bluuebunny/crossref_metadata_2025 huggingface dataset:
Changes in the phenological development of lime tree under future climate conditions
Authors: Hájková Lenka , Možný Martin , Vlach Vojtěch , Musilová Adéla
Published: July 2024

Climate change affects ecosystems and biodiversity, and the phenological development of plants has been changing. In this paper, we focused on the beginning of the flowering of the lime tree (Tilia cordata) in urban and rural settings.  The long-term series were evaluated from phenological and meteorological stations of the Czech Hydrometeorological Institute (period 1923–2023). The trends (using the Mann-Kendall test) and Pearson’s correlation coefficients between the beginning of flowering and meteorological variables were calculated.  Climate scenarios SPP2 4.5 and SPP5 8.5 (ALADIN-CLIMATE/CZ) were used to predict future climate conditions for lime tree phenological development. The main finding of this research is that the beginning of flowering of the lime tree during the period 1923–2023 is negative, and it varies in urban and rural environments. The results express the most significant acceleration of the beginning of flowering of T. cordata by -0.227 day per year in urban area and by -0.119 day per year in rural area. Moreover, the study proved that the beginning of flowering of T. cordata closely correlated with temperature (the Pearson’s correlation coefficient is in the range from -0.498 to -0.508.), and the correlation with precipitation is significantly weaker in both environments. The increase in air temperature in June and July will be significant in the period 2081–2100 (by 1.5 °C). Based on these findings, we expect a further shift in the beginning of T. cordata flowering to earlier dates. A change in the beginning of lime tree flowering will also affect the course of the pollen season. 
The Copernicus Climate Change Service’s data rescue and surface meterological data collection effort 
Authors: Healion Kevin , Thorne Peter , Noone Simon , Andersson Axel , van der Schrier Gerard , McKinstry Alastair , Poli Paul
Published: July 2024

 
Access to climate data is essential if we are to better understand the climate of the past, present and future. Climate scientists require data to reconstruct past climate and extreme weather events, to create seasonal forecasts and to produce climate projections. Various private and public sector actors also require climate data as part of their climate-related decision-making and planning. Historical data can assist the insurance sector by providing information on past extreme weather events. Farmers require data to understand how the future climate will impact their output. The data can also help populations who live along coastlines better understand the changing nature of storm surges. Finally, those concerned about biodiversity can use the data to understand how climate change may impact flora and fauna in future.  

Our proposed poster will provide a visual representation of the various services offered by the Copernicus Climate Change Service for data rescue and surface meteorological data access over land and marine domains. We shall outline key tools available including the data rescue service and the data deposition service. We will also provide an overview of the data available via the C3S data store (CDS). We shall outline progress to date on improved curation of the fundamental data record of surface meteorological holdings. This includes key collections that have been recently secured via Copernicus agreements and new open data policies from various European National Meteorological and Hydrological Services. We will provide information on what data will be available in the next data release and plans for the very final release of the current contract. Finally, we will highlight how you can get involved to help improve the curation and access to the fundamental data record. 

The core mission of the Copernicus Climate Change Service is to “support adaptation and mitigation policies of the European Union by providing consistent and authoritative information about climate change”. Rescuing historical data and making that data freely accessible forms an important part of this core mission and is vital as Europe and the rest of the world prepare for further changes to the global climate. 
Extreme air temperature in Toruń (Poland), 1871–202
Authors: Pospieszynska Aleksandra , Przybylak Rajmund
Published: July 2024

Extreme air temperatures in Toruń were analysed for the period 1871–2020. The air temperature data were collected from data of the Prussian, German and Polish (Archives of the Institute of Meteorology and Water Management − National Research Institute) networks. Possible deficiencies in the monthly average data were supplemented by the method of constancy of differences based on data from the station in Bydgoszcz (correlation of air temperature data from Bydgoszcz and Toruń reaches 0.99 and is statistically significant). The data was then homogenised using AnClim software (Štěpánek, 2008). Based on quantile thermal classification criteria (Czernecki & Miętus, 2011), months, seasons and years were determined to be: extremely warm (>95.00 percentile), anomalously warm (90.01–95.00), very warm (80.01–90.00), warm (70.01–80.00), slightly warm (60.01–70.00), normal (40.01–60.00), slightly cool (30.01–40.00), cool (20.01–30.00), very cool (10.01–20.00), anomalously cold (5.00–10.00), extremely cold (
Amazing grass: a numerical study of atmosphere-grass interactions
Authors: Van de Wiel Bas , Yi Dai , Boekee Judith , Ten Veldhuis Marie-Claire , Sauerbier Julian , Vis Gijs , ter Horst Tijn , van der Linden Steven
Published: July 2024

The usage of roughness length concept in atmosphere-vegetation modeling is convenient due to its simplicity. However, where the roughness length concept often leads to reasonable results, its empirical nature comes also at the cost of biases and parameter uncertainty as parameters become situation-dependent. For tall canopies, therefore, successful attempts have been made to explicitly model atmosphere-vegetation interaction as to avoid roughness length concepts and replace them by more physical parameterizations. For small canopies, such as grass, studies with that philosophy are very few. Yet, similar problems with roughness length concepts exist. Progress was hampered by our inability to probe the microclimate of grass due to its limited vertical extent. But with the appearance of miniaturized sensors, more and more observations of this thin,  intriguing layer have become available. Inspired by this, we aim to numerically simulate grass-atmosphere interaction with respect to momentum and heat and moisture exchange. We use direct numerical simulation (DNS) to explicitly simulate flow-obstacle interactions. Flow over rigid and non-rigid (flexible) grass elements are studied for different idealized configurations. We combine those 3D simulations with simplified 1D surrogates and analytical solutions (parameterizations) with the aim to replace the roughness length concept for grass with simple alternatives. It is shown that indeed such alternatives are possible and that, apart from the Reynolds numbers, there are strong physical parallels between flow over grass and flow of tall canopies. By exploiting those similarities, we think that biases due to usage of roughness length concepts can be avoided in the future.
Climate Transition Risks for Lithuania’s Economy
Authors: Kapilovaite Justina , Daugvila Darius
Published: July 2024

Climate transition risks arise during the global transition to a low-carbon economy. These risks encompass four principal subcategories: legal and policy, technological, market, and reputational. This study reviews climate transition risks for Lithuania's economic sectors and evaluates them by using the methodology developed by the authors of this work. In this study, non-financial enterprises were analysed. Six economic sector types (NACE branches) with the highest turnover in 2022 were selected for analysis: (1) manufacturing, (2) wholesale and retail trade, (3) electricity, gas, steam, and air conditioning supply, (4) information and communication, (5) construction, and (6) transportation and storage. For each selected economic sector type, thirty companies with the highest revenues in 2021 were selected for analysis. This study analysed selected companies' strategies for reducing greenhouse gas (GHG) emissions by 2030 and 2050, alongside evaluating their GHG reports across three scopes. Furthermore, financial metrics (debt-to-equity ratio and return on assets), as well as organization structures were considered. Based on the results of a study by the European Central Bank (ECB/ESRB, 2021), each economic sector type was assigned a score that assesses the long-term impact of a rapid transition from fossil fuels. Transition risks were categorized into five tiers: very high, high, moderate, low, or very low, and subsequently attributed to the analysed companies. The results are presented by aggregating the risks according to NACE branches. Obtained results indicate that in Lithuania, a very high transitional risk arises for companies engaged in transportation and storage, as well as construction activities. It was found that Lithuania's companies engaged in construction activities most often do not have climate and energy strategies for reducing GHG emissions by 2030 and 2050, and neglecting potential climate change-related risks. High risk was identified for Lithuanian companies engaged in wholesale and retail trade, and energy supply companies. The low risk was identified for companies engaged in information and communication activities. The financial results of information and communication companies are good, with many of them having 2030 and 2050 climate and energy strategies and the climate transition risk long-term impact for them is low as well. The conducted research is important as an early warning indicator of existing climate transition risks and the increasing need to adequately prepare for them.
A Variance Budget to estimate the Growth and Interaction of Uncertainties on Convective Scales 
Authors: Matsunobu Takumi , Keil Christian , Craig George
Published: July 2024

Convection-permitting ensemble prediction systems (EPS) are often underdispersive. To build a more reliable EPS, it is crucial to represent not only uncertainty in initial and lateral boundary conditions (IBC) but also uncertainty arising from model deficiencies. Uncertainty representations like stochastic parameterizations can add substantial variability to deterministic forecasts. However, it has been shown that when additional model uncertainty representations are implemented into an existing EPS that includes IBC uncertainty, they increase the total ensemble variance only slightly. This study aims to quantify this redundancy in ensemble variance by introducing a new "variability budget" framework. The framework decomposes the total ensemble variance into the sum of individual variances and their correlations, measuring the efficiency in increasing the total variance in relation to pre-existing variance.The variance budget framework is applied to a convective scale EPS based on operational IBC uncertainties, augmented by two model uncertainty representations, the physically-based stochastic perturbations scheme (PSP) and microphysical parameter perturbations (MPP). By adding these two model uncertainty representations we find a marginal increase in total variance of wind, temperature and humidity at various heights. In particular, PSP introduces variability at convection initiation, while MPP prolongs the lifetime of variance. Both model uncertainties work in sub regions of areas influenced by IBC uncertainty and impact convective activity especially at small scales. Since the impact of PSP and MPP is mostly negatively correlated with the existing impact, this only leads to a slight increase in the total variance. A flow-dependent assessment based on the strength of convective forcing reveals that the model uncertainties show larger variances in weakly-forced conditions but with stronger negative correlations. This negative correlation is primarily attributed to random displacements of convection, with a stronger effect during weak forcing when convection is more intermittent compared to strong forcing. 
Nowcasting applications of remote sensing to reduce weather risks to traffic.  
Authors: Müller Richard , Barleben Axel , Jerg Matthias
Published: July 2024

In recent years, DWD has developed a ground-breaking nowcasting methods for thunderstorms (TS) and strong convection based on intelligent combination of lightning data, satellite information and Numerical Weather Prediction (NWP). The respective NowCastSat-Aviation (NCS-A) method covers the complete geostationary ring and is used in cockpits of many airlines around the world in order to reduce risks of injury by thunderstorms and associated turbulences. However, NCS-A covers only forecasts up to 2 hours.  Thus, recent developments focus on the extension of the forecast horizon by deep learning and combination with an ensemble analysis of the Lightning Potential Index (LPI), provided by the DWD NWP model ICON. Both approaches attempt to overcome the limitations associated with the pure extrapolation of  objects with Atmospheric Motion Vectors (AMVs).  Although blending with NWP enables an improvement of the forecasting capability for predictions up to 3 hours, the CSI drops below 0.5 afterwards. This results from the chaotic nature of thunderstorms, which makes it difficult to model TS accurately. Within this scope it is discussed whether Artificial Intelligence will be able to replace numerical weather modeling in the near future. In fact, the University of Mainz has shown in a joint project that neural networks can learn something about the lifecycle and thus the physics of thunderstorms, leading to an improvement of CSI compared to classical nowcasting.The talk presentation will start with an overview of the current 24/7 thunderstorm nowcasting. This is followed by a presentation and discussion of the current developments at DWD aimed at providing accurate forecasts of thunderstorms up to 6 hours, including a discussion of NWP versus AI. The presentation will close with overview about the status and the further plans concerning volcanic ash and turbulence nowcasting, both danerous for traffic as well.References:Barleben, A.; Haussler, S.; Müller, R.; Jerg, M. A Novel Approach for Satellite-Based Turbulence Nowcasting for Aviation. Remote Sens. 2020, 12, 2255. https://doi.org/10.3390/rs12142255 Müller, R.; Barleben, A.; Haussler, S.; Jerg, M. A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms. Remote Sens. 2022, 14, 3372. https://doi.org/10.3390/rs14143372 4936-Brodehl, S.; Müller, R.; Schömer, E.; Spichtinger, P.; Wand, M. End-to-End Prediction of Lightning Events from Geostationary Satellite Images. Remote Sens. 2022, 14, 3760. https://doi.org/10.3390/rs14153760 Müller, R.; Barleben, A. Data Driven Prediction of Severe Convection at DWD. An Overview of Recent Developments. Preprints 2024, 2024031179. https://doi.org/10.20944/preprints202403.1179.v1. In the meanwhile accepted for publication in Atmosphere.
Mapping urban heat islands in Pärnu/Estonia by leveraging satellite data
Authors: Dehkordi Amir , Hoy Andreas , Jama Kelli Marie , Tuhkanen Heidi
Published: July 2024

Urban heat islands (UHIs) pose significant challenges for cities of various sizes worldwide. Extreme summer temperatures strongly increased in frequency and intensity in all parts of Europe including Estonia, especially since the millennium. This development increased exposure and vulnerability of people and infrastructure to heat impacts. This study contributes to the Horizon Europe funded Regions4Climate project by investigating the characteristics of land surface temperatures (LST) in Pärnu city and county, located in Estonia (north-eastern Europe). We employ remote sensing data from satellites to identify areas experiencing the most intense heat in recent summers.We studied available satellite data for the region via a web and literature research, focussing on the May-September timeframe. Considering available spectral bands and despite a comparably rare revisit frequency, Landsat-8 emerged the most applicable choice, offering comparably high-resolution data and an available data range of >10 years (2013-2023). Data are available for midday (around 12:30 local time), when the solar altitude is highest. We are additionally investigating into alternative remote sensing options are well (Modis, Ecostress). Furthermore, we obtained necessary ancillary data including city/county borders, building structure data, and open/green space maps.To identify relevant days, we analyse both available Landsat-8 data (around 16 days revisit cycle) and local weather station records. We use days with maximum air temperatures exceeding 25°C at Pärnu station, combined with minimal cloud cover. The chosen days are processed in a GIS environment to derive LST values for Pärnu city/county. We employ an intuitive colour scale and legend to represent absolute temperatures alongside anomalies from a predefined baseline. Finally, we apply threshold values to identify local areas experiencing overheating, by classifying pixels exceeding specific LST thresholds or exhibiting significant temperature anomalies.Our final results, tailored for integration into Pärnu city/county's urban planning processes, will be developed through reiterative consultation to ensure relevance. This information will inform strategies for mitigating UHI effects, improving thermal comfort, and promoting equitable and just climate adaptation. These results will serve as the foundation for a digital decision-making tool by Pärnu City, empowering stakeholders in urban development and land-use planning.
Improving prediction of heavy rainfall with Neural Networks using both observation and Numerical Weather Prediction data
Authors: Pujol--Nicolas Killian , Baggio Roberta , Filippi Jean-Baptiste , Lambert Dominique , Muzy Jean-François , Pantillon Florian
Published: July 2024

Heavy Precipitation Events (HPE) can cause significant human fatalities and material damages. Therefore, their prediction is crucial but challenging due to the complex processes involved. In this context, artificial intelligence methods have recently been shown to be competitive with state-of-the-art Numerical Weather Prediction (NWP). Our work focuses on improving the prediction of the occurrence of HPE based on Neural Network (NN) models and using both observation and NWP data.We use the MeteoNet open source database from Meteo-France on northwestern and southeastern France from 2016–2018 including station observations (OBS) and forecasts from the NWP models Arome and Arpege. We train a NN model to predict the occurrence of daily rainfall above a threshold of 10 mm / 24 h at the location of the stations. Our verification metric is the Peirce Skill Score (PSS) with Arome forecasts as a benchmark.Our results for both northwestern and southeastern regions are 1) the NN model using both OBS and NWP data as inputs has the highest PSS, 2) the NN model using only Arome data as input has higher PSS than the benchmark, 3) the NN model trained only with OBS data has lower PSS than the benchmark, showing the crucial contribution of NWP forecast data at a lead time of 24 h, and 4) due to the rarity of rainfall events meeting the threshold, training the NN model with a weighted loss function significantly increases the PSS.When extending the results to shorter time scales, we find that the contribution of OBS data to the NN model is dominant at 1–3 h lead times, while including NWP forecast data allows to mitigate the degradation of prediction skill with longer lead time. Finally, the results for northwestern France show slightly lower PSS than for southeastern France due to the different rainfall climatology.
TeWA: A new approach to predict the seasonal and subseasonal anomalies
Authors: Redolat Darío , Monjo Robert , Gaitán Emma
Published: July 2024

It is widely understood from analyzing energy balances that the global oceans play a crucial role in shaping atmospheric seasonal variations through interconnected mechanisms. Despite advances, numerical weather prediction models still face significant challenges in accurately predicting long-term trends due to their nonlinear response to initial deep ocean conditions. Given the inherently erratic nature of the Mediterranean climate, we have formulated an alternative approach based on several underlying assumptions: (1) recognizing that delayed teleconnection patterns offer valuable insights into the complex interactions between the ocean and atmosphere, particularly on shorter timescales, (2) noting the presence of predictable cyclic oscillations, and (3) leveraging the discernible predictive signals by isolating them from the surrounding noise over a continuous time frame. To empirically test these theoretical underpinnings, we conducted an extensive analysis of the subseasonal predictability of temperature and precipitation at 11 reference points in the Mediterranean region spanning the period from 1993 to 2021. Our innovative method involves integrating lag-correlated teleconnections (comprising 15 indices) with self-predictive techniques for residual quasi-oscillations, utilizing both Wavelet (cyclic) and ARIMA (linear) analyses. The predictive performance of this Teleconnection-Wavelet-ARIMA (TeWA) method was cross-validated and benchmarked against the SEAS5-ECMWF model (3 months ahead). The results clearly demonstrate that the TeWA method significantly enhances the predictability of temperature and precipitation anomalies for the first month by an impressive margin of 50–70% compared to the SEAS5 forecast. Furthermore, on a daily moving-average basis, we identified optimal prediction windows of 30 days for temperature and 16 days for precipitation. Particularly, these predictable intervals exhibit strong alignment with atmospheric connections observed in teleconnection patterns (e.g., ULMO) and are further corroborated by spatial correlations with sea surface temperatures (SST). Finally, our findings underscore the potential of integrating the TeWA approach with existing numerical models to unlock new frontiers in subseasonal-to-seasonal forecasting research.
Questions Arising While Co-producing Climate Projection Information Relevant to Philadelphia Public Health Extreme Heat Interests
Authors: Dixon Keith , Adams-Smith Dennis , Le Roy Benjamin , Zenes Nicole
Published: July 2024

The heat index metric (a function of temperature and humidity) is used often in the United States as a quantitative measure associated with human heat exposure.  Heat index values are part of warm season forecasts issued by the US National Weather Service, incorporated into heat warning systems used by governmental entities, and appear in many scientific publications, especially interdisciplinary studies linking public health with weather and climate. Yet, there is more than one way to calculate heat index values from observational data and from model predictions and projections. Not surprisingly, the choice of input data sets, computational algorithms used, and other methodological choices can lead to different quantitative results and hence uncertainties. Our experiences suggest that some sources of uncertainties are usually considered (e.g., scenario and model uncertainty, using the nomenclature of Hawkins and Sutton [2009]) by those without formal climate science training, whereas other uncertainties are not necessarily appreciated. Neglecting sources of uncertainty can lead to overconfidence in quantitative results produced in a particular study.  Here we present information developed during a research project investigating historical and projected daily maximum heat index values for the northeastern United States, with a focus on the city of Philadelphia. We illustrate how, in some cases, uncertainties associated with the choice of downscaling or bias correction methods, the observational data product used when bias correcting, the temporal resolution of weather and climate data, and common estimation methods used in some calculations, can lead to uncertainties whose magnitudes can rival those of scenario and model uncertainty. Comments will be offered regarding the practical challenges of seeking to promote improved practices when limited by data availability or other resources.Hawkins, E., and R. Sutton, 2009: The Potential to Narrow Uncertainty in Regional Climate Predictions. Bulletin of the American Meteorological Society.
Climate-trend analysis of air temperature and precipitation in the Extended Alpine Region (1961-2020)
Authors: Bongiovanni Giulio , Matiu Michael , Crespi Alice , Napoli Anna , Majone Bruno , Zardi Dino
Published: July 2024

The Alpine area is one of the most vulnerable and sensitive regions to the continuous warming of climate and it is considered an important hotspot of climate change. In particular, climate change is expected to exert a strong influence on all components of the hydrological cycle, including river regimes, with consequent effects on the services offered by the freshwater ecosystem, as well as on water availability for users, thus affecting  several socio-economic sectors. Climate change assessment in the Alpine region relies on direct application of climate observations and, thus, their quality may strongly impact climate and hydrological studies results and predictions in terms of reliability, accuracy and precision. Here, we present an extended climatological-trend analysis focused on the Alpine region over the 1961-2020 period. The potential of a recently developed and unprecedented observational dataset, addressing the key issues in terms of spatial density, data quality, time resolution and completeness, was exploited. About 300 time series of air temperature and 1000 of precipitation, covering homogeneously the extent and the elevation gradient of this area, were selected depending on World Meteorological Organization (WMO) requirements. Climatological and trend analysis was carried out on different time scales considering both mean values and a selection of ETCCDI indices. The phenomena of elevation-dependent warming (EDW) and precipitation change (EDPC) were also assessed. A pca-based clustering approach was applied to identify climatic regions and evaluate the spatial-dependency of trends. A further analysis concerned the relationship between detrended time series and the main teleconnection patterns. The present study aims to provide a reliable analysis of the evolution of key climate variables in the Alpine region. Being a study based on the most comprehensive spatial coverage in this area to date, the related results significantly increase the amount of information available to involved stakeholders to prevent and quickly plan for disaster management, risk mitigation and formulating proper locally relevant adaptation strategies.
Improving Forecast Accuracy of Ensemble Regional Tide and Storm surge Model
Authors: Kim Jiha , La Nary , Chang Pil-Hun , Kang Hyun-Suk , Park Jeong-Hyun , Cho Ik Hyun
Published: July 2024

The storm surge forecast model holds significant importance in minimizing coastal damages caused by strong low-pressure system (e.g., typhoon). In general, the model accuracy is largely influenced by atmospheric forcing. In the case of a deterministic forecast model, therefore, the surge forecast can vary significantly depending on the uncertainty of the atmospheric model. In the present study, we developed the Ensemble Regional Tide·Storm Surge Model (ETSM) for the sea around Korean Peninsula, utilizing the forecast results of 26 members calculated from an ensemble atmospheric model to address the uncertainty of a single forecasting model. For the assessment of ETSM, we carried out numerical experiment from July to September 2022 (ETSM_0). In addition, three sensitive experiments were conducted, considering initial bias correction (ETSM_1), the increase of ensemble members by using time-lag method (ETSM_2), and both methods (ETSM_3). When we compared ETSM_0 to additional experiments, the ensemble spread range generally widened in ETSM_2 and ETSM_3 due to the increase of ensemble members especially in the period of Typhoon HINNAMNOR, which leads to the inclusion of observations within the spread. Verification of the probability of surge heights exceeding the threshold of 10 cm for each forecast time (0, 24, 48, 72, 96, 120hr) showed that the Brier Skill Score (BSS) was 0.14 on average for ETSM_O, while it was 0.47 for ETSM_1, 0.19 for ETSM_2, and 0.47 for ETSM_3. Furthermore, the Relative Operation Characteristic (ROC) was 0.73 on average for ETSM_O, 0.79 for ETSM_1, 0.75 for ETSM_2, and 0.81 for ETSM_3. This indicates an overall improvement in probability forecast performance when applying initial bias correction and increasing the number of ensemble.
Effect of Variable Resolution System Based on Stretched Global Grid in KIM
Authors: Nam Hyun , Hahm Jae-Hee
Published: July 2024

   As science and technology develop, the operation of global numerical weather prediction models at high-resolution is considered to demonstrate more accurate forecast performance. However, when using the high-resolution numerical forecast model, the size of the grid becomes smaller, the number of grids increases by the square of the horizontal resolution change rate, and a relatively small time-step size must be applied according to the Courant-Friedrichs-Lewy condition. For these reasons, the overall amount of calculation increases and the limits of computer resources are encountered.   Now, in this study, we intend to use a variable resolution system based on a stretched global grid in Korean Integrated Model (KIM) to see the effect of high-resolution numerical forecasts at least in the region of interest without increasing the number of grids due to resolution changes. This system uses the Schmidt transform to expect a high-resolution effect using fine grids for the area of interest depending on the contraction/relaxation magnification, and shows a low-resolution effect using coarse grids for the other side of the Earth. In other words, in a variable resolution system, only the size of grid in each region changes depending on the contraction/relaxation ratio without structural modification of the dynamical core in a uniform grid system, and the number of grids is the same as the number of uniform grids. For the real case, we will use the variable resolution system to show the effect of the high-resolution in the region of interest (surrounding of the Korea Peninsula) by adjusting the contraction/relaxation scale. As a primary result, this study aims to analyze the numerical results of forecast performance and computational efficiency in variable grid systems compared to uniform grid system in KIM.
Improved understanding of warning evaluation: will you contribute to the survey?
Authors: Sivle Anders Doksæter , Wapler Kathrin
Published: July 2024

From the perspective of National Meteorological and Hydrological Services verification is needed to assess the quality of severe weather warnings, to determine their strengths and weaknesses and consequently to lead to improvements. Apart from the challenges that arise when analysing rare meteorological events, a set of statistical methods are suitable for comparing weather forecasts or warnings with observations to assess the (physical) accuracy of forecasts. For impact-based warnings, the complexity is increased since it is not enough to compare the forecasted meteorological values against observed values. The societal consequences of the hazard (e.g. comparison with damage compensation statistics) should also be taken into account, as well as the preventive measures taken by various actors - based on the information in the warning - to reduce risk and damages. What determines how valuable a warning is from a user’s point of view? Do users and providers have the same understanding of the value of a warning, and the thresholds for issuing warnings? How can the value of a warning be best communicated to users to support their decisions to mitigate risk or prepare for it as best as possible? Some potential factors to consider in an evaluation could be how accessible the warning is, timeliness, how understandable the information about the weather, the consequences and the risk is, and how useful the information is in their decision-making processes. The latter might be affected by e.g. how trustworthy they consider the (provider of the) warning to be, how skillful or accurate the information is, or how relevant the information is to their specific situation or context. Among users, e.g. emergency services, authorities in civil protection, media and communication, and the general public, there is a wide variety in the way warnings are used and understood and in requirements concerning severe weather warnings.  A user-oriented verification and evaluation could help the NMHSs understand the capabilities and limits of warnings among the different user groups, and consequently improve their decision-making capabilities, such as safeguarding life and property. Here, we will present results from a survey conducted at the second Weather and Society Conference (WMO) in February 2024, and at the seventh conference on weather warnings and communication (AMS) in June 2024. The respondents were experts from various fields, participating in the conference. They gave input on verification and evaluation activities in the field of weather warning and its communication to users, and opinions on how to best perform user-oriented evaluation of warnings. Further, to increase the impact of the study, we would also like to invite all conference participants at the EMS annual meeting in Barcelona to respond to the short survey (ten minutes), by accessing this URL: https://forms.gle/eBe6S8qeweL1LukV8. 
Near-Surface Turbulence Structure in the Presence of a Nocturnal Low-Level Jet: Results from the MATERHORN Field Experiment
Authors: Leo Laura Sandra , Barbano Francesco , Brogno Luigi , Tampieri Francesco , Di Sabatino Silvana
Published: July 2024

The presence of Low-Level Jets (LLJs) in the nocturnal boundary layer over complex terrain has proven to generate complex interaction with turbulence. Of notice, LLJs represent one of the most common mechanisms triggering the occurrence of the so-called “up-side down” configuration of the nocturnal boundary, where turbulence kinetic energy (TKE) is transported downward to the surface instead of upwards as in a traditional boundary-layer configuration. In this context, we combined eddy-covariance measurements from flux towers with tethered balloon soundings collected in an open valley to investigate turbulence regimes and characteristics in the stably stratified layer between the nose of the nocturnal LLJ and the ground. Data for the analysis are taken from the existing database of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program, considering measurements collected within the valley floor at the Dugway Proving Ground (Utah, USA) during nights of weak synoptic forcing. The theoretical frame is given by the budget equations of the second-order moments (SOMs), taking into account explicitly the effects of the unsteadiness and the divergence of third-order moments (TOMs), under the simplifying assumption of horizontal homogeneity. The focus of this presentation is on 'simple' cases, characterized by a well-defined shape of the LLJ nose and negligible rotation of the wind with height. Although unsteady, such cases exhibit time derivatives of the normalized SOMs negligible in the relevant budget equations. The traditional resistance, or defect law is rephrased in terms of the velocity and the height characterizing the nose and the surface stress. The relationship shows a dependence on stability and quantifies the interaction between the LLJ dynamic and the turbulence below. The TKE budget turns out to be not satisfied, especially in the lower levels where the residual (i.e., the divergence of TOMs normalized on the dissipation of TKE) is often positive, suggesting a transport of TKE from the considered layer. This observation links the local budget to the vertical structure of the whole turbulent layer and is broadly connected to the vertical profile of TKE, in the flux-gradient closure frame. The ratio between the vertical velocity variance and the TKE is investigated as a function of the flux Richardson number to highlight specific features of local turbulence and their representation in closure models.
Field-level analysis of phenological cycles and dynamics of sunflower (Helianthus annuus L.) and oil seed rape (Brassica napus L.) flowering within various regions of Hungary
Authors: Vincze Csilla , Birinyi Edina , Leelőssy Ádám , Kristóf Dániel , Mészáros Róbert , Kern Anikó
Published: July 2024

Phenological observations are expensive and demanding in terms of manpower to monitor the vegetation stages. Therefore, satellite products have opened new possibilities for easier and more widespread data collection. Nowadays biomass estimations extensively rely on these tools, given their extensive spatial and temporal coverage, which are defined by indicators such as vegetation indices, which describe the biomass growth, canopy structure, vegetation health and even water management etc. However, the detection of flowering stages through remote sensing is less explored, with fewer established methods available.This study investigates temporal phenological changes during the blooming period of the most widely cultivated oilseed crops in Hungary in 2021, specifically the sunflower (Helianthus annuus L.) and the winter-cultivated oilseed rape (Brassica napus L.). The objective is to characterize the blooming phase and dynamics of these two crop species utilizing various vegetation indexes and satellite-derived products. The investigation is conducted across seven distinct regions, using honey bees as bioindicators of the fields.Methodologies outlined in prior scientific literature, focusing on the analysis of anthesis timing and duration in major nectar-producing crops utilizing Sentinel-1 SAR and Sentinel-2 optical products, serve as the basis for this research. Within each radius study areas, the parcel-averaged and smoothed daily time series were acquired. The estimation of the blooming phases was achieved through parcel smoothing methods using daily non-parametric local regression (loess) approach which showed better performance compared to the Savitzky-Golay (SG) algorithm. In our flowering detection analysis, we also examined the differences in the ascending and descending orbits and their combined results. In the case of oil seed rape, the NDVI index reached its maximum after flowering, while for sunflower it varied. Additionally, we investigated the outcomes of all polarization and method combinations within each crop type.Our study enables the comprehension of temporal flowering patterns in bee pasture crops through the integration of SAR and optical measurements. Additionally, it supports the utilization of beehive scales to provide field-based reference data for estimating anthesis.The research was funded by the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project. Project No. 993788 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2020 funding scheme.
Regional climate scenario products in a global warming level perspective. Heat indicators in Switzerland.
Authors: Herrmann Michael , Rajczak Jan , Mülchi Regula , Kotlarski Sven
Published: July 2024

With ongoing climate change, national climate scenarios based on current scientific knowledge are indispensable for developing regional and local mitigation and adaptation strategies. In this context, the project Klima CH2025 currently develops the upcoming edition of Swiss climate change scenarios, facilitated by collaborative efforts involving the Swiss Federal Office of Meteorology and Climatology MeteoSwiss, ETH Zurich and further partners. Klima CH2025 will provide an updated basis of user-relevant climate information and related products containing regional and local assessments of future climate change in Switzerland. The backbone of the data production chain within Klima CH2025 is a comprehensive ensemble of CMIP-driven EURO-CORDEX climate simulations. These simulations are bias-adjusted and downscaled through quantile mapping, ensuring the reliability and accuracy of the derived climate scenarios on a localized level. By combining model simulations and observations through quantile mapping, also regional to local scale heat indicators representing the current and future climates are computed. These heat indicators portraying both current climatic conditions and projected future trends are presented in a global warming level framework. The significance of understanding heat-related extremes within a changing climate cannot be overstated. Such extremes exert direct and indirect impacts on several levels such as the human well-being and critical infrastructure. Thus, gaining insights into changes of heat extremes is crucial for formulating effective adaptation strategies and provides an important basis for decision-making across different sectors. In this contribution, our aim is to validate the representation of different heat extreme indicators formulated within the framework of Klima CH2025 and present their projected future changes for different global warming levels.
The actualization of the Catalan weather glossary as a scientific tool for communicating atmospheric sciences: an analysis based on the diversity index of Shannon and the complex network theory.
Authors: Mazon Jordi , Pino David , Hernández Antonio , Costa Marcel , Luque Jordi
Published: July 2024

The actualization of the Catalan meteorological glossary concluded in 2024. It has been based based on the original work of Fontserè (1948), a piooner work for disseminating weather in catalan language. The result has been an online terminology, including all new meteorological and climatological new terms that have been appeared from 1948 until now, related with current topics like climate change, climatic crisis, numerical simulation or teledetection, among other. They have been grouped in eleven meteorological families, from the original 840 terms to the near 2300 currently. The presentation aims to show two main goals. Firstly, to show the new online web glossary, which includes new and current terms not published yet in classical glossaries of meteorology, and also includes pictures for all those terms linked to the clouds and meteorological phenomena. The final result is an excellent tool for communicating the atmospheric science among different targets (media, scholar, ...). Secondly, to show the results of two pioneer analysis that have been done with the terms included in the glossary. The first has consisted in applying the diversity Shannon index to the whole terms grouped in the eleven meteorological families, to define a new index to quantify how the meteorological terms spread out in Catalan language, and comparing the original glossary of 1948 with the current one. This diversity Shannon index of terms is proposed as a new parameter to define any meteorological glossary, which can account how a certain language spread their terms along the 11 meteorological families. The second study has consisted in apply complex network theory to quantify and analyze the interconnected terms of the glossary.  
Fine-scale evolutionary characteristics of Super Typhoon Doksuri (2023) revealed by multi-source observations
Authors: Wang Qian , Zhao Dajun , Duan Yihong , Wang Hui , Sun Ziyao , Xu Yinglong
Published: July 2024

The variations in the track and intensity of a tropical cyclone (TC) are closely correlated with the fine-scale evolution of its structure. The fine-scale track, intensity, and structural evolution of TC can be comprehensively analyzed by combining multi-source observations. Based on observations from space-borne synthetic aperture radars (SARs), the Advanced Microwave Scanning Radiometer-2 (AMSR2), the Special Sensor Microwave Imager/Sounder (SSMIS), weather radars, in-situ buoys and island stations, an analysis was conducted on the fine-scale evolutionary characteristics of high-impact Super Typhoon Doksuri (2023). The results show that Doksuri (2023) experienced secondary eyewall formation (SEF), concentric eyewall maintenance (CEM), and eyewall replacement cycle (ERC) processes when entering the South China Sea and prior to landfall. These processes can be further delineated into three subsequent stages. In the first stage, the SEF phase, the secondary (outer) eyewall formed, exhibiting features that were non-concentric with the inner eyewall. Concurrently, the track of Doksuri (2023) displayed notable oscillations in both its forward translational direction and speed, accompanied by the emergence of two radial maxima centers of wind speed. Subsequently, during the second stage, the CEM phase, the geometric centers of the inner and outer eyewalls of Doksuri (2023) coincided, initiating a rapid intensification process characterized by an accelerated forward translational speed. Both the inner and outer eyewalls further contracted during this phase. In the third stage, the ERC phase, the asymmetry of the inner eyewall increased, and the outer eyewall gradually contracted while the inner eyewall dissipated until the replacement was completed prior to landfall. Accordingly, Doksuri (2023) experienced rapid weakening. With special attention been paid to the possible connection between the evolution of the typhoon structures and the changes in its track and intensity, it is found that the substantial track oscillations were closely associated with the asymmetric development of TC inner and outer eyewalls during the SEF, and, the rapid intensity changes were correlated with the CEM and ERC processes. These findings have the potential to enhance our understanding of the physical mechanisms governing the intricate structures of TCs at fine scales, bolstering the forecast accuracy of TC tracks and intensities.