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Atmosphere 2018, 9(4), 127; https://doi.org/10.3390/atmos9040127

Challenges and Opportunities for Data Assimilation in Mountainous Environments

1,†,* , 2,3,†
and
1,†
1
Jupiter, Boulder, CO 80305, USA
2
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309, USA
3
Physical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO 80305, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 15 February 2018 / Revised: 16 March 2018 / Accepted: 21 March 2018 / Published: 27 March 2018
(This article belongs to the Special Issue Atmospheric Processes over Complex Terrain)
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Abstract

This contribution aims to summarize the current state of data assimilation research as applied to land and atmosphere simulation and prediction in mountainous environments. It identifies and explains critical challenges, and offers opportunities for productive research based on both models and observations. Though many of the challenges to optimal data assimilation in the mountains are also challenges in flatter terrain, the complex land–atmosphere interactions and increased surface heterogeneity in the mountains violate key assumptions and methods in the data assimilation algorithms and the underlying models. The effects of model inadequacy are particularly acute in complex terrain. Recent research related to some of the key challenges suggest opportunities to make gains in both land and atmospheric data assimilation in the mountains. Research directions are suggested, focusing on model improvement in a data assimilation context, and design of field programs aimed at data assimilation. View Full-Text
Keywords: data assimilation; mountains; land–atmosphere coupling; model errors; observation errors data assimilation; mountains; land–atmosphere coupling; model errors; observation errors
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Hacker, J.; Draper, C.; Madaus, L. Challenges and Opportunities for Data Assimilation in Mountainous Environments. Atmosphere 2018, 9, 127.

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