bluuebunny/crossref_metadata_2025 huggingface dataset:
GAW-qc: A data science-based dashboard for quality control of atmospheric composition measurements
Authors: Brugnara Yuri , Steinbacher Martin , Emmenegger Lukas
Published: July 2024

The Global Atmosphere Watch (GAW) Programme of the World Meteorological Organization coordinates a worldwide network of hundreds of ground-based in-situ monitoring stations that provide reliable scientific data on the chemical composition of the atmosphere. In the framework of the GAW Programme, the Quality Assurance/Scientific Activity Centre at Empa has developed an interactive dashboard based on data science to support station operators in timely detecting issues in their in-situ measurements of various trace gases.The application (GAW-qc), currently in beta testing, makes use of a mixture of purely data-driven and hybrid anomaly detection techniques. It exploits historical measurements made at the target station as well as the archive of gridded numerical forecasts by the Copernicus Atmosphere Monitoring Service (CAMS). The accuracy of the latter for the specific site is improved through machine learning using various predictors, including meteorological parameters and aerosol concentrations.GAW-qc allows station operators to upload their latest measurements, visualize the data with different temporal aggregations, and easily detect anomalous values using just their internet browser. By combining the information gathered from the dashboard with logbook entries and local expertise, they can effectively flag problematic measurements and even detect instrumental issues that would remain unnoticed otherwise. First case studies indicate that this process can indeed facilitate the detection of malfunctionings in the analytical setup and reduce the ingestion of erroneous data into the international data repositories. Moreover, it has the potential to shorten data gaps if applied timely. Therefore, it may become a game-changer towards reliable, comparable and traceable world-wide datasets in the field of air quality and greenhouse gases. The software is freely available through a GitHub repository and can be adapted to analyze other atmospheric variables.
Evaluation of ensemble prediction systems in terms of global horizontal irradiance in Germany
Authors: Kurzrock Frederik , Mähnert Marie , Mayer Bernhard , Gregor Philipp , Ben Slama Mehdi , Schmutz Nicolas
Published: July 2024

In solar power forecasting, the consideration of uncertainties in the forecast is becoming increasingly important and is being taken into account more and more. Ensemble predictions systems can provide information about the uncertainty of cloud cover and solar irradiance. The goal of this study is to evaluate the quality of different operational ensemble predictions systems (EPS) in Germany in terms of global horizontal irradiance (GHI). Therefore, the models ICON-EU-EPS (horizontal grid spacing approx. 20 km, 40 members), IFS-ENS (horizontal grid spacing approx. 20 km, 50 members), GEFS (horizontal grid spacing approx. 28 km, 30 members), and WRF-Solar-EPS (horizontal grid spacing 3 km, 30 members) are evaluated. The 00UTC run is considered for all models. The evaluation period is summer 2022 with observational GHI data for 25 sites from Deutscher Wetterdienst (DWD). A quality check of the observational data with the python library libinsitu reveals that more than 95% of the observational data is of high quality. For reasons of computational time, WRF-Solar-EPS forecasts are evaluated for selected days only. The forecast quality is evaluated using rank histograms and the continuous ranked probability score (CRPS) among other metrics. The results show that the quality is similar for all models, while WRF-Solar-EPS does not necessarily stand out despite its higher spatial resolution. The rank histograms reveal that all models are highly over-confident, meaning that most observations lie outside of the range of all members. Post-processing methods and model calibration is not part of this study but has the potential to increase the forecast quality.
Operational Meteorologist-Led O2R Activities at the Met Office (or What I Do When I’m Not Forecasting Weather)
Authors: Suri Dan
Published: July 2024

The last ten to fifteen years have seen a marked growth in the range of forecasting operations-focused Operations-to-Research (O2R) activities at the Met Office. The range and maturity of many of these activities has developed such that in more recent years dedicated resource has been drawn from the chief operational meteorologist community to lead, manage and develop these activities. The goal is to ensure an operational, forecasting-focused perspective is represented in the O2R/R2O (Research to Operations) space.Here, an overview of the range of activities O2R is active in is presented. Activities can be categorized as follows: process groups providing governance and steering, model evaluation and testbed co-organisation.A number of process groups exist within the Met Office to draw on senior representation from key areas across the organisation to communicate and seek feedback on plans for requirements, research, development and delivery of new capabilities and upgrades. Interests here extend across a wide range of activities including the interests of NWP and post-processed outputs and some of the associated IT frameworks. O2R are invited to ensure that the plans align with forecasters present and future requirements.Model evaluation activities effectively fall into two categories, one providing subjective analysis of proposed future model packages and formulations and the other a retrospective assessment of model output. The former process supplements objective evaluation and verification of these packages and formulations, providing an operational perspective as to what they mean to and how they could be used by the forecasting community and how they could be impacted by their introduction. The latter category reviews a log of model performance called the Daily Forecast Assessment, completed by operational teams and then retrospectively assessed if model performance is considered particularly poor. These assessments are reviewed by O2R or O2R subgroups and Met Office Science and informs both the Met Office’s formal model characteristics documentation and model development processes.Since 2013 the Met Office has run a series of testbeds aimed at allowing operations and researchers to come together to evaluate new tools and share experience, the results then helping drive future developments of forecasting tools. In recent years, O2R has taken a leading role in organising testbeds and delivering their results.Other O2R activities include helping develop and test new forecasting tools, representation of the operational perspective to internal groups taking a more detailed look at specific elements of model performance, evaluation of impactful weather events to consider model performance, public service warning strategy and verification and forecasting best practice.
Comparing Deep Learning methodologies for Downscaling between meteorological models
Authors: Benitez Marc , Margalef Tomàs , Rodríguez Mirta , Dutta Omjyoti
Published: July 2024

The ability to obtain high spatial resolution meteorological data from coarse sources is a crucial skill needed to study local phenomena happening at finer scales such as severe storms or convective systems. This spatial downscaling can be achieved by reproducing the atmospheric state of a small region using numerical weather prediction (NWP) models that use low-resolution (LR) data as boundary conditions. However, running NWP models at high resolutions is computationally expensive and time consuming. A different approach is to establish statistical relationships between LR and HR data to increase the spatial resolution by interpolating intermediate points. In recent times machine learning (ML) based statistical methods have proven to be a cheap yet accurate alternative to dynamical downscaling. 
This work aims to develop a downscaling methodology from ERA5 to Weather Research and Forecasting (WRF) data based on deep learning. We study how the training dataset affects the downscaling performance and generalization capabilities of deep learning models and how it compares against traditional downscaling methods such as bilinear interpolation. Our models estimate the downscaling function for daily average 2-meter air temperature, between a LR dataset, and a high-resolution (HR) Weather Research and Forecasting (WRF) model outputs. The LR inputs come from different sources for each model. The first dataset is created by upscaling the HR WRF ground truth data to our target LR, and the second one is the ERA5 reanalysis used as boundary conditions to drive the NWP simulation. For validation purposes, we select data from regions that share similar climatology with data present in the training set that has been excluded from the training. To evaluate the performance of the model, we use Root Mean Square Error (RMSE) and metrics typically used in image super resolution problems such as Peak Signal-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). 

With this study we have taken a first step in the ML modelling of weather downscaling and its generalization capabilities. However, further work is needed to understand the capabilities and behavior of these models when faced with challenges such as reproducing local-scale patterns, downscaling discrete variables (e.g. precipitation, hail) or the transferability of their results to similar climatic zones outside the simulation domain. Lastly, in future works we plan to study the performance of different deep learning model architectures, such as Vision Transformers or Latent Diffusion, on downscaling. 
Can AI-based weather prediction models simulate the butterfly effect?
Authors: Selz Tobias , Craig George
Published: July 2024

Recently, weather prediction models based on artificial intelligence (AI) have become equally to slightly more accurate than the established operational weather prediction models in terms of deterministic scores. The much lower computational cost of the AI-models facilitates the generation of large ensembles, hence it is important to assess if their error growth properties are realistic. Here, we investigate error growth from initial condition perturbations with varying amplitudes, simulated with the AI-weather prediction models (PANGU, GraphCast, FourCastNet) and with a “classic” fluid equation-based weather model (ICON). From past research and the global convection-permitting ICON simulations, it is expected that small-amplitude initial condition perturbations would grow very fast initially in areas with latent heat release, then spreading out to larger and larger scales, eventually setting a fixed and fundamental limit to the predictability of weather. This phenomenon is known as the butterfly effect. We find however, that in contrast to ICON, the AI-based models completely fail to reproduce the rapid initial growth rates. Instead their growth rates remain similar to those typically found on synoptic-scales, which incorrectly suggests an unlimited predictability of the atmosphere. In contrast, if the initial perturbations are large in amplitude and comparable to current uncertainties in the estimation of the initial state, the AI-based models basically agree with results from the “physically-based” ICON simulations, although some deficits are still present, mostly related to their particularly low effective resolution. This provides an example of how machine learning models can fail to reproduce a fundamental physical principle, even though they can accurately mimic many observed behaviors.
Anthropogenic Influence on 2022 Extreme Winter Precipitation in Southern China
Authors: Hu Yamin , Dong Buwen , xie Jiehong
Published: July 2024

From January to February (J–F) 2022, Southern China (SC) experienced abnormally heavy precipitation, with the regionally averaged total precipitation reaching 248 mm, making it the second-largest value since 1961. It was the second highest value since 1961 with the anomaly being about 2.3 standard deviations (2.3ơ, using as the threshold for the 2022 event) above the climatology in the period of 1961-2005. This extreme event resulted in significant damage to transportation, power supply, and crop production. Decreases in precipitation occur in both HadGEM3 and CMIP6 simulations over SC, accompanied by anomalous positive sea level pressure (SLP) and an anomalous anticyclonic circulation over SC.
The two most important anthropogenic forcings are greenhouse gases and atmospheric aerosols and they can have different effects on SC J–F precipitation. Anthropogenic influence reduced the likelihood of extreme events like 2022 by about 50% (55%) in HadGEM3 (CMIP6). Furthermore, the greenhouse gas (GHG) forcing leads to a rightward shift of probability density functions (PDFs) to a wetter climate relative to natural forcing (NAT), while the anthropogenic aerosol (AA) forcing shifts to a drier world. The estimated PGHG and PAA indicate that a ~26 years event becomes a ~15 years event with PRGHG=1.75(1.60–2.00) in GHG and a ~83 years event in AA with PRAA=0.31 (0.24–0.37).
Analyses of single forcing experiments using CMIP6 model ensembles demonstrate different roles of changes in GHG and AA in J–F precipitation over SC with GHG forcing inducing an increase and AA forcing inducing a decrease. However, the magnitude of AA-induced precipitation decrease is larger than that of GHG-induced increase, leading to the reduced likelihood of the J–F precipitation event similar to that of 2022 in SC by the combined effect of anthropogenic forcing.
KNMI-Global: Lessons learned during the first phase of the SOFF as a peer advisor to 5 countries 
Authors: Pereira Marghidan Carolina , van der Schee Marlies , Kraai Aline , Verver Gé , Wijngaard Janet , Struijk Sophie , Konijn Rubert , van Aalst Maarten
Published: July 2024

In light of the rapidly changing climate, it is crucial to advance weather and climate services across the world. Cooperation and partnerships are important to accelerate such efforts around the globe. Due to the existing gaps in early warning systems, UN’s Early Warning 4 All (EW4ALL) campaign was launched to ensure that every person on earth is covered by early warning systems by 2027. In 2021, the Royal Netherlands Meteorological Institute (KNMI)-Global program was launched to support weather and climate services focusing on countries with the largest gaps.Effective early warnings require chains from observations to analysis to communication to their use to avoid impacts. As one contribution to such chains, KNMI contributes to the Systematic Observations Financing Facility (SOFF) as a fundamental step towards achieving EW4ALL. SOFF is a financing mechanism to improve (sharing of) basic weather and climate observations in around 60 of the Least Developed Countries. KNMI acts as a peer-advisor for five countries, i.e. Cabo Verde, Sao Tome & Principe, Uganda, Senegal, and Suriname. In the first phase of SOFF, the Readiness phase, we have analysed and reported on the country’s capacity gaps and hydro-met status, through collaboration with the hydro-meteorological service and local stakeholders. In many countries, there is already a high density of weather stations, yet issues around data policy, data management and communication hamper the international sharing of such data. Stakeholders often recognize the high value of climate and weather services, but stronger collaboration and partnerships are needed to advance products and services.Additionally, SOFF Uganda is an important pillar of the Water at the Heart of Climate Action programme, which launched in 2023. This programme is an ambitious and collaborative initiative geared towards mitigating the impacts of water-related risks and disasters and increasing the resilience of communities in Ethiopia, Sudan, South Sudan, Uganda, and Rwanda through effective early warning systems.Drawing from practical experiences and lessons learned, we will highlight best practices, quick wins, and common challenges encountered during our collaborative international efforts under KNMI-Global to advance weather and climate services in diverse contexts.
The impact of ocean–atmosphere coupling on heavy precipitation events over eastern Adratic and Dinaric Alps
Authors: Ivusic Sarah , Güttler Ivan , Horvath Kristian
Published: July 2024

The Adriatic region is one of the rainiest areas in Europe, particularly its coastal mountainous region. This area is often affected by severe weather events like heavy rainfall and flash floods, which pose a severe risk to people and property. Air-sea interactions play a significant role in this region, particularly in the autumn when heavy precipitation events (HPEs) are frequent, and the gradients are high between the sea-surface temperature (SST) and atmospheric temperature.Atmosphere-only regional climate models (RCMs) are forced by SST as a lower boundary condition, which comes from reanalysis or global models. These models typically have a much lower resolution than the atmosphere-only RCM. On the other hand, regional atmosphere-ocean coupled models (RAOCMs) explicitly resolve air-sea interactions at high resolutions resulting in improved cyclogenesis and precipitation.Our objective is to assess the impact of ocean-atmosphere coupling on heavy precipitation events that occurred during the first HyMeX Special Observation Period from 5 September to 6 November 2012. We focus on the six intensive observation periods (IOPs) during which heavy precipitation events affected the eastern Adriatic region. Additionally, we assess the performance of the coupled simulations in representing heavy and extreme precipitation at climatological scales.To achieve this, we use the evaluation atmosphere-only RCMs and RAOCMs simulations from the Med-CORDEX framework. As a reference dataset, we use the precipitation analysis system MESCAN-SURFEX, available at 5.5 km resolution every 6 h, and the rain gauge data from the local observational networks. The main verification method for the HPEs is the quality object-based measure SAL which takes into account the structure (S), the amplitude (A), and the location (L) of the precipitation field. For the climatological scale, we focus on a range of heavy and extreme precipitation indices.
Innovative approaches to science communication for climate and air quality services.
Authors: Martín del Real Inés , Urquiza Diana , Terrado Marta
Published: July 2024

Science communication and dissemination play a key role in research projects aimed at addressing critical societal challenges such as climate change and air pollution. By effectively conveying scientific knowledge to diverse audiences, these efforts contribute to transformative change and foster public awareness and engagement.An essential aspect of effective science communication is the strategic selection of communication formats and channels tailored to the needs and preferences of different audiences. In this poster we would like to pay special attention to the role of innovative approaches such as storytelling, art, and visual elements in communication and engagement strategies. These elements are key to evoke emotions, appeal to the audience's interest and enhance their learning by making complex scientific information more accessible and relatable. In addition, data visualisation, art, and multimedia representations are important components in making scientific information more accessible and understandable to non-specialist audiences.This poster illustrates several examples of communication products and activities that have proven to be effective and innovative in explaining complex issues related to climate and air quality services, among them: The use of scrolling narratives and augmented reality as communication and education tools is an effective way to raise awareness and allow users to interact with the basic concepts of air quality. These innovative approaches not only inform users about the intricate dynamics of air quality, but also demonstrate how such knowledge can inform regulatory and planning decisions. Through immersive experiences, individuals gain insight into the complexity of environmental issues and are empowered to take informed action towards sustainable solutions.
A scientific digital comic that introduces the science of climate services co-production to a broad audience and the importance of services for climate change adaptation and mitigation.
A theatre play developed as a result of a co-production process between artists and scientists, which contributes to increase the scientific literacy of the general public on how climate science is done and what are their opportunities and limitations.A roadshow to showcase a digital art project in 5 different locations in Southeast Europe, exploring the potential of data, research and climate services. This innovative approach aims to broaden the community of climate service users in this currently under-represented European region, whereas increasing the resilience of the area through the promotion of climate services.
Comparing the seasonal predictability of ENSO and the Tropical Pacific variability in EC-Earth3 at two different horizontal resolutions
Authors: Carreric Aude , Ortega Pablo , Doblas-Reyes Francisco , Lapin Vladimir
Published: July 2024

Seasonal prediction is a field of research attracting growing interest beyond the scientific community due to its strong potential to guide decision-making in many sectors (e.g. agriculture and food security, health, energy production, water management, disaster risk reduction) in the face of the pressing dangers of climate change.Among the various techniques being considered to improve the predictive skill of seasonal prediction systems, increasing the horizontal resolution of the climate models is a promising avenue. There are several indications that higher resolution versions of the current generation of climate models might improve key air-sea teleconnections, decreasing common biases of global models and improving the skill to predict certain regions at seasonal scales, e.g. in tropical sea surface temperature.In this study, we analyze the differences in the predictive skill of two different seasonal prediction systems, based on the same climate model EC-Earth3 and initialized in the same way but using two different horizontal resolutions. The standard (SR) and high resolution (HR) configurations are based on an atmospheric component, IFS, of ~100 km and ~40 km of resolution respectively and on an ocean component, NEMO3.6, of ~100 km and ~25 km respectively. We focus in particular on the Tropical Pacific region where statistically significant improvements are found in HR with respect to SR for predicting ENSO and its associated climate teleconnections. We explore some processes that can explain these differences, such as the simulation of the tropical ocean mean state and atmospheric teleconnections between the Atlantic and Pacific tropical oceans. A weaker mean-state bias in the HR configuration, with less westward extension of ENSO-related SST anomalies, leads to better skill in ENSO regions, which can also be linked to better localization of the atmospheric teleconnection with the equatorial Atlantic Ocean. It remains to be assessed if similar improvements are consistently identified for HR versions in other forecast systems, which would prompt their routine use in seasonal climate prediction.
Spatial analysis of 2m relative humidity over Scandinavia
Authors: Ersland Olav , Lussana Cristian , Seierstad Ivar Ambjørn , Nipen Thomas Nils
Published: July 2024

We improve the representation of relative humidity fields over Scandinavia by combining outputs from our numerical weather model (MEPS) with real-time weather observations from citizen-managed stations. The method has to work operationally as a part of the automatic forecast at Yr.no, so both computational speed and accuracy are important evaluation metrics.   The method of choice is Optimal Interpolation (OI), a Bayesian statistical analysis technique. In this approach, the model output serves as the prior information, which is updated by incorporating the more accurate and precise observations from real-time data. OI operates under the assumption that both model predictions and observations have associated uncertainties. By weighing these uncertainties, OI adjusts the model output towards the observations, aiming to minimise the overall error in the final data product.  A challenge with real-time observations from citizen-managed stations is that they cannot always be trusted, for various reasons. Therefore, we need a method of quality assurance of the observations. A second challenge is to incorporate OI in a practical way in the codebase. For the first challenge we use the titanlib library, developed at the Norwegian Meteorological Institute (MET Norway). This library deals with automatic quality control of weather observations. For the second challenge we use the library gridpp, also developed at MET Norway.  The quality of the reconstructed relative humidity fields is then evaluated by comparing them against a set of independent observations, which are known to be trusted and accurate. An example would be the weather station at Blindern, outside the headquarters of MET. The software are openly available on: Titanlib: https://github.com/metno/titanlibGridPP: https://github.com/metno/gridpp
High-resolution temperature profiling in the Π Chamber
Authors: Grosz Robert , Shaw Raymond , Chandrakar Kamal , Malinowski Szymon
Published: July 2024

The Π Chamber, a turbulent aerosol–cloud reaction chamber, stands as a highly advanced facility for conducting controlled experiments on cloud microphysics. Its unique design enables reproducible and controlled measurements across a broad range of temporal scales, from hours to days, while maintaining consistent thermodynamic conditions. The chamber induces Rayleigh-Bénard convection (RBC), wherein air is heated from below and cooled from above. In this study, we focus on analyzing small-scale temperature fluctuations at selected points along the vertical axis to characterize convection inside the chamber. Understanding these fluctuations is crucial for comprehending the variability in thermodynamic conditions within the chamber, which significantly influences the formation, growth, and evaporation processes at the smallest scales of turbulence.Our methodology involved collecting high-resolution temperature time series (2 kHz) using the UltraFast Thermometer (UFT) and comparing this data with Direct Numerical Simulations (DNS) conducted under similar thermodynamic conditions. The UFTs, developed at the University of Warsaw, are designed for airborne in-cloud measurements with resolution extending down to scales within and below 1 cm (i.e., reaching the dissipation range). The current sensor version (UFT-2B) features a resistive platinum-coated tungsten wire, 2.5 μm thick and 3 mm long, mounted on a miniature wire probe, facilitating easy replacement of the sensing head. This sensor design allowed for undisturbed vertical temperature profiling spanning from 8 cm above the floor to 5 cm below the ceiling.The research investigated three distinct temperature gradients (10℃, 15℃, and 20℃) between the top and bottom, maintaining convective conditions inside the chamber. The dataset comprised both long (19 min) and short (3 min) measurement time series, revealing intriguing inhomogeneities near the vertical plates associated with local thermal plume dynamics. We identified two spectral regimes termed inertial and dissipative ranges, characterized by slopes oscillating around -5/3 and -7, respectively. Furthermore, our analysis unveiled a robust relationship between the periodicity of large-scale circulation (LSC) and the temperature gradient, describable by an exponential relation. Notably, the experimental findings demonstrate strong agreement with DNS data, illustrating a rare comparative analysis of this nature.In our presentation, we will delve into the outcomes of our investigations, engaging in a comprehensive discussion and outlining future research directions.
Opportunities and challenges in providing wind information for the German offshore wind auctions according to WindSeeG 
Authors: Möller Thomas , Michaelis Janosch , Hansen Akio , Spangehl Thomas , Hüttl-Kabus Sabine , Brast Maren , Hahn Johannes , Andersson Axel , Leiding Tina , Grüter Mirko , Kühn Bettina
Published: July 2024

The construction of offshore wind farms in the North Sea and Baltic Sea in Germany's Exclusive Economic Zone (EEZ) is crucial for the successful implementation of the energy transition. Since 2021, the Federal Network Agency has put potential sites out to tender every year. Therefore, detailed information on the wind conditions at the sites is essential for the bidders' energy yield calculations. The tender preparation is conducted in cooperation with the Federal Maritime and Hydrographic Agency (BSH) according to the Offshore Wind Energy Act (WindSeeG). The German Meteorological Service (DWD) supports the BSH in compiling the required wind information.To assess the local wind conditions, an externally contracted one-year floating LiDAR measurement campaign is carried out for the potential site. This data is combined with the long-term in-situ measurements from the FINO1, FINO2 and FINO3 research platforms in the North Sea and Baltic Sea (https://www.fino-offshore.de/de/index.html). Additionally, the DWD provides corresponding model data and evaluation results of the COSMO-REA6 and ERA5 reanalyses. These data sets form the basis for the preparation of the comprehensive wind reports for the sites. All reports and the data are made publicly available to bidders by the BSH via the PINTA portal (https://pinta.bsh.de). The first two tendering processes have been successfully completed for three sites in the south-eastern North Sea (N-3.7, N-3.8, N-7.2) and for one site in the Baltic Sea (O-1.3) in September 2021 and September 2022, respectively. In 2023, a call for tender for sites N-3.5, N-3.6, N-6.6 and N-6.7, located in the North Sea, has been published and in 2024 for the North Sea sites N-9.1, N-9.2 and N-9.3. A detailed investigation of the seasonal variability as well as an in-depth assessment of the current and historical wind conditions for the respective site is possible with the unique measurement and reanalysis data. The floating LiDAR wind measurements focus on the heights relevant for future wind turbine types and extend up to 250 m. The reanalyses are evaluated for the grid points closest to the sites and for the surrounding grid points. The data is validated using existing measurement data. Previous evaluations show a very good correlation, highlighting the added value of the reanalyses for determining the wind conditions at the sites. As future wind farms are planned for about 30 years, information on long-term variability is required. Long-term time series of geostrophic wind, derived from air pressure data from coastal stations from 1877 onwards enable an assessment of multi-decadal variations.Future wind farms in the North Sea will be located even further off the coast. This will pose new challenges for the pre-investigations in all disciplines.
The latest satellite-based advancements on the characterization of potential Mediterranean tropical-like cyclones
Authors: D'Adderio Leo Pio , Sanò Paolo , Sebastianelli Stefano , Rysman Jean-Francois , Casella Daniele , Camplani Andrea , Miglietta Mario Marcello , Panegrossi Giulia
Published: July 2024

Mediterranean cyclones often exhibit phenomenological features typical of tropical (or sub-tropical) cyclones (e.g., a cloud free eye surrounded by spiraling rain bands around the center and a closed vortex associated with strong near-surface winds and heavy precipitation) causing extensive devastation over the coastal regions. More rarely (1-3 times per year) these cyclones undergo a tropical-like cyclone transition during their mature phase, exhibiting at some point during their evolution a deep warm core of diabatic origin and deep convection in proximity of the center. These cyclones are referred to as Medicanes (Mediterraean Hurricane). Generally, the term Medicane has been used for both cyclones with tropical-like characteristics, and for weaker warm seclusions present in the late stage of extra-tropical cyclones. The present work analyzes three Mediterranean cyclones (i.e., Helios, Juliette and Daniel) that occurred between February and September 2023. The analysis is mainly carried out using satellite passive microwave (PMW) measurements from different radiometers, which allow to monitor and to characterize the thermodynamic and microphysics characteristics during the cyclones’ life time, in particular the occurrence of a warm core and deep convection.  Moreover, the surface wind field is characterized by exploiting all the available scatterometers onboard LEO satellites (MetOp ASCAT and FY-3E WindRAD) highlighting the differences between the developing and the mature stage of the cyclones. Finally, a preliminary analysis about the role of dynamics and air-sea interaction processes favoring the tropical-like transition is also carried out.The goal is to determine if the three considered cyclones exhibit tropical-like characteristics during their mature phase and can be considered as medicanes. The three cyclones show a very similar evolution during the initial phases, with stratospheric intrusion followed by the development of a warm anomaly in the low/mid-troposphere around the cyclone center, clearly driven by baroclinic processes. While for Helios, the PMW precipitation microphysics diagnostics show a near complete lack of deep convection near the cyclone center (within 100 km), during the final stage of the mature phase of Juliette and Daniel this feature is identified, indicating that diabatic heating plays a key role in the warm core development. This evolution is observed in nearly all the medicanes which have been studied, as they eventually develop a warm core driven by diabatic processes (e.g., Zorbas, Numa). Based on this analysis, it can be concluded that Helios is warm seclusion, while Juliette and Daniel can be classified as medicanes.
On some aspects of the predictability of the 18 August 2022 derecho using a 910-members convective scale ensemble prediction system
Authors: descamps laurent , mounier arnaud
Published: July 2024

On 18 August 2022, Europe was hit by an extreme convective phenomenon. In the second half of the night, a derecho formed in the Mediterranean and successively hit several countries such as Spain, France, Italy, Slovenia, Austria and the Czech Republic. This exceptional meteorological event, with wind gusts reaching a maximum of 62.2 m/s, caused extensive damage in its path and led to numerous fatalties in several countries with the highest death tolls reported in Corsica and in the Alps.The aim of our work is to better understand and document various aspects of the predictability of such extreme phenomena. Using the operational convective ensemble forecasting system of Météo-France AROME-EPS, we built a large ensemble of hundreds of forecasts. The initial states of the forecasts come from the 26 (25 perturbed + 1 unperturbed) analyses of the operational AROME ensemble of data assimilations and the 35 (34 perturbed + 1 unperturbed) lateral boundary conditions come from the operational global ensemble prediction system of Météo-France, leading to a 910-members convective-scale forecast ensemble. The automatic bow echo detection tool developed by Mounier et al (2022) has been used to examine several aspects of the predictability of the derecho. First, we evaluated whether using hundreds of members in an ensemble prediction system improves our ability to anticipate the occurrence of the derecho. We examined several aspects of the predictability of the phenomenon, such as its trajectory, chronology and intensity. We also assessed the ability of this extended ensemble to produce more consistent probabilistic forecasts over time than the operational ensemble forecasting system. Finally, we examined the sensitivity of the forecasts ability to predict such an event to their initial states and to their coupling members.
A regional coupled ocean-atmosphere-wave model (SHYFEM-WRF-WW3) for intense meteo-marine events in the Mediterranean Sea
Authors: Arpaia Luca , Bajo Marco , Ferrarin Christian , Umgiesser Georg
Published: July 2024

We are developing a new regional coupled ocean-atmosphere-wave model to study the air-sea interaction during intense meteo-marine events such as mesoscale cyclones and tropical-like cyclones. The coupled model is based on the following open-source community model components: (1) an ocean component with the SHYFEM finite element coastal ocean model, (2) a wave component with the unstructured WW3 spectral wave model, (3) an atmospheric component with the Weather Research and Forecasting model (WRF). The wave and the ocean components are hard-coupled and run on the same triangular grid. The resulting ocean-wave and atmosphere components are coupled through the Earth System Modelling Framework library (ESMF), an integrated coupling framework which leads to very clean, abstract and efficient coupled models. The implemented coupler can handle explicit and semi-implicit coupled time-stepping schemes. With this instrument we study the interaction of tropical cyclone-like vortices with the sea. We show with simplified experiments how this feedback affects the trajectories and the intensity of the vortex as well as the sea-surface temperature and the ocean circulations. We show our model scales well compared to the baseline (uncoupled) runs of the single components. It can be thus applied to large scale configurations with high resolution. We focus on a series of storm events that occurred on the Mediterranean Sea: storm Vaja in 2018, Medicane Ianos in 2020 and a mesoscale cyclone in the northern Adriatic Sea occurred in 2019.  The results derived from the coupled model are compared with those from the simulations using either only the ocean or the atmospheric model, with external forcing (uncoupled). The comparisons made against observational data show that the coupled model outperforms the uncoupled one. 
Predicting Tree Effects on Urban Microclimate of Megacity Dhaka: Considering the 2022 building construction regulation changes 
Authors: Sharmin Tania , Rahman Mizanur Rahman
Published: July 2024

Megacities like Dhaka are facing unprecedented challenges due to the rapid expansion of their populations, resulting in a severe shortage of habitable space. To accommodate this burgeoning population, construction activities often encroach upon urban vegetation and natural landscapes, leading to their rapid degradation and destruction. The consequences of this loss of greenery are far-reaching, impacting the local microclimate in many ways.
Urban vegetation, including trees, shrubs, and green spaces, serves as a crucial natural regulator of the climate within cities. These elements play a pivotal role in moderating air temperature, humidity levels, air quality, and overall comfort for residents. However, the exceptional pace of urbanisation in Dhaka has led to a staggering loss of green spaces over the years. For instance, between 1989 and 2020, Dhaka witnessed a staggering 56% reduction in its greenspace, leaving only a mere 2% of healthy vegetation. This alarming trend is primarily attributed to unplanned urbanisation practices that prioritise construction over environmental preservation.
In response to the escalating challenges posed by unplanned urbanisation, the government of Bangladesh has introduced a series of building regulations over the years. However, these regulations, such as the Building Construction Rules of 1996, 2008, and 2022, were primarily focused on controlling the haphazard construction of buildings without adequately considering their environmental implications. Particularly, the focus on compact, high-density building structures to accommodate population growth has worsened the urban heat island (UHI) effect.
To address these issues, the government has progressively upgraded its building regulations, introducing concepts like Floor Area Ratio (FAR) and location-based FAR in 2008 and 2022, respectively. These regulatory measures aim to create a balance between urban development and environmental sustainability by ensuring the preservation of green spaces while accommodating population growth. However, the efficacy of these regulations in mitigating the adverse effects on the microclimate remains uncertain.
This study aims to comprehensively evaluate the impact of these building regulations on Dhaka's microclimate as a response to the above challenges. It employs advanced methodologies, including GIS-based data, 3D modeling, microclimate measurements, and simulation using the ENVI-met model. By comparing different scenarios based on varying building regulations, the study aims to provide valuable insights for urban planners and policymakers to formulate sustainable building policies that prioritise green and open spaces and improve the city's microclimate, ultimately contributing to the development of more liveable and resilient urban areas.
Numerical Sensitivity Study on a Mega Sand Dust Storm in Korea Using WRF-Chem
Authors: Yoon Ji Won , Lee Seungyeon , Lee Ebony , Park Seon Ki
Published: July 2024

This study evaluates the performance of the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) in forecasting a mega Asian Dust Storm (ADS) event that occurred over Korea on March 28–29, 2021. Since the introduction of the ADS crisis warning system in Korea in 2015, a nationwide caution stage was announced for the first time in six years on March 29, 2021. We specifically evaluated a combination of parameterization schemes: five for dust emission and four for land surface schemes, which are crucial for the accurate numerical forecasting of dust storms. Using in-situ and remote sensing data, we comprehensively evaluated meteorological and air quality variables, including 2 m temperature, 2 m relative humidity, 10 m wind speed, PM10, and aerosol optical depth (AOD) over Korea. Our results indicate that the prediction performance of surface meteorological variables was influenced more by the land surface scheme than by the dust emission scheme, with scheme combinations based on Noah land surface model with Multiple Parameterization options (Noah-MP) generally showing good performance. In contrast, in predicting air quality variables, including Particulate Matter 10 (PM10) and AOD, there was a very large difference in performance depending on the scheme combinations due to the influence of the dust emission scheme, which is directly related to the generation and amount of dust. Based on the evaluation results of various combinations of dust emission and land surface schemes in this study, we can suggest appropriate combination schemes for the WRF-Chem model in cases of mega ADSs affecting Korea.
Enhancing Finland's ceilometer network
Authors: O'Connor Ewan , Le Viet , Filioglu Maria , Shang Xiaoxia , Vakkari Ville , Moisseev Dmitri
Published: July 2024

The Finnish Meteorological Institute (FMI) ceilometer network now has about 45 ceilometer stations across Finland that report the full vertical profile of attenuated backscatter profiles, which are processed and displayed in real time on a dedicated website (ceilometer.fmi.fi). This data is also forwarded to E-PROFILE, part of the surface observation network in Europe coordinated by EUMETNET.The primary objective of ceilometers is for measuring cloud base, but, with the improvements in the sensitivity of the ceilometers now available, it also possible to diagnose the presence of aerosol, precipitation, and differentiate between liquid and ice clouds. For Finland, one important achievement of the ceilometer network is the reliable identification of supercooled liquid layers. This has led to the development of an 'icing' product which highlights the likelihood of icing (e.g. for trees, masts, aircraft) and has been used to evaluate the icing forecast generated by FMI in conjunction with in-situ measurements of ice accretion. Other products are also being developed (such as fog forecasts) for real time generation and display.Recently, the FMI ceilometer network has been augmented with 5 ceilometers having depolarisation capability (Vaisala CL61). The performance is such that episodes of dust, pollen and smoke are readily identifiable at the nominal instrument resolution without further averaging. In addition, it is also possible to distinguish between solid precipitation (ice and snow) and freezing rain, which has proved extremely useful for aviation forecasters. In this presentation we will demonstrate both the characterisation and quality control applied to the depolarisation instruments to ensure reliable measurements, particularly close to the surface, and compare remote sensing with surface-based identification of freezing rain events.
Hydrostatic Seasonal Forecast System Development within the Unified Forecast System at NOAA
Authors: Zhou Xiaqiong , Yang Fanglin
Published: July 2024

NOAA is undertaking the development of a Seasonal Forecast System version 1 (SFSv1) to replace the current Climate Forecast System version 2 (CFSv2) due to its limitations and the need for enhanced forecast accuracy. The SFS's development is primarily based on the capabilities of the NOAA Global Ensemble Forecast System (GEFSv13) which utilizes a non-hydrostatic Finite-volume dynamical core and is tailored for extended-range forecasting at a 25 km resolution. SFSv1, intended to operate at approximately 100 km, with a target resolution of about 50 km, requires careful examination to ensure the effectiveness of the existing physics suite and dynamics at these resolutions. Within the resolution range of 50 km to 100 km, the hydrostatic assumption remains the hydrostatic assumption retains its viability, offering reduced computational costs and intimating that the non-hydrostatic alternative may be superfluous. To comprehensively evaluate these alternatives, sensitivity experiments with both AMIP-type and fully coupled experiments, have been conducted. Adjustments to the schemes and parameters governing artificial dissipation, horizontal advection, and vertical remapping have been made to align with the hydrostatic option.The verification of atmosphere-only sensitivity experiments indicates that the hydrostatic option exhibits comparable performance compared to non-hydrostatic in terms of the prediction of large-scale environmental patterns. It also shows superior performance in predicting the quasi-biennial zonal wind oscillation at the stratosphere. However, further model refinement is deemed necessary, particularly when coupled with the ocean model to mitigate cold SST biases over the NINO3.4 region. Ongoing investigations are also addressing other issues arising from the transition to the hydrostatic option for SFS.