bluuebunny/crossref_metadata_2025 huggingface dataset:
Surface compound extremes and Mediterranean cyclones
Authors: Portal Alice , Romppainen-Martius Olivia , Raveh-Rubin Shira , Catto Jennifer
Published: July 2023

The co-occurrence of extreme events, resulting generally in stronger surface impacts compared to isolated extreme events, constututes a hazard for the public and can be associated with severe damage to buildings, infrastructure and means of transport. Within the context of the MedCyclone initiative (see e. g. the Mediterranean cyclone track dataset defined in [1]), we investigate the "impact area" of the cyclones crossing the Mediterranean region by linking their passage with the appearance of hazards at the surface, in particular extremes of accumulated precipitation, near surface wind intensity, height of combined swell and wave. This analysis is expected to produce an estimate of the fraction of compound extremes - combinations of the extremes listed above - associated with the MedCyclones tracks in the recent climatological period, including geographical and seasonal details. The large-scale characteristics of the cyclones are taken into account by using a classification based on their upper-level potential vorticity pattern, and eventually by considering dynamical features with strong impacts on surface weather, such as cold fronts, warm conveyor belts and dry intrusions.
[1]  Flaounas, E., Aragão, L., Bernini, L., Dafis, S., Doiteau, B., Flocas, H., L. Gray, S., Karwat, A., Kouroutzoglou, J., Lionello, P., Pantillon, F., Pasquero, C., Patlakas, P., Picornell, M. A., Porcù, F., D. K. Priestley, M., Reale, M., Roberts, M., Saaroni, H., Sandler, D., Scoccimarro, E., Sprenger, M., and Ziv, B.: A composite approach to produce reference datasets for extratropical cyclone tracks: Application to Mediterranean cyclones, Weather and Climate Dynamics Discuss. [preprint], https://doi.org/10.5194/wcd-2022-63, in review, 2023. 
Changes in design precipitation over the Nordic-Baltic region as given by convection-permitting climate simulations
Authors: Dyrrdal Anita Verpe , Medus Erika , Dobler Andreas , Hodnebrog Øivind , Arnbjerg-Nielsen Karsten , Olsson Jonas , Thomassen Emma Dybro , Lind Petter , Gaile Dace , Post Piia
Published: July 2023

The increased risk of flooding due to global warming and subsequent heavy rainfall events in the Nordic-Baltic region call for recommendations directed at long-term planning. Climate change allowances are often based on expected changes in design precipitation as given by climate model simulations, and are used as a buffer on top of current design values to avoid a future increased damage potential as a consequence of climate change. We here compute expected changes in precipitation design values, so-called climate factors, for the Nordic-Baltic region, based on convection permitting simulations. These simulations have the advantage of explicitly resolving convection, which has been shown to be the main contributor to increased rainfall, and not explicitly resolving convection is a main source of error in modeled precipitation. We compute climate factors and assess their dependence on rainfall duration, return period, and geographical location, focusing on the summer (convective) season, short durations and the high emission scenario RCP8.5. We also compare these climate factors to those computed from a more conventional (not convection permitting) regional climate model ensemble.
We found higher climate factors for the longer return period, with only few exceptions, and distinctly higher climate factors going from daily to sub-daily durations. However, the different simulations give conflicting results for very short-duration rainfall (< 3 hours). The huge difference in the climate sensitivity of driving GCMs dominates the magnitude of estimated return levels. Our analysis is shaped by the high computational costs of running convection permitting models, resulting in a very limited ensemble representing a single emission scenario. The value lies in a holistic assessment of a combined dataset, with their different strengths and weaknesses, supporting the assessment of robust climate change allowances for heavy precipitation in the Nordic-Baltic region.
 
Reference: Dyrrdal et al., 2023. Submitted to Weather and Climate Extremes. 
Modelling anthropogenic heat emissions from residential buildings: Comparison between Berlin and London
Authors: Hertwig Denise , Liu Yiqing , McGrory Megan , Paskin Matthew , Frid Martina , Han Jiayi , Chen Yilin , Lo Piano Samuele , Ramirez-Mendiola Jose Luis , Smith Stefán T. , Grimmond Sue , Tsirantonakis Dimitris , Poursanidis Dimitris , Somarakis Giorgos , Chrysoulakis Nektarios , Iqbal Nimra , Wendnagel-Beck Angela , Ravan Marvin , Birkmann Jörn
Published: July 2023

Heat emissions from buildings in many cities play an important role for the urban surface energy balance (USEB) and the urban micro-climate. Heat generated indoors from human activities (e.g., use of electrical appliances, space heating, metabolic rate) is conducted through the building fabric and affects the USEB through long-wave radiation and turbulent sensible heat flux. The radiative and thermal properties of materials used in the building’s structural components (e.g., external walls, roof, windows) determine the storage of heat in the building volume and therefore the rate and timing of heat exchange between indoor and outdoor environments. This also affects the overall indoor thermal comfort.We use a building energy model (STEBBS – Simplified Thermal Energy Balance for Buildings Scheme) to simulate and compare the anthropogenic heat flux from residential buildings in the city centres of London, UK, and Berlin, Germany, in different seasons. Occupancy schedules and timings of indoor heat gains for STEBBS can be determined from the agent-based model DAVE (Dynamic Anthropogenic actiVities and feedback to Emissions; McGrory et al. – this meeting). Both cities feature a diverse set of building types (determined by common morphological attributes) and a range of typical construction types that are derived from information on building age bands, building regulations and building typologies (EPISCOPE, TABULA). This poster discusses the generation of characteristic and comparable building archetypes for the two cities, considering and discussing differences in morphology markers (e.g., building height, volume, exposed walls), building age, construction materials (e.g., presence or not of wall insulation, roof types) and the typical state of refurbishment / retrofitting of the residential building stock. Seasonal diurnal variations of the building energy balance in terms of energy consumption, turbulent sensible heat flux and net storage heat flux are compared for common building archetypes in the two cities to assess the main controls on the anthropogenic heat emissions into the urban canopy layer.Funding supporting this work includes ERC urbisphere, NERC APEx, and EPSRC CREDS.
Forecasting lightning probabilities derived from the Lightning Potential Index using neural networks
Authors: Baumgartner Manuel , Schröder Guido , Primo Cristina
Published: July 2023

Forecasting the occurrence of thunderstorms is a well-known challenge in weather prediction. Since a thunderstorm is by definition accompanied by at least one lightning, we aim to forecast the occurrence of at least one lightning within a pre-defined area and a pre-defined time-interval. Numerous prior research studies on forecasting and detecting lightnings provide a rich database of model diagnostics and observational data. One example of such a model diagnostic is the so-called (subgrid-scale) Lightning Potential Index (LPI), that was recently implemented in the operational ICON-model (1) and is now available operationally, even in the ICON-EU ensemble. On the other hand, there are extensive observation networks that provide lightning observations, such as the Linet-network (2) that provides lightning observations over Europe. 
In our work, we use lightning observations from the Linet-network as ground truth and establish a translation from LPI to lightning probabilities. For this task, we trained neural networks to predict the desired lightning probabilities in a "global postprocessing mode", i.e. using the same network for the forecasts on the whole domain of ICON-EU, which is significantly larger than the domain of the Linet-network. We present the setup of the postprocessing method together with details of the training of the neural networks and show first results from its forecasts and their evaluation. In particular, these lightning probabilities outperform raw model probabilities as derived by counting the exceedance of a LPI-threshold and thereby avoid the need to even define such a threshold. We will also address aspects of producing stable operational forecasts by applying an ensemble of neural networks.
 
References:
(1) Schröder et al., 2022: Subgrid scale Lightning Potential Index for ICON with parameterized convection. Reports on ICON (10), DOI: 10.5676/DWD_pub/nwv/icon_010
(2) Betz et al., 2009: LINET - an international lightning detection network in europe. Atmos. Res., 91, 564–573, DOI: 10.1016/j.atmosres.2008.06.012
Cooling potential of green spaces in the Vienna metropolitan area during extended periods of drought
Authors: Thaler Sabina , Eitzinger Josef , Formayer Herbert , Gützer Christian , Hörbinger Stephan , Masson Valéry , Mursch-Radlgruber Erich , Perny Katharina , Preiss Jürgen , Pröll Tobias , Rauch Johann Peter , Sadriu Melissa , Schmidt Stefan , Schoetter Robert , Szocska Debora , Wittkowski Max , Trimmel Heidelinde , Wöß David , Weihs Philipp
Published: July 2023

Heat stress during heat waves in urban areas can have devastating effects on human health. To mitigate this heat stress, major efforts are being made to plant more vegetation and remove sealed surfaces. But many green roofs and facades are not irrigated, which in turn leads to drought stress for plants during prolonged periods of heat in the summer without precipitation. As a result, evapotranspiration from vegetated areas is reduced and the desired cooling effect is not achieved. During dry periods, the agricultural environment also cannot develop its full cooling effect even during the day.In the project Imp_DroP (Impact of longer Drought Periods on Climate in Greater Vienna: appropriate Mitigation measures), the cooling potential of green spaces in and around Vienna is to be determined via evapotranspiration and future irrigation requirements. Thus, four measurement sites were established on green roofs in different local climate zones of Vienna for two years in spring 2022. At each of the selected sites, two lysimeters were installed for extensive and intensive green roofs, as well as soil moisture sensors in the upper and lower soil layers.These measurement data from 2022 are used in a further step for the calibration of the crop growth model AquaCrop, and the data collected in 2023 are taken for validation. Therefore, soil moisture can be simulated for selected dry periods on green roofs. For the surrounding agricultural areas of Vienna, soil moisture is simulated using the ARIS model.All collected data are applied in a final step to initialize, run and validate the coupled WRF-TEB model. Here, the atmospheric conditions as well as the urban microclimate for current and future summer drought episodes are simulated to estimate the expected thermal stress for the Viennese population.
An high resolution coupled ocean-atmosphere numerical simulation for the Adriatic Sea
Authors: Memmola Francesco , Coluccelli Alessandro , Neri Francesca , Garzia Angela , Ricchi Antonio , Ferretti Rossella , Falco Pierpaolo
Published: July 2023

The role of Sea Surface Temperature in the weather forecast has been investigated in the past years and its role in modulating local circulation has been assessed. In this context, a coupled ocean-atmosphere numerical simulation has been developed. The three models WRF, ROMS and SWAN are coupled together within the Coupled Ocean-Atmosphere-Wave-Sediment Transport modeling system (COAWST) for the Adriatic Sea. For what concerns the atmospheric component the WRF-ARW model is used. Two domains run independently: a low resolution domain (15 km) initialized using NCEP analyses at 0.25 degrees and a high resolution domain (3 km) covering the Adriatic regions, initialized using the WRP low- resolution output. For what concern the oceanographic component, the circulation model ROMS and the wave driver SWAN run on the same high horizontal resolution grid (1km). The vertical discretization of ROMS is performed using 30 sigma layers while the river inputs consist of 67 sources of fresh water (Po river included). The exchange of data between models it is set to happen every 1O minutes of model time. The exchange is two-ways, therefore each component of the system influences the dynamics of the other components.
In order to resolve the coastal dynamics, the oceanographic component above described was also used as parent grid for a one-way nested grid covering the area of sea facing the Marche rigion (child grid) with an horizontal resolution of 200 m.
In this effort, the model system validation and results from a case study will be presented. The impact of the ocean-atmosphere coupling will be briefly addressed by comparing results with the ones obtained by a weather forecast (no coupled) and an ocean forecast (only ROMS+SWAN coupling). While the capability of the system in resolving the coastal dynamics is it assessed comparing results from the parent grid and results from the child grid with observed data.
Quantifying the atmospheric boundary layer evolution during a 2020 heat wave over western Germany
Authors: Burgos Cuevas Andrea , Löhnert Ulrich , Pospichal Bernhard , Marke Tobias , Vicencio Veloso José Miguel
Published: July 2023

During June 2022 surface air temperatures across most of Europe were above the 1991-2020 average and daily maximum temperatures reached over 40 ºC over southern Europe (according to Copernicus.eu). Unusually high temperatures were also reached in Germany, where heat waves took place with over 35 ºC. In the Jülich Observatory for Cloud Evolution (JOYCE), these extreme temperature and humidity conditions were registered. JOYCE combines a rather unique set of ground-based remote sensing instruments that provide information about Boundary Layer thermal structure. The present investigation utilizes some of those measurements to diagnose total mean flux of heat and moisture within the Boundary Layer. In order to analyze the daily evolution of these fluxes during a heat wave, we utilize measurements of temperature and humidity from an Atmospheric Emitted Radiance Interferometer (AERI) and plot them in the mixing diagram approach, i.e.,  in an energy space (Lq versus , where L is the latent heat of vaporization and is the specific heat). Additionally, we quantify, in a 2D vector representation, the contributions of surface, advection and entrainment fluxes to the total mean flux. Estimates of horizontal temperature and humidity advection are obtained from measurements of the 30º elevation scan of a Microwave Radiometer (MWR) and from wind velocities measured by a Doppler Lidar. Additionally, surface flux measurements from the Integrated Carbon Observation System (ICOS) in a near by station in Selhausen are utilized to quantify the contribution of these surface fluxes in the mixing diagram. The total mean flux shows a daytime evolution with both sensible and latent heat components observed in days before the heat wave; whereas a high sensible heat flux dominates during the heat wave and the advective contribution becomes more important when the heat wave ends. We discuss the daily evolution of these fluxes, as well as the implementation of the mixing diagram approach for their study utilizing measured quantities. The present investigation can shed light on the Land-Atmosphere interaction and the closure of the surface energy and water budgets. Furthermore, understanding how the surface conditions can affect the atmospheric variables is valuable for a better characterization, and subsequent prediction, of extreme events such as heat waves in a warming climate.
PurpleAir-II sensor correction factors and its use in measuring neighborhood-scale temperature and particulate matter anomalies
Authors: Couzo Evan , Godfrey Christopher
Published: July 2023

The PurpleAir-II sensor provides low-cost in situ measurements of meteorological variables including temperature and humidity, as well as fine particulate matter (PM2.5) in real time.  The sensors have been used in several studies investigating intracity differences in temperature and PM2.5.  While the adoption and use of low-cost sensors has many benefits, care must be taken to ensure proper calibration and testing.  We have shown previously that the PurpleAir-II sensor has a high bias of 4-5 degrees Fahrenheit and a relative humidity bias of about -17%.  These results were obtained during a continuous measurement period from December 2022 to August 2023 in Asheville, North Carolina.  PurpleAir-II values were compared to temperature measurements with a Campbell Scientific 107 temperature probe and Vaisala HMP45C relative humidity probe.  We now have a longer data record (December 2022 – Summer 2024) spanning multiple seasonal changes and have updated our bias and error calculations.  We have also determined and applied correction factors for PurpleAir-II temperature and relative humidity measurements.  PurpleAir-II PM2.5 concentrations were previously compared to a nearby regulatory PM2.5 monitor maintained by the Asheville Buncombe Air Quality Agency during the same December 2022 – August 2023 measurement period.  PurpleAir-II bias exhibited concentration dependence with low bias at low concentrations and high bias at high concentrations.  We have updated bias and error calculations for PM2.5 concentrations using the longer measurement period, and we have also determined and applied correction factors.  Finally, we describe an upcoming investigation using the PurpleAir-II sensor to measure neighborhood-scale temperature, relative humidity, and PM2.5 in Asheville, North Carolina.  Such measurements will enable us to determine if certain populations are at greater risk for heat stress and PM2.5 exposure.
Long-term homogenized and gridded precipitation data for Hungary
Authors: Szentes Olivér , Lakatos Mónika , Pongrácz Rita
Published: July 2023

A more accurate understanding of climate and its changes requires temporally and spatially representative climate databases. However, measurement conditions change frequently: relocation of stations, instrument changes, changes in measurement time, changes in environmental conditions can all cause inhomogeneities in the data series, and therefore homogenization is needed.
For homogenization of data series, quality control and filling in the missing values we use the MASH (Multiple Analysis of Series for Homogenization) procedure (MASHv3.03 software) at the Climate Department of the Hungarian Meteorological Service (OMSZ). Inhomogeneities are estimated using monthly data series. Monthly, seasonal and annual inhomogeneities are harmonized in all MASH systems, constructed for homogenization of various station systems which consist of stations with different length of data. After homogenization, we have temporally representative data series.
However, weather stations are not evenly distributed, the station network consists of both densely and sparsely covered subregions. In order to estimate the values of meteorological variables at points where no measurements are available, a spatial interpolation method must be used. Our gridded climate datasets are generated using the MISH method (MISHv1.03 software). After interpolation, we have spatially representative climate database.
Currently, the start of the Hungarian precipitation climate database is 1901, but the beginning of regular precipitation measurements started decades earlier, so it is possible to extend the precipitation database in time. In addition, the 131 datasets from the first half of the 20th century that are currently used can be significantly extended, as there are still many undigitized datasets before the 1950s. The collection of monthly precipitation data stored still on paper made it possible to use many more stations from the first half of the 20th century than before, and thus, the precipitation patterns in Hungary in the second half of the 19th century can be analyzed.
In this poster presentation, we will present the new precipitation station systems used for homogenization, the most important verification statistics of the homogenization of precipitation data series, and analyze the gridded spatial means (national averages for Hungary) from the beginning of the measurements to the present.
Acknowledgements:
The research presented was carried out within the framework of the Széchenyi Plan Plus program with the support RRF 2.3.1 21 2022 00008 project.
How thermally comfortable are cities in the EMME region simulated to be in the future?
Authors: Constantinidou Katiana , Hadjinicolaou Panos
Published: July 2023

Urbanization significantly alters a region's land surface properties, which in turn modifies the surface energy balance, having an impact on the climate at the local, regional, and global levels. In particular, urban areas experience higher temperatures compared to their rural surroundings which in combination with humidity have an effect on the quality of the citizens’ lives. Wet-bulb temperature (WBT) is an essential and widely used metric to assess the effects of humid heat. A WBT of 35◦C is considered to be the survivability limit, and prolonged exposure beyond this value could be deadly even for the fittest of humans. The region of eastern Mediterranean and the Middle East (EMME) is highly susceptible to high WBT values, especially in densely populated areas, where humid heat events with WBT≥35◦ C are projected by the end of this century. It is therefore crucial to assess the projected levels of humid heat over this particular area.
This work uses the Weather Research and Forecasting (WRF) model to simulate climate over the EMME coupled with the bulk urban parametrization and NoahMP land surface scheme. The simulations are performed at 4 km horizontal resolution for a recent past (2000-2004) and a future (2056-2060) period driven by the Representative Concentration Pathway (RCP) RCP 8.5. The aim of this study is to investigate how the simulated minimum, minimum and wet bulb temperatures are projected to change in the future during the summer period over the EMME region and, more specifically, over eleven cities located in the region of interest. The simulated summer climatologies of the two periods of time of the maximum and minimum temperatures are derived from hourly timeseries, which are then to calculate the WBT using also the relative humidity by the model output.
Development of a weakly-coupled atmosphere-ocean data assimilation system for the KIM coupled model
Authors: Kim Jiyoun , Ko Eunbyeol , Clayton Adam , Kwon In-Hyuk
Published: July 2023

It has long been known that coupling between the various Earth system components (the ocean, atmosphere, sea ice, and land) produces improved forecasts on seasonal and longer time scales (Neelin et al. 1994). KIAPS (Korea Institute of Atmospheric Prediction Systems) has developed a global atmospheric model called KIM (Korean Integrated Model) that has been running operationally at KMA (Korea Meteorological Administration) since April 2020, with deterministic analyses produced by the KIAPS hybrid-4DEnVar system, and ensemble analyses produced by an LETKF. KIAPS is now developing a coupled atmosphere-land-ocean-sea ice model aimed at extended-range forecasts, using NEMO as the ocean model. Here, we describe our progress in developing an ocean data assimilation (DA) component for this new coupled model.When atmospheric and ocean initial conditions are produced by independent DA systems, inconsistencies at the ocean surface can lead to imbalances in the coupled model (Lea et al., 2015). Therefore, we are developing a weakly-coupled atmosphere-ocean DA system to provide more balanced initial conditions. After investigating possible DA cycling strategies – including issues such as assimilation window lengths, observation cut-off times, and use of “catch-up” cycles – we decided to develop an atmosphere-ocean DA system where the ocean DA system uses the same cycling strategy as the atmospheric DA system, with 6-hour DA windows and a 6-hour cycle.KMA operates an ocean-only DA system to initialize ocean states for seasonal forecasts, which are based on a coupled model that combines the Met Office Unified Model (UM) atmosphere with an older version of the NEMO ocean model, and a different sea ice model (CICE rather than SI3) to that used by the KIM coupled model. The DA system is based on NEMOVAR, using a 3DVar-FGAT approach with 24-hour DA windows. We are now adapting this system to produce an ocean-only DA system that will eventually be coupled to the KIM atmospheric DA system. The key steps are:1. Change the surface forcing model from the UM to KIM.2. Change to the 6-hour DA windows used by the atmospheric DA system.3. Upgrade to the NEMO version and sea ice model used by the new KIM coupled model.We will present results illustrating the impact of these changes, and then summarize our progress in coupling the new ocean DA system with the existing KIM atmospheric DA system, within a single NWP suite.
Ocean DA experimets with 6-hour cycles, targeting a future weakly-coupled atmosphere-ocean DA system
Authors: Jang Hyeyeong , Ko Eunbyeol , Clayton Adam , Kwon In-Hyuk
Published: July 2023

     KIAPS (Korea Institute of Atmospheric Prediction Systems) has recently been adding Earth-system components to the atmospheric version of KIM (Korean Integrated Model), to produce a prediction model that considers various geophysical factors such as atmosphere, ocean, sea-ice, and land, to support skillful forecasting out from the very short range (~6 hours) to the extended medium range (up to ~30 days). The ocean component is a version of the NEMO (Nucleus for European Modelling of the Ocean) ocean model. For data assimilation (DA), our first goal is to develop a weakly-coupled DA system that combines KIM’s existing atmosphere/land DA system with a separate DA system for NEMO. This ocean DA system is based on NEMOVAR, and uses a 3-Dimension Variational – First Guess at Appropriate Time (3DVar-FGAT) DA method. The assimilated observations include SST observations from satellites and moored buoys, temperature profiles collected by Argo floats, and satellite observations of sea-level anomaly and sea ice concentration.     The ocean DA system is being developed from an existing ocean-only NEMOVAR system that is based on 24-hour DA windows. In order to make this system compatible with the existing atmospheric DA system, we have changed the ocean DA cycling strategy to match the 6-hourly strategy used by the atmospheric system, including reductions to the ocean observation cutoff times. After introducing the details of this system, we will present results from experiments designed to test (a) the impact of changing the DA window lengths, without changing observation usage, and (b) the further impact of using earlier observation cutoff times.
Impacts of soil-frost on wind-related energy grid disturbances
Authors: Láng-Ritter Ilona , Mäkelä Antti , van Kooten Sebastian , Perrels Adriaan , Gisnås Kjersti , Laapas Mikko
Published: July 2023

A large share of the disruptions in electricity distribution in Finland is caused by wind and windstorms. Snow loads on trees are another or complementary cause for such disruptions. The disruption proneness of power supply to strong winds in Finland, and in similar densely forested countries, seems to reduce in the presence of soil frost.
In a still ongoing study, NordicLink, funded by Nordforsk, we combined the power distribution disruption data (2015-2023, by event) with weather and soil temperature information and estimated the impact of wind and soil frost on disruption occurrence probability. The objective of our work was to find a relation between wind gust speed and the number of power supply interruptions and to determine the effect of soil frost on wind-related faults in the electricity distribution system in Eastern Finland. 
The results indicate that when soil frost is present, trees are less likely to fall onto power lines during high-wind events, reducing the risk of power outages. The frozen soil provides additional support to the trees, making them less likely to uproot under the force of the wind. As expected, we found a positive correlation between the number of power outages and wind speed and a negative correlation between soil frost and wind-related power outages.
The warmer climate will most likely decrease the soil frost, and thus increase the possibility of wind-related power outages in Finland or similar high-latitude countries. However, further research is needed to fully understand the complex relationship between soil frost, wind events, and power outages, and to identify the most effective strategies for mitigating the risk of such outages in the future.
Verification and Improvement of Impact-based Forecast Using Multi Model Ensemble
Authors: Yun Sug-gyeong , Shin Hyun-Cheol , Kim Eun Jung , Park Jong Im , Ha Jong-Chul , Kwon Young Cheol , Lee KieWoung
Published: July 2023

  The Korea Meteorological Administration(KMA) is producing an impact-based forecast data based on both the deterministic forecast and ensemble forecast for heat waves (HW) and cold waves (CW). Ensemble prediction system for impact-based forecast is Multi-Model ensemble system which integrates UM(global, global ensemble, local, and local ensemble models), ECMWF(global and global ensemble models) and KIM(Korean Integrated Model) global model(Hereafter, impact-based forecast  based on the deterministic forecast and Multi-Model ensemble system are called `IMPC` and ‘MEPS’, respectively.). MEPS determine the risk level by using the probability of occurrence of abnormal temperatures on the Korean Peninsula. Once maximum perceived temperature(HW) or lowest temperature(CW) from all 93 Multi-Model Ensemble members were extracted, their probability distribution was determined by using a Generalized Extreme Value (GEV) distribution.  The performance of MEPS for HW was compared with IMPC for July and August 2022. Verification was conducted by evaluating how well impact-based forecast level(safe, concern, caution, warning, alarm) was matched to the observed risk level in 175 regions.   As a result of verification it was found that IMPC forecasts ‘concern’ more frequently and MEPS forecasts ‘caution’ more frequently. In July, IMPC's prediction performance is excellent for ‘safe’ and ‘concern’ level and MEPS is excellent at a ‘caution’ and ‘warning’. IMPC underestimates ‘caution’ and ‘warning’ while MEPS tends to overestimate ‘safe’ and ‘concern’. In August, ETS score of MEPS is excellent for ‘safe’ levels as well as ‘caution’ and ‘warning’ levels. In case studies, there is many cases in which MEPS detected HW better than IMPC when a high-level heat wave was observed on the Korean Peninsula. Overall, MEPS is expected to be a good reference data in the impact-based forecast where predictive ability for high risk levels is important.   The MEPS guidance uses only daily temperatures, but according to KMA’s forecast guideline, HW is defined a phenomenon in which a high temperature lasts for more than 2 days. By reflecting these conditions, future guidance needs to be improved. In the HW guidance, the risk level re-established by considering whether the risk level lasts longer tha two days improved the predictive performance of ‘safe’ and ‘concern’, and the CSI (Critical Success Index) and ETS increased at all risk level.
Exploring the use of 3D radar measurements in predicting the evolution of convective rain cells
Authors: Chen Yu-Shen , Wang Li-Pen
Published: July 2023

Object-based radar rainfall nowcasting is a widely-used technique for convective storm prediction. Due to the data and algorithmic limitations, most existing object-based nowcasting methods focus on predicting the movements of each rain object (or cell). The evolution of rain cells’ properties (e.g. cell size, shape and intensity) themselves is often neglected. It is however critical to account for the temporal changes in cells’ properties in order to improve the predictability for convective storms. 
 
In the literature, three-dimensional (3D) radar images have been used for observing the vertical feature changes through the formation process of convective rain cells. This shows the potential of extracting useful information from 3D images to facilitate characterising the life cycle of rain cells. Most of these works however focused on analysing or reconstructing the life cycles of individual convective rain cells or storm events. It remains an open challenge to incorporate 3D radar rainfall information into object-based radar rainfall nowcasting. 
 
In this research, we would like to explore the use of deep learning techniques to predict the evolution of convective rain cells. The proposed work comprises two main parts. The first part is rain cell data preparation. An enhanced TITAN storm tracking algorithm proposed by Muñoz et al. (2018) is employed to identify 2D rain cells and their temporal associations (or tracks) across successive time steps. The information of 2D cells are then used to extract cell properties from 3D radar images. These include mean reflectivity, area, major and minor axis lengths and the convective core altitude of each rain cell. In the second part of the work, a LSTM-Encoder-Decoder model is developed, which uses cells’ properties from the past 15 min to predict the evolution of these properties in the next 15 min. 
 
A total of 4708 lifespans of rain cells extracted from high-resolution (5-min, 1 km, 24 levels) 3D radar images are used to train the model, and a total of 1177 extracted lifespans are used to validate the prediction result. The result suggests that the proposed LSTM-Encoder-Decoder model can well predict the evolution of cells’ properties, and, with the employed 3D information (core altitude), the prediction errors of mean reflectivity can be further reduced by 20-25% at 15-min forecast lead time.  
 
Reference
Muñoz, C., Wang, L.-P., and Willems, P. (2018). Enhanced object-based tracking algorithm for convective rain storms and cells. Atmospheric Research, 201:144–158.
 
Overview of applied statistical interpolation methods in SPARTACUS, a spatial dataset of the surface climate in Austria
Authors: Tilg Anna-Maria , Hiebl Johann , Höfler Angelika , Rohrböck Anna
Published: July 2023

Spatially comprehensive information of climate variables that extends over many decades is essential for various modelling, reanalysis, monitoring, and planning applications, like snow cover modelling, drought monitoring, model evaluation or environmental planning. For the territory of Austria, a gridded dataset called SPARTACUS is produced by the national weather service of Austria, by spatially analysing daily station observations. This operational climate-monitoring dataset has a spatial resolution of 1 km to 1 km and provides data since 1961 with a temporal resolution of one day and further temporal aggregates (month, season and year).
The current operational version covers the parameters minimum and maximum air temperature, precipitation amount and sunshine duration. Parameter-specific methods are used to interpolate the surface in-situ observations. An overview of the applied geostatistical interpolation methods including key results from evaluation will be presented, focusing on temperature and precipitation. Moreover, requirements on input station data, like constant station networks over time, will be specified. Limitations in the analysis of such a gridded observation dataset, due to for example conditional biases, will be pointed out as well.
A gridded dataset of air humidity is currently under development. Such a dataset is highly relevant, for example for the investigation of droughts or snowmaking potential. The actual spatial analysis is performed for the parameter of dew-point depression, which allows adapting the interpolation method previously used for air temperature. Preliminary findings of the current approach including results from the evaluation will be shown. They might be relevant for similar projects in other regions with complex topography.
Development and evaluation of new weather types classification for Croatia
Authors: Marinovic Ivana , Beck Christoph
Published: July 2023

Weather type classification in Croatia has been based on subjective Poje’s classification since 1965. In the first stage of the analysis the results of the COST733 action “Harmonization and Applications of Weather Types Classifications for European Regions” were applied to the Croatian area to improve previous knowledge and practice, and to make the whole classification process automatized. Based on the obtained results and conclusions from the first stage, analysis in the second stage is done by running COST733class software for different settings. This analysis aims to determine the most suitable combination of main classification parameters such as domain size, number of types, input variables and classification methods to develop a classification that is intended to be the most appropriate to capture precipitation conditions in Croatian regions. The assessment of evaluation results takes into account the effect of varying numbers of types and specifies temporal (among seasons) and as well spatial (among regions) variations. For this purpose, Croatia is divided into five regions to acknowledge the orography and climate diversity of the Croatian area which lead to different weather conditions within the same atmospheric circulation. Two regions belong to the continental part of Croatia (Eastern and Central Croatia), while the rest, including mountainous parts, are placed along with the Adriatic sea (North, Middle and South Adriatic). The efficacy of different objective classifications developed using COST733class is examined utilizing Croatian meteorological station data. For evaluating the discriminative power (synoptic skill) of the classifications and the relevance of specific settings several statistical metrics are used and the statistical significance of the differences in statistic metrics between classifications is tested. The results from the first stage have shown better classification performance for optimization and threshold based methods among other methods, hence we proceed with GWT (GrossWetterTypes) and DKM (dkmeans) methods in the second stage. Results for all different settings have shown better classification performance along the Adriatic coast and in the mountainous parts than in the more continental parts. Further, some preliminary results show that classifications based on DKM methods perform better when using input data with spatial-mean-removed and that domain size and position should be tailored for each input variable. Also, the improvement of classification is found when including some of the additional variables together with the main variable (usually mean sea level pressure). However, it is not clear the inclusion of which additional variable is the most relevant and statistically significant, and how many of them should be included at the same time.
Added value of WRF as a convection permitting regional climate model in simulating precipitation over a mountain region in Southern Europe
Authors: García-Valdecasas Ojeda Matilde , Solano-Farías Feliciano , Donaire-Montaño David , Cepeda-Ventura Luna , Romero-Jiménez Emilio , Rosa-Cánovas Juan José , Castro-Díez Yolanda , Gámiz-Fortis Sonia , Esteban-Parra María Jesús
Published: July 2023

The climate of Sierra Nevada affects many relevant aspects for the living systems that inhabit it as well as the water resources of a region with semi-arid characteristics. Climate change in the Sierra Nevada can be especially exacerbated by its condition of mountainous region and its location in the Mediterranean area, which makes it a double climate change hotspot. Monitoring and studyng its present and future climate are crucial to define adaptation strategies to climate change and the use of very high-resolution regional models becomes a useful tool in this area of complex topography.
In this work we evaluate climate simulations at a resolution of 1 km using the Weather Research and Forecasting (WRF) model as a convection permitting model and we analyze the added value of these simulations with respect to another with a resolution of 5 km for the Sierra Nevada area. The simulations cover the period 2001-2020 and the analysis focuses on the study of precipitation. The simulated precipitation is compared with observational data mainly from the Climanevada database (https://climanevada.obsnev.es/).
In addition to analyzing the mean precipitation values and some extreme indices, the evaluation is also focused on the comparison of the simulated data for some extreme events in order to analyze both the increasing improvement in the spatial and temporal resolutions.
Keywords: Sierra Nevada, precipitation, WRF, convection permitting model, added value.
ACKNOWLEDGEMENTS
This research was financed by the project “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AIe-Services University of Granada-SierraNevada” (LifeWatch-2019-10-UGR-01), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program2014-2020 (POPE), LifeWatch-ERIC action line, the project P20_00035 funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades, and by the project PID2021-126401OB-I00 funded by MCIN/AEI/ 10.13039/501100011033/FEDER Una manera de hacer Europa.
Human thermal load in the fog in the Hungarian lowland
Authors: Ács Ferenc , Kristóf Erzsébet , Szalkai Zsófia , Zsákai Annamária
Published: July 2023

Fog is one of the special weather phenomena of the Hungarian lowlands in autumn and winter. It has been studied from many points of view (for example frequency, type, predictability), but not yet from the point of view of human thermal load. The aim of this work is to fill this gap. We observed a total of 132 fog events at Martonvásár (Hungary, Central Transdanubian region) in the period 2017-2023. During our observations, we documented the period of its existence, air pressure, air temperature, air humidity and wind speed. Based on the meteorological and human (body mass, body length, sex, age, person walking at a speed of 1.1 m/s) data, we estimated the thermal load of the foggy environment using a clothing thermal resistance (rcl)-operative temperature model. The model is very simple, based on the calculation of the energy balance of the human body covered with clothing. The main results are as follows. In the fogs that cause the greatest lack of heat, the rcl is around 2.5 clo, the operative temperature is around -7 ℃.  In fogs with a much smaller heat deficit, the rcl is around 0.5 clo, and the operative temperature is around 15-17 ℃. In the vast majority of cases, rcl varies between 1-2 clo. We were able to make sure that the thermal load of the fog is mostly determined by the temperature and radiation conditions. Further observations are needed. For instance, one should observe the smallest thermal deficit, in which fog can still exist.
Boundary layer coherent structures and circulations viewed through collocated wind lidar and cloud radar profiling
Authors: Dias Neto José , Nuijens Louise
Published: July 2023

The horizontal resolution of weather models is increasing, which demands a careful consideration of momentum and energy transport carried across different scales. Deep convective transport and even shallow convective transport, which were previously parameterized, are now resolved, while turbulence remains parameterized. Can observations help constrain the transport by turbulence, coherent structures associated with convection and mesoscale circulations coupled to organized cloud systems?
 
Using a novel experimental setup for deriving high-resolution continuous wind profiles across the boundary layer, our objective is to provide a fresh view on the variability in horizontal and vertical wind in the presence of a range of (shallow) cloud systems over land, as well as to derive the wind variance and momentum fluxes, and a quantitative assessment of the relevant scales.
 
The experimental dataset is collected during the Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment (CMTRACE). The field campaign occurred at the experimental Cabauw site (The Netherlands) between 13.09.2021 and 03.10.2021 and between 16.05.2022 and 13.06.2022. For this experiment, a cloud radar and wind lidar were operated at 75 degrees elevation, providing horizontal and vertical wind observations within and below the cloud layer. The combined lidar-radar's wind profiles have a vertical resolution of 50 m and a temporal resolution of ~1.5 minutes (which is representative of a horizontal scale of 450 - 1800 m). During CMTRACE, a large variety of cloud regimes were sampled, from non-precipitating shallow convection to deep convective clouds and stratiform clouds.
 
The observations reveal both coherent thermals (up and downdrafts) and mesoscale divergence and convergence patterns. The scale growth of horizontal momentum variance and flux is well explained by vertical velocity variance, whereby larger vertical velocity variance corresponds to a larger contribution of scales < 35 km to total momentum variance and flux. Additionally, precipitation is shown to separate days on which larger mesoscales contribute to horizontal momentum flux, and the presence of larger cloud structures is confirmed using cloud spatial statistics as viewed from satellite.  In an outlook, the representation of these different flows in large-eddy simulation and regional weather hindcasts from the Dutch weather model HARMONIE is evaluated.