bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-term biometeorological conditions in the mid-sized European city – A case study of Banja Luka (Bosnia and Herzegovina)
Authors: Trbic Goran , Savic Stevan , Milosevic Dragan , Ivanisevic Marko , Garic Bojan , Markovic Mirjana
Published: June 2022

Intensive urbanization and global warming are impacting the health and well-being of urban population. Nevertheless, urban environments with different design will have different micro and local climate conditions. Therefore, the application of parameters that assess thermal stress in urban areas, such as outdoor thermal comfort (OTC) indices are of paramount importance.

In this study the long-term (1961–2020) datasets of daily OTC indices for the city of Banja Luka (Bosnia and Hercegovina) were used. Detailed temporal analysis using Physiological Equivalent Temperature (PET), Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (Tmrt) was performed for: a) the entire research period, b) the decadal level, and c) defined extreme heat/cold stress categories. The results show intensive increase in extreme/strong heat days in the last twenty years, and the number of these days is five times higher than in the 70s and 80s. Decreasing tendencies are noticed in extreme/strong cold days towards the last two decades.

We also performed modeling of changes in mean daily air temperature according to IPCC climate scenarios RCP 2.6, RCP 4.5 RCP 8.5 until the end of the XXI century. The results point to significant positive changes under all three climate scenarios.

Furthermore, we analyzed PET, Tmrt and modified Psychologically Equivalent Temperature (mPET) indices during the hot summer days in June 2021 for different urban spaces (downtown, urban park, riverside) in Banja Luka. Results show that the downtown is the most most uncomfortable area in terms of OTC indices. Also, riverside had lower average Taduring summer daytime compared to urban park and downtown likely due to the synergy between river cooling effect (evaporation and sensible heat transfer) and tree shade.
Rapid Update Cycle in DWD's Seamless INtegrated FOrecastiNg sYstem (SINFONY)
Authors: Ulbrich Sven , Welzbacher Christian , Hanisch Thomas , Potthast Roland , Blahak Ulrich
Published: June 2022

The SINFONY project at Deutscher Wetterdienst (DWD) aims to produce seamless precipitation and radar reflectivity ensemble forecast products for a time-range from minutes up to 12 hours. It combines numerical weather predictions (NWP) and forecasts from Nowcasting (NWC) with an initial focus on severe summertime convective events with associated hazards such as heavy precipitation, hail and wind gusts. Nowcasts at DWD are currently initialized with an update frequency of 5 minutes, while standard short-range numerical weather prediction (SRNWP) systems are initialized every three hours. Hence, in the worst case a three-hour old SRNWP-forecast had to be combined with an up-to-date Nowcast.

To overcome this issue, a rapid update cycle (RUC) is implemented, which initializes forecasts every hour with a potentially much shorter observation cutoff for the subsequent data assimilation step due to time criticality. To avoid growing differences between the atmospheric states in RUC and SRNWP, the RUC data assimilation cycle has a limited lifetime and branches off the SRNWP-cycle every 24 hours.

Besides the initialization frequency of the forecasts, the RUC also differs from the standard SRNWP in the data assimilation and the atmospheric model . The prediction of extreme convective events benefits for example from additionally available observation systems with huge data amount (e.g. satellites). Also, a more sophisticated microphysics scheme is applied differing from the SRNWP leading to a spin-up phase after branching off. However, this microphysics scheme helps to improve the results in reflectivity space compared to the SRNWP making a combination with NWC more feasible.

The large amount of involved additional observational data and more frequent forecasts is a challenge regarding stable data production, timeliness and handling at DWD.

We present ideas in terms of atmospheric parameters, related data flow, involved infrastructure, and results for the convective season of 2022. The latter topic will also touch upon outcomes of a first evaluation of the SINFONY-RUC with the DWD forecasters.
Storylines: A severe rainfall-landslide event in Past, Present & Future climate scenarios
Authors: Mishra Aditya Narayan , Maraun Douglas , Knevels Raphael , Truhetz Heimo , Bevacqua Emanuele , Proske Herwig , Petschko Helene , Leopold Philip , Brenning Alexander , Zappa Giuseppe , Schaffer Armin
Published: June 2022

In the June of 2009 central Europe witnessed a rampant rainfall spell that spread across populated areas of the country. High-intensity rainfall caused 3000+ landslides in the southeastern Austrian state of Styria, and property damages worth €10 Million. Elsewhere in Austria, flooding amounted to reparations worth €40 Million. Numerous synoptic-scale studies indicated the presence of a cut-off low over central Europe and excessive moisture convergence behind the extreme event. In a warmer climate change scenario, such an extreme precipitation event may manifest into a more intense event due to the higher water holding capacity of air with increased temperatures, but this reasoning may not be so straightforward considering the complex physics of precipitation, more so in a topographically heterogeneous region such as the GAR (Greater Alpine Region).

The flooding and landslides caused in the region raise an alarm and thus motivate this study whereby we investigate if the rainfall event did become stronger with time due to climate change compared to how it would have been in a counterfactual (climate change free) past. Here we have deployed the CCLM high-resolution regional model coupled with a statistical landslide model to simulate this event (rainfall and landslides) in a pseudo (surrogate) warming scenario. A marked decrease in rainfall intensity is observed in the simulations for a 1 K cooler climate (pre-industrial past) and the consequent landslide risk could reduce by up to 20%. In the future,  depending on the changes in rainfall and soil moisture, the area affected during a 2009-type event could grow by 45% at 4 K global warming, although a slight reduction is also possible.

In this novel event-based study, we discuss the results from the lens of attribution perspective - how conditional attribution is much more useful compared to the conventional risk-based approach of attributing extreme events. We develop physical storylines to address the increasing risks of landslides in the region.
Quality control of weather observations using machine learning
Authors: Horan Kevin
Published: June 2022

A key component of a weather service’s mission is to capture accurate observations of weather conditions in real-time, which are subsequently fed into forecasting models for prediction, used in climate research, and form the basis of the official climate statistics. The accuracy of such measurements is ensured by following a set of robust procedures set out in the World Meteorological Organisation guidelines. In addition to appropriate maintenance and calibration of equipment, procedures for quality control (QC) of observations are necessary in order for users to have maximum confidence in the data. Much of this QC tends to be done manually by expert climatologists who examine data and flag erroneous values. However, due to the current proliferation of climate data, more and more organisations are seeking to implement automated, real-time QC to prevent poor quality data being used in operational products. One potential approach is to use Anomaly Detection algorithms to identify potentially erroneous values. Such methods are a well-established field in Machine Learning and are a potential tool for QC of environmental data.

This paper develops and evaluates models to perform automatic QC of Irish weather data (from the Irish meteorological agency Met Éireann) using appropriate Machine Learning techniques such as Convolutional Neural Networks (CNN’s). The outcomes are evaluated in comparison to standard statistical tests which have traditionally been used for these purposes (such as linear regression spatial variability tests). The dataset in question consists of over 10 years of automated temperature observations taken at one minute intervals using Platinum Resistance Thermometers (PRT’s) located at sites across the country. This is complemented by a related data set that has been manually quality-controlled using well established methods, which can be used for comparison and verification.

The paper aims to investigate whether anomaly detection algorithms can correctly identify erroneous values in the time-series to at least the same standard as that achieved through traditional manual approaches. Furthermore, it examines whether these anomalies, once discovered, can be classified into categories with interpretable physical meaning.
Interpreting Meteorological and Oceanographic Data to Provide Optimal and Safe Ship Routing
Authors: Castellano Dana , Dib Matthew , DeMoss Jeremy , Nuss Matt , Converse Darrell , Bakke Adam
Published: June 2022

For the past 50+ years, Fleetweather has provided ship routing and marine forecasting services to ship owners, charterers, and vessel crews across the globe. Over time as meteorological and oceanographic data has become more abundant and accessible, Fleetweather has evolved and improved their methods and products to enhance the value of the services delivered to clients.

Our group of approximately thirty Marine Routers utilise and interpret a variety of data from numerous sources and agencies, including marine observations, meteorological and oceanographic forecast model output, satellite and radar data, tropical warnings, and marine ice data. This data, in combination with expertise in ship performance characteristics, marine navigation, maritime threats such as piracy, and environmental regulations, allows our routers to provide optimal route recommendations to vessels in every ocean basin. The safety of crew and cargo is always the number one priority, but many other considerations factor into recommendations, such as minimising fuel consumption, voyage distance, or voyage time.

Additionally, historical data is analysed to both examine the past performance and predict future performance for vessels and fleets. As more data becomes accessible, more precise analysis and prediction results, to the benefit of all parties involved.

Given the plethora of data available to the public, proper and clear communication of what the data actually means is vital to the end user, and conveying this information as accurately and concisely as possible is key to maximising the value to the client. Expertise in meteorology and oceanography is a requirement, but equally important is having a strong understanding of the concerns and priorities of those in the maritime industry, and the ability to communicate the same is a crucial part of the services Fleetweather provides. 

Fleetweather is positioned to take the available high-value datasets, applying our knowledge and expertise, and then delivering a high value product at the end of the value-chain.  This end product/deliverable is of high value to our clients in the maritime industry, and also provides societal benefits to the population at large, through reduction of fuel consumption and increased vessel efficiency, leading to a contribution to reducing global greenhouse gas emissions worldwide.
A simulation study of the co-benefit of in-canyon trees for heat mitigation and air quality
Authors: Morakinyo Tobi Eniolu
Published: June 2022

The co-benefit of urban trees for  heat mitigation and air quality improvement is not fully understood, and conflicting recommendations on whether trees should be integrated into (core) urban areas exist in literature. For climate-sensitive urban design,  trees help mitigate urban overheating. However, in terms of air quality-related effect, there is conflicting recommendation depending on the perspective of the study: dispersion (aerodynamic effect) or deposition (removal of pollutants). For the former, trees minimize pollutant dispersion and increase concentration levels, thus not recommended for urban integration. On the other hand, trees have been found to remove particulate pollutants through their filtration and deposition mechanisms, thus, improving air quality – recommended. This study leveraged the Particle(gas) Dispersion and Deposition Module (PDDM) and atmospheric modules in the ENVI-met modelling system to investigate the relationship between 3D tree form, urban morphology, urban microclimate and air quality. Parametric models were developed with variable urban morphology (SVF:0 to 1) and different 3D tree form based on tree height, trunk height, crown diameter and foliage density; with fixed traffic and meteorological information as boundary conditions. Thereafter, the micro-climate and air-quality related output were analysed using a proposed integrated climate-air quality index that accounts for the impact of trees on pollutant dispersion, deposition, and heat mitigation to estimate the co-benefits of different trees forms in different urban morphology. Results show  Findings are useful in practice as it helps recognize a set of tree form (species) with similar and simultaneous heat mitigation and air quality improvement capacities thereby aiding decision making in selecting the right set of species for the right place.
Preliminary assessment of tourists interest for tourism-tailored climate and environmental products
Authors: Velea Liliana , Gallo Alessandro
Published: June 2022

Despite the strong relationship between tourism and weather/climate and the sustained research in the area, little climate and environmental information customized for tourism is easily accessible for tourists. Several aspects may be considered as possible explanations, as for example tourists satisfaction with the current products and less interest for other new ones, availability, costs, efficiency in providing and delivering of new products and services to tourists etc.

We aim to identify some characteristics of a potential climate service of interest for tourists that may contribute to a better user uptake, by addressing in particular three aspects: (1) which are the weather, climate and/or environmental features most commonly marked ‘of interest’ in the general case of ‘any destination type’ and for the particular case of rural destinations; (2) which are the delivery and presentation forms of greatest interest for tourists; (3) how willing would be the tourists to pay for such information/service.

To this end, we used a questionnaire with 5 closed questions regarding the aspects mentioned before; four questions involve a five-fold Likert -type scale, while one is a multiple-choice question. The questionnaire was available online and disseminated through social media and e-mail.

The results confirm some expectations based on scientific literature like the interest for air quality and highlight the user interest for information encompassing several climate and/or environmental aspects preferably in one single product, like the thermal comfort, weather fit for outdoor activities, air quality. The survey also highlighted that mobile phone application would be the preferred mean of access, while the information should be presented in graphical form and preferably associated with some descriptive text. The financial attractivity of such information is quite low, as most of the respondents would not pay for such information and only 30% would agree to one single payment for a broader package.
Assessment of summer precipitation on James Ross Island, Antarctic Peninsula based on the WRF model output and in-situ observations
Authors: Matějka Michael , Láska Kamil , Zbyněk Engel , Ondřej Nedělčev
Published: June 2022

Summer precipitation in the polar regions is important for many aspects of local environment. Examples include moss and lichens growth, snow-melt runoff, or snow accumulation on glaciers. However, precipitation observations, especially in Antarctica, are very limited due to complicated logistics and  frequent blowing-snow transport and sublimation processes. One of the novel methods how to improve the in-situ precipitation estimates is an application of a very‑high‑resolution numerical atmospheric model. A state-of-the-art, Weather Research and Forecasting (WRF) model, was run for two summer months in early 2022 over northern Antarctic Peninsula and James Ross Island, respectively. Model setup included 300 m horizontal resolution, 65 vertical levels and a new 3D TKE scheme which is especially suitable for subkilometer-scale WRF simulations. James Ross Island was selected because of availability of in-situ observations at proximity of the Johann Gregor Mendel Czech Antarctic Station. The observations were conducted by a Thies laser precipitation monitor (disdrometer) providing precipitation data at 1-minute resolution. Summer precipitation from the Thies measuring system and manual rain gauge observation was investigated in terms of duration, intensity, and phase (liquid/solid) and compared with corresponding WRF variables. In case of snow accumulation on the ground surface, snow height from the model output was compared with a sonic distance sensor data (Judd Communications). The influence of atmospheric circulation on heavy summer snowfalls was analyzed to show both the regional synoptic patterns and local (orographic) atmospheric processes. Finally, the ability of the WRF model to provide accurate and reliable precipitation records for other applications, e. g. snowpack storage and snowmelt-runoff modelling, was assessed.
An adapted deep convolutional RNN model for spatio-temporal prediction of wind speed extremes in the short-to-medium range for wind energy applications
Authors: Scheepens Daan , Schicker Irene , Papazek Petrina , Hlavackova-Schindler Katerina , Plant Claudia
Published: June 2022

The amount of wind farms and wind power production in Europe, both on- and off-shore, increased rapidly in the past years. To ensure grid stability, on-time (re)scheduling of maintenance tasks and mitigate fees in energy trading, accurate predictions of wind speed and wind power are needed. It has become particularly important to improve wind speed predictions in the short range of one to six hours as wind speed variability in this range has been found to pose the largest operational challenges. Furthermore, accurate predictions of extreme wind events are of high importance to wind farm operators as timely knowledge of these can both prevent damages and offer economic preparedness. We propose in this work a deep convolutional recurrent neural network (RNN) based regression model for the spatio-temporal prediction of extreme wind speed events over Europe in the short-to-medium range (12 hour lead-time in 1 hour intervals). This is achieved by training a multi-layered convolutional long short-term memory (ConvLSTM) network with imbalanced regression loss, to which end we investigate three different loss functions: the inversely weighted mean absolute error (W-MAE) loss, the inversely weighted mean squared error (W-MSE) loss and the squared error-relevance area (SERA) loss. 

The results indicate superior performance of the SERA loss, showing significant improvements on high intensity extreme events. The W-MAE and W-MSE shows no improvements over the standard MSE loss and we thus discourage the usage of the inverse weighting method. We conclude that the SERA loss provides an effective way to adapt deep learning to the task of imbalanced spatio-temporal regression and its application to the forecasting of extreme wind events in the short-to-medium range. 

 This work was performed as a part of the MEDEA project, which is funded by the Austrian Climate Research Program to further research on renewable energy and meteorologically induced extreme events.
Hour-scale cloud cover forecasting using WRF and an infrared all-sky imager
Authors: Kurzrock Frederik , Hochebner Andrea , Millerioux Quentin , Schmutz Nicolas , Reinhardt Maria , Acevedo Walter , Potthast Roland
Published: June 2022

Numerical weather prediction (NWP) models such as the Weather Research and Forecasting (WRF) model allow to forecast cloud processes at high spatio-temporal resolutions of a few kilometres and minutes. Nevertheless, WRF is known to underestimate the presence of clouds, which has negative impacts on solar irradiance forecasts for example. Therefore, drawing the forecasts closer to observations of clouds can be an efficient means for improving hour-scale cloudiness forecasts. Determining precise initial conditions from both observations and model output in terms of clouds is a challenge. This study explores the use of four-dimensional data assimilation (FDDA) nudging to take into account cloud cover observations from an infrared all-sky imager. Experiments with two WRF domains (9 km and 3 km grid spacing), driven by Global Forecasting System (GFS) forecasts, are performed over Central Europe. The cloud cover observations are determined by the Sky InSight, an all-sky infrared imager, installed at the Lindenberg Meteorological Observatory – Richard-Assmann-Observatory (MOL-RAO) in eastern Germany. Compared to observations in the visible range, this instrument has the advantage to deliver cloud cover observations of the same quality at day and night time. The observations are compared to cloud cover values from the WRF background. Depending on the difference between the two, the WRF background humidity profile at the location of the imager is either increased, decreased or unchanged to obtain a new profile for the nudging. Cloud base height values are determined by a ceilometer and empirical values are used for the cloud top height to limit the vertical extent of the derived humidity profiles to be nudged. Reuniwatt’s custom Meteosat Second Generation (MSG) Satellite Application Facility NoWCasting (SAFNWC) cloud products are used to evaluate the impact of the nudging on WRF forecasts of cloud cover. The fast and cost-effective nudging method leads to an improvement of WRF cloud cover forecasts. It could be easily upscaled to a large amount of ground-based camera observations.
Objective verification for development and monitoring of automated weather forecasts
Authors: Bhend Jonas , Spirig Christoph , Moret Lionel , Liniger Mark A.
Published: June 2022

Objective forecast verification provides the basis to motivate changes to the forecast system. At MeteoSwiss, we are introducing statistical ensemble postprocessing into our automated forecast production. These automated forecasts are accessed by the Swiss general population through the MeteoSwiss website and mobile app and they will form the basis for a range of derived products. Therefore, it is crucial to evaluate the new forecasts broadly, i.e. on the diverse aspects of forecast quality relevant for the variety of products used across all of Switzerland.

The core component of this evaluation system consists of a web portal for interactive visualization of verification measures. This web portal provides the means to compare forecast meteograms at stations with the corresponding observations for a quick visual inspection akin to the information available on the mobile app. In addition, forecast quality of the most recent forecasts is monitored using various verification measures. Finally, re-forecasting with experimental configurations is integrated to produce in-depth reporting on the effect of novel forecasts to support decisions on the development of the forecasting system. To facilitate near real-time analyses, atomic verification scores per meteorological parameter, forecast source, forecast issue time, station, and time are pre-computed and stored in a partitioned database. The partitioning allows for rapid multi-threaded access at analysis time from the interactive web portal. The objective verification is complemented with feedback by forecasters on duty on individual cases during the pre-operational phase of new forecast developments. Here, we will showcase how each of these parts is used to assess release candidates for the automatic forecast production. 
Non-persuasive communication as a strategy to deliver climate information 
Authors: Hoffmann David , Holmes David , Healy Ella
Published: June 2022

The Monash Climate Change Communication Research Hub (MCCCRH) is Australia’s leading research centre dedicated to improving public understanding of climate change. 

Utilising what is known as ‘non-persuasive communication,’ MCCCRH delivers simple, short, factual climate messages over longer-time frames, that are repeated often. The approach is large scale, evidence-based and non-partisan and is delivering the information that Australians are seeking to actively bridge deep divisions on climate change.

In this paper we present non-persuasive communication in action that is done by translating climate science into simple and engaging formats for two of MCCCRH’s major programs:

‘Climate Communicators’, which is an innovative national climate education program that produces simple, long-term climate graphics for Australian television weather presenters and currently broadcasts in every state of Australia with more than 500+ broadcasts to date. It broadcasts to metropolitan and rural television audiences and harnesses the existing science communication capacity of weather presenters.

 

‘Changing Climates’, which is a climate column in collaboration with Australia’s largest media organisation News Corp Australia that increases the visibility of climate science and scientists and has published 2000+ print and digital columns across 58 mastheads. 

Most up to date and accurate climate science and data is brokered from the Bureau of Meteorology, CSIRO and Monash University scientists. Easy-to-process data visualisations are continuously researched with leading science communications specialists to ensure clarity and shared with partners from the most visible and trusted forms of media including television weather presenters and community newspapers. The effectiveness of these strategies is routinely evaluated, ensuring their design and delivery are specifically tailored for maximum impact. 

 

Corresponding/presenting author: David Hoffmann, Monash University, Melbourne, Australia; david.hoffmann@monash.edu

 
Temporal evolution of features that control 10-m wind gusts in moist baroclinic wave simulations identified using non-linear regression
Authors: Bouvier Clément , Cornér Joona , Sinclair Victoria
Published: June 2022

A majority of insured losses over Europe are related to Extra-Tropical Cyclones (ETC) which are characterised by strong winds, heavy precipitation and powerful ocean waves. Baroclinic wave simulations (BWS) are used to study ETC by varying their background state and measuring their different intensities. However, two main issues limit an exhaustive exploration of ETC intensity and background state relationship: 1) the dimensionality of the feature space, 2) a large number of intensity measures. To alleviate this issue, this study proposes to use a wrapper Feature Selection Algorithm (wFSA) combined with a non-linear regressor applied to an intensity measure. The selected subsets are analysed through time.

BWS was performed in the moist case using OpenIFS version Cy43r3v2 configured as an aqua planet with full physics and the radiation scheme deactivated. The atmospheric state proposed by Jablonowski and Williamson was used. The spatial resolution of the simulation was set to TL319/L137 and the time resolution to 20 minutes for 15 days. The initial perturbation was located in 40°N 20°E. A number of 55 measures -called features- were extracted from the BWS and the 10-m wind gust was selected as the intensity measure. A stable wFSA was performed using weighted Random Forest Regressor in the framework proposed by Meinshausen and Bühlmann. The regression was run 10 times on 60% of randomly selected points in the northern hemisphere to infer the 10-m wind gust. Finally, the average feature importance and its variance were computed for each feature every 12 hours.

The forecast surface roughness and the specific humidity were the most important features for the first 2 days. Afterwards, mean sea level pressure became predominant for 5 days. For the remaining days, forecast surface roughness, specific humidity and large scale precipitation were the most important features to infer 10-m wind gust. Further work will aim at increasing the number of BWS by modifying the average temperature of the background state. All results will be compared to propose an efficient dimension reduction to study BWS and their evolution.
Intense summer Arctic cyclone and its relationship with Arctic sea ice simulated by the Korean Integrated Model
Authors: Kim Shin-Woo , Shim Taehyoun , Kim Junghan , Park Junseong
Published: June 2022

Sea ice declines have been continuously observed in recent decades over the Arctic Ocean by global warming. In recent years, intense storms over the Arctic have been observed more frequently, consistent with events of extreme sea ice loss. However, the interaction of storms and sea ice is not yet fully understood due to limited observations. In August 2016, an intense summer Arctic cyclone covering the Arctic ocean was occurred and maintained for almost one month, with the recorded the minimum central surface pressure of 967 hPa. During the developing stage of the cyclone, strong baroclinic instability near the cyclone center mainly contributed to the cyclone development. In addition, enhanced upward latent heat flux is also attributable to the development of Arctic cyclone. The presence or absence of sea ice would have had a significant impact on the development of storms. A large loss of sea ice was also observed along the storm's path.

In this study, a suite of sensitivity experiments with sea ice conditions are performed using the Korean Integrated Model (KIM), a global numerical weather prediction system developed by the Korea Institute of Atmospheric Prediction Systems (KIAPS) and used as the operational system at the Korea Meteorological Administration (KMA) since April 2020. The results of control experiment based on surface cycling with the reanalysis data are compared with those of sensitivity simulations using the fixed sea ice data before and after the storm. The effects on baroclinic instability, heat flux, and cyclone intensity induced by the changes in sea ice are compared. Numerical simulations are additionally performed using the initial version of KIM coupling system with earth system components including land surface, ocean, and sea ice.
Validation of extratropical cyclone characteristics in sub-seasonal ECMWF forecasts
Authors: Büeler Dominik , Sprenger Michael , Wernli Heini
Published: June 2022

Extratropical cyclones strongly interact with the midlatitude waveguide and can thus actively influence onset, maintenance, and decay of large-scale weather regimes on sub-seasonal timescales (10 – 60 days). For instance, individual cyclones over the Pacific-North American region have shown to be able to trigger positive or negative phases of the North Atlantic Oscillation, depending on the type of Rossby wave breaking they are associated with. Likewise, strongly diabatically driven cyclones have shown to be relevant for ridge amplification and thus blocking onset and maintenance. Biases in cyclone activity and characteristics in sub-seasonal numerical weather prediction models might therefore hinder exploiting the potential large-scale predictability on these timescales. We thus, for the first time, identify and track extratropical cyclones in 21 winters (2000 – 2020) of sub-seasonal ensemble hindcasts from the European Centre for Medium-Range Weather Forecasts. This quasi-Lagrangian, object-oriented approach allows us to validate various cyclone life cycle characteristics such as the deepening rate, location of genesis, maximum intensity, and decay, propagation direction and speed, and size, age and lifetime. Overall, the hindcasts reproduce the climatology of cyclone activity and characteristics remarkably well up to 6 weeks lead time, both over the North Atlantic and the North Pacific. However, the hindcasts tend to underestimate the frequency of the strongly intensifying subset of North Atlantic cyclones. This underestimation is likely linked to an underestimation of cyclogenesis frequency along the southeastern U.S. coast. As strongly intensifying cyclones are particularly relevant for the reorganization of the large-scale flow downstream, this bias might influence sub-seasonal forecast skill and deserves further attention.
Forecasting extreme precipitation in the central Mediterranean: Different products for different timescales
Authors: Mastrantonas Nikolaos , Furnari Luca , Magnusson Linus , Senatore Alfonso , Mendicino Giuseppe , Pappenberger Florian , Matschullat Jörg
Published: June 2022

Extreme precipitation events pose a great threat to society, the economy, and the environment with devastating consequences like floods and landslides. Improved forecasting of this hazard, combined with adequate Early Warning Systems, can thus support the mitigation of the associated negative impacts. Here, we propose new forecasting products that substantially improve predictions of extreme precipitation events at different timescales, from short- to medium- and extended-range forecasts. Our study focuses on forecasting extreme rainfall in Calabria, south Italy. This region, located in the central Mediterranean, has a complex and abruptly varying topography that poses additional challenges in precipitation formation and forecasting. For this work we use data from three sources; observational precipitation from the EOBS dataset, modelled atmospheric fields from ERA5 reanalysis, and forecasted atmospheric fields from ECMWF reforecasts for 1 up to 45 days lead time. Based on our analysis we show that different forecasting horizons require different forecasting products. More specifically, bias-correcting the precipitation forecasts for short- to medium-range lead times provides the most informative outputs to end-users. For extended-range forecasts though, this method is not sufficient, as it cannot outperform the reference score based on climatological information. For such extended lead times, it is beneficial to use the large-scale weather variability to infer reliable information about extreme precipitation. This can be accomplished by connecting Mediterranean-wide weather patterns, with the probability of extreme precipitation in Calabria. We present the benefits of the methods, based on long-term statistical analysis using a range of indicators, such as the Brier skill score, and the reliability diagram.
How Will Extreme Weather Events Change due to Climate Change?
Authors: Schultz David
Published: June 2022

Headlines from newspapers and websites scream out the soundbite that anthropogenic climate change is causing more extreme weather. 

“Climate change means more extreme weather – here’s what the UK can expect if emissions keep increasing.”

“Why extreme weather is the new normal.”

“Man-made climate change had role in some of 2015’s extreme weather.”

For better or worse, the potential and deadly serious effects of anthropogenic climate change are often communicated through this soundbite. This soundbite has become popular with scientists and the media, but neglects important caveats.  What do we mean by “extreme weather”?  Where does this extreme weather occur on Earth?  Will all types of extreme weather be affected equally?  Have we already witnessed changes in extreme weather?  Is anthropogenic climate change responsible? How will these changes affect society?  Thus, the seemingly simple question of how does climate change affect extreme weather becomes much more complicated upon further reflection.

No doubt these questions may be obscured or even unasked in the modern media landscape, but that does not mean that they aren’t important questions to ask.  In this talk, I will present the science behind how different extreme weather events may – or may not – change in the future as the global climate warms.  Specifically, I will present results from studies on tropical cyclones and tornadoes, and I will consider the factors that have affected, and will affect, their changes in intensity, frequency, and spatial distribution on Earth.  I will also discuss the relatively recent approach called climate-change attribution where the role of anthropogenic climate change in individual extreme weather events can be quantitatively addressed.  By the end of this talk, I hope to give the audience the tools to evaluate with a critical eye media stories about climate change and extreme weather.
Measuring weather data with a self-build low-cost sensor 
Authors: Schoster Daniela , Rust Henning , Sobottke Vincent , Göber Martin , Kox Thomas
Published: June 2022

Citizens as voluntary weather observers have long contributed to weather and climate science. The density of the professional observation network is enriched by lay observations of weather phenomena and their impacts. Some statements about the impact of climate change on the environment and people would not be possible without them. A better public understanding is also particularly interesting in order to build up decision-relevant knowledge about climate change, as citizens not only gain key scientific insights, but also increase their understanding of the topic and gain a growing interest in the research process. 

In two projects in Germany  (in Brandenburg, within the FESSTVaL (Field Experiment on submesoscale spatio-temporal variability in Lindenberg) measurement campaign initiated by the Hans-Ertel-Center for Weather Research, and in Bavaria, in the KARE-Citizen Science  project), we use a weather station to be assembled by pupils as a participatory vehicle to increase interest in and understanding of weather and climate, as well as of weather forecasting, and to generate high resolution data for research. The pupils measure weather data such as temperature and precipitation with self-built weather stations out of a 3D-printer. They also report weather impacts such as observed damages. These data are evaluated in workshops involving the students, their teachers, local partners and scientists. Interesting meteorological phenomena were discovered in the dataset, e.g. a cold pool that can form during a thunderstorm and trigger new ones. Thus, our network of higher spatial and temporal resolution data collected by the pupils has the potential to study these small-scale phenomena in more detail than with professional networks of about 25 km spacing.
Implication of ensemble selection and weighting on the performance of regional climate simulations in Austria
Authors: Lehner Sebastian , Schellander-Gorgas Theresa
Published: June 2022

The plethora of available General Circulation Model (GCM) data allows an inclusion of model evaluation techniques to weigh the contribution of GCMs for the use in a statistically downscaled ensemble. We present the development of a pipeline in which GCM data is evaluated based on different criteria, downscaled using an analog technique and the performance impact of the evaluation is validated via the SPARTACUS dataset for Austria.

For the evaluation of GCMs we employ multiple metrics, which are calculated for all GCMs as well as reanalysis data. For the latter we use JRA55 and ERA5 reanalyses, which are used as historical reference. The inclusion of two reanalysis data sets allows the estimation of reanalysis uncertainty contained in the employed evaluation methodology. A principal component based Northern Atlantic Oszillation Index is calculated from which various performance measures based on the time coefficients and empirical orthogonal functions are determined. Furthermore the Central European Zonal Index is used as an additional performance metric. Time-independent quantities are then calculated for all performance measures to reflect the non-time-synchronous characteristic of GCMs. Finally, different weighting schemes are determined based on the performance metrics and additional factors such as model independence, yielding weighting coefficients for each model. The GCMs are downscaled using an analog approach with a random choice analog selection based on the ten best analogs for each given day. Predictands to be downscaled are the near-surface temperature and precipitation totals with a daily resolution. The downscaled ensemble is then compared to the SPARTACUS dataset and weighting schemes are evaluated by minimizing the error of the ensemble mean over a historical period.
Heat Health Warning System in Germany - New Developments and Lessons Learned
Authors: Matzarakis Andreas
Published: June 2022

After the heat waves in the year 2003 and the statements of IPCC about the increase and the related consequences several countries in Europe decided to develop or implement a Heat Health Warning System (HHWS) and provide information for general public and public health. In Germany, weather Forecast is used to predict heat episodes, which are associated with negative health impacts. Therefore, a heat balance model of the human body and an extracted equivalent temperature (Perceived Temperature) is applied. Thresholds for strong and extreme heat stress based on thermal perception classification are used and build the first approach of the HHWS. Furthermore, the threshold of strong heat stress includes a short term adaptation component and considers the previous thermal stress conditions of the last 30 days. The second step includes nocturnal conditions, based on forecasted minimum air temperature or a simulated maximum indoor temperature for typical houses. The indoor temperature is calculated also based on a urban heat model for cities with a population over 100.000 inhabitants. Both criteria are important for the decision about warnings for the present and next days. Warnings are generated by daily weather forecast automatically and are additionally confirmed or adjusted by a biometeorological forecaster. The warning is valid on county level considering several elevation classes. The heat warning is available as a map on the internet and registered users can receive information by a daily newsletter. A specific smartphone app is also available for general use. The main target groups are the public, nursing homes and ministries of the federal states and other authorities. The HHWS is in operation since 2005 and preliminary studies indicate a reduction of the heat related mortality after implementation.  The HHWS with his regional differentiation of heat stress warnings is also part of the heat health action plans in Germany.
Global verification of numerical solar radiation forecast 
Authors: Reiß Alexandra , Bader Nico , Bührer Michael , Schlögl Sebastian
Published: June 2022

Renewable energies are becoming increasingly important in energy production due to proceeding climate change. This makes the energy sector one of the fastest growing and changing sectors worldwide. Solar energy is expected to account for large parts of the world’s future energy supply.

Solar radiation forecast will become more important with the future expansion of existing energy supply structures with solar energy resources. Due to the natural fluctuation of solar radiation, a reliable forecast is required for an efficient use of solar energy. This study deals with a solar radiation verification of the global numerical weather prediction models NEMSGLOBAL, GFS, ICON, ARPEGE-World, as well as the reanalysis model ERA5. The aim is to find out which models performs the best and how multi-model approaches can lower the forecast error.

For the verification 81 global solar radiation measurements from the Basic Surface Radiation Network (BSRN) and the World Radiation Data Center (WRDC) were used. Hourly forecast data are compared to quality controlled and aggregated measurement data for the years 2018 until 2020. Statistical analysis is conducted for each measurement location separately to evaluate and compare the performance of each raw or multi-model by using the error metrics like mean absolute error (MAE) and mean bias error (MBE).

Among all models, the reanalysis model ERA5 performed the best with a MAE of 43 Wm-2 and a MBE of 8 Wm-2. For the weather forecast models, ICON showed the lowest error with a MAE of 48 Wm-2 followed by GFS, NEMSGLOBAL and ARPEGE-World with MAEs of 51 Wm-2, 61 Wm-2 and 67 Wm-2. Unlike the other forecast models, NEMSGLOBAL and ARPEGE-World tend to underestimate solar radiation. Within the best performing multi-models, ICON is usually weighted the highest with up to 50 %. Implementing a multi-model approach for a solar radiation forecast, the MAE can be reduced as the number of models increases. Including two models in the multi-model the MAE can already be reduced by 4.6 Wm-2, while three models reducing the MAE by 7 Wm-2, and four models by 8.8 Wm-2.

Results indicate that the multi-model approach can improve solar radiation forecast. That shows the potential of further investigation of forecast models and their combination.
Efficient generation of synthetic near-infrared satellite images for model evaluation and data assimilation
Authors: Baur Florian , Scheck Leonhard , Stumpf Christina , Köpken-Watts Christina , Bach Liselotte , Potthast Roland
Published: June 2022

Satellite images in the solar spectrum provide high-resolution cloud and aerosol information and present promising observations for data assimilation and model evaluation. While visible channels contain information on the cloud distribution and cloud optical thickness, near-infrared channels are in addition more sensitive to cloud microphysical properties and can be used to distinguish between water and ice clouds. The assimilation of these channels is therefore expected to improve the vertical cloud structure and correct errors in effective droplet and ice particle radii.

The direct assimilation of satellite radiance in operational systems has so far been restricted to infrared and microwave observations. This is because sufficiently fast and accurate forward operators for visible and near-infrared radiances were not yet available, which is related to the fact that multiple scattering makes radiative transfer at solar wavelengths complicated and standard radiative transfer solvers computationally expensive. MFASIS, a 1D radiative transfer method based on compressed look-up tables, is sufficiently accurate and orders of magnitude faster, but limited to visible channels and clouds.

After discussing the limitations in the current version of MFASIS that prevent it from simulating near-infrared channels accurately, we present an alternative approach that increase the accuracy significantly for near-infrared channels. In this novel approach, the look-up tables are replaced by a neural network reducing the computational costs and allowing for additional input parameters. Those parameters enable us to describe the vertical distribution of cloud parameters, in particular the effective radius profiles, more accurately. We will demonstrate that the errors are reduced considerably, compared to the original MFASIS method. The new approach is tested for the SEVIRI 1.6mu channel using model output from IFS and the convective-scale data assimilation system KENDA, which is based on the ICON-D2 model.