Operational Dust Prediction View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Angela Benedetti , José Maria Baldasano , Sara Basart , Francesco Benincasa , Olivier Boucher , Malcolm E. Brooks , Jen-Ping Chen , Peter R. Colarco , Sunlin Gong , Nicolas Huneeus , Luke Jones , Sarah Lu , Laurent Menut , Jean-Jacques Morcrette , Jane Mulcahy , Slobodan Nickovic , Carlos Pérez García-Pando , Jeffrey S. Reid , Thomas T. Sekiyama , Taichu Y. Tanaka , Enric Terradellas , Douglas L. Westphal , Xiao-Ye Zhang , Chun-Hong Zhou

ABSTRACT

Over the last few years, numerical prediction of dust aerosol concentration has become prominent at several research and operational weather centres due to growing interest from diverse stakeholders, such as solar energy plant managers, health professionals, aviation and military authorities and policymakers. Dust prediction in numerical weather prediction-type models faces a number of challenges owing to the complexity of the system. At the centre of the problem is the vast range of scales required to fully account for all of the physical processes related to dust. Another limiting factor is the paucity of suitable dust observations available for model, evaluation and assimilation. This chapter discusses in detail numerical prediction of dust with examples from systems that are currently providing dust forecasts in near real-time or are part of international efforts to establish daily provision of dust forecasts based on multi-model ensembles. The various models are introduced and described along with an overview on the importance of dust prediction activities and a historical perspective. Assimilation and evaluation aspects in dust prediction are also discussed. More... »

PAGES

223-265

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-94-017-8978-3_10

DOI

http://dx.doi.org/10.1007/978-94-017-8978-3_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010432804


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