A Precipitation Classification Scheme for China with Special Consideration of Extreme Events

Drop size distributions observed by surface based disdrometer and vertical pointing micro rain radar during June and August 2018 extreme events over the coastal station Thumba are used to diagnose rain characteristics during intensive rainfall episodes. Rain integral parameters like Rainfall rates R, mass weighted median volume diameters Dm, reflectivity Z, drop size distributions (DSDs), and gamma DSD parameters were derived and compared between the micro rain radar and surface based disdrometer. Accumulated rainfall observed by disdrometer is in good agreement with the co-located tipping bucket measurements. Over a 4-day period (14 August 2018 to 17 August 2018), these extreme events generated 150 mm of rain. There exist a distinct DSD in two extreme rainfall events. Rainfall was characterized by a large number of small- to medium-sized raindrops (diameters smaller than 1.5 mm) resulting in small values of Z (40 dBZ), and $\mathrm{D}_{m} (\lt2.5$ mm). The disdrometer-derived Z–R relationships reflect how unusual the DSDs were during these extreme events. These data sets were stratified based on rainfall rate and results show that the extreme event DSD exhibit significant variation in the DSD compared to non-extreme events. The big drops are almost absent in extreme DSD, whereas the small and medium sized drops are larger in number than they are in non-extreme rain. The vertical profiles of DSD observed using MRR shows an interesting feature. The retrieved DSD profiles are divided into classified into (convective, transition and stratiform), based on classification scheme, to examine salient microphysical characteristics and the vertical variability of DSD in extreme events. DSD parameters estimated using gamma fit method revealed that these extreme events are dominated by small to medium size rain drops. The vertical extent of these extreme events is shallow in nature. From the observed vertical variation of DSD parameters and the median volume diameter various microphysical processes like drop sorting, coalescence, evaporation and breakup were assessed.

Extreme weather events and their resulting impacts threaten all levels of society. Climate change can amplify the frequency and intensity of associated hazards. One of the key-challenges for decision-makers in civil protection is adapting to the changing landscape of weather-induced impacts driven by climate change. It is therefore essential to assess and estimate the changing conditions for extreme weather events under climate change.This study investigates the changing landscape of regional extreme weather events in the Alpine region by utilizing weather circulation type classification through its relationship with weather-induced potential extreme events. Therein, large-scale weather types take the role of relevant precursors for regional extreme events. The local-scale potential impact events that are associated with prevailing weather types are derived by using percentile-based methods on high-resolution, gridded precipitation data. ERA5 mean sea level pressure and the classification scheme GWT ('Gross-Wetter-Typen') are used to derive 18 classes of cyclonic and anti-cyclonic weather types, representing a prevailing circulation on a daily basis for the whole Alpine region. Subsequently, unsupervised hierarchical clustering (Agglomerative clustering) is used to evaluate overlaps between cluster families in order to derive a subset of 'high-impact' weather types. The relationship between those weather types and local-scale extreme events is further characterized by analyzing percentile-based indicators from station data and high-resolution observational data. Finally, we extend our analysis by applying found relationships to state-of-the-art climate models from the Coupled Model Intercomparison Project 6 (CMIP6) to investigate the changing landscape for extreme weather events under different climate change scenarios.Our findings indicate that a subset of weather types, related to specific cyclonic circulation patterns, is mostly responsible as precursor for extreme precipitation events. Those patterns furthermore show increases in frequency under the scenario SSP3-7.0, that are consistent across the climate model ensemble. Associated changes of precipitation totals suggest increases in intensity, however these intensity changes are not as conclusive due to large inter-model spread.

Extreme weather events and associated natural hazards pose a significant global threat to all levels of society. It is scientific consensus that climate change contributes to an increasing frequency and intensity of these events. One of the key challenges for decision-makers in the field of civil protection is to deal with the changing landscape of weather-induced impact events, that are driven by climate change. Hence, assessing the current and changing conditions across spatiotemporal scales for extreme weather events under a changing climate is essential. This study explores the potential of utilizing weather circulation type classification through its correlation with observed weather-induced extreme events and their potential impacts on the local-scale. Thereby, high-impact weather types can be determined as a relevant background field, serving as a measure about the potential of severe weather hazards. We employ ERA5 reanalysis data as baseline meteorological input data to derive long-term and robust time series of weather types from mean sea level pressure that are relevant for the cross-border region of Austria and Italy. The classification scheme 'Gross-Wetter-Typen' (GWT) with 18 classes was used to assign each day a prevailing weather type class. The overlap between derived classes is further investigated by means of unsupervised clustering techniques, to evaluate clusters of groups across all GWT classes. Additional meteorological fields (e.g. equivalent potential temperature, geopotential height, precipitable water, ...) are validated on top of the GWT classes for further characterisation of extreme weather events. Days exhibiting extreme weather-induced potential impact events are derived via percentile methods applied to precipitation data from observational gridded datasets (Enigl et al., 2024, EGU24-10058). Finally, we extend our analysis with an evaluation of potential changes by applying found relationships to state-of-the-art climate model data from the Coupled Model Intercomparison Project 6 (CMIP6) to investigate the changing landscape of potential weather extremes. Our findings indicate that a specific subset of large-scale weather circulation patterns acts as a crucial precursor to high-impact weather extremes. Furthermore, considering the climate change scenario SSP3-7.0, the frequency and associated precipitation totals linked to these weather patterns exhibit an increase. This suggests a potential rise in both the frequency and intensity of extreme weather events and their corresponding impacts if emissions continue to increase.

A significant concern for public health and visibility is airborne particulate matter, especially during extreme events. Of most relevance for health, air quality, and climate is the role of fine aerosol particles, specifically particulate matter with aerodynamic diameters less than or equal to 2.5 micrometers (PM2.5). The purpose of this study was to examine PM2.5 extreme events between 1989 and 2018 at Mesa Verde, Colorado using Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring data. Extreme events were identified as those with PM2.5 on a given day exceeding the 90th percentile value for that given month. We examine the weekly, monthly, and interannual trends in the number of extreme events at Mesa Verde, in addition to identifying the sources of the extreme events with the aid of the Navy Aerosol Analysis and Prediction (NAAPS) aerosol model. Four sources were used in the classification scheme: Asian dust, non-Asian dust, smoke, and “other”. Our results show that extreme PM2.5 events in the spring are driven mostly by the dust categories, whereas summertime events are influenced largely by smoke. The colder winter months have more influence from “other” sources that are thought to be largely anthropogenic in nature. No weekly cycle was observed for the number of events due to each source; however, interannual analysis shows that the relative amount of dust and smoke events compared to “other” events have increased in the last decade, especially smoke since 2008. The results of this work indicate that, to minimize and mitigate the effects of extreme PM2.5 events in the southwestern Colorado area, it is important to focus mainly on smoke and dust forecasting in the spring and summer months. Wintertime extreme events may be easier to regulate as they derive more from anthropogenic pollutants accumulating in shallow boundary layers in stagnant conditions.

We investigate the future changes in the climate zone and six extreme temperature indices in Korea, using the 20-km high-resolution atmospheric general circulation model (MRI-AGCM3.1S). The Trewartha and Koppen climate classification schemes are applied, and four summer-based extreme temperature indices (i.e., summer days, tropical nights, growing degree days, and cooling degree days (CDD) and two winter-based indices (frost days and heating degree days (HDD) are analyzed. To represent significantly the change in threshold indices, the monthly mean bias is corrected in model. The model result reasonably captures the temporal and spatial distribution of the present-day extreme temperatures associated with topography. It was found that in the future climate, the area of the subtropical climate zone in Korea expands northward and increases by 21% under the Trewartha classification scheme and by 35% under the Koppen classification scheme. The spatial change in extreme climate indices is significantly modulated by geographical characteristics in relation to land-ocean thermal inertia and topographical effects. The change is manifested more in coastal regions than in inland regions, except for that in summer days and HDD. Regions with higher indices in the present climate tend to reveal a larger increase in the future climate. The summer-based indices display an increasing trend, while the winter-based indices show a decreasing trend. The most significant increase is in tropical nights (+452%), whereas the most significant decrease is in HDD (−25%). As an important indicator of energy-saving applications, the changes in HDD and CDD are compared in terms of the frequency and intensity. The future changes in CDD reveal a higher frequency but a lower temperature than those in HDD. The more frequent changes in CDD may be due to a higher and less dispersed occurrence probability of extreme temperatures during the warm season. The greater increase in extreme temperature events during the summer season remains an important implication of projecting future changes in extreme climate events.

Suspended sediment is a natural component of rivers, but extreme concentrations can have substantial impacts on water quality, aquatic ecosystems, floods, hydropower production, etc. In mountain environments, sediment availability and transport are modified by a changing climate through changes in erosive precipitation, snow cover and glacier retreat. As it is well known that the majority of suspended sediment load is transported during a few extreme events, it is essential to better understand the spatial and temporal dynamics of suspended sediment concentration (SSC) during extreme events, now and in the future. To date, most studies have attempted to predict SSC dynamics based on catchment characteristics and hydroclimatic factors, however, mostly for individual catchments or specific events, which limits our understanding of SSC dynamics at larger spatial scales. This research aims to identify the main factors that influence the spatio-temporal variability of SSC and the occurrence of SSC extremes in the Alps.We use 10 years of observed subdaily SSC data from 38 gauging stations in Switzerland and Austria to study the temporal and spatial variability of SSC. First, we examine spatial patterns in the annual SSC regime. We identify three main types of annual SSC regimes after applying hierarchical clustering based on regime differences in magnitude, timing and shape. Our results show that snow and ice significantly influence the annual SSC regime in small mountainous catchments, in contrast to low-elevation and larger catchments where rainfall is more important. The presence of glaciers and the timing and amount of snowmelt play a crucial role in shaping the annual SSC regime and determining when peak SSC occurs, whereas geological and soil characteristics and the annual runoff regime have a smaller influence.Second, we move from the annual to the event scale at a subdaily time step by analyzing extreme events. We introduce a new classification scheme to categorize the 2,398 extreme SSC events into nine distinct types, based on their dominant transport processes. Our study reveals that rainfall is the main cause of these extremes, responsible for 80% of the events. However, in high-altitude and partially glaciated catchments, up to 40% of the events are driven by snow and glacial melt. Events triggered by both glacial melt and intense rainfall produce the highest sediment concentrations and area-specific yields. These insights into the large-scale and catchment-specific variations in SSC and their extremes are valuable for improving our understanding of the complex hydrology-sediment system response.

Nonvascular photoautotrophs (NVP), including bryophytes, lichens, terrestrial algae, and cyanobacteria, are increasingly recognized as being essential to ecosystem functioning in many regions of the world. Current research suggests that climate change may pose a substantial threat to NVP, but the extent to which this will affect the associated ecosystem functions and services is highly uncertain. Here, we propose a research agenda to address this urgent question, focusing on physiological and ecological processes that link NVP to ecosystem functions while also taking into account the substantial taxonomic diversity across multiple ecosystem types. Accordingly, we developed a new categorization scheme, based on microclimatic gradients, which simplifies the high physiological and morphological diversity of NVP and world-wide distribution with respect to several broad habitat types. We found that habitat-specific ecosystem functions of NVP will likely be substantially affected by climate change, and more quantitative process understanding is required on (1) potential for acclimation, (2) response to elevated CO2 , (3) role of the microbiome, and (4) feedback to (micro)climate. We suggest an integrative approach of innovative, multimethod laboratory and field experiments and ecophysiological modelling, for which sustained scientific collaboration on NVP research will be essential.

The last twenty years has seen the birth and subsequent evolution of a fundamental new idea in nonlinear wave research: Rogue waves, freak waves or extreme events in the wave field dynamics can often be classified as coherent structure solutions of the requisite nonlinear partial differential wave equations (PDEs). Since a large number of generic nonlinear PDEs occur across many branches of physics, the approach is widely applicable to many fields including the dynamics of ocean surface waves, internal waves, plasma waves, acoustic waves, nonlinear optics, solid state physics, geophysical fluid dynamics and turbulence (vortex dynamics and nonlinear waves), just to name a few. The first goal of this paper is to give a classification scheme for solutions of this type using the inverse scattering transform (IST) with periodic boundary conditions. In this context the methods of algebraic geometry give the solutions of particular PDEs in terms of Riemann theta functions. In the classification scheme the Riemann spectrum fully defines the coherent structure solutions and their mutual nonlinear interactions. I discuss three methods for determining the Riemann spectrum: (1) algebraic-geometric loop integrals, (2) Schottky uniformization and (3) the Nakamura-Boyd approach. I give an overview of several nonlinear wave equations and graph some of their coherent structure solutions using theta functions. The second goal is to discuss how theta functions can be used for developing data analysis (nonlinear Fourier) algorithms; nonlinear filtering techniques allow for the extraction of coherent structures from time series. The third goal is to address hyperfast numerical models of nonlinear wave equations (which are thousands of times faster than traditional spectral methods).

Non‐coastal flood events in the UK are usually associated with extreme rainfall and can last from minutes to weeks. Efficient management and mitigation of flood risk requires accurate and reliable precipitation forecasts as inputs to flood risk models. We constructed an archive of British Rainfall data from 1866 to the present day to improve our understanding of historical extreme rainfall events. The relationship between record rainfall and flooding is nonlinear and uncertain, implying that probabilistic forecasts of rainfall are required. We developed an objective classification scheme of extreme rainfall events consisting of eight types, analysed extreme rainfall events and produced probabilistic forecasts by combining statistical techniques with the outputs of ensemble predictions from a numerical weather predictions model. Copyright © 2010 Royal Meteorological Society

<p>Research on heat waves and extreme events in general is highly motivated by their impacts on human life and the economy. Therefore, the focus is on near surface variables and less research has been done on the state of the lower atmosphere as a whole. In a study of the mega heat waves of 2003 in France and 2010 in Russia, Miralles et al. (2014) investigate which factors have to come together to enable such extremes. One interesting finding is the gradual increase in planetary boundary layer height during those events. Also, their simulations display a correlation between mean potential temperature in the lower atmosphere and the boundary layer height. For these reasons, we believe that a systematic analysis of the planetary boundary layer during heat waves may provide valuable insights into their formation and persistence. We investigate whether these features are common traits of European heat waves in general. To this end, we apply a classification and regression scheme to vertical profiles taken from COSMO-REA6 data for the summers of 2014 to 2018. The reanalysis data is also used to identify heat waves, such that a comparison of boundary layers during normal and extreme conditions is possible. We analyse the distributions of planetary boundary layer heights for every grid point to check for regional differences. For validation, we make a comparison to radio soundings taken from the DWD Open Data service. The results of our work could possibly be used to improve the discriminability of different severity levels of heat waves or to formulate a heat wave measure that is not based solely on surface variables.</p>

Although impacts of heavy rain on buildings in urban areas are often extensive, the public usually underestimates the negative consequences of that environmental risk compared e.g. to storm or hail events. However, the intensification of extreme weather events due to climate change as well as the rising physical vulnerability of assets are going to trigger the increase of impacts on the built environment. Heavy rain events are often highly localised that makes it difficult to estimate their probability and magnitude accurately. High intensity rain affects buildings both directly as well as indirectly. First, engineering surveys have already proven the broad variety and high physical vulnerability of building constructions that are directly and frequently affected by invading water during heavy rainfall events: flat roofs, roof terraces and balconies, connections between steep roofs and other building parts, soil-covered slabs of underground parking, soil-contacted basement walls and bottom plates as well as windows and external doors. It became evident that most damage is avoidable if exposed constructions become more resilient. Nevertheless, any adaptations require expertise on the demanding damage processes in order to explore deficiencies and to reduce physical vulnerability of building constructions exposed to heavy rainfall. In response to that challenge, the paper describes an engineering approach for the systematic classification of physical vulnerability criteria based on empirical research. A developed classification scheme allows the ex-ante examination of typical failure modes and the evaluation of negative consequences of heavy rainfall at individual building level. The topic is of high relevance, because the classification scheme may act as a capable tool for the prospective planning of adaptations towards more resilient buildings. Second, heavy rain may result in urban pluvial flooding due to sewer overflow that cause severe damage to buildings. A comprehensive study of the impacts and the consequences in Dresden (Germany), presented in the paper, revealed that the potential risks of flooding from sewers due to hydraulic overload can be estimated on building scale using the model approach IVART (Integrated Spatial Vulnerability and Risk Assessment Tool). Modelling results provide the basis to quantify the effectiveness and efficiency of flood resilience technologies.

The morphology of rivers is the result of complex relationships between sediment supply, hydrological regime, geological and vegetation conditions, and human disturbances. Numerous river channel classifications have been developed in different geographical contexts over the last few decades. Nevertheless, channel characterization in active volcanic environments under humid tropical conditions is almost completely absent in geomorphological research.We carried out a detailed characterization of river morphology in an active volcanic complex, the Irazú-Turrialba, characterized by extreme rainfall conditions (>7000 mm/yr.), frequent volcanic eruptions (>10/100 yrs.), high-magnitude earthquakes (>Mw5), and dense tropical vegetation. This volcanic complex is located in the central volcanic chain of Costa Rica and at its foothills, most of the country’s population and economic activities (>60%) can be found. A total length of 166.5km of the river network was mapped to understand the occurrence and distribution of channel morphologies in this high-energy and dynamic environment.Using remote sensing techniques (RGB and multispectral satellite imagery, digital terrain models, spaceborne imaging radar products, and unmanned aerial vehicles), 74 river reaches located on four rivers within the volcanic complex were analyzed using 13 morphometric variables, including channel slope, channel width, confinement index, braided index, and others. Then, channel morphology for each reach was defined referring to four internationally recognized classification schemes, examining how such schemes adapt to the active tropical volcanoes. Further on, to characterize with more detail the river reaches and the volcanic complex, a morphometric index was developed to identify sediment sources and erosion-sedimentation areas. The morphometric index relies on vegetation height, terrain roughness index, slope degree, and average precipitationResults allow a novel understanding of river morphology and processes in active volcanic environments under humid tropical conditions. The main outcomes are:  (i) channel morphology in this volcanic complex is dominated by steep, confined and coarse sediment river reaches; (ii) there is a strong difference in channel morphology and processes between the north and south parts of the volcanic complex due to climatological, geological, and tectonic aspects; (iii) established classification schemes partially failed when applied in this specific environment which is characterized by very high energy and a large amount of sediments; (iv) the morphometric index developed to analyze the volcanic complex and river reaches turned out to be useful for mapping sediment sources and detecting landforms such as lava flows, debris avalanches, landslides, and volcanic cones. Overall, this study provides novel insights about river morphology under highly dynamic and active volcanoes with extreme rainfall events, resulting in steep, confined, coarse sediment channel morphologies that are quite uncommon in other environments (e.g., boulder and cobble-bed braided rivers).

Arid environments are extremely diverse in terms of their land forms, soils, fauna, flora, water balances, and human activities. Because of this diversity, no practical definition of arid environments can be derived. However, the one binding element to all arid regions is aridity. Aridity is usually expressed as a function of rainfall and temperature. A useful representation of aridity is the following climatic aridity index: p/ETP, where P = precipitation; ETP = potential evapotranspiration, calculated by method of Penman, taking into account atmospheric humidity, solar radiation, and wind. Three arid zones can be delineated by this index: namely, hyper-arid, arid and semi-arid. Of the total land area of the world, the hyper-arid zone covers 4.2 percent, the arid zone 14.6 percent, and the semiarid zone 12.2 percent. Therefore, almost one-third of the total area of the world is arid land. Arid climate, is a climate that does not meet the criteria to be classified as a polar climate, and in which precipitation is too low to sustain any vegetation at all, or at most a very scanty scrub. An area that features this climate usually (but not always) experiences less than 250 mm (10 inches) per year of precipitation and in some years may experience no precipitation at all. In some instances an area may experience more than 250 mm of precipitation annually, but is still considered a desert climate because the region loses more water via evapotranspiration than falls as precipitation. Although different classification schemes and maps differ in their details, there is a general agreement about the fact that large areas of the Earth are arid. These include the hot deserts located broadly in subtropical regions, where the accumulation of water is largely prevented by either low precipitations, or high evaporation, or both. Abiotic are associated with non-living causal factors such as weather, soils, chemicals, mechanical injuries, cultural practices and, in some cases, a genetic predisposition within the plant itself. Abiotic may be caused by a single extreme environmental event such as one night of severe cold following a warm spell or by a complex of interrelated factors or events. A biotic plant problems are sometimes termed disorders that reflects the fact that the injury or symptom, such as reduced growth, is ultimately due to the cumulative effects of the causal factors on the physiological processes necessary for plant growth and development (Schutzki & Cregg, 2007). Abiotic stresses, such as drought, salinity, extreme temperatures, chemical toxicity and oxidative stress are serious threats to agriculture and the natural status of the environment. Increased salinization of arable land is expected to have devastating global effects, resulting

Climate change is increasing the frequency and intensity of Extreme Weather Events (EWEs), which causes widespread disruption globally. As these events intensify, the need for better hazard identification becomes critical. While machine learning (ML) is already enhancing forecasts, and has huge potential for identifying future hazards. To unlock this potential, we need comprehensive training datasets of historic EWEs that integrate and harmonize diverse datasets, account for data collection discrepancies, and address gaps in temporal and spatial records.This presentation initially discusses the development of an Aotearoa New Zealand EWE database from 1996 to 2021, which currently includes occurrence data derived from subjective classifications from the national weather service, research organizations, and insurance information. Careful analysis of that database and ancillary reanalyses output can successfully characterise rainfall extreme intensities by deriving duration, peak rainfall, and total accumulation.Building on that work, this presentation will discuss the development and testing of a methodology to integrate extreme weather event (EWE) occurrence, intensity, and storm track data into a unified database. By processing this combined dataset, we aim to harmonise data from the disparate sources and improve data accuracy and reliability, making it robust for future ML analyses. We also use our experience of applying ML classification schemes in climate research to provide proof-of-concept applications demonstrating the value of our harmonisation methodology.

Simulation of an extreme dust episode using WRF-CHEM based on optimal ensemble approach