Research Model Research Articles

Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG

Abstract. Atmospheric transport models are often used to simulate the distribution of greenhouse gases (GHGs). This can be in the context of forward modeling of tracer transport using surface–atmosphere fluxes or flux estimation through inverse modeling, whereby atmospheric tracer measurements are used in combination with simulated transport. In both of these contexts, transport errors can bias the results and should therefore be minimized. Here, we analyze transport uncertainties in the commonly used Weather Research and Forecasting (WRF) model coupled with the greenhouse gas module (WRF-GHG), enabling passive tracer transport simulation of CO2 and CH4. As a mesoscale numerical weather prediction model, WRF's transport is constrained by global meteorological fields via initialization and at the lateral boundaries of the domain of interest. These global fields were generated by assimilating various meteorological data to increase the accuracy of modeled fields. However, in limited-domain models like WRF, the winds in the center of the domain can deviate considerably from these driving fields. As the accuracy of the wind speed and direction is critical to the prediction of tracer transport, maintaining a close link to the observations across the simulation domain is desired. On the other hand, a link that is too close to the global meteorological fields can degrade performance at smaller spatial scales that are better represented by the mesoscale model. In this work, we evaluated the performance of strategies for keeping WRF's meteorology compatible with meteorological observations. To avoid the complexity of assimilating meteorological observations directly, two main strategies of coupling WRF-GHG with ERA5 meteorological reanalysis data were tested over a 2-month-long simulation over the European domain: (a) restarting the model daily with fresh initial conditions (ICs) from ERA5 and (b) nudging the atmospheric winds, temperatures, and moisture to those of ERA5 continuously throughout the simulation period, using WRF's built-in four-dimensional data assimilation (FDDA) in grid-nudging mode. Meteorological variables and simulated mole fractions of CO2 and CH4 were compared against observations to assess the performance of the different strategies. We also compared planetary boundary layer height (PBLH) with radiosonde-derived estimates. Either nudging or daily restarts similarly improved the meteorology and GHG transport in our simulations, with a small advantage of using both methods in combination. However, notable differences in soil moisture were found that accumulated over the course of the simulation when not using frequent restarts. The soil moisture drift had an impact on the simulated PBLH, presumably via changing the Bowen ratio. This is partially mitigated through nudging without requiring daily restarts, although not entirely alleviated. Soil moisture drift did not have a noticeable impact on GHG performance in our case, likely because it was dominated by other errors. However, since the PBLH is critical for accurately simulating GHG transport, we recommend transport model setups that tie soil moisture to observations. Our method of frequently re-initializing simulations with meteorological reanalysis fields proved suitable for this purpose.

Confirmatory factor analysis for the implementation of Green Lean Six Sigma (GLSS) in Malaysia's wastewater treatment industry

ABSTRACT This study investigates the critical enablers influencing the implementation of Green Lean Six Sigma (GLSS) in Malaysia's wastewater treatment industry. Through an extensive literature review and insights from the industry, 30 distinct enablers were identified and categorized based on their characteristics within wastewater treatment plant (WWTP) organizations, aimed at ensuring successful GLSS execution. Structural equation modelling was employed to validate the research model, utilizing data from 296 certified professionals in Malaysia. The analysis revealed five significant enablers, indicating moderate to high levels of GLSS adoption within the industry, with the ‘strategic’ and ‘resource’ enablers emerging as particularly influential factors. Subsequent confirmatory factor analysis further affirmed the validity and reliability of these enablers. Moreover, the findings demonstrated both convergent and discriminant validity, reinforcing the efficacy of these factors in measuring GLSS implementation in Malaysian WWTPs. The study highlights the critical importance of strategic planning and resource allocation while emphasizing the need to address cultural and environmental factors for successful GLSS adoption in the industry. However, enablers based on linkages, particularly those pertaining to supplier relationships and customer satisfaction, garnered the least consensus among respondents, indicating areas necessitating further attention and improvement.

Development of Learning Modules for Senior High School Semester V Based on the Independent Curriculum

This research is about the creation of a learning module for the 5th semester of high school based on the Independent Curriculum with the theme "die Familie (Family)". This research focuses on the creation of printed learning modules. This study uses a qualitative descriptive method through data collection using questionnaires with data sources from the Independent Curriculum, German books and information from the Internet. This development research is a development that uses Richey and Klein's theory. The research model consists of 3 steps, namely: 1) Planning, 2) Making, 3) Evaluation. The result of this research is a printed module in the form of a German language learning module for the theme "die familie (Family)". This learning module has gone through the process of material and design validation and received an assessment from experts of 93.75.

University entrepreneurship: entrepreneurial orientation, networks, market orientation, and sustainable development

PurposeThe literature identifies the need to understand better the role of universities’ entrepreneurial orientation (EO), even while this remains an unexplored field. This study seeks to overcome this shortcoming and put forward empirical evidence on the EO of universities and it examines the moderating effects of networks, knowledge and trust, market orientation, and implementing sustainable development goals (SDGs) on the design and development of entrepreneurial universities.Design/methodology/approachTo test the conceptual model, the authors used a sample of 125 questionnaires obtained from Portuguese higher education institutions, and it was deployed a structural equation model by a partial least squared as the estimation method.FindingsThe results show that the different dimensions of EO significantly influence the design and development of entrepreneurial universities. Furthermore, our findings show how market orientation and SDGs have moderating effects on the development of university entrepreneurship.Practical implicationsAs for practical implications, the results point to the influence EO holds over entrepreneurial universities, and this causal relationship undergoes moderation by networks, knowledge and trust, market orientation and SDGs. As such, HEI rectors, deans and directors need to leverage these moderating effects, fostering human capital and universities’ active initiatives and policies to conceive and develop more entrepreneurial universities.Originality/valueOur research model seeks to contribute to advancing studies on the EO of universities and assists in better understanding EO within the scope of influence of the third university mission as entrepreneurial institutions.

Investigating social streaming app dependency: a mixed-methods analysis

PurposeThere is a dearth of knowledge regarding how user dependency offers valuable resources to develop the intellectual capital of social streaming apps (SSAs) companies. This study aims to integrate major conceptual components of the UandD model, identify contextualized goal-oriented SSA dependency and empirically evaluate their interrelated user-dependency relationships in the SSA context.Design/methodology/approachA mixed-methods approach was utilized in this study. First, user gratifications were elicited through a qualitative approach, considering the exploratory stage of the SSA phenomenon. Second, statistical methods were applied to investigate and extract the sub-dimensions of SSA dependency. At last, a research model was developed grounded on the UandD model and empirically validated using the quantitative approach.FindingsThe results validated the gratification-dependency-attitude-behavior relationships hypothesized by the UandD framework in SSA. The role of user-SSA dependency in enhancing intellectual capital in the social media industry has been highlighted in this study.Research limitations/implicationsThis research not only provides an opportunity for the UandD model to realize its theoretical potential as envisioned by scholars but also contributes to the scholarship on social streaming apps and media dependency theory by conceptualizing goal-oriented dependency in SSAs.Practical implicationsThe research results will guide digital media practitioners to a more nuanced understanding of the relationships between their users and modern digital media apps and thus empower the practitioners to better manage their intellectual capital based on the facilitation of their users’ dependency.Originality/valueThis work is one of the pioneers in contextualizing the UandD model in the SSA field, refining and measuring the SSA dependency and its distinct subdimensions and employing mixed-methods to offer a comprehensive understanding of how user dependency boosts intellectual capital in the SSA industry.

Leveraging large language models for daily tourist demand forecasting

ABSTRACT Large Language Models have attracted the attention of tourism researchers, and many discussions have been published in leading tourism journals. However, little research has been conducted on how to use Large Language Models in tourism research. In this paper, we propose a new tourist arrival forecasting model based on the Large Language Model. The Large Language Model is used to extract and produce the satisfaction scores from the review comments by the tourists. The generated satisfaction scores are incorporated into the tourist arrival forecasting model to take full advantage of the extracted information from the review comments. We have applied the Large Language Model based forecasting model to predict the tourist arrival in Macao. Experiment results show that the proposed model has produced forecasts with improved forecasting accuracy.

The Influence of Religiosity, Attachment to God and Education Level on Mental Health of Youth in Gereja Kristen Jawi Wetan (GKJW) Rungkut Surabaya in Indonesia

The purpose of this study was to assess the mental health conditions influences by religiosity, education level and attachment to God on the mental health of youth in GKJW Rungkut, Surabaya. Data were collected using a questionnaire administered to youth in GKJW Rungkut, Surabaya, aged between 16 and 35 years. The sample comprised 120 respondents. The questionnaire used a Likert scale (1-5), and statistical analysis was conducted using Warp-PLS. The results indicate that the levels of religiosity and mental health among the youth in GKJW Rungkut, Surabaya, are significant. However, aspects such as anxiety and low self-esteem are still at a moderate level. This suggests that a significant level of religiosity alone has not been able to fully address anxiety and low self-esteem. Statistical testing revealed that religiosity, education level, and attachment to God have a simultaneous influence on the mental health of the youth in GKJW Rungkut. The research model demonstrated adequate predictive relevance (Q² > 0), indicating that it can be used for further research.

Linking work meaningfulness, engagement and job crafting to career commitment

PurposeThe purpose of this study is to examine the relationships among meaningful work, employee engagement, job crafting and career commitment.MethodsThe study primarily employed structural equation modeling to test the proposed research model and hypotheses. Data were collected through an online questionnaire from employees working at multiple companies.FindingsThe findings indicated that meaningful work affected employee engagement, job crafting and career commitment. In addition, employee engagement and job crafting mediated the relationships between meaningful work and career commitment.OriginalityThis study identifies three important antecedents (meaningful work, employee engagement and job crafting) that improve employees’ level of career commitment. Based on previous findings indicating that career commitment can promote behavioral and attitudinal outcomes, this study focuses on contributors of career commitment, which ultimately may link to the positive outcomes of career commitment.

The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) Engagement Core: A culturally informed, community-engaged research (CI-CER) model to advance brain health equity.

The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) Engagement Core was launched to advance Alzheimer's disease (AD) and AD-related dementia (ADRD) health equity research in underrepresented populations (URPs). We describe our evidence-based, scalable culturally informed, community-engaged research (CI-CER) model and demonstrate its preliminary success in increasing URP enrollment. URPs include ethnoculturally minoritized, lower education (≤ 12 years), and rural populations. The CI-CER model includes: (1) culturally informed methodology (e.g., less restrictive inclusion/exclusion criteria, sociocultural measures, financial compensation, results disclosure, Spanish Language Capacity Workgroup) and (2) inclusive engagement methods (e.g., the Engagement Core team; Hub Sites; Community-Science Partnership Board). As of April 2024, 60% of ADNI-4 new in-clinic enrollees were from ethnoculturally or educationally URPs. This exceeds ADNI-4's≥50% URP representation goal for new enrollees but may not represent final enrollment. Findings show a CI-CER model increases URP enrollment in AD/ADRD clinical research and has important implications for clinical trials to advance health equity. The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) uses a culturally informed, community-engaged research (CI-CER) approach. The CI-CER approach is scalable and sustainable for broad, multisite implementation. ADNI-4 is currently exceeding its inclusion goals for underrepresented populations.

Digitalisation Empowerment of Business to Digital Innovation in the Achievement of SDGs: A Survey on Creative Msme Actors in Indonesia

Objective: This study aims to test and analyse the relationship patterns of digital business-based training, empowerment of MSME actors, digital self-efficacy and digital innovation in creative MSMEs for the achievement of SDGs in MSMEs in West Java and Banten Provinces, Indonesia. Theoretical Framework: Digital literacy is needed so that a person has the ability to access information related to digital-based media. MSMEs are required to have renewable digital-based business methods in order to provide optimal and satisfying services. When MSMEs have high digital literacy through digital-based training, they could increase high self-efficacy. Empowering MSMEs based on digital business is very necessary in the digital era. Human resources in the organisation is expected to have digital competencies (skills, knowledge, attitude). Also, employees are expected to increase digital self-efficacy in order to increase digital innovation and MSME performance in the long term. Method: This study uses quantitative method by distributing questionnaires to 250 natural colour batik MSMEs in West Java and Banten Provinces. 207 questionnaires are filled in completely and then processed with Partial Least Square statistical techniques. Results and Discussion: This study finds that digital-based business training has a significant positive impact on the empowerment of MSME actors, and digital self-efficacy of MSME actors. In addition, empowerment of MSME players has significant positive impact on digital innovation and digital self-efficacy. On the other hand, digital self-efficacy of MSME actors has significant positive impact on digital innovation and is able to mediate the effect of business digital-based training on digital innovation. However, empowerment of MSME actors cannot mediate the effect of business digital-based training on digital innovation. Research Implications: This study provide guidance for the government and MSME empowerment organisations to design more targeted digital-based training. In addition, this study highlights the importance of supporting technological infrastructure and providing wider access to digital technology, especially for creative MSMEs in Indonesia. This study also enriches the literature on the role of digital training on digital empowerment and digital self-efficacy. Likewise, it provides implications for the role of digital empowerment and digital self-efficacy in encouraging digital innovation. Originality/Value: This study offers a business digitalisation empowerment model for creative MSMEs in order to increase digital innovation for creative MSMEs in achieving SDGs in Indonesia. The research model considers digital-based business training, empowerment of MSME players, and digital self-efficacy.

Harnessing Ascidians as Model Organisms for Environmental Risk Assessment

Environmental Risk Assessment (ERA) often relies on a restricted set of species as bio-indicators, introducing uncertainty when modeling complex environmental variables. This may lead to oversimplified or erroneous risk assessments. Ascidians, marine filter-feeding sessile chordates, are valuable models for scientific research in various biological fields such as stem cell biology, embryogenesis, regeneration, innate immunity, and developmental biology. Their global distribution, sensitivity to pollutants, high abundance, mass sexual reproduction, and habitation in coastal areas impacted by anthropogenic pollution make them excellent indicators for monitoring marine pollution and global environmental changes, including biological invasions and species diversity diminution cases. Despite their potential as environmental bioindicators, ascidians remain underutilized in ERAs (≤0.13% of ERA studies), particularly in the field of chemical pollution impact assessment, primarily due to a lack of standardization. This underrepresentation poses a challenge for accurate modeling, especially in models relying on a broad range of species (e.g., Species Sensitivity Distributions). Given these constraints, expanding the use of ascidians in ERAs could improve the comprehension and precision of environmental changes and their assessments. This underscores the necessity for future research to establish standardized testing protocols and choose the most suitable ascidian species for inclusion in ERAs.

Pathways to corporate green technological innovation: An analysis from the perspective of dynamic capabilities

Green technological innovation has become a pivotal tool for driving economic restructuring and achieving environmental sustainability. However, in the face of complex and volatile market and policy environments, a critical question remains: how can enterprises effectively select and implement innovation pathways to address environmental turbulence? Drawing on data from 14,751 Chinese listed companies from 2012 to 2022, this study employs dynamic capabilities theory and a fixed-effects panel model to examine the impact of dynamic capabilities on firms’ choice of green innovation pathways. Results show a significant positive influence of dynamic capabilities on green innovation, although this effect varies depending on the research and development model and the level of environmental turbulence. Firms with high dynamic capabilities tend to favor independent research and development, with an effect coefficient of 0.778 (p<0.01); firms with moderate dynamic capabilities benefit more from cooperative research and development, with a coefficient of 0.045 (p<0.01); and firms with lower dynamic capabilities primarily pursue green innovation through technology transactions, with a coefficient of 0.052 (p<0.01). Furthermore, environmental turbulence significantly moderates the relationship between dynamic capabilities and green innovation negatively, with an interaction coefficient of -0.088 (p<0.05), indicating that environmental uncertainty weakens the positive impact of dynamic capabilities.These findings suggest that firms can select suitable research and development models aligned with their level of dynamic capabilities and adopt flexible innovation strategies to effectively manage environmental turbulence. This study proposes the following policy recommendations: enterprises should strengthen their dynamic capabilities, particularly in knowledge acquisition, resource integration, and adaptability to environmental changes, to enhance their green innovation capacity. Additionally, the government should increase policy support for green technological innovation by offering financial subsidies, tax incentives, and intellectual property protections, helping enterprises maintain their innovation momentum amid environmental turbulence.

Implementation of an HIV Prevention Intervention at Historically Black Colleges and Universities and Predominantly Black Institutions

Black Americans and the Southern United States are disproportionately represented in the HIV epidemic. Historically Black Colleges and Universities (HBCUs) and Predominantly Black Institutions (PBIs), often located in communities that have been historically underserved, are uniquely positioned to implement HIV prevention interventions focused on Black young adults. The purpose of the current study was to conduct a qualitative study, using the Consolidated Framework for Implementation Research (CFIR) model as a guide, to identify the barriers and facilitators to implementing an HIV intervention pre- and post-implementation. Pre-implementation key informant interviews with administrators, faculty, and staff, alongside focus groups with students, highlighted several themes as potential influencers on intervention rollout. These included perceived need, campus health resources, cost, personnel availability, student priorities, HIV-related stigma, and institutional culture. Post-implementation interviews with campus liaisons further revealed themes including institutional culture, external partnerships, internal communication, student health resource accessibility, and peer educator recruitment and retention. These findings underscore the complexities of implementing public health interventions in academic settings and may guide future efforts at HBCUs and PBIs to effectively address HIV prevention.

Идиостиль автора: вопросы параметризации

The paper presents a systematization of the parameters identified for the study of the author’s individual style, which are found in scientific works of recent years, with the aim of creating a general research model. The work presents the results of analysing dissertations from 1996 to 2024, in which authors address the analysis of the author’s individual style in terms of material, object, or subject of study. As a basis for further systematization, features such as research approach, material, understanding of individual style, and, finally, the analyzed parameters were adopted. This work is the first one to present a systematization of parameters included in the system of the author’s individual style, based on dissertation research conducted within different fields, using different approaches and diverse material, with the aim of creating a general model, which accounts for the scientific novelty. The research findings are as follows: a system of parameters has been identified and subjected to authorial classification, consisting of six levels, which includes elements of a level model of language, a pragmatic component (functionality), and the author’s worldview.

Sustaining Economic Growth: E-Service Quality’s Role in Fostering Customer Loyalty in Nigeria SMEs

The aim of this study is to investigate the impact of e-service quality on customer loyalty with customer satisfaction as a mediator in the small and medium-sized business sector in Nigeria. The dimensions investigated in this study are reliability, web design, trust, empathy, convenience, and cultural inclusion. To the best of the researchers’ knowledge, there has not been any research done on this subject investigating the above dimensions among Nigerian SMEs across various cultural groups. Therefore, this study fills this gap by investigating the impact of culture, web design, trust, empathy, convenience, and reliability on SMEs. A questionnaire was used for the purpose of the study to collect primary data. Four hundred and ninety-two responses were obtained from the respondents, who were age 20 and above. The survey was carried out online across the four major poles of Nigeria (east, west, north, and south) because of their varying and distinct cultural background. The data collected were analyzed using SPSS 28, Smart PLS 4 and AMOS 29 for descriptive analysis and structural equation modeling to test the research model. The results showed that the service quality dimensions have a positive significant impact on customer satisfaction and also show that customer loyalty is positively impacted by customer satisfaction.

What drives social shopping in social commerce platform? The effects of affordance, self-congruity and functional-congruity

PurposeSocial group system and social tagging system, which leverage the psychological mechanisms underlying group influence and social tags to drive consumer behaviors, have been prevalent in the social commerce platform. However, limited studies have examined how the affordances of social group system and social tagging system influence consumers’ social shopping behavior. The purpose of this study is to examine the formation of social shopping behavior in the social commerce platform.Design/methodology/approachCombining affordance theory with dual-congruity theory, we develop a model to examine how the affordances of social group system and social tagging system influence consumers’ social shopping behavior through the underlying self-congruity and functional-congruity processes. We empirically validate the research model using a multimethod approach, including an instrument development study and a field survey study.FindingsOur empirical findings show that social support positively influences relational identity, while it has a nonsignificant effect on social identity. Social interactivity positively influences relational identity and social identity. Furthermore, social tagging quality and social endorser credibility positively affect perceived diagnosticity and perceived serendipity. Finally, relational identity, social identity, perceived diagnosticity and perceived serendipity collectively determine consumers’ social shopping intention.Originality/valueThis study contributes to the theoretical understanding of social shopping in social commerce and offers practical implications for designing an effective social group system and social tagging system to boost product sales.

Assessing the impact of consumption values on satisfaction and usage intentions of millet-based food products

PurposeThe purpose of this study is to assess the impact of consumption values on customer satisfaction and intention to consume millet-based foods. This study examines both tangible and intangible values of the millet-based food to the customer.Design/methodology/approachBased on consumption value theory (CVT), the present study evaluates the impact of consumption value factors such as emotional, epistemic, health, price, prestige and taste/quality values on satisfaction and intention to use millet-based foods and develops a unique research model. Later, leveraging a dataset comprising more than 410 responses from Pune city in India, a rigorous empirical examination of the proposed model was conducted employing SmartPLS 4.0 software.FindingsThe study's finding unveils both the significant and insignificant impacts of various consumption values, both from the tangible and intangible consumption value perspectives on satisfaction and intention to consume millet-based foods. The study also provides a validated food consumption model which can be used for other similar food consumption behaviour of the customers.Research limitations/implicationsThis study provides insights as to how different consumption values impact the satisfaction of the customers for the millet-based food. This study illuminates additional aspects of the tangible and intangible factors impacting the satisfaction level of customers to consume millet-based products. Also, this is a cross-sectional study, and the respondents are only based in Pune, India. Thus, the study results cannot be generalised.Originality/valueThis study employs CVT to explore the unexplored impact of consumption values on satisfaction and intention to use millet-based foods, which is unexplored in the past literature. Moreover, this study develops a theoretical model with high explanatory power, and the research model adds value to the existing body of literature from areas such as food preference, consumer behaviour and value consumption insights.

Multi-scale hierarchical brain regions detect individual and interspecies variations of structural connectivity in macaque monkeys and humans

Macaques are representative animal models in translational research. However, the distinct shape and location of the brain regions between macaques and humans prevents us from comparing the brain structure directly. Here, we calculated structural connectivity (SC) with multi-scale hierarchical regions of interest (ROIs) to parcel out human and macaque brain into 8 (level 1 ROIs), 28 (level 2 ROIs), or 46 (level 3 ROIs) regions, which consist of anatomically and functionally defined level 4 ROIs (around 100 parcellation of the brain). The SC with the level 1 ROIs showed lower individual and interspecies variation in macaques and humans. SC with level 2 and 3 ROIs shows that the several regions in frontal, temporal and parietal lobe show distinct connectivity between macaques and humans. Lateral frontal cortex, motor cortex and auditory cortex were shown to be important areas for interspecies differences. These results provide insights to use macaques as animal models for translational study.

Dynamic analysis of landscape drivers in the thermal environment of Guanzhong plain urban agglomeration

Climate change caused by rapid urbanization in the Guanzhong region of China is becoming an increasingly significant problem. Previous empirical studies have confirmed that landscape patterns inextricably linked with the thermal environment, but static results based on a single temporal cross section of image data provide only a partial understanding. In this paper, we constructed a dynamic framework using Weather Research and Forecasting Model (WRF) for temperature simulation and Geodetector to study the landscape factors and their interactions that influence near-surface temperature (NST) changes in the Guanzhong Plain Urban Agglomeration (GPUA) between 2000 and 2020. Results showed that the GPUA average NST increased by 0.012 °C and 0.053 °C in January and July from 2000 to 2020, respectively. In terms of the dynamic correlation between landscape patterns and NST, cropland (CPL) was negative, urban land (UBL) was positive, and the remainder of the landscapes differed in winter and summer. Furthermore, results from the Geodetector showed that UBL embodied a stronger influence in summer than during winter months. This finding helps to explain why the average NST increase is higher in summer than during winter. The Dynamic Q values (DQ) of the area-based landscape metrics were generally larger than those of other spatial configuration metrics, and the interaction results showed that the landscape metrics of various land-cover classifications were enhanced, indicating that the superposition effect among landscape metrics needs to be taken into account in landscape planning in addition to area factors. The study of the relationship between landscape patterns and thermal environment considering dynamic perspective using WRF offers an important theoretical reference allied with practical guidance for understanding and adapting to forthcoming change in our climate through which we can help drive sustainable development decisions of the GPUA.

Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without a Summer of 1816

Abstract. The “Year Without a Summer” in 1816 was characterized by extraordinarily cold and wet periods in central Europe, and it was associated with severe crop failures, famine, and socio-economic disruptions. From a modern perspective, and beyond its tragic consequences, the summer of 1816 represents a rare opportunity to analyze the adverse weather (and its impacts) after a major volcanic eruption. However, given the distant past, obtaining the high-resolution data needed for such studies is a challenge. In our approach, we use dynamical downscaling, in combination with 3D variational data assimilation of early instrumental observations, for assessing a cold-air outbreak in early June 1816. We find that the cold spell is well represented in the coarse-resolution 20th Century Reanalysis product which is used for initializing the regional Weather Research and Forecasting Model. Our downscaling simulations (including a 19th century land use scheme) reproduce and explain meteorological processes well at regional to local scales, such as a foehn wind situation over the Alps with much lower temperatures on its northern side. Simulated weather variables, such as cloud cover or rainy days, are simulated in good agreement with (eye) observations and (independent) measurements, with small differences between the simulations with and without data assimilation. However, validations with partly independent station data show that simulations with assimilated pressure and temperature measurements are closer to the observations, e.g., regarding temperatures during the coldest night, for which snowfall as low as the Swiss Plateau was reported, followed by a rapid pressure increase thereafter. General improvements from data assimilation are also evident in simple quantitative analyses of temperature and pressure. In turn, data assimilation requires careful selection, preprocessing, and bias-adjustment of the underlying observations. Our findings underline the great value of digitizing efforts of early instrumental data and provide novel opportunities to learn from extreme weather and climate events as far back as 200 years or more.