Introducing the Bulletin of Atmospheric Science and Technology

Autor: Silvana Di Sabatino, Frank S. Marzano, A. Buzzi, Paolo Di Girolamo, Giorgio Budillon, Joan Cuxart, Carlo Buontempo, Stefano Serafin, Tiziana Paccagnella, Richard Rotunno, Cristina L. Archer, Gabriele Curci, Francesco Pilla, Dino Zardi, Evelyne Richard, Mario Marcello Miglietta, Rossella Ferretti, Simona Bordoni, Giacomo Gerosa, Carmine Serio, Domenico Cimini, Alberto Troccoli, Silvio Davolio, Marcello Petitta
Rok vydání: 2020
Předmět:
Zdroj: Bulletin of Atmospheric Science and Technology. 1:1-11
ISSN: 2662-1509
2662-1495
Popis: The rapid technological development of the past few decades has allowed for an unprecedented wealth of data about ourselves and our planet. The cost reduction of space platforms, the microelectronic revolution and the nearly exponential increase in computer power have been generating novel opportunities to explore and understand the world around us. Tools and theoretical approaches, capable of putting together all the insights we may possibly gain from all these new streams of data in a multidisciplinary framework, are still being developed. We are hence faced with both a unique challenge and an opportunity to make a significant progress in many scientific fields, first and foremost in the atmospheric and climate sciences. We are pleased to announce here the launch of the Bulletin of Atmospheric Science and Technology (BAST), a new peer-reviewed journal which is meant to bridge this gap in the broad area of the atmospheric sciences. The journal encourages a cross-disciplinary approach with an emphasis on new sensor technologies and systems, combined observational and modeling techniques, innovative numerical methods, data analysis, and retrieval techniques. BAST offers a platform to share new ideas and fresh developments to stimulate research activities focusing on urban, coastal, marine, rural, and mountain environments. Particular attention will be given to cross-disciplinary studies, especially those involving citizens for the collection of crowd-sourced data and those devoted to the characterization of uncertainties and homogenization of methods.
Databáze: OpenAIRE
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