Technological opportunities for sensing of the health effects of weather and climate change: a state-of-the-art-review

Sensing and measuring meteorological and physiological parameters of humans, animals, and plants are necessary to understand the complex interactions that occur between atmospheric processes and the health of the living organisms. Advanced sensing technologies have provided both meteorological and biological data across increasingly vast spatial, spectral, temporal, and thematic scales. Information and communication technologies have reduced barriers to data dissemination, enabling the circulation of information across different jurisdictions and disciplines. Due to the advancement and rapid dissemination of these technologies, a review of the opportunities for sensing the health effects of weather and climate change is necessary. This paper provides such an overview by focusing on existing and emerging technologies and their opportunities and challenges for studying the health effects of weather and climate change on humans, animals, and plants.

Exploring differences in spatial patterns and temporal trends of phenological models at continental scale using gridded temperature time-series

Phenological models are widely used to estimate the influence of weather and climate on plant development. The goodness of fit of phenological models often is assessed by considering the root-mean-square error (RMSE) between observed and predicted dates. However, the spatial patterns and temporal trends derived from models with similar RMSE may vary considerably. In this paper, we analyse and compare patterns and trends from a suite of temperature-based phenological models, namely extended spring indices, thermal time and photothermal time models. These models were first calibrated using lilac leaf onset observations for the period 1961-1994. Next, volunteered phenological observations and daily gridded temperature data were used to validate the models. After that, the two most accurate models were used to evaluate the patterns and trends of leaf onset for the conterminous US over the period 2000-2014. Our results show that the RMSEs of extended spring indices and thermal time models are similar and about 2 days lower than those produced by the other models. Yet the dates of leaf out produced by each of the models differ by up to 11 days, and the trends differ by up to a week per decade. The results from the histograms and difference maps show that the statistical significance of these trends strongly depends on the type of model applied. Therefore, further work should focus on the development of metrics that can quantify the difference between patterns and trends derived from spatially explicit phenological models. Such metrics could subsequently be used to validate phenological models in both space and time. Also, such metrics could be used to validate phenological models in both space and time.

Short communication: emerging technologies for biometeorology

The first decade of the twenty-first century saw remarkable technological advancements for use in biometeorology. These emerging technologies have allowed for the collection of new data and have further emphasized the need for specific and/or changing systems for efficient data management, data processing, and advanced representations of new data through digital information management systems. This short communication provides an overview of new hardware and software technologies that support biometeorologists in representing and understanding the influence of atmospheric processes on living organisms.

Supporting sustainability initiatives through biometeorology education and training

The International Society of Biometeorology (ISB) has covered significant breadth and depth addressing fundamental and applied societal and environmental challenges in the last 60 years. Biometeorology is an interdisciplinary science connecting living organisms to their environment, but there is very little understanding of the existence and placement of this discipline within formal educational systems and institutions. It is thus difficult to project the ability of members of the biometeorological community-especially the biometeorologists of the future-to help solve global challenges. In this paper, we ask: At present, how we are training people to understand and think about biometeorology? We also ask: What are the current tools and opportunities in which biometeorologists might address future challenges? Finally, we connect these two questions by asking: What type of new training and skill development is needed to better educate "biometeorologists of the future" to more effectively address the future challenges? To answer these questions, we provide quantitative and qualitative evidence from an educationally focused workshop attended by new professionals in biometeorology. We identify four common themes (thermal comfort and exposures, agricultural productivity, air quality, and urbanization) that biometeorologists are currently studying and that we expect to be important in the future based on their alignment with the United Nations Sustainable Development Goals. Review of recent literature within each of these thematic areas highlights a wide array of skill sets and perspectives that biometeorologists are already using. Current and new professionals within the ISB have noted highly varying and largely improvised educational pathways into the field. While variability and improvisation may be assets in promoting flexibility, adaptation, and interdisciplinarity, the lack of formal training in biometeorology raises concerns about the extent to which continuing generations of scholars will identify and engage with the community of scholarship that the ISB has developed over its 60-year history.

Developing a Workflow to Identify Inconsistencies in Volunteered Geographic Information: A Phenological Case Study

Recent improvements in online information communication and mobile location-aware technologies have led to the production of large volumes of volunteered geographic information. Widespread, large-scale efforts by volunteers to collect data can inform and drive scientific advances in diverse fields, including ecology and climatology. Traditional workflows to check the quality of such volunteered information can be costly and time consuming as they heavily rely on human interventions. However, identifying factors that can influence data quality, such as inconsistency, is crucial when these data are used in modeling and decision-making frameworks. Recently developed workflows use simple statistical approaches that assume that the majority of the information is consistent. However, this assumption is not generalizable, and ignores underlying geographic and environmental contextual variability that may explain apparent inconsistencies. Here we describe an automated workflow to check inconsistency based on the availability of contextual environmental information for sampling locations. The workflow consists of three steps: (1) dimensionality reduction to facilitate further analysis and interpretation of results, (2) model-based clustering to group observations according to their contextual conditions, and (3) identification of inconsistent observations within each cluster. The workflow was applied to volunteered observations of flowering in common and cloned lilac plants (Syringa vulgaris and Syringa x chinensis) in the United States for the period 1980 to 2013. About 97% of the observations for both common and cloned lilacs were flagged as consistent, indicating that volunteers provided reliable information for this case study. Relative to the original dataset, the exclusion of inconsistent observations changed the apparent rate of change in lilac bloom dates by two days per decade, indicating the importance of inconsistency checking as a key step in data quality assessment for volunteered geographic information. Initiatives that leverage volunteered geographic information can adapt this workflow to improve the quality of their datasets and the robustness of their scientific analyses.

Lilac and honeysuckle phenology data 1956-2014

The dataset is comprised of leafing and flowering data collected across the continental United States from 1956 to 2014 for purple common lilac (Syringa vulgaris), a cloned lilac cultivar (S. x chinensis ‘Red Rothomagensis’) and two cloned honeysuckle cultivars (Lonicera tatarica ‘Arnold Red’ and L. korolkowii ‘Zabeli’). Applications of this observational dataset range from detecting regional weather patterns to understanding the impacts of global climate change on the onset of spring at the national scale. While minor changes in methods have occurred over time, and some documentation is lacking, outlier analyses identified fewer than 3% of records as unusually early or late. Lilac and honeysuckle phenology data have proven robust in both model development and climatic research.