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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Gridded time series of climatic variables are key inputs to phenological models used to generate spatially continuous indices and explore the influence of climate variability and change on plant development at broad scales. To date, there have been few efforts to evaluate how the particular source and spatial resolution (i.e., scale) of the input data might affect how phenological models and associated indices track variations and shifts at the continental scale. This study represents the first such assessment, based on cloud computing and volunteered phenological observations. It focuses on established extended spring indices (SI‐x) that estimate day of year (DOY) for first leaf (FL) emergence and first bloom (FB) emergence in plants particularly sensitive to accumulation of warmth in early to mid‐spring. We compared and validated gridded SI‐x products obtained using Daymet (at 1, 4, 35, and 100 km spatial resolution) and gridMET (at 4, 35, and 100 km) temperature data. These products were used to estimate temporal trends in DOY for FL and FB in the coterminous United States (CONUS) from 1980 to 2016. The SI‐x products, and their resulting patterns and trends across CONUS, affected more by the source of input data than their spatial resolution. SI‐x estimates DOY of FL and FB are about 3 and 4 weeks more accurate, respectively, using Daymet than gridMET. This leads to significant differences, and even contradictory, rates of change in DOY driven by Daymet versus gridMET temperatures, even though both data sources exhibit advancement in DOY of FL and FB across most regions in CONUS. SI‐x products generated from gridMET poorly estimate the timing of spring onset, whereas Daymet SI‐x products and actual volunteered observations are moderately correlated (r = 0.7). Daymet better captures temperature regimes, particularly in the western United States, and is more appropriate for generating high‐resolution SI‐x indices at continental scale. Extended spring indices (SI‐x) are being used increasingly as national and official indicators of climate change in the United States. We represent the first assessment of the influence of source and scale of Daymet and gridMET (the most accurate gridded daily weather data sets in the coterminous United States) on SI‐x indices, and associated indices track variations and shifts from 1980 to 2016. SI‐x products generated from gridMET poorly estimate the timing of spring onset, whereas SI‐x products generated from Daymet and actual volunteered observations are correlated. [ABSTRACT FROM AUTHOR]

Information about the annual meeting organized by the organizations the International Society of Biometeorology (ISB) and the Students and New Professionals (SNP) held in Norfolk, Virginia from July 28 to August 1, 2016 is presented. The event was organized to improve the teaching methods of teachers and learning of students on high education biometeorology and the presentations, practical sessions and group discussions participated by attendees.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The increasing availability of volunteered geographic information (VGI) enables novel studies in many scientific domains. However, inconsistent VGI can negatively affect these studies. This paper describes a workflow that checks the consistency of Volunteered Phenological Observations (VPOs) while considering the synchrony of observations (i.e., the temporal dispersion of a phenological event). The geographic coordinates, day of the year (DOY) of the observed event, and the accumulation of daily temperature until that DOY were used to: (1) spatially group VPOs by connecting observations that are near to each other, (2) define consistency constraints, (3) check the consistency of VPOs by evaluating the defined constraints, and (4) optimize the constraints by analysing the effect of inconsistent VPOs on the synchrony models derived from the observations. This workflow was tested using VPOs collected in the Netherlands during the period 2003–2015. We found that the average percentage of inconsistent observations was low to moderate (ranging from 1% for wood anemone and pedunculate oak to 15% for cow parsley species). This indicates that volunteers provide reliable phenological information. We also found a significant correlation between the standard deviation of DOY of the observed events and the accumulation of daily temperature (with correlation coefficients ranging from 0.78 for lesser celandine, and 0.60 for pedunculate oak). This confirmed that colder days in late winter and early spring lead to synchronous flowering and leafing onsets. Our results highlighted the potential of synchrony information and geographical context for checking the consistency of phenological VGI. Other domains using VGI can adapt this geocomputational workflow to check the consistency of their data, and hence the robustness of their analyses. [ABSTRACT FROM AUTHOR]