bluuebunny/crossref_metadata_2025 huggingface dataset:
TRANSLATE: Providing Standardised National Climate Services for Ireland
Authors: Scannell Claire , Lambkin Keith , O'Brien Enda , Nolan Paul , Holloway Paul , Ryan Paraic , Duffy Catriona , Griffin Seanie , Delmar Jordan
Published: July 2023

The TRANSLATE project is a Met Éireann lead initiative, steered by national and international experts, to standardise future climate projections for Ireland and develop equitable and accessible climate services that meet the climate information needs of decision makers across Ireland.Working alongside national experts, from the Irish Centre for High End Computing (ICHEC), NUI Galway, and the SFI Research Centre for Energy, Climate and Marine (MaREI), UCC, TRANSLATE aims to:Produce robust standardised national climate projections for Ireland (June 2023).
Co-develop prototype climate services, implementing TRANSLATE’s standardised projections and demonstrating how this climate information can meet the needs of the Irish adaptation sector.
Provide information and documentation that informs the standardisation of cross-sectoral decision making for climate sensitive sectors.
Ensure that standardised projections, summary information and climate services are easily accessible and useful to all potential users.
Facilitate the development of climate services using standardised climate information within the National Framework for Climate Services (NFCS).
TRANSLATE critically scrutinises existing climate scenarios, effectively choosing the most suitable for Ireland. It follows international best practice for combining multiple climate simulations and applies downscaling and statistical techniques to provide a robust range of high resolution (1.5 km), future climate projections for all of Ireland. Using such approaches TRANSLATE aims to improve the accuracy and resolution of existing high-resolution projections. Outputs consist of 30-year time slices of daily projections of temperature and rainfall over a range of different scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP 8.5). From these, sector specific climate indices can be computed to support climate-based decision making across Ireland.Here we will present each element of the program culminating in how these standardised projections can be used to inform decision making across sectors. We will present the projections in the form of national summaries for Ireland across global warming levels (GWL), presenting the most up to date picture of Irelands changing climate, creating national and sub-national narratives. This will include a first look at output from a provisional ensemble of CMIP6 data chosen for Ireland and presented across a range of global warming levels.
An aviation support tool for satellite remote detection of in-flight icing.
Authors: Zollo Alessandra Lucia , Bucchignani Edoardo
Published: July 2023

In-flight icing, i.e. the accretion of ice on airplane’s surfaces during flight, is caused by supercooled water droplets that freeze instantly when they impact the airframe and it represents a critical meteorological risk to aviation as it affects aircraft performance, stability and controllability. Therefore, the remote detection of weather conditions leading to in-flight icing is a goal of great interest to the scientific community.  
In 2017, the Meteorological Laboratory of CIRA has developed a first satellite-based tool for in-flight icing detection in collaboration with Italian Air Force Meteorological Service. This tool is based on several high-resolution satellite products of Meteosat Second Generation (MSG) and a set of experimental curves and envelopes describing the interrelationship of icing-related cloud variables that represent the icing reference certification rules, namely Appendix C to FAA 14 CFR Part 25 / EASA CS-25. However, Appendix C data do not consider Supercooled Large Droplets (SLD), which have been the cause of tragic accidents over the last decades and that have been introduced in new certification procedures and guidelines through the Appendix O, effective as of 2015. In the framework of the H2020 EU project SENS4ICE (SENSors and certifiable hybrid architectures for safer aviation in ICing Environment) started in 2019, CIRA is working on a further maturation of the previously developed icing detection algorithm, in order to consider also Appendix O Icing Conditions. The developed tool is targeted to identify areas potentially affected by in flight icing hazard, giving an estimate of the altitude and of the severity of the phenomenon (light, moderate, severe) with indication of possible SLD conditions.
In the present work an overall description of the implemented tool is provided along with an analysis of its performance. Due to the lack of suitable in-situ observations of icing conditions, a complete validation of the developed product is challenging. A comparison with significant weather charts has been performed and other validation activities based on the comparison with soundings data are ongoing, showing quite good results. Furthermore, this tool is currently being used in the framework of the SENS4ICE flight test campaign (scheduled in April 2023), which represents a good opportunity to evaluate its performance in environmental icing conditions. During the flight tests, information on monitoring of icing conditions are provided in the pre-flight phase and updated in near-real time. The outcomes of the flight test campaign will be exploited to identify the strengths and weaknesses of the algorithm.
Acknowledgment: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N° 824253 (SENS4ICE project).
Assessing cloud representation of two global atmospheric models using multiple overpasses of CloudSat-CALIPSO over the same Arctic cyclone
Authors: Wimmer Meryl , Aubry Clémantyne , Rivière Gwendal , Delanoë Julien , Bazile Eric , Vignon Etienne , Hofmann Lukas
Published: July 2023

Mixed-phase clouds are common in Arctic cyclones, but their representation in global atmospheric models is still challenging. The aim of the study is to evaluate and improve the ratio of ice and liquid water within mixed-phase clouds simulated by two general circulation models (GCM): ARPEGE (operational at Météo-France) and LMDZ (the atmospheric component of the IPSL-CM Earth System Model). For that evaluation, we consider ERA5 reanalysis for the overall dynamics, and for the cloud representation, DARDAR satellite products which provide the ice water content (IWC) as well as categorization masks along CloudSat-CALIPSO tracks. This work focuses on an Arctic cyclone that occurred in May 2019 near Svalbard and has been sampled several times by the two satellites CloudSat and CALIPSO.
Compared to satellite observations, LMDZ simulations provide quite realistic IWC while ARPEGE underestimates this quantity. However, by comparing liquid and ice occurrences between models and observations, both GCMs clearly miss occurrences of liquid phase at temperature lower than -30°C but they all overestimate the liquid occurrence in the whole negative temperature range between 0°C and -30°C.
In order to improve such liquid and ice occurrences, different liquid/ice partition functions are tested in the two models. In particular, different shapes and extensions to lower temperatures are considered. This helps to improve the occurrence of liquid water at the lowest temperatures but does not correct the overestimated occurrences between 0°C and -30°C. As temperature does not discriminate liquid-ice partition properly, different other predictors are tested and distance from the cloud top appears to be the most relevant.
A Record Breaking Extratropical Cyclone in 2022: A Sign of Climate Change ?
Authors: Hewson Tim
Published: July 2023

In October 2022 a cyclone that deepened explosively over the South Pacific - the "Peter I Storm" - acquired a central pressure close to 900hPa, which by some margin is lower than has been seen before (in modern times) for a cyclone in either the northern or southern hemisphere extratropical regions. Whilst observation coverage in the area was sparse, the exceptional central pressure value was supported by operational numerical model output from both ECMWF and GFS models, and also by the ERA5 re-analysis. This talk will examine the evolution of the cyclone, that spanned more than 10 days: from its tropical origins near to Tonga to its final decay on the fringes of Antarctica. We pose the question: what might have contributed to the occurrence of such an extreme? Surface characteristics, such as roughness and sea ice coverage, and large-scale dynamics will be referenced. Operational ECMWF ensemble predictions will also be shown; these suggest that the event was remarkably predictable for lead times of 5 days and less. The ECMWF forecasts will also be compared with some modern data-driven AI-based forecasts, to examine if they were as good. 
The extreme event will then be placed in a climatological context, using ERA5, to reference questions about 'trends in extremes', examining also northern hemisphere behaviour, and comparing the cyclone's life-cycle with that of the northern hemisphere "record holder" from 1986. We will also cross-reference another extreme cyclone case from 1941, to further examine the question of re-analysis integrity in the face of sparse data coverage. It seems that the ERA5 representation of cyclones with extreme central pressures is quite robust. This is probably because such cyclones tend to have a large areal extent. The talk will conclude with contrasting remarks about representativeness for smaller cyclones, that can have particular relevance for high impact weather over Europe.
Convolutional neural network downscaling to improve sub-seasonal wind-speed predictions in Europe
Authors: Tian Ganglin , Le Coz Camille , Alexandre Charantonis Anastase , Tantet Alexis , Goutham Naveen , Plougonven Riwal
Published: July 2023

Wind power systems' maintenance, deployment, and management, as well as the balance between energy supply and demand, are highly dependent on wind speeds and their temporal variability. Wind speed prediction at the sub-seasonal time scale presents a challenge since the skill of surface wind prediction sharply declines after two weeks. Nevertheless, the predictability of large-scale variables is higher than that of surface variables at this time scale. Goutham et al. (2022) improved the skill of surface wind speed forecasts by downscaling 500 hPa geopotential height (Z500)t forecasts using redundancy analysis based on a linear framework. Leveraging their work, we investigate whether Convolutional Neural Networks (CNNs) can be used to further improve the skill of subseasonal wind speed predictions over Europe.
 
To answer this question, this study proposes a non-linear statistical sub-seasonal ensemble forecasting method for the boreal winter over Europe based on applying a supervised learning model to dynamic forecasts. Specifically, the proposed statistical CNN model regresses weekly-mean surface wind speeds from dynamic forecasts of Z500. The dynamical forecasts of surface wind speed from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the statistical forecasts from Redundancy Analysis (RA, Goutham et al. 2022) are selected as benchmarks for comparison, known for their superiority to climatology in different domains, lead times, and assessment indicators. Initially, we access the skill of a deterministic version of the network, utilizing ERA5 reanalysis data (trained for 15 years and tested for 5 years) to regress wind speed from geopotential height, find that the network is, on average, more skillful than RA based on the root mean squared error. Subsequently, the same network (without retraining) is applied to the subseasonal predictions from the ECMWF  covering the boreal winters from 2015 to 2022, with the same variables. Several indicators, including the anomaly correlation coefficient, continuous ranked probability score, and corresponding skill scores, compare the skill of the statistical forecasts (RA and CNN) and the dynamical forecasts (ECMWF). This comparison aims to determine if the statistical CNN has superior skill in the primary wind energy-producing countries in Europe and whether different models exhibit specific spatiotemporal patterns of skill in the sub-seasonal range. This study also investigates the performance of statistical and dynamical wind speed forecasts under normal and extreme conditions by analyzing the probability density distribution of ensemble members at given areas. Our preliminary findings reveal that statistical forecasts exhibit superior skill in normal
Worldwide Köppen Geiger Climate classification changes projections (SSP2-2.6 and SSP5-8.5)
Authors: Andrade Cristina , A. Santos João , Fonseca André
Published: July 2023

Climate strongly influences not only the distribution and abundance of species on Earth but also the distribution of ecosystem types. Since the climate is defined as a long-term pattern of weather conditions at a given location or region both the distribution and evolution of its conditions are considered highly relevant. The Köppen-Geiger climate classification is based on temperature and precipitation and has been widely used to assess climate shifts worldwide.
In this study, high spatial resolution (10 min) monthly precipitation (in mm) and maximum, minimum, and mean temperatures (in °C) gridded datasets were retrieved from the WorldClim data website. The Köppen-Geiger climate classification was then computed worldwide between 1970 and 2000 (historical baseline) and between 2041 and 2060 under two Shared Socio-economic Pathways (SSPs) SSP2‒2.6 and SSP5‒8.5; from an ensemble of 14 Global Climate Models (GCMs).
Results point out an overall decrease of type E (Polar) around 4.6% for ET (Tundra) and 3.8% for EF (Icecap) under both SSPs. A noticeable decrease of type Dfc (Temperate subpolar oceanic) 7.34% and 7.53% under SSP2-2.6 and SSP5-8.5, respectively is also projected. Climate types Csa (Temperate with hot summer) and Cfb (Temperate oceanic with warm summer) are also projected to decrease by about 1% under both SSPs. Conversely, in decreased order of magnitude, types Dfa (Continental humid with hot summer, 6%), Cfa (Temperate humid subtropical, 2.8 and 2.9%), BWh (Hot desert, 1.4%), BSh (Hot semi-arid or steppe, 1.3%), As (Tropical, 1.2%), Dwa (Monsoon-influenced hot-summer humid continental, 1%) are projected to increase under SSP2‒2.6 and SSP5‒8.5, respectively. The projected changes mainly in locations like Siberia (reduction of type E areas, for example), will have a detrimental effect on the environment since the melting of the permafrost will release methane a powerful greenhouse gas, thus contributing to global warming.
 
Acknowledgments: This research was funded by National Funds by FCT ‒ Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020.
Evaluation of the Warning Value Chain as an Educational Approach for University Students
Authors: Hoffmann David , Ebert Beth , Mooney Carla , Majumdar Sharan , Goeber Martin , Golding Brian
Published: July 2023

The weather information value chain provides a framework for characterising the production, communication, and use of information by all stakeholders in an end-to-end warning system. It covers weather and hazard monitoring, modelling and forecasting, risk assessment, communication, and preparedness activities.  

A 4-year international project under the WMO World Weather Research Programme is using value chain approaches to describe and evaluate warning systems for high-impact weather by integrating physical and social science. One of the project’s key outputs is a database questionnaire for high-impact weather event case study collection and analysis. The questionnaire is primarily aimed at scientists and practitioners to review, analyse and learn from previous experience using value chain approaches. Project scientists are using it to analyse high impact weather events that have occurred in recent years.  

Beyond the professional use for severe event assessment, the questionnaire has proven to be an effective educational tool for university students to learn about high-impact events. Undergraduate students at the University of Miami used the questionnaire to study the warning value chain for Hurricanes Ida (2021) and Ian (2022) as an assignment in an undergraduate tropical meteorology course. Similarly, undergraduate interns at the Bureau of Meteorology completed the questionnaire for the Black Summer Bushfires in south-east Australia (2019/2020) and did a comparative study of the warning value chains for Hurricane Isaias (2020) for the Caribbean and the US. Using available online resources, the students prepared their responses and collaborated in teams to present syntheses of their evaluations. Engaging students in such a cross-disciplinary study enhanced their critical thinking about high-impact weather event forecasting, impacts, warning communication and response. In this presentation we introduce the database questionnaire and how it can be used for educational purposes. 

The questionnaire and accompanying guide are freely available for anyone to use and can be downloaded at http://hiweather.net/Lists/130.html. We encourage not only the research and operational communities but also academic institutions to participate in this project by contributing case studies of high impact events and collaborating in their analysis.  

 

 Corresponding/presenting author: David Hoffmann, Bureau of Meteorology, Melbourne, Australia; david.hoffmann@bom.gov.au  
Climatologies of Anisotropy in the Reynolds Stress Tensor
Authors: Mack Laura , Pirk Norbert , Vercauteren Nikki
Published: July 2023

Turbulent mixing processes in the atmospheric boundary layer are usually described using Monin-Obukhov similarity theory (MOST), which combines the mean flow properties in the dimensionless stability parameter. Originally, this theory was derived for steady flows over flat surfaces and fails to describe turbulence under unsteady conditions or in complex terrain. However, recent research showed that the inclusion of geometric properties of the Reynolds stress tensor can help to overcome this problem. From the eigenvalues of the Reynolds stress tensor, three topological limiting cases (one-, two- and three-component) can be derived, where the three-component limiting state describes isotropic turbulence. Additionally including a measure of the deviation from isotropy, i.e. anisotropy, to MOST reduces the scatter of observations around the parametrized stability correction function and thereby improves MOST. However, general climatologies of anisotropy based on eddy-covariance measurements have not been studied so far.Based on long-term eddy-covariance measurements in Finse, an alpine valley in southern central Norway, the seasonality and diurnal variation of anisotropy is investigated. It is shown that the flow is more anisotropic in winter than in summer. In winter, the wave-dominated one-component limiting state dominates at night and the two-component limiting state dominates during the day. In summer, the flow becomes more isotropic during the day. Correlation analysis shows that anisotropy correlates mainly with wind speed shear. Compared to the two-component limiting state, the one-component limiting state occurs more frequently with a high intermittency factor.In addition, flux tower measurements from the FLOSS2 experiment are used to study climatologies of the vertical profiles of anisotropy. It is shown that turbulence is strictly anisotropic near the ground and becomes more isotropic with height. During the day, the flow tends towards the two-component limiting state near the ground and from about 10 m altitude towards the three-component limiting state. At night, the one-component limiting state dominates, especially at higher altitudes.Such climatologies of anisotropy and their relation to large-scale meteorological variables can help in the development of new parametrizations for numerical weather prediction models.
Development of Real-time Urban-scale Weather Monitoring Network and Experimental Hourly Temperature Forecasts in Hong Kong
Authors: Kwok Yu-ting , Lau Stephen Po-wing , Wong Wai-kin , Lee Olivia Shuk-ming
Published: July 2023

The high-density building environment and complex, heterogeneous landscape in Hong Kong result in significant spatial variability of local temperatures, winds and other weather elements down to the street level.  To enhance meteorological observation and forecasting services, the Hong Kong Observatory (HKO) has been actively developing an urban-scale weather observing network in recent years through in-house efforts and collaboration with local communities.  In this presentation, a novel real-time monitoring network, based on economical sensors deployed at various sites within the urban areas, providing basic temperature and relative humidity measurements will be introduced.  They are found to provide useful data for analyses of daily temperature variations in urban areas under very hot or prolonged heat stress episodes.  The new observations offer indispensable meteorological information and reference for studies of urban micro-climate and the impact of heat stress on public health or activities.  The data of the urban-scale weather monitoring network are then utilized to generate location-specific hourly forecasts of air temperatures and relative humidity for the next nine days through post-processing of downscaled forecasts from the global NWP models.  It is found that the mean absolute errors of daily minimum and maximum temperature forecasts at the urban-scale monitoring network stations are generally within 2 degrees Celsius in the next 5 days.   The real-time forecast products have been launched to public since mid-2022 on HKO’s Automatic Regional Weather Forecast web portal.   The automatic urban-scale forecast shows useful information for the public and serves as potential early alert for communities to get prepared for potential heat stress brought by the very hot weather in the next few days.  The methodology and recent development of the forecast products will be presented, including artificial intelligence technique to consider different meteorological factors for improving forecast of daily minimum and maximum temperatures, as well as machine learning methods to generate urban-scale forecasts under different weather regimes.
Plans for the evaluation of the KIAPS LETKF-based radar reflectiviity DA system
Authors: Choi Dayoung , Clayton Adam , Kwon In-Hyuk
Published: July 2023

   The renewed KIAPS (Korea Institute of Atmospheric Prediction Systems) aims at developing a unified framework for seamless prediction from the very short range (~6 hours) to the extended medium range (~30 days), including coupling to various Earth system components, such as the land surface, oceans, and sea ice. For very short range forecasts, KIAPS is currently developing a high-resolution (1-5km) model targeting storm-scale high-impact weather over East Asia, centered on the Korean peninsula. In order to start developing methods for assimilating Korean radar reflectivity observations, we have developed a “testbed” system based on a high-resolution, limited-area version of WRF, configured by the Korea Meteorological Administration (KMA) to use the same physics packages as the Korean Integrated Model (KIM) global model. The data assimilation (DA) system is based on the Local Ensemble Transform Kalman Filter (LETKF), and currently only includes support for assimilation of 3D synthesis data derived from the radar reflectivity observation.   In order to get reasonable ensemble analysis fields at the convective scale through the LETKF-based radar DA system, we have been evaluating and tuning the reflectivity assimilation method. In order to improve the analysis of synoptic scales, we are now also adding support for the assimilation of conventional observations (Sonde, Surface, Aircraft, etc.). To allow this, the KIM Package for Observation Processing (KPOP), which has so far only been able to use background fields from the KIM global model, is being extended to support use WRF limited-area background fields. When this work is completed, the LETKF system will be extended to assimilate the conventional observations provided by KPOP in addition to radar reflectivity observations. We will then continue evaluation and develop a full cycling system. The final goal will then be to replace WRF with the new high-resolution KIM model.
Deep Learning Approaches for High-resolution Solar Irradiance and Solar Power Nowcasting
Authors: Papazek Petrina , Schicker Irene
Published: July 2023

PV / solar power production is fostered to become one of the most powerful renewable energy sources in Central Europe to tackle our growing energy demands. While the just recently emerging PV power observations often record highly resolved real-time data for a relatively short time-horizon, solar irradiance offers long time-series using automatized weather station networks. Being closely linked, irradiance can act as a relevant input and estimate for PV forecasting. Particularly, long time series may be exploited for generating synthetic training datasets. However, both irradiance and PV can be challenging in forecasts using machine learning methods as they embrace a high degree of diurnal and seasonal variation.
Deep learning offers us new opportunities to generate highly resolved weather forecasts by learning relations in complex datasets. In this study, we investigate the suitability of several deep learning techniques for irradiance and PV nowcasts. Our main models investigated includes a sequence-to-sequence LSTM (long-short-term-memory; a type of artificial neural network) model using a climatological background model or NWP (numeric weather forecasting model) for post-processing, a Graph ANN (artificial neural network) model, and an analogs based deep learning method. Relevant input features include 3D-fields from NWP models (e.g.: AROME), satellite data and products (e.g.:  CAMS), radiation time series from remote sensing, and observation time-series.
Results for selected topologically diverse locations obtained by the developed method yield, in general, high forecast-skills, where we elaborate on interesting cases studies from a meteorological point of view. Different combinations of inputs and processing-steps are considered. We compare obtained forecast results to forecasts produced by traditional methods.
Observing the effects of roll-vortices on the Tropical Cyclone Boundary Layer (TCBL) using Synthetic Aperture Radars (SAR)
Authors: Dey Ipshita , O'Neill Morgan
Published: July 2023

The Tropical Cyclone Boundary Layer (TCBL) is uniquely favorable for the formation of highly asymmetric coherent structures called roll-vortices. They are kilometer-scale eddies that are aligned in the mean tangential wind direction and are generally associated with a combination of shear and convective instability. Roll-vortices can cause periodic enhancements of winds as well as surface fluxes and may assist the intensification process via vertical transport of tangential momentum. Furthermore, the up-gradient transfer of surface heat and momentum fluxes associated with roll-vortices can energize the large-scale motions, possibly resulting in an increased convective activity. It is therefore important to understand and quantify the effects of roll-vortices in setting the overall TCBL structure as well as near-surface winds.
Synthetic Aperture Radars onboard low-earth orbit (LEO) satellites such as Sentinel-1A/B and RadarSat-2 have often been used to identify TCBL roll-vortices. Based on the positive correlation between microwave backscattering and sea surface roughness, sea surface wind speed can be retrieved at high spatial resolution (1–3 km), without any saturation for high wind speed. However, the spatial distribution of roll-vortices in the TCBL and their subsequent impact on the vertical momentum and enthalpy fluxes are not well understood.  In this study, we use standard two-dimensional spectral analysis to detect roll-vortices in SAR-observed winds over multiple tropical cyclones (TCs) and use TC-relative composites to analyze their spatial distributions. We will also study the vertical wind and thermodynamic structures in the vicinity of SAR-observed roll-vortices using collocated aircraft observations whenever available. This may also allow us to extract empirical relationships between the roll-averaged vertical profiles of near-surface fluxes and maximum near-surface winds.
Results from a one-year test of the WindRanger® Doppler Lidar at GM Falkenberg
Authors: Bohne Charlotte , Beyrich Frank , Detring Carola , Kirtzel Hans-Jürgen , Peters Gerhard
Published: July 2023

Doppler lidar systems are nowadays widely used for measuring the 3D wind vector within the atmospheric boundary layer (ABL). Different systems tailored towards different fields of application are commercially available. The WindRanger® is a compact, cost-effective and easy-to-use FMCW lidar for vertical wind profiling across the lowest 200 m metres above ground. It performs continuous velocity-azimuth-display scans of the laser beam at a zenith angle of 10 degrees.
To assess the performance of this instrument, a WindRanger® was operated at the boundary layer field site (in German: Grenzschichtmessfeld, GM) Falkenberg of the Deutscher Wetterdienst (DWD) over a period of 13 months. We have compared the wind measurements with this instrument at a height of 90 m above ground versus sonic data collected at the 99m-tower and (for a sub-period) versus lidar wind data obtained with a pulsed Doppler lidar Streamline (Halo Photonics). Limitations of the WindRanger® measurements occurred in connection with precipitation and with low cloud base heights such that the data had to be filtered accordingly. The comparison provided RMSD values around 0.4 ms−1 for the wind speed and < 5° for the wind direction (after removing a bias due to improper alignment towards North).  
The WindRanger® allows for a time resolution of 0.5 s for one complete scan cycle. This lead us to investigate the possibility to derive wind gusts from the high-resolution time series. We found, however, that the wind speed from single-scan data exhibits a high variation throughout the day and derived gusts exceed the values determined from both the sonic and the Streamline Doppler lidar wind data in the convective boundary layer. This result is attributed to the small zenith angle (10 deg); this implies that the retrieved radial velocity values basically represent the vertical wind vector component which is typically characterized by strong fluctuations under convective conditions.
Synthetic Data Generation for Deep Learning based Renewable Energy Forecasts
Authors: Papazek Petrina , Schicker Irene
Published: July 2023

Renewable Energy is gaining more importance in tackling globally growing energy demands. However, time-series obtained form production sites are often limited in time-horizon and also different in nature due to present technology in power plants. Due to their high resource demands high-resolution NWPs, such as AROME, can often not be fully stored long-term and change significantly in each update cycle, making them a challenging input for data hungry deep learning approaches. Still, in short- and medium range forecasts selected NWP parameters often improve forecast results of machine learning approaches.
In this study, we investigate how to setup a synthetically generated/extended training dataset based on diverse spatially and temporary heterogeneous inputs. For practical reasons, we concentrate on a solar power production case study. Data considered includes NWP models (e.g.: AROME, ECMWF), satellite data and products (e.g.:  CAMS), radiation time series from remote sensing, and observation time-series. In particular, we present a machine learning methodology on extending time-series from AROME in accordance with close observations.  Our synthetic data generator learns from long observation time series, and longer available but still coarse ECMWF. It yields both synthetic observations for missing data in the observation, synthetic production optimized for a relatively new established site, as well as a prolongation of numeric model data for studied renewable energy sites.
Results are evaluated by our deep learning framework (e.g.: LSTM) and cross-validation or achieved data of the data-set. The combination of real and synthetic data generally offers benefits in forecasting by more complex machine learning methods relying on sufficiently long training data-sets.
Variability of flash droughts and their circulation drivers in Central Europe
Authors: Rehor Jan , Brazdil Rudolf , Trnka Miroslav , Balek Jan
Published: July 2023

Events with unusually rapid onset and intensification (usually called flash droughts) are emerging into the spotlight as dangerous subseasonal climatic phenomena. Even though they bear a potential for significant socio-environmental impacts, research of their spatiotemporal variability and meteorological drivers in Central Europe has been so far limited. This contribution implements and adapts established methods of flash drought detection and conducts their comprehensive spatiotemporal analysis based of gridded soil moisture data from the SoilClim model. Studied area consists of the Czech Republic, Slovakia and north part of Austria for the 1961–2021 period. The gridded flash drought results were divided into four clusters using Wards hierarchical agglomerative method. Individual flash drought episodes were delimited for each cluster, divided into three phases (onset, course, end) and investigated separately in terms of drivers, represented by three meteorological variables (precipitation, actual evapotranspiration, maximum temperature) and atmospheric circulation types based on the objective classification (derived from flow strength, direction and vorticity). The study found that frequency of flash droughts was slightly decreasing in the winter half-year and slightly increasing in the summer half-year, with substantial amplification in April–June season. The increase was slower than in the case of seasonal droughts, being driven by longer-term accumulation of water deficit. Circulation drivers exhibited much stronger and more direct influence in the summer half-year, particularly causing the onset of flash drought episodes during predominance of anticyclonic types and absence of cyclonic types, while the course of flash drought episodes was also connected to increased temperatures, often connected to warm airflow. Compared to soil drought in general, flash droughts in Central Europe were more tightly connected to circulation, particularly to anticyclonic patterns and southeastern airflow. 
Analog-based post-processing of the operational temperature NWP in Croatia
Authors: Vujec Ivan , Odak Plenković Iris
Published: July 2023

NWP models have been a staple of modern weather forecasting for a long time. Although their skill is steadily improving, their limitations are still notable. An additional forecast improvement obtained by applying statistical post-processing for the locations where the measurements are available is thus a natural next step. Such an example of various NWPs improvements is achieved by the analog method.  For that reason, a potential post-processing contribution of the analog method is analyzed by verifying forecasts against the temperature measurements across the Republic of Croatia.
 
The raw NWP model used in this work is the ALADIN model with a 4 km horizontal resolution. The analyzed analog-based method includes weight optimization and the correction for more extreme values. Weight optimization aims to improve the process of similarity search by determining the ideal weight for each predictor variable, and prevents overfitting. The correction for more extreme values tends to improve forecasts on both ends of the temperature distribution by adding the correction factor if the starting NWP forecast exceeds the before-determined threshold.
 
The verification of the hourly temperature forecasts is performed using both the continuous and categorical approaches. In the categorical approach, the analysis is performed for both common and more extreme events. Considering the sensitivity of forecast accuracy on the terrain type, verification is also performed for different groups of stations. Results reveal the benefits of using the more advanced version of the analog method. Moreover, such an advanced analog method forecast outperforms raw NWP in most cases. When results are compared depending on the location, it is shown that the greater post-processing improvement can be seen in the continental than in the coastal area of Croatia.
 
Considering that the minimal and maximal daily values are a pragmatic way to provide forecasters with a quick and summarized overview, such forecasts are additionally examined. In addition to the statistical verification approach, a few interesting cases were also singled-out and analyzed more thoroughly. Finally, the amplitude of diurnal temperature variation seems to be more realistic for the analogs during heat or cold waves.
Impact of tropical SSTs on the late-winter signal over the North Atlantic-European region
Authors: Herceg-Bulić Ivana , Ivasić Sara , Popović Margareta
Published: July 2023

The North Atlantic-European (NAE) region is influenced by many climate factors that could be cooccurring and/or coinciding. These various influences, combined with the pronounced internal atmospheric variability, make separating the distinct impacts on the NAE area difficult. Besides the North Atlantic Oscillation (NAO), which is the strongest pattern of climate variability in the mid and high latitudes of the Northern hemisphere, the NAE region is also affected by the El Niño-Southern Oscillation (ENSO). ENSO is an atmosphere-ocean phenomenon involving SST changes in the tropical Pacific. Its remote influence can be manifested directly through the atmosphere, but also indirectly involving different parts of the ocean.In this study, we use a set of sensitivity experiments to investigate the potential impact of tropical sea surface temperatures (SSTs) on the signal of geopotential heights at 200 hPa (GH200) over the NAE region. Using an intermediately complex atmospheric general circulation model (ICTP AGCM), we have designed five experiments based on 35-member ensembles of long integrations with SST anomalies prescribed in various regions acting as lower boundary forcing for the model. In the AGCM experiments, the SST forcing was prescribed globally, in the tropical zone of all oceans, only inside the tropical Atlantic area, in the tropical Indian Ocean and limited to the tropical Pacific. Additionally, an experiment containing only climatological SSTs was analysed.The monthly GH200 signal was calculated based on the difference between the ensemble mean of each experiment and the climatological mean for the considered period. In addition, to separate the impact of the ENSO from other influences, the signal was calculated for ENSO and non-ENSO years, respectively. The influence of ENSO was analyzed in the context of the direct tropical Pacific SST forcing, but also from the aspect of the indirect contribution of other parts of the tropical oceans. The linearity of the response was analyzed with regard to the amplitude of the forcing, but also with regard to the contribution of individual parts of the tropical oceans.Results have shown that the GH200 signal is the strongest in the late-winter months (January-March) in all experiments. The AGCM experiment with SST boundary forcing prescribed only in the tropical Atlantic consistently yielded the least amount of signal. The strongest signal linked to ENSO events was found in the experiment with the SST forcing prescribed only in the tropical Pacific. It is also demonstrated that the potentially predictable ENSO-related signal over the NAE region is projected onto the East Atlantic pattern.
Modelling Methane Dispersion in Rotterdam using Large-Eddy Simulations
Authors: van der Linden Steven , de Roode Stephan
Published: July 2023

Last summer (Aug—Sep 2022) an extensive measurement campaign took place in Rotterdam, the Netherlands, within the Ruisdael program focusing on greenhouse gas emissions and local air quality in cities. As part of this campaign, methane was released (controlled flux) and subsequently measured in the direct surroundings (along line transects of a few 100 meters) in the Kralingen neighborhood with the aim of improving models for emission quantification. The limited spatial extent of the measurements, however, still makes it challenging to accurately determine the dispersion of methane on the wider neighborhood scale.
Here, we explore the possibility to use high-resolution Large-Eddy Simulations (LES) with explicitly resolved buildings using an immersed boundary method to provide detailed information on the local dispersion of methane. Recent development in our model now enables us to model gradients of concentration, wind and temperature on the street-level scale. The controlled release experiment serves as a benchmark for validation and evaluation of the LES approach for urban dispersion, with the aim of reproducing the observed concentrations along the line transects in an averaged sense.
We will present preliminary results of our simulations and discuss the model complexity needed for an accurate representation of methane concentration. Specifically, we will look into a number of challenges: how much detail/resolution is needed to capture local variations in concentrations; and what is the dependency of the results on large-scale meteorological conditions like wind speed and direction, and stability. Part of these challenges is dictated by the tradeoff between desired accuracy and numerical costs.
A validated LES approach may in turn aid us in interpreting measurements or predicting concentrations of trace gasses, when observations are scarce. Possibly, such LES may also be used in the future in an inverse modelling approach to obtain the most likely source of emission from a few local observations.
A flood storyline tool to train first responders and civil protection forces
Authors: Martius Olivia , Mosimann Markus , Munz Lukas , Kauzlaric Martina , Zischg Andreas
Published: July 2023

The Swiss first responders and civil protection agencies work with scenarios to train and prepare for high-impact weather events. These scenarios have traditionally been focusing on events with moderate severity (return periods of 30 to 100 years). After the flood event in the Ahr catchment in summer 2021 a need for more extreme and worst-case scenarios has been recognized.   The online-tool flood dynamics (https://flooddynamics.floodrisk.ch/) addresses these needs.
We have used the UNSEEN method, i.e. re-forecast archives from S2S ensemble predictions,  to identify nine scenarios (storylines) of very extreme precipitation accumulations over 3 to 5 days in Switzerland that serve as meteorological input for the tool. The precipitation storylines are processed through a chain of hydrological and hydraulic models to identify flooded areas. The flooded areas are then intersected with exposure data (buildings, roads, residents, critical infrastructure such as hospitals) to illustrate and quantify the impacts of the flooding. Impacts are quantified i) in monetary damages on buildings, ii) as number of affected persons, workplaces, hospitals, nursing homes, and schools, and iii) as the locations and length of impassable roadsections. 
A novel aspect of the tool is that it provides physically plausible, temporally evolving flood storylines. The evolution of the flooded areas is illustrated at an hourly time-resolution. Because this information includes both flooded buildings and flooded and hence impassable roads, first responders can for example see how access to critical infrastructure such as hospitals or to their own headquarters is interrupted during the events. The detailed visualizations in the form of maps and time evolutions available in the tool provide information far beyond classical training scenarios. The tool furthermore provides a regional overview on the (timing of) flood impacts over large parts of Switzerland. This large-scale perspective is relevant to prepare emergency organisations for extreme events, when many river sections are affected at the same time.
Deriving point-scale climate data for arbitrary locations – The “station transfer” method and an example application
Authors: Frei Christoph , Begert Michael , Isotta Francesco A.
Published: July 2023

Many planning applications require statistical information about the climate at the scale of a point. In civil engineering, for example, the design of heating and cooling systems relies on estimates of the occurrence of cold/warm extremes at the location of the building. An emerging practice of climate service providers is to derive such information from interpolation-based or reanalysis-based climate datasets on a fine (km-scale) grid. Yet, such datasets are poorly suited for this purpose. The effective resolution is limited and, as a consequence, this data represents regional area-mean conditions, not the point scale. As a result, the frequency of extremes is underestimated, and cross-parameter relationships are compromised. Here, we propose and illustrate a statistical procedure to derive point-scale climate data, at any point in space, without the artefacts of traditional optimal-prediction-based interpolation.
The proposed method, denoted “station transfer”, derives a climate series for the target location by transferring the observations of a suitable station and applying an adjustment, so that the result is representative for the target location. The choice of station and the adjustment are fully data driven. Unlike conventional interpolation, where concomitant observations in the neighborhood are combined, the “station transfer” insists on a single station as the origin of an adjustment, which is less invasive and better preserves point-scale tail statistics and cross-parameter relationships. We introduce a stochastic model for the transfer where the adjustments and the areas of representativity are jointly estimated from the entire station network. The method is illustrated with an example application that predicts point-scale surface air temperatures over the territory of Switzerland, using data from 65 stations.
The development and experimental application of this presentation are preliminaries of an upcoming project, where MeteoSwiss will derive new building-design climate basics for the Swiss Society of Engineers and Architects. But “station transfer” may become a much more widely used complement to conventional gridding, because of the wide-spread request for point-scale and high-frequency climate information in civil planning. 