bluuebunny/crossref_metadata_2025 huggingface dataset:
ERA5-ecPoint: what it is and its power in the contextualisation of severe temperatures and rainfall
Authors: Pillosu Fatima , Hewson Tim , Cagnazzo Chiara
Published: July 2024

The continuous breaking of temperature and rainfall records worldwide is a stark reminder that climate is changing. These record-breaking events underscore the urgent need to understand and adapt to these changes as they can lead to more frequent and severe weather events, e.g. heatwaves (or cold waves), extreme (localised) rainfall, and (flash) floods. Hence, investing in sustainable practices, enhancing forecasting and response strategies, and implementing policies to mitigate climate change effects is critical. But to do so, we need appropriate data to inform our decisions.Observation-based climatologies help detect trends and patterns in the climate over a long period of time and can contextualise extreme, high-impact weather events. However, observations can be inaccurate and unevenly distributed in space and time. Reanalysis such as ERA5 provides a good alternative to observation-based climatologies because it provides accurate, temporally consistent, gridded estimates of the past state of the Earth system worldwide. However, this only satisfies some needs due to ERA5's relatively coarse model resolution (precluding the representation of localised extremes) and some intrinsic biases.Within the Highlander project, co‑financed by the EU and coordinated by Italy's Cineca computing centre, ECMWF's ecPoint post-processing technique was applied to raw ERA5 'deterministic' fields to address ERA5 limitations and create much more reliable (probabilistic) point-scale climatologies. With ERA5-ecPoint to be released in the Copernicus Climate Data Store later this year, this presentation is addressed to future users of ERA5-ecPoint. The main goal of the presentation is to provide examples of how this dataset can help understand and contextualise the occurrence of low-probability but high-impact weather, such as extremely low or high temperatures and extreme (localised) rainfall, and how to do so in a changing climate.
On the need and applications of intermediate-complexity climate risk models 
Authors: Ruggieri Paolo , Di Sabatino Silvana
Published: July 2024

Adaptation to climate change requires a timely implementation of effective adaptation actions. A major effort in this context is devoted to extreme weather and climate events that are a major driver of climate change impacts. Planning and designing effective and efficient adaptation actions ideally requires the assessment of risk posed by impactful events. Complex and conceptual risk models already cohabit in the scientific literature in the form of a disorganised ecosystem of non-homogeneous models. This study explores the concept and mathematical formalism of intermediate complexity (IC) models for assessing climate risk and their significance in supporting climate change adaptation efforts. Existing climate risk models are classified into categories of different complexity and intermediate-complexity climate risk models identified and conceptualised to fill gaps in the hierarchy. The formulation of an intermediate-complexity model for flood risk under future climate conditions is introduced, drawing inspiration from established large-scale methodologies and utilizing freely available data. The simulations combine data of hazard, exposure and vulnerability provided by the European Copernicus Services and local datasets to perform high resolution (order 100 m) risk assessments. We discuss the application of IC climate risk models as a conceptual laboratory to develop the concept of parametrizations to take into account the effect of small scale (order tents of meters) engineering actions in a risk model with a coarser resolution (typically hundreds meters or kilometers). In this sense, they will mimic traditional parametrizations used to model subgrid process for geophysical flows in numerical climate models. The use of IC models to rank the impact of extreme events simulated with climate models with the aim to replace hazard-based metrics with impact-based metrics for the definition of an extreme events is discussed. While acknowledging that these models may not encompass all factors influencing risk, the authors argue that they are a crucial component of model hierarchies, capable of promptly addressing critical knowledge gaps and providing valuable insights to decision-makers. 
RODEO Project – bringing meteorological data open for all users
Authors: McCairns Willie
Published: July 2024

The RODEO project responds to the requirements of the EU Directive (2019/1024) on Open Data and its Implementing Regulation in order to boost the re-use and combination of open public data across the EU. The Implementing Regulation defines High Value Datasets (HVD) for six thematic categories of public data of which weather observation data, climate data, warnings, weather radar data and Numerical Weather Prediction (NWP) data are defined as meteorological High Value Datasets. These HVDs shall be shared free of charge, under the conditions of the Creative Commons BY 4.0 licence or a less restrictive open licence, and will be openly accessible via Application Programming Interfaces (APIs), machine-readable and bulk downloadable.The three-year RODEO project is a joint effort by 11 European meteorological institutions, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the network of 31 European National Meteorological and Hydrological Services, EUMETNET. The project strengthens the capacity of the European meteorological data providers byDeveloping a user interface and a data catalogue for making data discoverable;
Developing APIs, using open licences, for accessing weather observation data, climate data, weather radar data, warnings, and AI datasets;
Engaging with the data owners and user communities; and
Supporting the deployment of national data portals and APIs.
Implementing a comprehensive user engagement strategy involving
A Project External Advisory Board, composed of public and private sector partners
Frequent communications updates through several channels

The project is now in its second year. It has moved on from its design phase into developing and testing some of the APIs that are going to enable a large increase in the open availability of near real time European meteorological data. This will bring large benefits across society as a whole from the public through to decision makers. In particular, small and medium-sized enterprises (SMEs) will benefit from the greater data availability by creating new digital products and services and eventually attract new investors.
For a Pluralism of Climate Modelling Strategies
Authors: Thompson Erica , Baldissera Pacchetti Marina , Jebeile Julie
Published: July 2024

The continued development of General Circulation Models (GCMs) towards increasing resolution and complexity is a predominantly chosen strategy to advance climate science, resulting in channelling of research and funding to meet this aspiration. Yet many other modelling strategies have also been developed and can be used to understand past and present climates, to project future climates and ultimately to support decision-making. We argue that a plurality of climate modelling strategies and an equitable distribution of funding among them would be an improvement on the current predominant strategy for informing policy making. To support our claim, we use a philosophy of science approach to compare increasing resolution and complexity of General Circulation Models with three alternate examples: the use of machine learning techniques, the physical climate storyline approach, and Earth System Models of Intermediate Complexity. We show that each of these strategies prioritises a particular set of methodological aims, among which are empirical agreement, realism, comprehensiveness, diversity of process representations, inclusion of the human dimension, reduction of computational expense, and intelligibility. Thus, each strategy may provide adequate information to support different specific kinds of research and decision questions. We conclude that, because climate decision-making consists of different kinds of questions, many modelling strategies are all potentially useful, and can be used in a complementary way.  The outcomes of active diversification of climate modelling strategies would be to broaden the kinds of decision questions we are capable to answer, as well as to have more justified confidence in the robust core of projections, more potential input from those who will be affected by decisions, and thereby more effective consensus building for climate action.
Approaching the sub-mesoscale globally at 10 min temporal resolution through assimilating clear-sky radiances measured by geostationary satellites
Authors: Schröttle Josef , Lupu Cristina , Burrwos Chris
Published: July 2024

A refined 4D-Var assimilation system within DestinE allows us to assimilate the Meteosat-10/SEVIRI clear-sky radiances over Europe, as well as globally at a spatial scale of 75 km instead of the previous 125 km in the ECMWF Integrated Forecasting System (IFS). Higher resolution observations can potentially improve the analysis and therefore the prediction of extreme weather events over Europe, as well as globally. The effects of using higher resolution observations have been investigated with a detailed set of experiments and the impact on wind, temperature, and humidity has been evaluated. A broad range of experiments indicate that exploiting the higher spatial density clear-sky radiances leads to an improvement of humidity sensitive fields in short- range forecasts with the IFS as independently measured for example by instruments on low-earth- orbiting satellites (IASI, CrIS, SSMIS, or ATMS). Due to a reduced displacement and representativeness error, these changes could further lead to improvements in longer range forecasts as these errors propagate upscale nonlinearly. First experiments show an upscale propagation of initially very localised increments in the analysis fields of vertical wind, as well as humidity above the Pacific or the North Atlantic. Over time, these incremental improvements from the 4D-Var system lead to an improvement in forecast scores of the IFS up to 5 days ahead.

In addition, pre-processed GOES-16/ABI and GOES-18/ABI observations by NOAA have been assimilated with 10 min sampling rates at 75 km spatial density. Exploring how to best assimilate relatively small spatial and temporal scales for one geostationary satellite, will allow us to approach these smaller scales with other satellites such as HIMAWARI/AHI above the Pacific or MTG-I/FCI above Europe. Data from both satellites will be available for us early in 2024. Preliminary experiments demonstrate the ability of IFS to assimilate observations at the highest available temporal resolution for the GOES-16 and GOES-18 satellites. Higher resolution radiances observed at these shorter time intervals naturally capture smaller scale atmospheric features such as mesoscale convective systems. In our experiments, simultaneously assimilating observations at a higher spatial and temporal resolution leads to an impact that is only marginally better than assimilating higher density observations alone, suggesting a combined investigation of optimal time-assignment, as well as assessment of the observation error are needed to optimise the integration of rapid update measurements in 4D-Var.
Weather and Climate Services in Support of the German Energy Transition: Use cases from Germany’s national meteorological service (Deutscher Wetterdienst, DWD)
Authors: Kaspar Frank , Fundel Vanessa
Published: July 2024

The energy sector has a wide range of requirements for weather and climate data and services. As in many other countries, the share of renewable energies has increased in Germany during the last years and the German government aims at a further increase within the next decades. As a consequence, there is an increasing need for meteorological information on several time scales. In this presentation, we will discuss the experience of Germany’s national meteorological service (Deutscher Wetterdienst, DWD) with the provision of user-oriented services for various stakeholders in the energy sector.DWD hosts several national and international climate data centres that provide observation-based climatological data suitable for assessments of the site-specific renewable energy potential, but also suitable for evaluating weather variability and the resulting risks of shortfall events or complementarity of energy resources (e.g. https://doi.org/10.1016/j.renene.2020.10.102). The datasets are derived from traditional weather observations, satellite data (e.g. https://www.cmsaf.eu) or model-based atmospheric reanalyses (e.g. https://doi.org/10.5194/asr-17-115-2020). Climate simulations can help to assess whether relevant changes in critical parameters are to be expected in the future. The debates in the recent winters about the risks of a shortage in gas availability have also led to an increased interest in seasonal forecasts.Weather forecasts are of particular interest for the daily operation of the energy system, the predictive balancing of production and consumption, the management of congestion and the weather dependent thermal rating of the overhead lines in the electrical grid. In close contact with our key user groups, DWD optimizes and co-designs forecast products and services tailored to the user needs. One special focus lies on the co-development of products that make use of ensemble forecast information and to foster its integration in the user’s decision process.DWD's open data policy (https://www.dwd.de/opendata) also supports the cooperation with this community. In addition to the national data, DWD’s international data centres also provide relevant datasets for Europe, or beyond (e.g. https://doi.org/10.3389/fclim.2021.815043).In the presentation we will discuss the results and lessons learned of the cooperation with our users and we will also provide information on products that may be of interest to other users.
Changes in population exposure to drought in the Iberian Peninsula under 1.5 and 2º GWLs
Authors: Russo Ana , Bento Virgílio , Ribeiro Andreia , C.A. Lima Daniela , A. Careto João , M.M. Soares Pedro , Libonati Renata , M. Trigo Ricardo , M. Gouveia Célia
Published: July 2024

In the context of escalating temperatures under climate change conditions, drought events are growing globally, particularly affecting Mediterranean Europe, a recognized climate change 'hotspot'.  Consequently, the vulnerability of ecosystems and human populations is increasingly pronounced in areas such as the Iberian Peninsula (IP).This study delves into the repercussions of 1.5ᵒ and 2ᵒC Global Warming Levels (GWL) by the end of the 21st century on drought occurrences and population exposure in the IP. Utilizing EURO-CORDEX experiments, incorporating 13 simulations as a weighted multi-variable multi-model ensemble, spanning historical periods, 1.5ᵒ and 2ᵒC GWL years, and projections for the end of the century, this research employs the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI) to assess droughts. Three greenhouse gas emission scenarios are considered alongside demographic projections up to 2100 for Portugal and Spain (RCP2.6, RCP4.5 and RCP8.5).Under the RCP8.5 scenario, a significant increase in moderate, severe, and extreme droughts is anticipated, with SPI and SPEI projections indicating a substantial rise in drought occurrences due to additional warming. Population exposure to extreme droughts is projected to be higher under the 2ᵒC scenario, emphasizing the impact of rising temperatures in the IP. By the end of the century, population exposure to extreme droughts could surge by varying percentages, particularly under RCPs 4.5 and 8.5. The study highlights a projected surge in population exposure to droughts across the IP, primarily driven by climate change. These findings stress the critical need for regional authorities, policymakers, and society to prioritize adaptation planning and enhance understanding of vulnerabilities to tackle challenges posed by dry extreme events. This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020 (https://doi.org/10.54499/UIDB/50019/2020), UIDP/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020) and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020). This work was performed under the scope of project https://doi.org/10.54499/2022.09185.PTDC (DHEFEUS) and supported by national funds through FCT. DL and AR acknowledge FCT I.P./MCTES (Fundação para a Ciência e a Tecnologia) for the FCT, 2022.03183.CEECIND/CP1715/CT0004 (https://doi.org/10.54499/2022.03183.CEECIND/CP1715/CT0004) and (https://doi.org/10.54499/2022.01167.CEECIND/CP1722/CT0006 (Complex), respectively. 
Changes in Land Surface Model Thermodynamics and Hydrology: Implications for Arctic Amplification 
Authors: Meabe Nagore , Gonzalez-Rouco Jesus Fidel , García- Pereira Félix , Martínez-Vila Alex , Steinert Norman Julius , de Vrese Phillip , Jungclaus Johann , Lorenz Stephan
Published: July 2024

The Arctic’s response to global warming is primarily intensified by sea-ice and lapse- rate feedbacks. This mechanism, usually known as Arctic amplification (AA), significantly contributes to permafrost thawing, impacting Arctic soil carbon emissions and hydrology. However, significant uncertainties arise in permafrost-related processes. This study will analyze various experiments with different Arctic thermo-hydrodynamic set-ups to com- prehend how different soil hydrology parameterizations in permafrost areas affect Earth’s climate, particularly AA. Additionally, different vertical discretizations of the land surface model are considered. Although these discretizations show minor differences in surface temperature, the varied model hydro-thermodynamic configurations result in relatively distinct climate background states in the Arctic. A positive sea-ice-snow-albedo feedback is shown to enhance the warming signal under a climate change scenario. The magnitude of the feedback depends on the background state and available snow and sea-ice. By assessing the AA ratio we conclude that all configurations show considerable (internal) AA variabillity in the 20th and the first quarter of the 21st century, but end up converging to a factor of 2-3 times larger warming in the Arctic regions than globally by the end of the century. This suggests that high AA values recently found in observations are related to internal variability, and that near 0 temperature and sea-ice area trends in the Arctic lead to highly variable and non-significant AA values.
Furthermore, changes in the Arctic have the potential to affect the circulation, not only at a local scale but also up to mid-latitudes and the tropics via a series of teleconnections. The physical processes linking AA and sea-ice loss to lower latitude weather are still being discussed in the community. In this study we also assess how those different hydro- thermodynamical set-ups shape some atmospheric circulation patterns. Preliminary results shown a comparison of the response of extratropical (Arctic and Antarctic oscilla- tions) and intertropical (monsoons and ENSO) modes across the ensemble of simulations.
Climate Initiative for Iberian Mountain Areas (CIMAs): improving our understanding of climate variability over mountain areas using high resolution modelling.
Authors: Greciano-Zamorano Emilio , González Rouco Jesús Fidel , Vegas Cañas Cristina , García Pereira Félix , Navarro Montesinos Jorge , García Bustamante Elena , Rodríguez Camino Ernesto , Rodríguez Guisado Esteban
Published: July 2024

Mountain areas are particularly sensitive to global warming as they usually present a complex distribution of climates and ecosystems and feedbacks tend to amplify the effects of climate change. Additionally, the large spatial variability of temperature gradients and heterogeneity in the occurrence, amount and distribution of precipitation and snow cover in mountainous areas are especially relevant for water resources and stresses the need for high altitude observations and high-resolution modelling over complex terrain. However, harsh meteorological conditions and the complex orography associated with this environment that, as part of the Mediterranean domain, has been underscored as a climate change hot-spot, hinder the obtention of a good coverage of high-altitude observations and pose challenges for regional climate models. CIMAs is a joint effort aiming at improving our understanding of climate variability over mountain regions in Iberia. A pilot area has been selected over the Sierra de Guadarrama (Spanish Central range, about 50 km from Madrid) aiming at studying climate variability through very high (1 km) resolution simulations, exploring models’ ability to capture relevant processes at that scale. A set of observational sites ranging from high altitudes to low levels at both sides of the mountain range has been used. ERA Interim, ERA5 and different WRF nested simulations, spanning the last three decades and reaching 1 km resolution, have been compared to a dense network of in situ observations. Results show a clear improvement with increasing resolution for temperature, but some altitude-related biases for precipitation. In this sense, some sensitivity tests to changing convection parameterisations and to convection permitting configurations have been assessed.
Resolving inconsistencies in the dry period properties of bias-corrected daily precipitation timeseries: an example from the TRANSLATE climate projections for Ireland.
Authors: Griffin Seánie , O'Brien Enda , Duffy Catriona , Nolan Paul
Published: July 2024

Data from the TRANSLATE project was used to examine the behaviour of standard climate indices associated with prolonged deficits in precipitation, particularly those that rely on the property of “consecutiveness”. Ireland has a temperature oceanic climate and usually gets a large amount of rain, but it is not uncommon for shortfalls to occur also. Having access to reliable climate information about these phenomena is important for planning of future infrastructure and for water-reliant sectors, such as agriculture and utilities.The TRANSLATE dataset is a bias-corrected ensemble of regional climate model projections over Ireland, with quantile-mapping used to produce the bias-corrections. The regional climate model data comes from a combination of the EURO-CORDEX simulations and an ensemble of COSMO and WRF downscaled simulations over Ireland, both of which were driven by CMIP5 global model data. Bias-correction techniques, such as quantile-mapping, can successfully adjust daily time series to remove overall biases, but generally do not account for “consecutiveness”, and so can introduce occasional wet-day interruptions into otherwise dry periods in the detrended and bias-corrected daily precipitation time series. This has the potential to produce inconsistent results between the raw and bias-corrected projections if the bias-corrected daily time series is used to calculate indices which rely on this property.Results for the standard climate index Consecutive Dry Days (CDD), the related “Dry Periods” (count of periods of more than 5 consecutive dry days) and the Standardised Precipitation Index (SPI) are presented to highlight where these inconsistencies may arise, and the steps taken to address them. It was found that calculating the indices first, using the raw projections, and then applying the bias-correction to this index data resolved this apparent contradiction. Meanwhile results based on single-day extremes and occurrence frequencies were unaffected by the choice of technique. This research helps to provide the best representation of future projected dry periods in Ireland, while adequately highlighting the uncertainty in these projections.
Convective and stratiform precipitation under future climate
Authors: Beranova Romana , Rulfova Zuzana
Published: July 2024

Convective and stratiform precipitation differ not only in their origin, but also in their characteristics. Different risks arise from them, and they also exhibit distinct trends under climate change. Convective precipitation is associated with intense storms and tends to develop rapidly and locally, whereas stratiform precipitation is linked with longer lasting and less intense rainfall events. Their distinct characteristics have varied impacts on ecosystems, agriculture, infrastructure, and human lives.In this study, we focus on analyzing the current characteristics of convective and stratiform precipitation and assess their future changes using ALADIN-CLIMATE/CZ model. Due to uncertain future concentration of greenhouse gases in the atmosphere, two CMIP6 scenarios are analyzed: SSP5-8.5 and SSP2-4.5. The ALADIN-CLIMATE/CZ model is based on the ALARO canonical model configuration of the ALADIN System. The model domain covers Central Europe, with a horizontal grid size of 2.3 km and it belongs to the so-called convection permitting models. We utilize data from 19 stations in the network of the Czech Hydrometeorological Institute to validate the model. The historical validation period spans from 1990 to 2014. Employing an algorithm that uses information from the SYNOP report, we separate precipitation into convective and stratiform. We have found that the ALADIN-CLIMATE/CZ model overestimates both stratiform and convective precipitation in all months. Convective precipitation is better captured by the model in summer than stratiform precipitation. We examine precipitation characteristics across 25 winter and summer seasons for both the near future (2050-2074) and for the end of the 21st century (2074-2099). In future climate assessments, we focus on extreme events in addition to total seasonal precipitation.
Extraction of information for forecasters in convective-scale ensembles: supercell detection and rainfall synthesis
Authors: Mounier Arnaud , Soulard-Fischer Louis
Published: July 2024

The use of ensemble prediction systems (EPS) enables the quantification of forecast uncertainty. However, the use of EPS is challenging due to the large amount of information it provides. Forecasts from EPS are typically summarised using statistical measures (such as quantiles maps). Although this mathematical representation is effective in capturing the ensemble distribution, it lacks physical consistency, which raises issues for many applications of EPS in an operational context. Following the application of a bow echo detection tool appreciated by forecasters at Meteo-France, we propose two different approaches for providing physically consistent synthesis of French convection-permitting AROME-EPS forecasts.The first approach is similar to bow echo detection but it is applied to supercell. Supercell can especially produce hail, strong winds or tornadoes. To summarise the risk of supercell in AROME-EPS forecasts, a convolutional neural network has been trained to automatically detect these supercells in AROME-EPS members based on updraft helicity, reflectivity and hail diagnosis. Then, different synthesis plots are produced, based on these detections. A case study will be presented to better understand the usefulness of these plots.The second approach is a rainfall synthesis. The aim is to automatically classify the members into different classes pre-defined by rainfall climatology. To design a rainfall synthesis, the procedure can be divided into two parts. The first step aims to extract relevant features from each EPS member to reduce the problem dimensionality. Then, clustering is performed based on these features. The originality of our work is to leverage the capabilities of deep learning for feature extraction. For this purpose, we use a convolutional autoencoder (CAE) to learn an optimal low-dimensional representation (also called latent space representation) of the input forecast field. The clustering task is then accomplished using a SOM algorithm. In this work, the algorithm is developed to work on 1-hour accumulated rainfall from AROME-EPS. The rainfall synthesis plot summarises information concerning rainfall positions, the number of members in each class, and rainfall intensities. The rainfall synthesis will be presented for a case study in this presentation.The two methods proposed are shown to provide an additional and complementary information, useful for facilitating the human expertise. In addition, their design is generic enough to be applied to other events and variables.
Constraining near-term climate projections by combining observations with decadal predictions
Authors: Bonnet Rémy , Sanchez-Gomez Emilia , Boé Julien , Cassou Christophe
Published: July 2024

The implementation of adaptation policies requires seamless and relevant information on the evolution of the climate over the next decades. Decadal climate predictions are subject to drift because of intrinsic model errors and their skill may be limited after a few years or even months depending on the region. Non-initialized ensembles of climate projections have large uncertainties over the next decades, encompassing the full range of uncertainty attributed to internal climate variability. Providing the best climate information at regional scale over the next decades is therefore challenging. Recent studies have started to address this challenge by constraining uninitialized projections of sea surface temperature using decadal predictions or using a storyline approach to constrain uninitialized projections of the Atlantic Meridional Overturning Circulation using observations. Here, we develop a new method combining both observational and forecast information to provide the best climate information over the next decades. First, we select a sub-ensemble of non-initialized climate simulations based on their similarity to observed predictors with multi-decadal signal potential over Europe, such as Atlantic multi-decadal variability (AMV). Then, we try to further refine this sub-ensemble of trajectories by selecting a subset based on its consistency with decadal predictions. This study presents a comparison of these different methods for constraining surface temperatures in the Europe regions as defined in the IPCC over the next decades, focusing on CMIP6 non-initialized simulations. A retrospective evaluation is carried out over the historical period to assess the ability of this method to provide a good climate information in the near-term climate.
ClimateMeter: Putting Extreme Weather Phenomena in Climate Perspective 
Authors: Faranda Davide ,   The ClimaMeter team
Published: July 2024

Climate change is a global challenge with manifold and widespread consequences, including the intensification and increased frequency of numerous extreme weather phenomena. In response to this pressing issue, we introduce ClimaMeter, a platform designed to assess and contextualize extreme weather phenomena in relation to climate change. The platform provides near-real-time information on the dynamics of extreme events, serving as a resource for researchers, policymakers, and acting as a scientific outreach tool for the general public. ClimaMeter currently analyzes heatwaves, cold spells, heavy precipitation, and windstorms.Our methodology is based on looking for weather conditions similar to those that caused the extreme event of interest with physics-informed machine-learning methodologies. We focus on the satellite era, namely the period since 1979, when widespread observations of climate variables from satellites have become available. The object studied (i.e. "the event") is asurface-pressure pattern over a certain region and averaged over a certain number of days, that has lead to a extreme weather conditions. We split the dataset 1979-Present in two parts of equal length and consider the first half of the satellite era  as "past" and the second part as "present" separately. We use data from MSWX. We then compare how the selected weather conditions have changed between the two periods, and whether such changes are likely due to natural climate variability or anthropogenic climate change.This presentation sheds light on the methodology, data sources, and analytical techniques that ClimaMeter relies on, offering a comprehensive overview of its scientific foundations. To illustrate ClimaMeter, we present some examples of recent extreme weather events. Additionally, we highlight the role of ClimaMeter in promoting a profound understanding of the complex interactions between climate change and extreme weather phenomena, with the hope of ultimately contributing to informed decision-making and climate resilience. Follow us on X @ClimaMeter and visit www.climameter.org.
Urban effects on heatwave intensity and duration: a case study of the Vilnius city
Authors: Bukantis Arūnas , Klimavičius Laurynas
Published: July 2024

The aim of this paper is to determine the impact of the city on the intensity and duration of heat waves. The study was carried out in Vilnius city, Republic of Lithuania. Vilnius is the capital and largest city of Lithuania, with a population of 590 000 as of 2023. The area is 401 km2 and the density is 1450/km2. The central part of the city is located in the wide and deep valleys of the Neris and Vilnia rivers, while the other districts of the city are located on the surrounding hills and their slopes. The lowest point of the city is 97 m above sea level and the highest is 234 m.An urban heat island (UHI) can amplify and prolong heat waves. This is important for the design and assessment of the energy performance of buildings in urban areas and the impact of heat waves on human health. The role of UHI will be even greater in the future due to climate change and possible global warming, as temperatures are likely to rise and the UHI will exacerbate them.In this study, hourly air temperature data, as well as daily average, maximum, and minimum air temperature data were collected from Vilnius University automatic meteorological station (VU MS) located in the city centre, and automatic stations at Vilnius Airport (VA) in the suburbs during the summer seasons of 2022–2023.In Lithuania, a heatwave is a natural meteorological phenomenon where the daily maximum air temperature reaches 30 °C or more for 3 consecutive days (or more). In total, 30% of the days in the summer of 2022 had a daily maximum air temperature above 30 °C in VU MS, while only 16% of the days in the suburban VA meteorological station reached this threshold. In the summer of 2023, 20 % and 7 % of such days were found, respectively. According to the VU MS data, 7 heat waves with a total duration of 32 days have been recorded for 2022–2023, while only 3 heat waves with a total duration of 11 days have been recorded in the suburban (VA) area. In the central part of the city (VU MS), the average daily maximum air temperature during heatwaves was 3.2 °C higher than in the suburban VA. It was also found that tropical nights (daily minimum air temperature of at least 20 °C) were more frequent in the city compared to the suburbs, and the amplitude of the daily air temperature increased during the heat waves.
Towards Resilient Viticulture: Vineyard Microclimatic Zoning as a Tool for Sustainable Adaptation
Authors: Fonseca André , Cruz José , Fraga Helder , Andrade Cristina , Valente Joana , Alves Fernando , Neto Ana , Flores Rui , Santos João
Published: July 2024

In order to achieve sustainable and optimised grape production within vineyard plots, it is crucial to have a deep understanding of the spatial variability of microclimates. This study utilises a microclimate model (NicheMapR) in conjunction with multiple climate data sources to analyse microclimatic conditions in two vineyard plots: Quinta do Bomfim in northern Portugal and Herdade do Esporão in southern Portugal. The innovative approach achieves a spatial resolution of 10 meters for climate variables. Local station hourly data is combined with ERA5-land data using quantile mapping bias correction. The microclimate model output is further used to correct biases in a EURO-CORDEX model ensemble. Specific climate extreme and bioclimatic indices designed to viticulture are computed for each vineyard plot. By conducting analysis at the 10-meter scale, it becomes possible to identify any potential shifts in temperature extremes, precipitation patterns, and other crucial climatic variables that are relevant to grape cultivation within each individual plot. In regions with complex topography, the importance of microclimate analysis is highlighted, as there are significant variations in climatic variables. However, in areas with gentle slopes, the differences in climatic variables are minimal and therefore the significance of microclimate analysis is less pronounced. According to the projections, it is estimated that there will be a median temperature increase of around 3.5°C and 3.6°C in Quinta do Bomfim and Herdade do Esporão, respectively, when comparing future scenarios for the periods 2071–2100 and 1981–2010. Additionally, there is expected to be a decrease in precipitation of approximately 98 mm and 105 mm in these areas. Thus, this study provides a comprehensive and forward-looking approach to analysing microclimates in vineyard plots. By integrating geospatial data, ERA5-land data, and the microclimate NicheMapR model, the research aims to provide viticulturists with a better understanding of current microclimates and future climate scenarios. Acknowledgments: Research funded by National Funds by FCT – Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 and LA/P/0126/2020 (https://doi.org/10.54499/UIDB/04033/2020). Vine & Wine Portugal—Driving Sustainable Growth Through Smart Innovation, PRR & NextGeneration EU, Agendas Mobilizadoras para a Reindustrialização, Contract Nb. C644866286-011.
Improvements to Solar Energy Generation Predictions via Temporal Resolution of Meteorological Data
Authors: Testik Firat Y. , Marino Daniele , Ortega Laura , Le Tuan , Testik Murat C.
Published: July 2024

Solar energy generation using photovoltaic technologies has emerged as one of the core renewable energy sources.  Therefore, its prediction, which highly relies on meteorological information, is of paramount importance for various applications including power grid planning, integrating solar power effectively into the power grid, resource allocation, and sustainable energy management.  This study presents the results of our investigation on the impact of the temporal resolution of meteorological data on solar energy generation predictions using machine learning (ML) models and a methodology that we developed to improve these predictions.  Two independent solar energy generation datasets (one for urban rooftop solar panels in San Antonio, Texas, U.S., and the other one for a solar farm in India) along with the corresponding meteorological datasets that include solar irradiation, wind, temperature, and humidity information were utilized.  Our results demonstrate that the temporal resolution of the meteorological data has a profound influence in predicting solar energy during rapid meteorological changes, particularly during sunrise and sunset times when the time rate of change of the meteorological data may be relatively large.  We developed a simple, yet very effective, method to enhance machine learning model predictions through modifying the temporal resolution of the available meteorological data.  We will present, using quantitative metrics, the predictive capabilities of our ML-based model, the impact of the temporal resolution of meteorological data on prediction accuracy, and the method that we developed to enhance prediction accuracy.  This research was supported by the funds provided by CPS Energy (San Antonio, Texas, USA) through UTSA-TSERI.
Developing Method to Forecast the Short-term Instantaneous Wind Speed for Railways Operation Control under Strong Winds
Authors: Kurihara Aki , Araki Keiji
Published: July 2024

Japanese railway companies monitor strong winds along their lines with anemometers installed at intervals of several kilometers to several tens of kilometers on average, in order to prevent railway disasters on railways caused by strong winds. When strong winds are observed by anemometers monitoring each section, train operations are controlled. Specifically, if the instantaneous wind speed observed by an anemometer exceeds a certain threshold (usually 25 m/s, on conventional lines in Japan), the speed of train traveling on that section is limited (slowed down) or suspended. However, if strong winds continue intermittently, train operation is repeatedly suspended and resumed, resulting in reduced transportation service. Therefore, we are studying to develop a method for forecasting the short-term instantaneous wind speeds that can be directly used for operation control under strong winds. The Japan Meteorological Agency (JMA) and private weather companies provide wind speed forecast information, but their forecasts are averaged wind speeds and generally have a time resolution of more than one hour. On the other hand, operation control under strong winds requires forecasts of instantaneous wind speeds with a time resolution of about ten minutes.We have developed a method for forecasting 10-minute maximum instantaneous wind speeds using instantaneous wind speed data observed at anemometers and time series analysis. Our method can forecast 10-minute maximum instantaneous wind speeds up to two hours ahead by using different parameters for the time series analysis depending on the synoptic scales that cause strong winds: typhoon, low pressure, front, winter monsoon, and others. We have also developed a method to automatically classify these five types of synoptic scales from surface weather maps using deep learning.We have confirmed that the synoptic scale can be automatically classified from surface weather maps with an accuracy of about 70%. As a result of forecasting 10-minute maximum instantaneous wind speeds for 50 cases of strong winds, it was confirmed that 10-minute maximum instantaneous wind speeds can be forecast up to one hour ahead with an RMSE of about 5 m/s or less, regardless of the synoptic scale. Furthermore, we have obtained the prospect of reducing the number of operation control under strong winds and the total suspension time by using our forecasts, compared to the current operation control under strong winds that uses only observed wind speeds.
The Met Office's Operational Surface Analysis Chart
Authors: Suri Dan
Published: July 2024

The Met Office have routinely been producing surface analysis charts operationally since 1872. Early charts bear little resemblance to current operational analyses and evolved through the late 19th and early 20th centuries to take on board evolving concepts and notions, for example Bergen School frontal theory, with fronts starting to appear on these charts in the early 1930s. These charts, and subsequent charts can be found online in Met Office publications such as the Daily Weather Report.Originally hand-drawn based on surface observations, as forecaster workstations developed in the 1980s and 1990s, production of these charts began to shift to become a wholly workstation-based graphical production process. Currently, the analyst - the duty Principle or Expert Operational Meteorologist in the Met Office's Expert Weather Hub - blends observational and model data to produce the surface pressure field and then uses a range of observational data supplemented by NWP to aid drawing the fronts. Conceptual models considered now extend beyond the Norwegian Cyclone Model to, for example, incorporate Shapiro-Keyser and Browning and Roberts Cyclone Models.Meanwhile, improvements in satellite and radar imagery and NWP resolution have allowed analyses to become increasingly detailed, revealing structures and detail otherwise not present or immediately obvious in more conventional surface observations. This has undoubtedly led to an increase in detail in Met Office analyses. Concurrently, the scale of some features on the analyses can be relatively small, especially around the UK; this is quite deliberate and necessary as these charts are both used operationally and provide the UK's official weather analysis.Inspired by questions about how these charts are made from a number of delegates at the EMS Annual Meeting in 2023, this presentation will offer a brief history of the operational surface analysis chart at the Met Office and, as one of the team of operational meteorologists producing these charts, then focus on using real world examples to illustrate the processes by which these human-produced charts are prepared.That these charts remain human-produced, rather than automated and wholly-objective, is important to appreciate - this is felt to aid continuity between charts, can offer an indication of feature development and for many producing these charts maintains a link between the current and forecast weather situation, i.e. is a necessary part of the forecast process, and on a more romantic level for some of us these charts provide a link with our forefathers and pioneers.
A causality framework to decipher prediction windows of opportunity
Authors: Ardilouze Constantin , Muñoz Ángel , Materia Stefano
Published: July 2024

The evolution of the atmosphere on weekly to sub-seasonal time scales is driven by a combination of factors from different compartments of the Earth system. At these time scales, the atmospheric predictability can arise from the initial state of the atmosphere, the land surface and the ocean. The weight of these different sources and their joint contribution can change quickly depending on the season and the time scale considered. As an illustration, while subseasonal forecasts often exhibit limited skill across mid-latitudes, occasional improvements are observed in specific locations during certain periods, known as "windows of opportunity." Understanding these windows is complex due to the diverse and interdependent nature of predictors, their spatial and temporal variability, and the challenges in establishing causality relationships. A typical strategy could consist of assessing the time-lagged correlation or regression with potential predictors. The main caveat of this approach is that even a lagged relationship between two variables X and Y does not ensure causality, because of the many confounding factors involved in complex earth system interactions. Furthermore, a high correlation between X(t) and Y(t+dt) may just be the consequence of Y(t) causing X(t). In this study, we introduce a novel approach based on Liang-Kleeman information flow, allowing the assessment of causal links across various lead times. Applied to reforecast and reanalysis data, our method identifies significant predictability drivers, revealing their evolving patterns and prevalence from seasonal to subseasonal scales. Additionally, the comparison between reanalysis and reforecast results aids in assessing the capability of models to capture these causality features. We will illustrate the theoretical background by showcasing the causal factors influencing a window of opportunity identified from a multimodel subseasonal reforecast.