bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial modeling of Zaragoza urban temperature at seasonal scale, 2015-2022.
Authors: Barrao Samuel , Serrano-Notivoli Roberto , Saz Miguel Ángel , Cuadrat-Prats José María
Published: July 2024

The aim of this communication is to use Geographic Information Technologies (GIT)  to improve the characterization of the spatial variability of temperature in urban spaces. For this purpose, an interpolation method has been developed that allows to know the spatial patterns of the maximum, mean and minimum urban temperature of Zaragoza at seasonal scale. Using as a climate database the observatory network of the Clima, Agua, Cambio global y Sistemas Naturales research group, composed of 21 thermohygrometric sensors distributed in different urban areas of the city and its surroundings. We will use daily scale temperature data from March 2015 to December 2022 to apply them in a cokriging interpolation model, complemented with spatial information of different variables. These complementary variables of satellite and territorial origin will be subjected to a previous correlation and regression analysis with temperature, including in the interpolation only those significant variables with higher correlation, discarding those correlated with other covariates. For the evaluation of the model, different combinations of model, variables and principal components were calculated using different error metrics to check which was the most appropriate option. Finally, the first three principal components of the variables Sky View Factor, DEM, Green Leaf Index, Normalized Difference Turbidity Index, Normalized Built-up Area Index and Land Surface Temperature were chosen. Finally, we obtained as a result the spatial distribution of the temperature in the city of Zaragoza at 100 meters, a seasonal average interpolation of the study period. Obtaining a complete picture of the spatial distribution of urban temperature that allows analysis at detailed scales between different urban spaces.
Making ECMWF open weather forecast data more accessible to users: maintenance challenges
Authors: Vuckovic Milana
Published: July 2024

ECMWF are moving towards an extensive open data policy, providing data to a broader user base extending beyond operational forecasters in Member and Co-operating states and commercial customers. Beginning in 2020, the first phase saw the opening of hundreds of web forecast charts (www.charts.ecmwf.int) and made archived data available under a Creative Commons (CC BY 4.0) open licence. This momentum continued in January 2022 with the introduction of an open subset of real-time medium-range forecast data, with ongoing updates incorporating new parameters and datasets. Notably, the latest update in February 2024 included increasing the resolution from 0.4° to 0.25° and included the new Artificial Intelligence/Integrated Forecasting System (AIFS) model data.This phased move towards free and open data aims to support creativity, innovation and reproducibility in scientific research and weather applications. However, this can not be achieved by only opening the real time data. The users need to be able to find and easily use the data and integrate it into their own research work or application workflows.To address this, additional efforts are underway to improve the data's FAIR (Findable, Accessible, Interoperable and Reusable) attributes. Key developments include the creation of open source Python libraries for data processing and visualisation, alongside the introduction of a set of Jupyter notebooks, each of which is reproducing one open weather forecast chart - from the downloading the data to the visualisation.However, the tools and data constantly change, and keeping up with these changes presents a significant challenge if not designed with maintenance in mind.This talk will provide an overview of the open forecast web charts and the use of Jupyter notebooks for their reproduction, followed by an exploration of the maintenance challenges and future plans.
A Bayesian approach to quantifying uncertainties in surface fluxes from the eddy covariance method
Authors: Levy Peter E.
Published: July 2024

Eddy covariance is the state-of-the-art technique for measuring fluxes of energy and gases between the land surface and the atmosphere. However, many processing steps sit between the data that are actually measured (typically a 10-Hz time series of wind speed components and gas mixing ratios) and the calculated flux. In practice, the data processing constitutes a modelling exercise, requiring a model of the measurement system and of the surface layer of the atmosphere. In conventional data processing, we treat the parameters of this model as known constants, and allow only for a random uncertainty term which diminishes as the duration of the observations increases.  This inevitably fails to comprehensively propagate the true uncertainties.In a Bayesian approach, we treat the parameters of the model (of the measurement system and atmosphere) as uncertain parameters, which we characterise with probability distributions. We use our knowledge of the physics of the system and previous data to specify prior probability distributions for these parameters. We then update these with the data we actually observe (the high-frequency time series) to yield the posterior distributions for these parameters. By a simple extension, we can include a model of the biological processes governing uptake and emissions of gases (e.g. in the case of carbon dioxide, photosynthesis and respiration). In this way, we can produce estimates of the fluxes of interest, such as the long-term net carbon balance of an ecosystem, in the form of posterior probability distributions, in which the uncertainty is correctly represented following the axioms of conditional probability. We thereby correctly propagate all of the systematic uncertainties, giving a much more accurate representation of the true uncertainty.  For example, in most eddy covariance set-ups, the time lag between the sonic anemometer and the gas analyser is  estimated from the cross-covariance function, where it shows the maximum covariance. However, whenever there is noise in the data, this time lag can be misidentified, and results in systematic over-estimates in the calculated flux. The Bayesian approach represents this more correctly, as a system with two cross-correlated random variables where the time lag between them is itself a temporally-autocorrelated random variable with uncertainty. By estimating the distribution of plausible ("posterior") time lags to form a distribution of calculated fluxes, we incorporate this uncertainty in the results. We can apply similar principles to detrending the data, coordinate rotation, frequency-response corrections, and separating advection from the eddy flux.
How North Atlantic SST Anomalies Modify the Occurrence of Atmospheric Blocking
Authors: Lohmann Richard , Ahrens Bodo
Published: July 2024

Atmospheric blocking describes a quasi-stationary weather pattern in midlatitudes which is characterised by a disruption of the westerly flow. Within the blocking anticyclone, large-scale subsidence of air effects dissipation of clouds whereas the precipitation rate increases around the block. The stationarity of the weather system increases the probability of extreme events as heatwaves, droughts or floods. Reanalyses show (multi)decadal variability of blocking frequency. This finding encourages the investigation of potential drivers of blocking, i.e. parameters in the climate system favouring or hampering the occurrence of blocking. One important driver is the variability of sea surface temperatures (SST) in the North Atlantic. In this study, we focus on a region in the south of Greenland and investigate the relationship between SST anomalies in that region and blocking in reanalyses and a subset of selected CMIP6 climate simulations. The region in the south of Greenland is known as the so-called “North Atlantic Warming Hole” because the SST trend is negative unlike in overwhelming parts of the ocean. The negative SST trend in the south of Greenland increases the temperature gradient to surrounding, warming areas resulting in a modification of the atmospheric circulation over the Atlantic and Europe and consequently in the frequency of blocking. Our results show a decreased blocking frequency over the North Atlantic in the case of a strong warming hole, i.e. in the case of a strong negative SST anomaly in the south of Greenland. The effect is strongest in winter. Over Europe, blocking frequency tends to be higher than on average. A comparison of reanalyses and CMIP6 simulations reveals that the climate models are neither able to simulate the SST trend in the North Atlantic properly nor the link between SST anomalies and blocking. Furthermore, different realisations of one model show different SST trends and different blocking patterns indicating an incomplete modelling of physical processes and feedbacks.
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Authors: Radke Tim , Fuchs Susanne , Wilms Christian , Polkova Iuliia , Rautenhaus Marc
Published: July 2024

Detection of atmospheric features in gridded datasets from numerical simulation models is typically done by means of rule-based algorithms. Recently, also the feasibility of learning feature detection tasks using supervised learning with convolutional neural networks (CNNs) has been demonstrated. This approach corresponds to semantic segmentation tasks widely investigated in computer vision. However, while in recent studies the performance of CNNs was shown to be comparable to human experts, CNNs are largely treated as a “black box”, and it remains unclear whether they learn the features for the correct reasons. Here we build on the recently published “ClimateNet” dataset that contains features of tropical cyclones and atmospheric rivers as detected by human experts. We adapt the explainable artificial intelligence technique “Layer-wise Relevance Propagation” (LRP) to the feature detection task and investigate which input information CNNs with the Context-Guided Network (CG-Net) and U-Net architectures use for feature detection. We find that both CNNs indeed consider plausible patterns in the input fields of atmospheric variables, which helps to build trust in the approach. We also demonstrate application of the approach for finding the most relevant input variables and evaluating detection robustness when changing the input domain. However, LRP in its current form cannot explain shape information used by the CNNs, and care needs to be taken regarding the normalization of input values, as LRP cannot explain the contribution of bias neurons, accounting for inputs close to zero. These shortcomings need to be addressed by future work to obtain a more complete explanation of CNNs for geoscientific feature detection.
Austria data for climate normal period 1991-202
Authors: Chimani Barbara , Tilg Anna-Maria
Published: July 2024

Climate normal periods are a common way to describe climate and its changes. With the period 1991 -2020, the first independent climate period after the frequently used reference period of 1961-1990 is available. While, according to the recommendation of WMO, the period of 1961-1990 should still be used as a reference for the long-term climate development, the newer period provides the information for the current climate and is going to be used in many applications regarding also different sectors as agriculture, infrastructure and energy.The stations for which climate normal period values for 1991-2020 have been calculated were selected depending on their:inclusion in regular international data exchange
use in climate products and services provided by GeoSphere Austria
inclusion in former homogenization activities
use in former national climate normal period datasets
the current state of the measurements of the stations
completeness in the period 1991-2020.
The homogenization of the selected stations data was done for the daily mean temperature, the daily maximum temperature, the daily minimum temperature and daily precipitation and for an as long period as possible (if possible back to 1961). This was done in order to compare the most current climate period to the 1961-1990 period. For the calculation of both climate normal periods the same criteria of completeness have been used. The method used for homogenization was ACMANT. Finally, around 170 stations where selected for most of the calculated parameters (significantly less stations have been taken into account for snow-depth, radiation and sunshine duration). The homogenization was evaluated by the analyses of breaks and adjustments, by comparison with parallel data as well as by the effect on trends and climate normal values. Additionally the results for the 1991-2020 climate normal period where compared to the results from neighboring countries.The presentation will cover aspects of the homogenization, its evaluation and the effects of climate change.
Severe weather warnings for Sofia Bulgaria: May - September 2010-2019
Authors: Guerova Guergana , Tsalova Stanislava , Stoev Krasimir , Stoycheva Anastasiya
Published: July 2024

Severe weather monitoring and forecasting is the prime task of the National Meteorological Services according to World Meteorological Organization Guidelines (WMO).  Since 2009 Bulgarian National Institute of Meteorology and Hydrology (NIMH) issues 48 h ahead severe weather warnings as a contribution to the European warning system (METEOALARM). A total of 243 days with warnings for heat, thunderstorms and intense rain were issued for Sofia, Bulgaria the period May-September 2010-2019. The monthly maximum for heat is 42 days in July, for thunderstorms is 26 days in June and for intense rain is 27 days in June. The yearly heat maximum of 21 days is in 2012 from which orange code warnings are 8 and yellow code warnings are 13. The yearly thunderstorm maximum of 16 days with both orange and yellow code warnings is in 2018 where one day is with orange code and 15 days are with yellow code warnings. The yearly rain maximum of 20 days with both orange and yellow code warnings is in the same year 2018. There were 3 days of orange code warnings for rain, and 17 days with yellow code warnings. Conducted is objective circulation classification which shows that the majority of heat warnings are associated with Anticyclonic and West flow circulation types. About 40% of thunderstorm warnings are of Cyclone type followed by 28.6% Cyclonic Directional types. For rain warnings, most of the days are with Cyclone type (46.2%) and Cyclonic Directional types (27.5%). Analysis of Global Navigation Satellite Systems (GNSS) derived Integrated Water Vapor (IWV), from the  Bulgarian station in Sofia, shows  consistently higher values for the days with METEOALARM warnings for heat, thunderstorm and rain. 
Integrating Analog Methods with Other Machine Learning Techniques to Further Enhance Wind Speed Forecasting
Authors: Vujec Ivan , Odak Plenković Iris , Schicker Irene , Lozuk Jakov
Published: July 2024

NWP models have been crucial for modern weather forecasting for a long time. Although their skill is improving, the errors they exhibit can still be substantial. The additional forecast improvement is often obtained by applying statistical and machine learning (ML) post-processing techniques, especially for the locations where the measurements are available. Post-processing of wind speed using the analog-based method has been successfully implemented and analyzed at DHMZ for a long time. But considering the relatively recent surge of various other machine learning techniques, the next logical step is to combine  the analog method  with them to bring further improvements. In this work, we are trying to determine whether the output of the analog method can successfully be used as an input of deep-learning and gradient-boosting methods in order to benefit from both approaches.The raw NWP model used in this work is the ALADIN model with 2 and 4 km horizontal resolutions. The analog-based method is first applied to raw NWP, where the method includes weight optimization and the correction for more extreme values, as well as the novel variation with varying ensemble sizes. Then, the deep-learning and gradient-boosting methods are fed with both analog ensemble and raw NWP. Besides using all the analog ensemble members, the analog ensemble can also be characterized by the descriptive values, which therefore reduces the number of machine learning predictors. The forecasts are verified against the wind speed measurements across the Republic of Croatia.The continuous and categorical approaches are used for the verification of the hourly wind speed forecasts. In the categorical approach, verification is also performed for both common and more extreme events. Additionally, verification is also performed for different types of stations. Results show a clear benefit of using the analog method as a generator of additional ML predictors to raw NWP.  The exact improvement is dependent on the type of predictors used in the process. Finally, the results can be fine-tuned, depending on the main goal. Since the extreme events are particularly hard to predict, the variation which is able to further improve the performance for such events is emphasized.In conclusion, this work demonstrates the potential of integrating analog methods with machine learning techniques to improve wind speed forecasting. By combining the strengths of both approaches, the enhancements in forecast accuracy are achieved, even for extreme events. These findings underscore the importance of exploring novel methodologies to advance weather prediction capabilities and mitigate the impact of severe weather events.
Fast approximation for calculating deep convection cloud top heights from satellite brightness temperature
Authors: Šoljan Vinko , Jurković Jadran , Babić Nevio
Published: July 2024

Due to fuel efficiency most air traffic takes place at the top of the troposphere, always avoiding deep convective clouds because of associated hazards such as severe turbulence, icing, hail, and lightning. If deep convection cloud tops are not very high, airplanes can sometimes fly above them, deviating less from their planned flight paths and causing less delays. Because of this, deep convection cloud top diagnosis and forecast is very important in aviation meteorology. One of the methods to estimate altitude of existing convective cloud tops is to compare infrared satellite brightness temperature with a calculated parcel curve temperature (parcel theory). The premise here is that we already have deep convective clouds so we are not interested in the full vertical temperature profile (sounding). To calculate the cloud top pressure, which is directly related to altitude in the standard atmosphere, we only need the parcel curve and measured satellite brightness temperature. Parcel curve is usually calculated iteratively from surface temperature and dewpoint, but this calculation can be computationally intensive for many points. In the first part of this work we will test different approximations of moist adiabat calculation and compare them to classical iterative method to see which one gives us acceptable error for estimating cloud top heights from satellite data. The second phase will implement the best approximation in operational forecaster environment.We will also try to test some expected limitations of this approach, as calculation of parcel curve from only surface temperature and dewpoint is realistic only for diurnal deep moist convection, while in the case of elevated convection the most unstable layer temperature and dewpoint should be used. 
Area, volume and ELA changes of West Greenland local glaciers and ice caps over the last 35 years
Authors: Securo Andrea , Del Gobbo Costanza , Citterio Michele , Machguth Horst , Marcer Marco , Korsgard Niels , Colucci Renato R.
Published: July 2024

The behaviour of mountain glaciers on decadal time scales is a useful indicator for assessing climate change. Although less monitored and studied than the ice sheet, local glaciers and ice caps (GICs) along the coast of Greenland are significant contributors to meltwater runoff and sea level rise. This study analyses the cumulative area, ice mass and ELA changes occurred in 4100 GICs in West Greenland from 1985 to 2020, using remotely sensed data and including smaller glaciers in the calculations. GICs involved in the study decreased in area by almost 15%, which is equal to -1774 ± 201 km2. Their surface elevation decreased on average by 20.6 ± 2.9 m, corresponding to a rate of -0.5 m w.e. yr-1. The Equilibrium Line Altitude (ELA) shows a median regional rise of 150 m since the 1980’s with marked local variability and higher median rise in the northern areas of this study. Strong regional gradients in ELA of individual GICs are found, both towards the ice sheet and in areas where local orography affects precipitation. The observed high spatial variability of changes suggests that more monitoring on sub-regional level is needed to reduce uncertainty regarding the future of GICs. This study on West Greenland Local Glaciers and Ice Caps underscores their diverse nature in terms of glacier type, size, aspect, and hypsometry. Measurements show high variability in the net changes from 1985, including area and volume shrinkage, alongside an elevation rise of ELAs. We observe a nearly 15% reduction in overall glaciated area, accompanied by a mass balance rate of -0.5 m w.e. yr-1 (- 5.6 ± 0.7 Gt yr-1), aligning with observed rates in the Arctic.
The atmospheric boundary layer height during FESSTVaL derived from in-situ and ground-based remote sensing profile measurements
Authors: Beyrich Frank , Detring Carola , Matiaske Carina , Ripke Kim
Published: July 2024

The height of the atmospheric boundary layer (hABL) is an important integral variable to characterise the state of the lower atmosphere with respect to different applications such as the assessment of trace gas and aerosol transport and mixing, the prediction of low-level clouds, the description of sound propagation, and the scaling of ABL profiles for use in engineering models and parametrizations. Different methods have been suggested and qualified to derive hABL based on profile measurements of atmospheric state and process variables with ground-based in-situ and remote sensing techniques. During the Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) which took place around the Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory (MOL-RAO) of the German Meteorological Service (DWD) in summer 2021, a suite of ground-based remote sensing systems (including Doppler wind lidars, DWL, ceilometers, and microwave radiometer profilers, MWRP) was operated to characterize the status of the ABL over a heterogeneous land surface. Data from these instruments were used to derive estimates of hABL from the ceilometer backscatter intensity profiles, from the profiles of the vertical velocity variance measured with an upward looking DWL and from the wind and temperature profiles obtained from DWL and MWRP using the bulk Richardson number method. As an independent in-situ reference, different hABL estimates derived from 6-hourly operational radiosonde ascents were considered. In the presentation we discuss both case studies and results of a statistical comparison of the different hABL retrievals. Each of the methods employed has its strengths and weaknesses suggesting a synergistic approach to construct a reliable hABL composite data set. 
Long-Lead ENSO Predictability: the 2023/24 El Niño
Authors: Petrova Desislava , Rodó Xavier , Koopman Siem Jan , Tzanov Vassil , Cvijanovic Ivana
Published: July 2024

Long-lead climate predictions are increasingly in demand due to climate change. Through its well-known atmospheric teleconnections El Niño Southern Oscillation (ENSO) is a leading source of seasonal and interannual climate predictability, but currently operational ENSO forecasts are limited to about two seasons in advance. At the same time the scientific literature pointing to the feasibility of ENSO forecasts one year and even more in advance is increasing. The early anticipation of ENSO could prepare vulnerable communities around the world and help mitigate its most devastating impacts such as droughts, floods, poor harvests, and the spread of infectious diseases. Here we showcase real-time forecasts of the recent 2023/24 El Niño at lead times up to 1.5 years in advance of an expected peak in December 2023. We use a previously validated statistical ENSO model that relies on surface and subsurface ocean temperatures and zonal wind stress as predictors, and includes various dynamic components in the form of time-varying cyclical components. Real-time early forecasts with the same model were also issued for the 2015/16 and 2018/19 El Niños, when the long-lead forecasts were also coupled to an impact dengue fever model for the city of Machala in Ecuador to predict the probability of a dengue outbreak in the region up to 11 months in advance. In both cases the dengue predictions were successful indicating an outbreak in 2016 and a low dengue season in 2019. Therefore, such longer-lead ENSO forecasts could directly facilitate decision-making in the health sector, but also other key sectors of society.
Extreme precipitation in the past and future
Authors: Fauer Felix S. , Rust Henning W.
Published: July 2024

We create Intensity-Duration-Frequency (IDF) curves which describe the major statistical characteristics of extreme precipitation events (return level, return period, time scale) and are capable of considering large-scale influences. They provide information on the probability of exceedance of certain precipitation intensities for a range of durations from minutes to days and can help to visualize either how extreme (in terms of probability/frequency/return period) a specific event is or which intensity is expected for a given probability. We modeled the underlying distribution of block maxima with the Generalized Extreme Value (GEV) distribution. Maxima from different durations are used and enable a model that can evaluate different time scales. All durations are modeled in one single model in order to prevent quantile-crossing and to assure that estimated quantiles are consistent.The influence of climate change is included by letting the GEV parameters (covariates) depend on the variables NAO, temperature, humidity, blocking and year (as a proxy for climate change). We found an increase in probability of extreme precipitation with year and temperature, while the effect of the other variables depends on the season. This corresponds to the aim of the session UP3.1, to answer whether there have been significant changes in frequency or amplitude of extreme events. Since it is easier to project average values than to project extremes, we use the modeled relations between average large-scale covariates and extreme precipitation to create future IDF-relations based on climate projections and the projected average large-scale values. This poses some challenges because the polynomial dependencies of the past might not hold for an extrapolation into the future. Furthermore, we plan to add a spatial component to the model that enables the usage of data from several neighboring stations in one model and interpolate to ungauged sites. This will be the basis for investigating how gridded data sets can be used to complement the station-based approach. One focus will lie on the dependence between neighboring grid points.
Geographic validation of Satellite-Based Solar Irradiance Forecasting Models Across Europe
Authors: Meddahi Amar , Albergel Simon , Chea Nicolas , Tuomiranta Arttu , Guillon Sebastien , Saint-Drenan Yves-Marie , Blanc Philippe
Published: July 2024

Solar forecasting is a critical tool for mitigating the variability of solar power availability within the electric grid, notably influenced by atmospheric conditions. These models often fail to adapt to the diverse climatic and cloud conditions across different regions [1], complicating efforts for PV project developers to estimate forecast accuracy without historical dataset of in-situ and hindcast.Among a great number of solar forecasting applications and approach, we focus on intra-day solar forecasting based on geostationary satellite. We examine the sensitivity of solar irradiance forecasting accuracy to varying geographic locations and cloud conditions across Europe. Our goal is to assess how these models perform under different settings and their generalizability across diverse European regions and weather situations.To evaluate our methodology, we employed a cloud motion vector (CMV) algorithm through the high-resolution visible (HRV) channel of the Meteosat Second Generation (MSG) SEVIRI instrument [2]. The scope of our study was confined to Western and Central Europe to ensure comparable satellite spatial resolutions, thereby eliminating spatial resolution as a significant source of variability in performance comparison. We selected six BSRN stations [3], which underwent advanced quality checks [4], with half located in Cfb (temperate oceanic) climate zones and the other half in Dfb (humid continental) zones. We analyzed a complete year's dataset from these stations to evaluate the forecasting model’s performance across the different seasons, cloud coverage, meteorological regimes and spatial locations.Preliminary analysis using the persistence method has identified three distinct performance clusters of geographical locations, each represented by one meteorological station from both Dfb and Cfb climatic zones. To enhance this analysis, the CMV algorithm will be employed as it quantifies advection movements of clouds, thereby improving our understanding of performance variability across diverse sites. [1] Liu, Bai, et al. "Predictability and forecast skill of solar irradiance over the contiguous United States." Renewable and Sustainable Energy Reviews 182 (2023): 113359.[2] Cros, Sylvain, et al. "Reliability predictors for solar irradiance satellite-based forecast." Energies 13.21 (2020): 5566.[3] Driemel, Amelie, et al. "Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017)." Earth System Science Data 10.3 (2018): 1491-1501.[4] Blanc, Philippe, et al. "Data sharing of in-situ measurements following GEO and FAIR principles in the solar energy sector." (2022).
Cyprus GNSS (Global Navigation Satellite System) Meteorology Enhancement 
Authors: Oikonomou Christina , Haralambous Haris , Tymvios Filippos , Charalambous Demetris , Satraki Margarita , Kotroni Vassiliki , Lagouvardos Konstantinos , Loizou Eleftherios
Published: July 2024

The present work aims to describe the main objectives and activities of the research project “Cyprus GNSS Meteorology Enhancement” (CYGMEN) funded (€1.500.000) by the Cyprus Research Innovation Foundation (RIF) in the frames of "Strategic Research Infrastructures" Call for Proposals. CYGMEN was initiated at December 2023 with the aim to establish a Meteorological cluster (CyMETEO) in Cyprus that will strategically augment existing Frederick Research Center and Department of Meteorology infrastructure, through the introduction of: a) a Lighting detection network, b) a dense GNSS network for atmospheric water vapor estimation (supported by Cloudwater Ltd and Nicosia Development Agency, c) a Radar Wind Profiler (RWP) and d) a Microwave Radiometer (MWR). Within this framework, preliminary activities for the establishment of CyMETEO infrastructure will be presented here. The CyMETEO infrastructure will be also accompanied by an advanced CyMETEO service that will be developed in order to: a) process and provide in near real-time all different types of data generated by CyMETEO infrastructure (CyMETEO Observational Analysis Component) and b) provide advanced short-term weather forecasting through the assimilation of CyMETEO data into the state-of-the-art Weather Research and Forecasting (WRF) model currently employed operationally by the Cyprus DoM without, however, performing any Data Assimilation (CyMETEO Simulation Analysis Component). Thus, preliminary results from the CyMETEO service design and development will be also shown. Furthermore, another aim of this study is to evaluate the performance of the GNSS tropospheric analysis component of the CyMETEO system over Nicosia district, prior to its deployment. For this purpose, GNSS tropospheric product (IWV) as deriving from the hyper-dense CYGMEN GNSS station network will be validated by being compared with respective IWV values obtained from Nicosia Radiosonde station, as well as from ERA5 Reanalysis datasets.
Impacts of external forcing and internal variability on the evolution of hydroclimate during the last millennium
Authors: Roldán-Gómez Pedro José , González-Rouco Jesús Fidel , Smerdon Jason , García-Pereira Félix
Published: July 2024

Evidences from climate reconstructions and model simulations show that climate experienced important changes during the Last Millennium (LM). These changes can be associated with external forcing factors, such as cycles of solar activity, changes in the concentration of greenhouse gases in the atmosphere, volcanic eruptions, changes in orbital parameters, or changes in the land use and land cover, or with a redistribution of energy as part of climate internal variability. Both external forcing and internal variability contributed to alter temperatures, ice extent and atmospheric dynamics, with impacts on the hydroclimate in different parts of the world. The LM was characterised by a period of higher radiative forcing during the Medieval Climate Anomaly (MCA; ca. 950-1250), a period of lower radiative forcing during the Little Ice Age (LIA; ca. 1450-1850), and a rapid increase of the radiative forcing during the industrial era.This work analyses simulations of the LM from the Paleoclimate Modelling Intercomparison Project (PMIP3 and PMIP4); grids of reconstructed data based on tree rings from the Drought Atlases of Europe (OWDA), North America (NADA) and Asia (MADA); data assimilation-based products like the Paleo Hydrodynamics Data Assimilation product (PHYDA) and the Last Millennium Reanalysis (LMR); as well as individual reconstructions based on tree rings, marine and lake sediments, speleothems, ice cores and documentary information, with the main objective of analysing the hydroclimate variability from interannual to multicentennial timescales from a global perspective, how these changes impacted tropical and extratropical regions, their relation to changes in temperatures and atmospheric dynamics, and the impact of external forcing and internal variability on their occurrence.These analyses show coordinated changes in the hydroclimate of separate regions, both in extratropical and in tropical areas. These changes can be linked in extratropical areas to alterations of extratropical modes like the North Atlantic Oscillation (NAO), the Northern Annular Mode (NAM) and the Southern Annular Mode (SAM) and in tropical areas to alterations of the Intertropical Convergence Zone (ITCZ).
Assessing spatial biases of a meteorological model for real-time flood forecasts in the hydraulic node of Milan
Authors: Gambini Enrico , Ravazzani Giovanni , Mancini Marco , Valsecchi Ismaele Quinto , Cucchi Alessandro , Negretti Alberto , Davolio Silvio , Ceppi Alessandro
Published: July 2024

Meteorological forecasts are crucial for mitigating extreme weather impacts, but they always contain sources of uncertainty. One of these arises in the prediction of the location of intense convective precipitation systems: this issue is particularly critical for flood forecasting in small watersheds, where even a slight discrepancy in the predicted rainfall location can lead to significant inaccuracies in flow forecasts.In this study, we propose a methodology for quantifying the spatial biases of rainfall forecasts produced by the MOLOCH meteorological model on the hydraulic node of Milan (northern Italy), which is a strongly urbanized and sensible territory due to the presence of productive activities, critical infrastructures and more than 4 million inhabitants. The goal is to investigate whether the model exhibits “preferential” directions where it tends to misplace convective precipitation events.A total of 65 significative convective rainfall episodes were selected during the 2013-2022 period, comparing the rainfall forecasts at day+0 with the observed rainfall field obtained through a merging of radar and rain-gauge stations (“PRISMA” dataset, provided by Lombardy Region’s Civil Protection).The proposed procedure relies on the definition of a domain around the study area which is suitable to define the eastwards/northwards spatial misplacements of the forecast rainfall field, such domain was defined accordingly to an analysis on the Fractional Skill Score (FSS).Results indicate that the MOLOCH meteorological model tends to misplace convective rainfall systems towards the North-West and North-East directions, and this outcome could be significant to forecasters operating in the civil protection sector, especially for these river catchments with limited spatial extent.
Frequency and trajectories of the Mediterranean cyclones and their influence on precipitation in Bulgaria during the months of September-April for the period 2010-202
Authors: Popov Hristo , Georgieva Anna-Maria
Published: July 2024

The Mediterranean region is known for the formation of an area of low atmospheric pressure during the cold half of the year and the formation of cyclones. Creation of cyclones in this area is influenced by both cold air advections along the cold fronts of the Atlantic cyclones, the East European Maximum, and high pressure ridges coming from the Azores Maximum located to the west. The formed cyclones over the waters of the Mediterranean Sea have an impact on a vast territory covering southern Europe, the Middle East and the northern parts of Africa, which defines the zone of Mediterranean climates. Their frequency, mode, trajectories, speed of movement and duration of retention over a given territory determine to varying degrees the intra-annual rainfall regime, as well as the individual economic activities related to them.For the purposes of this article, data from synoptic maps at a height of 500 hPa were used, as well as average monthly precipitation data for 10 meteorological stations - four located in Northern Bulgaria (Vidin, Pleven, Ruse, Varna) and six in Southern Bulgaria: Burgas , Sliven, Plovdiv, Sofia, Kyustendil and Sandanski. They are collected from NIMH monthly newsletters.Pisarski's (1955) classification scheme was used as the basis for the types of trajectories of Mediterranean cyclones. The monthly, seasonal and annual total number of Mediterranean cyclones is calculated. The main areas of cyclogenesis and their percentage ratios in relation to the total number of formed cyclones have been established. For each trajectory, the shares of cyclone formation areas relative to the total number of Mediterranean cyclones formed were calculated. The monthly and seasonal distribution of cyclones passing along the different trajectories, as well as average, minimum and maximum duration, are presented.In order to analyze the influence of Mediterranean cyclones on the precipitation in Bulgaria, the correlation coefficient (r) between the precipitation amounts for the 10 selected stations and the frequency of occurrence of the formed cyclones was used.
The three-way feedback between North-Atlantic circulation regimes, Rossby wave breaking, and surface weather.
Authors: Tamarin Brodsky Talia , Harnik Nili
Published: July 2024

Rossby Wave Breaking (RWB) events describe the last stage in the life cycle of baroclinic atmospheric disturbances. These breaking events can strongly influence the large-scale circulation and are tightly related to low-frequency weather regimes. In addition, RWBs are often associated with weather extremes such as heat waves, blockings, and extreme precipitation events. Here we examine the three-way interaction between RWB, weather regimes, and surface weather in the North Atlantic. This is done by combining a RWB detection algorithm, a storm-tracking routine, and a clustering technique to identify low-frequency circulation regimes in the North-Atlantic. We find that regardless of weather regime, most cyclones and anticyclones are associated with an Anticyclonic Wave Breaking (AWB) and/or a Cyclonic Wave Breaking (CWB) at some point during their lifetime, while very few storms do not involve any upper-level wave breaking (~11%). Moreover, storm characteristics (e.g., actual and relative positions, intensities, and displacements) differ depending on the associated breaking type. In “same-pairing” cases (i.e., cyclones with CWB and anticyclones with AWB) the surface system is positioned so that its associated upper-level winds would enhance the breaking (the anomalous circulation is in the same direction as the background shear). In “opposite-pairing” cases (i.e., cyclones with AWB and anticyclones with CWB), the upper-level winds associated with the surface system do not act to enhance the breaking which occurs in the direction of the background shear. In addition, we find that the surface storm characteristics are significantly altered with the weather regime, with distinct and clearly preferred storm paths in each cluster. We suggest a picture in which the resulting RWB frequencies and positions in each cluster are modified by the corresponding tracks of cyclones and anticyclones, with the maximum breaking found where there is a constructive interaction with the low-frequency flow. The positions of RWBs, in turn, shape the overall cluster structure and contributes to the persistence of the weather regime. An improved understanding of the relation between weather systems, RWB events, and weather regimes can also help us improve our understanding of and confidence in projected future circulation changes (e.g., by relating changes in the frequency and positions of RWB events, storm-tracks, and the North-Atlantic jet).
National climate services to support standardisation - examples from Switzerland
Authors: Schwierz Cornelia , Schlegel Thomas , Aller Dörte , Wehrli Kathrin , Kotlarski Sven , Fukutome Sophie , Schröer Katharina , Croci-Maspoli Mischa
Published: July 2024

In the construction sector in particular, climate change is increasingly being factored into planning due to the long service life of structures. Among other things, standards aim to prevent damage through their specifications. Many of these are dependent on weather parameters. They are regularly reviewed and this offers the opportunity to incorporate more up-to-date or newly available data, to apply modernize the used methods, and to take climate change into account.MeteoSwiss has been working closely with the Swiss standardisation associations for many years with the aim of supporting optimal foundations for forward-looking standardisation and adaptation to climate change in the construction sector. This not only involves drawing on existing data, but also developing new foundations and co-designing climate services in joint projects. In these interactions, MeteoSwiss often takes on a coordinating role as well as providing the scientific expertise for the climate data and the statistical evaluations. In addition, as a national weather service, the experience as a provider of reliable operational services is key. On the stakeholder side, the knowhow about the specific problem, the risk landscape and the needs of the end-users are essential for the creation of the products and services.In this presentation, some of these collaborations and their results will be presented. These include the heating and cooling of buildings, hail protection and property drainage. These examples will also be used to reflect on which elements are important for the long-term interaction between a national weather and climate service and stakeholders, how uncertainties can be dealt with by engaing into a risk dialogue and what lessons have been learned by both sides over the years in order to put actionable climate services into practice.
Climate extreme values in Andorra, evolution in terms of frequency and intensity
Authors: Martín Guillem , Rivero Lucia , Albalat Anna , Sinfreu Gemma , Molné Toni
Published: July 2024

Climate change in mountain areas is faster than in other parts of the world. In addition, the rate of temperature increase in the context of climate change can be altered by changes in the environment, including the cryosphere. So, the loss snow cover and a dry floor can increase the temperature additionally.Temperature increase study shows how in recent decades the rise in temperature is accelerating. If we focus on the extremes, in some of climatic indices such as maximum and minimum temperature records, we see that in Andorra maximum temperature records continue to occur while minimum temperature records stopped occurring in the 1980s. The analysis is made with 90-year-old stations in order to have an initial 20 years for comparison and from this period we add registered values up to the present day. Before this analysis, the climate series have undergone a process of quality control and homogenisation.In this poster we will quantify the speed increase in temperatures rise in two historical weather stations from Andorra over the last decades and we will see how the frequency and intensity of extremes study demonstrates an alteration of the climate beyond a simple statistical distribution.In terms of precipitation, changes are also observed in the behaviour and distribution of extreme precipitation and drought periods.Through the SPI drought index we can see how it is becoming easier and easier for it to oscillate in short periods around 0 or negative values, while if we study the SPEI, which takes into account temperature, i.e. evapotranspiration, we will see how this index is increasingly capable of reaching negative extreme values.capable of reaching negative extreme values.
Variability and changes of snow cover in Central European mountains 
Authors: Wypych Agnieszka , Ustrnul Zbigniew , Sałaja Julia
Published: July 2024

Mountainous regions present a distinct vulnerability to climate change, serving as valuable barometers for tracking these shifts. Studies conducted across various global locales reveal a recent uptick in temperatures within mountainous terrains, notably evident since the 1980s, with variations in intensity across specific altitude bands. While temperature rises are not consistently accompanied by notable trends in annual precipitation totals, they do prompt alterations in the annual precipitation patterns and types, thereby impacting the frequency and duration of snow cover.Since snow cover plays a pivotal role in the Earth's systems, influencing hydrology, climate, and ecological environments, any alterations in snowpack patterns will yield multifaceted effects. This study aims to investigate the variability of snow cover metrics, including snow depth, duration, persistence, and extent.The study focused on mountainous regions in Central Europe, defined as areas with elevations exceeding 500 meters above sea level within the domain of 10°E - 25°E longitude and 47°N - 55°N latitude. Data analysis spanned from 1961 to 2023 and drew upon various meteorological sources, including on-site snow depth measurements and regional ERA5-Land reanalysis. To better capture the details of complex terrain, a new gridded dataset was generated by integrating measurements with reanalysis data at a spatial resolution of 0.5 kilometers.The primary findings underscore the significant influence of ongoing climate change on snow cover characteristics, with the intensity of this impact varying based on geographical and terrain factors such as altitude, landform, slope, and aspect. However, a consistent multiannual decrease in snowpack persistence was observed across the entire research area.A comprehensive understanding of the timing of snow accumulation and ablation is essential, as it governs springtime mountain runoff rates, water infiltration, groundwater storage, and transpiration rates—all critical components of the hydrological cycle.