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Every day, massive amounts of environmental data are routinely collected and processed from a dense constellation of Earth observing satellites and sensors, providing nearly continuous monitoring of the global biosphere. Other data are collected from aircraft, in situ and mobile weather station networks spanning the globe. Collectively, these data are complimentary and encompass a diversity of information and observational scales, which are processed and used in a broad range of environmental applications, including weather and climate forecasts, disaster risk assessments, water quality, and ecosystem health monitoring. The timeliness and quality of these 'big data' assessments have multiple direct benefits to human welfare and national economies, and they fundamentally improve our understanding of the environment. At the forefront of the big data revolution are the climate sciences, driven by the need to effectively distill and utilize information from increasingly greater volumes of global data. New approaches that enhance data reduction and distillation processes are continuously developed and environmental predictions and understanding are constantly improved by the information gained from large, diverse datasets.
Data-Driven Climate Sciences publishes innovative interdisciplinary research involving geospatial data processing and analyses, environmental assessments and applications in the climate sciences. Relevant topics include but are not limited to:
· water and carbon cycle interactions
· climate feedbacks
· land-ocean-atmosphere connections
· surface energy budget and climate change
· earth observation and data processing technologies
Studies involving innovative data processing and applications are emphasized as they pertain to the above topical areas, including remote sensing, modelling and data product development; multi-sensor remote sensing data fusion; model data assimilation and integration; ecological forecasting; climate services and data architectures.
General commentary articles and book reviews are only considered by invitation.
Indexed in: Google Scholar, DOAJ, CrossRef, CLOCKSS, OpenAIRE
Data-driven Climate Sciences welcomes submissions of the following article types: Brief Research Report, Clinical Trial, Community Case Study, Conceptual Analysis, Core Concept, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, New Discovery, Opinion, Original Research, Perspective, Policy and Practice Reviews, Review, Specialty Grand Challenge, Study Protocol, Systematic Review and Technology and Code.
All manuscripts must be submitted directly to the section Data-driven Climate Sciences, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Data-driven Climate Sciences will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Big Data.
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