) multivariate, (3) temporally compounding, and (4) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (1) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (2) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (3) In Portugal, deep-landslides are often caused by temporal clusters of moderate precipitation events. Finally, (4) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors

Assessment of subseasonal-to-seasonal (S2S) ensemble extreme precipitation forecast skill over Europe

Heavy precipitation can lead to floods and landslides, resulting in widespread damage and significant casualties. Some of its impacts can be mitigated if reliable forecasts and warnings are available. Of particular interest is the subseasonal-to-seasonal (S2S) prediction timescale. The S2S prediction timescale has received increasing attention in the research community because of its importance for many sectors. However, very few forecast skill assessments of precipitation extremes in S2S forecast data have been conducted. The goal of this article is to assess the forecast skill of rare events, here extreme precipitation, in S2S forecasts, using a metric specifically designed for extremes. We verify extreme precipitation events over Europe in the S2S forecast model from the European Centre for Medium-Range Weather Forecasts. The verification is conducted against ERA5 reanalysis precipitation. Extreme precipitation is defined as daily precipitation accumulations exceeding the seasonal 95th percentile. In addition to the classical Brier score, we use a binary loss index to assess skill. The binary loss index is tailored to assess the skill of rare events. We analyze daily events that are locally and spatially aggregated, as well as 7 d extreme-event counts. Results consistently show a higher skill in winter compared to summer. The regions showing the highest skill are Norway, Portugal and the south of the Alps. Skill increases when aggregating the extremes spatially or temporally. The verification methodology can be adapted and applied to other variables, e.g., temperature extremes or river discharge

High return level estimates of daily ERA-5 precipitation in Europe estimated using regionalized extreme value distributions

International audienc

High return level estimates of daily ERA-5 precipitation in Europe estimated using regionalised extreme value distributions

Accurate estimation of daily rainfall return levels associated with large return periods is needed for a number of hydrological planning purposes, including protective infrastructure, dams, and retention basins. This is especially relevant at small spatial scales. The ERA-5 reanalysis product provides seasonal daily precipitation over Europe on a 0.25 ×0.25 grid (about 27 × 27 km). This translates more than 20,000 land grid points and leads to models with a large number of parameters when estimating return levels. To bypass this abundance of parameters, we build on the regional frequency analysis (RFA), a well-known strategy in statistical hydrology. This approach consists in identifying homogeneous regions, by gathering locations with similar distributions of extremes up to a normalizing factor and developing sparse regional models. In particular, we propose a step-by-step blueprint that leverages a recently developed and fast clustering algorithm to infer return level estimates over large spatial domains. This enables us to produce maps of return level estimates of ERA-5 reanalysis daily precipitation over continental Europe for various return periods and seasons. We discuss limitations and practical challenges and also provide a git hub repository. We show that a relatively parsimonious model with only a spatially varying scale parameter can compete well against statistical models of higher complexity

On the temporal clustering of European extreme precipitation events and its relationship to persistent and transient large-scale atmospheric drivers

Extreme precipitation events that occur in close succession can have important societal and economic repercussions. Here we use 42 years of reanalysis data (ERA-5) to investigate the link between Euro-Atlantic large-scale pattern of weather and climate variability and the temporal clustering of extreme rainfall events over Europe. We implicitly model the seasonal rate of extreme occurrences as part of a Poisson General Additive Model (GAM) using cyclic regression cubic splines. The smoothed seasonal rate of extreme rainfall occurrences is used to (i) infer the frequency of significant temporal clustering and (ii) implicitly serves as the baseline rate when modeling the effects of atmospheric drivers on extreme rainfall clustering. We use GAMs to model the association between the temporal clustering of extreme rainfall events and seven predominant year-round weather regimes in the Euro-Atlantic sector as well as a measure of synoptic-scale transient recurrent Rossby wave packets. Sub-seasonal clustering of precipitation events is significant at all grid-points over Europe; the proportion of extreme rainfall events that cluster in time ranges between 2% to 27%. The most relevant weather regime is the Atlantic Trough (corresponding to NAO+ with a southward shift of the jet) explaining most of the significant increase in clustering probability over Europe. The Greenland Blocking regime explains most of the clustering over the Iberian Peninsula. The Scandinavian Blocking regime is associated with a significant increase in clustering probability over the western Mediterranean, with a northwards shift in the signal to central Europe in summer