bluuebunny/crossref_metadata_2025 huggingface dataset:
Sequential impact chains - a tool to explore compound and cascading impacts of weather extremes in the Alpine regions in the framework of the X-RISK-CC project
Authors: Crespi Alice , Steger Stefan , Bozzoli Laura , Zebisch Marc , Maines Elena
Published: July 2023

The assessment of risks related to weather and climate extremes in a specific area requires the understanding of the complex cause-effect chains that include all relevant factors and processes contributing to risk. In recent years, the Alps experienced intense meteorological events that caused compound impacts and domino effects with long-lasting consequences on the socio-economic systems. These events highlighted the need for an improved understanding of impacts of extreme weather events and of risk management adaptation in order to meet the challenges of the future.To this aim, a conceptual impact-chain framework for the identification of predisposing and triggering conditions, hazards and subsequent impacts occurring at multiple time scales represents a suitable tool for setting up proper impact models and identifying risk management gaps. In this context, the visualization of the temporal sequence of involved processes is crucial. Impact chains are well-known conceptual models used for climate risk assessment, especially in the context of adaptation planning. They represent and interlink all climate risk components and are built in a participatory manner with stakeholders and experts integrating local data and knowledge of the past. The standard structure of impact chains reports impacts that can be caused by one or more climate or climate change factors, in present or future conditions, while the temporal evolution of a specific process is not explicitly reported.In the framework of the Interreg Alpine Space X-RISK-CC project, the impact chain structure has been extended to conceptualize consequences of past extreme events by visualizing the temporal sequence of weather drivers inducing multiple compound (i.e. occurring at the same time) and cascading (i.e. domino effects) hazards and impacts. Weather drivers and non-climatic predisposing elements are linked to natural hazards which, in turn, are connected to subsequent socio-economic impacts acting on different time scales. Exposed sectors and vulnerability related to risk management are also linked to impacts and help to recognize the main management gaps and entry points for improvements.These sequential impact chains were applied to conceptualize all targeted events in the Alps and used as basis for local workshops with stakeholders and experts focusing on the lesson learnt of risk preparedness and management for the examined events. This contribution presents the novel sequential impact chain framework and discusses the results of its application in the project areas.The research leading to these results has received funding from Interreg Alpine Space Program 2021-27 under the project number ASP0100101, “How to adapt to changing weather eXtremes and associated compound and cascading RISKs in the context of Climate Change” (X-RISK-CC).
2m Temperature Downscaling with Dynamic Lapse Rates
Authors: Höhlein Kevin , Hewson Tim
Published: July 2023

Application of a global downscaling method for 2m temperature is investigated, using forecasts of the ECMWF Integrated Forecast System. We work with a spatial resolution of 9km, which is destined to become the operational medium range ensemble resolution at ECMWF from June 2023. The downscaling technique is broadly based on Sheridan et al (Meteorological Applications, 2010), in which regression between model topographic elevation ("coarse grid") and model 2m temperature at an instant, in the vicinity of a gridbox in question, is the basis for lapse rate assignment for that gridbox. The rate so-derived is used to adjust temperatures onto a fine grid orography (e.g. 1km) for the said gridbox. Wherever, on the fine grid, elevation is out of the range of the coarse grid model topographic elevations in the vicinity then we use either extrapolation, for lower points, or model level data, for higher points. This overall approach should innately adapt to many meteorological scenarios - unstable, frontal, inversions, etc. - delivering much more weather-dependant lapse rate assignments for particular locations. The 2m temperatures so computed should verify much better than when using just a fixed rate assumption - e.g. -6.5K/km - as ECMWF does currently for it's very widely used meteogram products. The key attractions of this approach are: (i) the potential for large skill gains, (ii) that we exploit model "situational awareness" by indirectly incorporating the model's in-built physical processes, (iii) no training period or training data is needed, (iv) there is complete transparency for users because the lapse rate derivation is clearcut, and one can store lapse rate values on the grid in question.
In this presentation we will describe the approach and discuss the results of early investigations, using unstable and stable examples. The stronger and weaker aspects will be highlighted, discussing how to deal with coastal effects and addressing the all-important questions of search radius for defining 'the vicinity', and the regression format. One innate weaker point is the absence of any bias correction; a proposal for how this could also be built in in future, using weather-type dependant gridbox-scale bias correction from ECMWF's ecPoint post-processing method, for 2m temperature, will also be touched on.
Modelling the cooling effect of Nature-based Solutions in densely built-up areas for a case study Vienna, Austria
Authors: Zuvela-Aloise Maja , Hahn Claudia , Bügelmayer-Blaschek Marianne , Schneider Martin
Published: July 2023

Reducing the Urban Heat Island effect and local implementation of climate adaptation strategies, especially Nature-based Solutions (NbS), are one of the key aspects of tackling climate change impacts in urban areas. Increasing green infrastructure of open spaces and buildings, such as implementing green roofs, unsealing of paved surfaces and planting vegetation, particularly increasing the number of street trees and park areas, is considered to have a cooling effect and can help to reduce extreme heat. In this study we examine the possibility to implement different NbS in a densely built environment and evaluate its cooling performance. The evaluation is done on a micro-scale using the ENVI-met model for a selected area in the City of Vienna and the cooling effect is further analysed in case these measures are implemented on a city-scale by using the MUKLIMO_3 urban climate model. The climate adaptation scenarios with different NbS included: 1) reduction of paved surfaces, 2) increase in surface albedo of paved surfaces, 3) implementation of green roofs, 4) new parks including trees and low vegetation and 5) a combination of NbS.  The extent of NbS was quantified for the selected area and proportionally scaled for all densely built areas in the city. Simulations were performed for a representative clear-sky heat day for each NbS scenario as well as for the combination of all NbS. The highest cooling effect is found for a combination of all NbS. The results show similar cooling intensity both in microscale and city-scale simulations. For a realistic proportion of NbS implemented in the models, a moderate cooling effect of about 1-2°C can be achieved. In case of city-scale simulations, the maximum difference of about 1.4°C is found in the densely-built areas where the measures were applied. Minor cooling effect can be detected in the surrounding areas as well. Additional modelling simulations with variable parameters describing land-use properties were conducted to estimate the uncertainties in the modelling results. Based on the different representation of land use characteristics in the model, variations in the spatial pattern of heat load can be found. The cooling effect also varies spatially, depending on the local implementation of NbS. However, the results show similar cooling efficiency of NbS with minor influence of the background data and the method applied.
Connecting physical and socio-economic spaces for urban agent-based modelling
Authors: Hertwig Denise , McGrory Megan , Paskin Matthew , Lo Piano Samuele , Liu Yiqing , Ramirez-Mendiola Jose Luis , Smith Stefán T. , Grimmond Sue
Published: July 2023

Holistic approaches for urban modelling need to simultaneously consider the urban form, i.e., the physical characteristics of a city (e.g., urban surfaces, building morphology and fabric), urban function driven by economic, societal, cultural activities, and human behaviour. DAVE (Dynamic Anthropogenic actiVities and feedback to Emissions) is an agent-based modelling system that connects physical and socio-economic urban spaces (McGrory et al. 2023 – this meeting) to model anthropogenic heat emissions and assess human exposure to heat stress and air pollution. In this approach, humans are modelled as active components of the urban system and human exposure to and impact on urban environmental stressors are dynamic. DAVE is intended to dynamically respond to changes of multiple drivers (e.g., urban background climate, physical environment, socio-economic factors). The spatio-temporal complexity and variability of urban form, function, human behaviour and climate puts high demands on the input data needed for DAVE.This poster presents a discussion on the approach taken to mine, process, connect and harmonise geo-spatial and socio-economic data sources for city-scale simulations based on the example of London, UK. This includes the generation of suitable building archetypes for the building-energy modelling (BEM) component of DAVE, encompassing assessments of building function, morphology and household size / occupancy. Using time-use surveys (TUS), domestic occupancy and appliance use profiles are generated for the BEM. Movement schedules of citizens of different demographic groups between different socio-economic areas of activity (e.g., home, work, school, shops, restaurants) are also based on TUS information and statistically invoked in the model. Non-domestic / commercial areas of activity and associated building archetypes are characterised by their baseline energy-demand. Available travel routes for different modes within the city are informed by public transport stops and timetables as well as the road network density and speed limits. DAVE is coupled to a land-surface model, for which landcover and morphology information is provided.Funding supporting this work includes ERC urbisphere, NERC APEx, and EPSRC CREDS.
Deep learning-based classification of atmospheric circulation types over Europe in a CMIP6 Large Ensemble
Authors: Mittermeier Magdalena , Weigert Maximilian , Rügamer David , Küchenhoff Helmut , Ludwig Ralf
Published: July 2023

The 29 circulation types by Hess & Brezowsky, called “Großwetterlagen”, are one of the most established classification schemes of the large-scale atmospheric circulation influencing Europe. They are widely used in order to assess linkages between atmospheric forcing and surface conditions e.g. extreme events like floods or heat waves. Because of the connection between driving circulation type and extreme event, it is of high interest to understand future changes in the occurrence of circulation types in the context of climate change. Even though the “Großwetterlagen” have been commonly used in conjunction with historical data, only very few studies examine future trends in the frequency distribution of these circulation types using climate models. Potential reasons for this, are the lack of an open-source classification method of the “Großwetterlagen” and their high range of internal variability. Due to the dynamic nature of the large-scale atmospheric circulation in the mid-latitudes, it is highly relevant to consider the range of internal variability when studying future changes in circulation patterns and to separate the climate change signal from noise.
We have therefore developed an open-source, automated method for classifying the “Großwetterlagen” using deep learning. We apply this method to the SMHI-LE, an initial-condition single-model large ensemble of the CMIP6 generation with 50 members on a daily resolution. A convolutional neural network has been trained to classify the circulation patterns using the atmospheric variables sea level pressure and geopotential height at 500 hPa at 5° resolution. The convolutional neural network is trained for this supervised classification task with a long-term historical record of the “Großwetterlagen” covering the 20th century. It is derived from a subjective catalog of the German Weather Service with daily class affiliations and atmospheric variables from ECMWFs’ reanalysis dataset of the 20th century, ERA-20C.
We present the challenges of the deep learning-based classification of subjectively defined circulation types and quantify the uncertainty range intrinsic to deep neural networks using deep ensembles. We furthermore demonstrate the benefits of this automated classification of “Großwetterlagen” with respect to the application to large datasets of climate model ensembles. Our results show the ensemble-averaged future trends in the occurrence of “Großwetterlagen” and the range of internal variability, including the signal-to-noise ratio, for the CMIP6 SMHI-LE under the SSP37.0 scenario.
Daily global aerosol forecasts with ICON-ART to reduce forecast errors for photovoltaic power generation caused by high loadings of aerosols
Authors: Filipitsch Florian , Bachmann Vanessa , Porz Nikolas , Seifert Axel , Förstner Jochen , Wagner Annette , Hoshyaripour Ali , Rohde Anika , Kumar Pankaj , Menken Julia , Vogel Heike , Doppler Lionel , Mattis Ina , Wacker Stefan
Published: July 2023

The shift towards renewable energy sources such as wind and solar poses major challenges for the entire energy sector. In order to ensure a secure energy supply in the future, accurate weather forecasts are becoming increasingly important. The majority of the operational numerical weather prediction models do not consider prognostic aerosol and aerosol-cloud radiation interaction. This repeatedly leads to erroneous weather forecasts during special weather conditions that involve aerosols, such as strong Saharan dust episodes or biomass burning events.
The project PermaStrom (May 2020 – April 2024), namely “Photovoltaic power prediction to better manage the influence of the atmospheric aerosol on the electricity grids in Germany and Europe” (translation from the German title), follows up on the developments of the predecessor project PerduS (March 2016 – February 2020), where global and refined regional forecasts of dust aerosol were established in a pre-operational version at DWD. Within PermaStrom, the model system is extended with more aerosol species (soot from biomass burning and sea salt) to further improve photovoltaic yield forecasts. Additionally, the treatment and parameterization of aerosol-cloud interactions are studied in a high-resolution LAM (limited area mode) setup along with ICON-ART ensemble predictions. The model developments are continuously validated against various ground- and satellite-based measurements of aerosol concentration and distribution, clouds and solar radiation. The dust forecasts are planned to become operational at DWD in its own data assimilation cycle in winter 2023/2024 with a global 26 km grid and a two-way 13 km nest covering Europe, North Africa and the North Atlantic.
This contribution will provide an overview of the research and model development activities within the PermaStrom project. Additionally, we will show validation results for several aerosol events with direct attention to photovoltaic power production in Germany.
Characteristics of systematic biases in the winter Northern Hemisphere of the Korean Integrated Model
Authors: Han Bo-Reum , Choi Hyun-Joo , Park Jeong-Hyun , Cheong Seong-Hoon
Published: July 2023

The Korean Integrated Model (KIM), operated by Korean Meteorological Administration (KMA) since April 2020, has been improved through continuous updates on model physical processes and data assimilation. As the model version was updated, performance was substantially improved during the summer season, but the improvement was not clear in the winter. Therefore, in this study, we examine a dropout case degrading performance of KIM in the northern hemisphere on 2022 winter in order to investigate the causes of systematic biases related with the KIM performance during the winter season. The worst performance in terms of 5-day forecast of 500 hPa geopotential height occurred at 12UTC on the 20th of January 2022 with cold biases in the lower Arctic and Siberia regions. Especially, the cold bias in Siberia is strong at the surface and is amplified as the forecast time increases from the initial time. The surface temperature cold bias in Siberia appears robustly in winter as well as this case. This is contrast with the summer season with warm bias, which mostly seems to be due to the underestimated soil moisture and surface latent heat flux. In winter, the latent heat and soil moisture seem to have little effect on the cold bias, and it is analyzed that lower tropospheric cloud may one of main factors that affect the cold bias in Siberia. The cloud and snow contents associated with the lower tropospheric cloud in Siberia are less compared with the ERA5 reanalysis, which in turn contributes to the underestimation in surface downward longwave flux through the cloud-radiation interaction. The reduction in radiative surface heating seems to be one of main causes of the lower tropospheric cold bias in Siberia.
Return values for extreme precipitation in Norway - a comparison of estimates from a new approach combining ensemble data and gridded observations to PMP values
Authors: Ødemark Karianne , Tveito Ole Einar Ellingbø
Published: July 2023

The occurrence of extreme precipitation events causing surface water excess and flooding is becoming an increasing societal expense due to the rise in precipitation levels. It is therefore crucial to understand and get better knowledge about extreme precipitation events to predict their likelihood and frequency, as well as to estimate design values for critical infrastructure and constructions. 
Analysis of extreme precipitation events requires long timeseries, which can be challenging using conventional or relatively short observational data records. To increase the event sample size we have applied a data set from the numerical seasonal prediction system SEAS5 at ECMWF. The data were fitted to a GEV-distribution and compared to an equivalent GEV-distribution for the gridded observational data set SeNorge. A method to estimate return values by combining the two datasets, taking advantage of the large sample size from SEAS5 and the spatial distribution from SeNorge is proposed. By using a normalized "growth curve" from both data sets and the location parameter from SeNorge the correct level of the frequency curve for short return periods is determined. An additional correction to the scale parameter was employed to ensure appropriate levels of the curve for return values at longer return periods, based on a spatial adjustment factor. 
The resulting return value estimates are considered to be more robust than previous calculated estimates, due to the inherited small confidence interval from SEAS5. We compare the new estimates of long return period values with existing values for PMP (Probable Maximum Precipitation), where we also evaluate the spatial variability of the traditional method for PMP values, which are point estimates, to the new spatially consistent approach. 
High-resolution air temperature forecast for urban heat wave management
Authors: Bader Nico , Schlögl Sebastian , Gutbrod Karl
Published: July 2023

Urban areas are becoming more vulnerable with their growth and an increasing number of heatwaves caused by anthropogenic climate change. Differences in the surface structure, such as between green spaces and sealed surfaces, cause differences in surface energy budget due to the different heat storage capacity, resulting in a high inner-city air temperature variability.
Initial conditions of NWP models do not accurately represent air temperatures in urban areas, since official (WMO) ground weather stations are typically located in city outskirts where air temperatures are lower than in the city center. Hence, the urban heat island effect is not fully resolved in NWP models which results in an underestimation of urban air temperatures. Since the urban air temperature variability occurs on a micro-scale, state-of-the art NWP models, with resolution in the order of a few km, are not able to resolve the air temperature field.
This study focuses on the development of the meteoblue city-climate model (mCCM), a dynamic statistical downscaling model which was developed to resolve the urban heat island effect and the small-scale air temperature variability in urban areas.
The mCCM is based on a high-resolution model domain with 10 m horizontal resolution and trained on air temperature data from measurement networks. These networks are operated by the municipality or based on crowdsourcing and help understanding the air temperature variability and dynamics in an urban environment. To resolve the differences in the surface energy budget, surface texture parameters are derived from the polar-orbiting, high-resolution satellites Sentinel-2 and Landsat 8. Further improvement is achieved with the parameterization of the coefficients of the statistical model, which depend on the prevailing meteorological conditions. The trained model is then fully driven by meso-scale NWP models and thus independent of any air temperature measurements, which allows a direct forecast of high-resolution air temperatures in urban environments.
Through statistical downscaling, the mCCM can resolve the urban heat island effect and the inner-city air temperature variability, reducing the bias to almost 0 K. Implementing the parameterization, the model considers dynamic processes which makes the model sensitive to different meteorological conditions and further reduces the hourly MAE by at least 25 % compared to NWP models.
The mCCM allows more reliable and location-specific air temperature forecasts in cities for the upcoming six days for areas where vulnerable groups live, such as hospitals, kindergartens, or schools. The approach helps decision-makers to improve the heat wave management in their cities and allows data-driven decisions.
Long range impact-based forecasts for drought early warning in Australia
Authors: Schepen Andrew , Gaydon Don , Hughes Neal , Bennett James
Published: July 2023

Early warning of drought is critical for national water security and farm business outcomes. The Drought Early Warning Service (DEWS) project is developing indicators to measure and forecast the extent and severity of drought impacts in the Australian water and agricultural sectors. Through a multi-disciplinary approach, long-range climate forecasts are combined with agricultural, hydrological and agro-economic models to generate impact-based drought indicators, translating climate data into specific impacts such as crop yields, pasture growth, farm business outcomes and inflows.
In this work, we develop a high-resolution spatial forecasting system to generate forecasts of rainfall, temperature, solar radiation, vapour pressure and evaporation on a 5km grid across Australia, at a daily time step out to 18 months ahead. Forecasts are derived from the Bureau of Meteorology’s ACCESS-S2 climate model. Forecast calibration and downscaling are implemented using Bayesian joint probability modelling and an empirical disaggregation approach, which seamlessly extends forecasts beyond the 7-month range of the climate model.  The ensemble forecasts are targeted at multiple observational datasets to drive a suite of models: APSIM, AussieGrass, FarmPredict and FoGSS.
The outputs of the models become indicators that will be presented as percentiles for a defined historical reference period. The definition of an appropriate reference period is a challenging problem given significant changes in both temperature and rainfall patterns across Australia over the last century. Drought indicators will be published online via the prototype Climate Services for Agriculture (CSA) platform currently under development. We discuss the impacts of the forecasting system and adoption for other use cases such as hydropower, which require long-range forecasts of inflows to assess sustainability.
From drizzle to downpour: investigating the statistical and dynamical properties of raindrop size distributions in the Netherlands using a network of co-located disdrometers and micro-rain radars
Authors: Schleiss Marc , Mackenzie Robert , Castro Gonçalves Andre , Unal Christine
Published: July 2023

The Ruisdael Observatory (https://ruisdael-observatory.nl/) is a network of advanced sensors and high-resolution models aimed at improving the accuracy of climate, weather, and air quality models at the regional scale in the Netherlands. Within Ruisdael, TU Delft oversees a broad network of over 30 sensors, ranging from basic weather stations to cutting-edge disdrometers, radiometers, micro-rain radars, X-band radars, and cloud radars.
In this presentation, we focus on analyzing the statistical and dynamic properties of raindrop size distributions recorded by our network of seven co-located optical disdrometers and micro-rain radars in the Rotterdam-Delft area. Our analysis covers a range of precipitation intensities, from drizzle to heavy convective rain exceeding 140 mm/h. We document some of the most intriguing cases we've encountered during the first three years of operation and perform case studies to understand the temporal variability of raindrop sizes, their moments, scaling properties, and vertical evolution from the melting layer down to the ground. We also investigate whether temperature has any notable influence on the shapes, characteristic sizes, and concentrations of the raindrop size distributions, which is important for understanding future rainfall extremes. 
Finally, we discuss some of our future plans for the observatory, including efforts to improve high-resolution precipitation measurements in the Netherlands. By providing a better understanding of the statistical and dynamic properties of raindrop size distributions, we can enhance our ability to forecast precipitation, mitigate the impacts of heavy rain events, and improve air quality predictions. The Ruisdael Observatory is a crucial component of these efforts and represents a significant step forward in advancing our understanding of atmospheric processes in the Netherlands.
Wind Drought Index: a proposal to assess low wind speed events over the Iberian Peninsula
Authors: Andrés-Martín Miguel , Azorín-Molina César , Vicente-Serrano Sergio M. , Bedoya-Valestt Shalenys , Utrabo-Carazo Eduardo , Plaza Martín Nuria P.
Published: July 2023

Near-surface wind speed (SWS) has been the forgotten part of the climate system, compared with other viables as temperature or precipitation, due to poor quality of observational data and the challenges in the homogenization process. During the last two decades the interest in long-term SWS variability and trends has increased and two main phenomena were discovered using observational data: the first one is termed “stilling” (decline until the 2010s), followed by the “reversal” (increase since then) of SWS. For a complete characterization of SWS changes, extremes also need to be evaluated as they have wide socioeconomic and environmental implications. The majority of the studies dealing with wind extremes are focused on high wind speeds associated with extratropical cyclones, hurricanes, tornados, storms, etc., which represent a threat for human lives and cause large damages. However, low wind speed events (known as “wind droughts”) have received less attention, representing a research gap in climate studies, and they are crucial due to the strong impacts on, for example, wind power generation a key motor of decarbonization. The main objective of this study is to define a Wind Drought Index (WDI) to identify and quantify the magnitude and extend of low wind speed events over the Iberian Peninsula. Our analyses are based on quality controlled and homogenized observational data series from 86 stations over Spain and Portugal for 1961-2022. Here we present for the first time the long-term and spatio-temporal changes in the occurrence and intensity of wind drought events over the study region.
Modelling the sea level in the Mediterranean Sea by assimilating data from coastal stations
Authors: Bajo Marco , Ferrarin Christian , Umgiesser Georg
Published: July 2023

In this work, we have used a hydrodynamic finite element model to study tides, surges and seiches in the Mediterranean Sea. The simulations were performed with and without data assimilation for a two-month period, November and December 2019, in which several intense meteorological events occurred in this area. The observations come from coastal stations along the Mediterranean and have been processed to select tides and surges. Surge simulations were used also to analyse seiche oscillations in the Mediterranean. These oscillations are caused by an initial perturbation and propagate freely with periods corresponding to the barotropic modes of the basin. The periods and the energy have been identified through spectral analysis from the observed data and their forecast improves significantly with data assimilation. Furthermore, data assimilation in reanalysis simulations shows a significantly improving in the results, both for the surge and for the astronomical tide. The error of the results reduces considerably even in areas far from the assimilated stations, such as the Eastern Mediterranean Sea. In the case of the astronomical tide, a comparison with the tide calculated from the harmonic components, extrapolated by altimetric data, demonstrates a considerable improvement not only in the coastal areas but also in the central part of the basin. This study is the first step to set up long-term reanalysis climatological simulations for tides, surges and total sea level in the Mediterranean Sea.
On the origin of North Atlantic Oscillation longitudinal displacements
Authors: Santolaria-Otín María , García-Serrano Javier
Published: July 2023

Previous studies have shown that the centers of action of the North Atlantic Oscillation (NAO), leading mode of regional atmospheric variability, shifted eastward in the mid-1970s compared to the preceding decades of the mid-twentieth century. They pointed at increasing greenhouse gas forcing and/or low-frequency oceanic variability as potential players. Here, the possible driving mechanisms of this shift are further investigated using long-term reanalysis data (NOAA 20CR, ERA 20CR) and two 500-year climate simulations performed with EC-EARTH 3.3 (CMIP6 version) keeping fixed the radiative forcing at present-day conditions [year 2000], in atmosphere-only and coupled ocean-atmosphere configurations. 30-year running empirical orthogonal functions (EOFs) of sea level pressure anomalies in the North Atlantic-European region are computed to track the longitudinal displacements of the NAO pattern, first showing that a similar eastward shift happened at the beginning of the observational record. Recurrent eastern and western locations of the NAO pattern are properly simulated by the model, with marginal differences between the atmosphere-only and coupled configurations. Moreover, the fraction of variance explained by the running EOFs varies with time displaying a comparable range (~30% to 60%) in observations and model simulations. Composites of eastern and western NAO patterns, based on upper and lower terciles, are built to show that the longitudinal shifts appear linked to changes in the variability of the regional eddy-driven zonal wind. The similarity in the NAO displacements between observations, atmosphere-only and coupled simulations suggests that the observed eastward shift could have occurred previously and might not be due to anthropogenic or oceanic forcing but to pure internal variability. 
Analysis-ready climate data with ESMValCore and ESMValTool
Authors: Andela Bouwe , Kalverla Peter , Kazeroni Remi , Loosveldt Tomas Saskia , Predoi Valeriu , Schlund Manuel , Smeets Stef , Zimmermann Klaus
Published: July 2023

We present new features of ESMValCore, a Python package designed to work with large climate datasets available from ESGF and beyond. The Earth System Grid Federation (ESGF) offers a wealth of climate data that can be used to do interesting research. For example, the latest edition of the Coupled Model Intercomparison Project (CMIP6) output features 20 petabytes of data. However, the heterogeneity of the data can make it difficult to find and work with. ESMValCore now provides a Python interface that makes it easy to discover what data is available on ESGF and locally, download it if necessary, and make it analysis-ready. The analysis-ready data can then be used as input to the ESMValCore preprocessor functions, a collection of functions to perform commonly used analysis steps such as regridding and statistics. When searching for data on ESGF as well as when loading the NetCDF files, the software intelligently corrects small issues in the metadata that otherwise make working with this data a time-consuming, manual effort. Data and metadata issues are fixed in memory for fast performance. The search and download features are user-friendly and will automatically use a different server if one of the ESGF servers is unavailable for some reason. Several Jupyter notebooks demonstrating these new features are available at https://github.com/ESMValGroup/ESMValCore/tree/main/notebooks. 

ESMValCore has been designed for use on computing systems that are typically used by researchers: it works well on a laptop or desktop computer, but also comes with example configuration files for use on large compute clusters attached to ESGF nodes. For reliable computations, ESMValCore makes use of the Iris library developed by the UK Met Office. This in turn is built on top of Dask, a library for efficient parallel computations with a low memory footprint. In 2023, we aim to improve our use of Dask in collaboration with the Iris developers, for even better computational performance. 

For easy reproducibility, ESMValCore also offers "recipes" in which standard analyses can be saved. A large collection of such recipes is available in the Earth System Model Evaluation Tool (ESMValTool), including recipes for estimating future drought risk. ESMValTool started out as a set of community-developed diagnostics and performance metrics for the evaluation of Earth system models. Recently it has also turned out to be useful for other users of climate data, such as hydrologists and climate change impact researchers. Both ESMValCore and ESMValTool are developed by and for researchers working with climate data, with the support of several research software engineers. An important recent achievement is the use of these packages to produce the figures for several chapters of the IPCC AR6 report. Documentation for both ESMValCore and ESMValTool is available at https://docs.esmvaltool.org. 
Data-driven s2s forecasting with s2spy & lilio
Authors: Liu Yang , Schilperoort Bart , Kalverla Peter , Vijverberg Sem , van Ingen Jannes , Alidoost Sarah , Verhoeven Stefen , Coumou Dim
Published: July 2023

Reliable S2S forecasts remain a huge scientific challenge. Only for specific ‘windows of predictability’, skillful forecasts are possible, in an otherwise largely unpredictable future. Due to a number of successes in S2S forecasting, the interest in machine learning (ML) is growing fast. However, we argue there is a need for more standardization, consensus on best practices, higher efficiency, and higher reproducibility. Typical S2S ML use-cases, such as (1) pure statistical forecasting based on observations, (2) transfer learning, and (3) post-processing of dynamical model ensembles, require a large coding and preprocessing effort. Such experiments are not trivial to set up, and without sufficient experience and expertise there is a large risk of improper cross-validation and/or improper and non-standard verification. 

Driven by the need for a reliable tool to integrate expert knowledge and artificial intelligence, we are developing two python packages to tackle the scientific challenge of (sub) seasonal (S2S) forecasting. s2spy is a high level python package designed to handle and optimize the entire data-driven forecasting workflow. Lilio is an advanced calendar system for resampling timeseries into training and target data for machine learning. Our aim is to make ML workflows more transparent and easier to build, and to facilitate standardization and collaboration across the S2S community. This also contributes to higher reproducibility and works towards a wider acceptance of standards and best practices. We will present our vision and the capabilities of our package, show-casing that we can build a model from raw climate data up to verification in only a few lines of code. 
Calibrating multi-scale deterministic and probabilistic forecasts
Authors: Huo Ziqiang , Liu Pu , Wang Yong
Published: July 2023

Over recent decades the deterministic and probabilistic NWPs have been improved significantly. It becomes the essential toll for the meteorological operation and applications. It is very often that there are several deterministic NWPs and EPSs with different resolution available for meteorological operation and applications. Those forecasts are with different characteristics of systematic bias and dispersion errors. Many statistical calibration methods have been proposed and been implemented in the operation, for example, ensemble model output statistics (EMOS) and standardized anomaly model output statistics (SAMOS). Further, Artificial intelligence (AI) based method has been used in different way for calibration.  In this study we applied EMOS and SAMOS to calibrate multi-scale deterministic and probabilistic forecasts. In the frame of SAMOS/EMOS we have introduced AI based methods for selecting the important variables and building the non-linearity for calibration. The CMA（China meteorological Administration) NWP model chain, a convection permitting NWP (3km resolution), a regional NWP (9km) and a global NWP (25km), a regional EPS (10km) and a global EPS (50km) have been used for the calibration. Two years observation and NWP data over Beijing region was selected for training the EMOS/SAMOS method.  EMOS and SAMOS, AI based variable selection and Boosting method etc. have been compared. 2m temperature, 10m Wind and precipitation forecasts have been calibrated and verified with statistical scores such as, root mean square error of ensemble mean, continuous ranked probability score（CRPS）and so on. The results of calibrated ensemble mean and ensemble spread are quite encouraging, which will be presented at the conference.
Temporal scaling dependency in precipitation simulated by the HCLIM regional climate model
Authors: Rasmus E.  Benestad Rasmus E. , Dobler Andreas , Parding Kajsa M. , Lutz Julia , Mezghani Abdelkader , Landgren Oskar , Haugen Jan Erik
Published: July 2023

Rainfall intensity-duration-frequency (IDF) curves play an important role in water management, for instance urban stormwater handling. They are often derived by fitting generalized extreme value distributions to observed annual maximum rainfall values and require long-term observations of rainfall at sub-daily scales to get robust estimates. However, mathematical models describing the IDF curves have been observed to have a fractal dimension, providing a possibility to avoid the challenging data requirement. In this case, we used a simple analytical formulation, where two key parameters specify the shape of the curves: the wet-spell mean precipitation and frequency. These two parameters also play a role for the fractal dimension of the temporal rainfall scales. 
This study aimed to take a step towards a better understanding of which conditions may play a role for the fractal shapes and how they are affected by geographical conditions. To this end, we have explored the dependency between wet-spell mean precipitation and frequency over different timescales, using data from convective-permitting (3km) climate simulations with the regional climate model HCLIM over northern Europe. The analysis was applied to HCLIM results with boundary conditions from the ERA Interim reanalyses, as well as the EC-Earth and GFDL-CM3 global climate models.
The simulated sub-daily fractal dimensions were influenced by geographical conditions for both the wet-spell mean precipitation and the wet-spell frequency. The results were consistent for the different boundary conditions representing current climate conditions (reanalysis and GCMs) and hence independent of the driving model. Similar simulations for the future, following the RCP8.5 scenario, hinted at changes in the fractal shapes at high latitudes. However, for large regions, there was little indication of change. This study is an example of using high-resolution regional climate model data for testing hypotheses where consistent, dense and temporally high-resolved long-term observational networks are lacking.
Spatiotemporal Analysis of Hourly Surface Air Temperature – an Application in Switzerland
Authors: Frey Louis , Frei Christoph
Published: July 2023

Today, most climate data sets derived by spatial analysis of station data have a maximum time resolution of one day. This is limiting in environmental modelling applications. For example, plant transpiration and snow melt depend on temperature non-linearly. Modelling these processes using daily mean temperatures only is therefore questionable. Our objective is to improve this situation and to advance spatial analysis of temperature into the sub-daily time scale. At this high temporal resolution, surface temperature fields are highly organized not just spatially but also temporally. This requires a new modelling approach that allows information from observations to also flow over time, not just over space like in traditional spatial climate analysis.
An attractive framework for this methodological extension is spatiotemporal (ST) statistical modelling. In this presentation we propose, and experiment with, a dynamic linear model (DLM). A DLM can be thought of as an extension of Kriging with external drift (KED, a Gaussian spatial process), where the trend coefficients are assumed to be serially correlated, i.e. smooth in time. These time series represent the characteristic variations of the temperature field, such as a diurnal cycle with a varying amplitude and phase, or the gradual variation of the height and amplitude of a temperature inversion.
In our study we develop and configure a DLM for surface air temperatures in Switzerland. The model can represent non-linear temperature profiles, such as inversions or well-mixed boundary layers. Our configuration involves several profiles across the domain to account for large-scale variation and across-ridge contrasts of the temperature distribution. We also account for mesoscale cold-air pooling with a tailor-made predictor. All model components are allowed to vary quasi harmonically to account for the diurnal variation.
We apply the DLM to hourly temperature observations from about 200 stations in Switzerland. Experiments are made for a set of test episodes including typical weather conditions like summertime high pressure and wintertime inversion situations. We find that the DLM reproduces physically plausible temperature distributions and evolutions during these weather conditions. The developments are continuous and reveal, for example, the generation, diurnal oscillation and dissolution of an inversion layer over several days. We also find a quite robust performance with respect to erroneous and missing observations. In a comparison with a traditional spatial-only analysis (sequentially applied KED), the DLM exhibits some added value. This benefit will be demonstrated in the presentation. In summary, the DLM framework turns out to be flexible and promising for the development of sub-daily temperature datasets, in Switzerland and likely in other regions of the world.
Identifying Sensitive Areas for Adaptive Observations by Using The CNOP-I Method to Improve Prediction of Asian Dust Storms
Authors: Lee Seungyeon , Qin XiaoHao , Park Seon Ki
Published: July 2023

The Asian dust storm is one of the important air pollution problems in Korean Peninsula. In air quality prediction, it is essential to find the upstream areas. The conditional nonlinear optimal perturbation for initial conditions (CNOP-I) is a suitable method for adaptive (targeted) observations. In the previous studies on improving the Asian dust storms (ADSs) prediction mainliy considered the kinetic energy calculation, which included only two variables (u-wind and v-wind). However, variables affecting the actual ADS's development and movement also include some other variables such as included in defining multiple energy equations (e.g., kinetic energy, dry energy, moist energy, etc.). The goal of this study is twofold: finding variables that strongly affect the ADSs and identifying sensitive areas for the adaptive observations. We examined the ADS events that occurred during the last 32 years (1990—2021) over South Korea. Through the PCA, firstly we recognize the top five variables⸻temperature, specific humidity, divergence, ozone mass mixing ratio, and eastward wind⸻which affect origination and translocation of the ADSs. Furthermore, the ADSs are significantly affected by vertical velocity, divergence, relative humidity, eastward wind, and potential vorticity; for example, strong downdraft and divergence make ADSs finally land on the Korean Peninsula. We are applying the CNOP-I to identify the sensitive areas for adaptive observations in air quality prediction. We expect to improve air quality forecasts by classifying the synoptic situations that brings about severe ADS outbreaks in Korean peninsula and by identifying the upstream areas for adaptive observations to which we can enhance observations, potentially through international collaborations.
CZEXWED: the unified Czech Extreme Weather Database
Authors: Müller Miloslav , Kašpar Marek
Published: July 2023

A crucial step in developing a strategy against natural hazards is the analysis of weather extremes in the past. Due to the multiplication of their impacts when occurring in a larger area, we strongly recommend not evaluating the extremes only at individual sites but assessing regional extreme weather events. The presented Czech Extreme Weather Database (CZEXWED) comprises six types of extreme events, namely, heat waves, cold waves, air temperature drops, windstorms, heavy precipitation events, and heavy snowfalls. To date, it covers the period 1961–2020. To minimize methodological differences in the process of evaluating various types of extreme weather events including compound events, we employed the weather extremity index (WEI), a universal indicator based on the evaluation of return periods of relevant variables. Each event is characterized not only by the WEI value but also by its spatial extent and duration.
Heat and cold waves in Czechia generally reach higher WEI values than other types of extreme weather because they usually affect larger areas. The number and extremity of heat waves are increasing significantly, while the opposite may be true for cold waves and windstorms. Air temperature drops defined by declines in daily maximum air temperature are frequent in the warm half-year, but three of four top events occurred in January. Windstorms and heavy precipitation events prevailed in the cold and warm half-years, respectively, but weaker events of these types also occurred during the opposite season. A comparison of CZEXWED with event lists from the wider Central European region shows that Czech and Central European extreme events correspond well with each other.