Next Article in Journal
Micrometeorological Measurements Reveal Large Nitrous Oxide Losses during Spring Thaw in Alberta
Next Article in Special Issue
Separation of Upslope Flow over a Plateau
Previous Article in Journal
A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models
Previous Article in Special Issue
Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

Challenges and Opportunities for Data Assimilation in Mountainous Environments

1,*,†, 2,3,† and 1,†
Jupiter, Boulder, CO 80305, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309, USA
Physical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO 80305, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Atmosphere 2018, 9(4), 127;
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)
PDF [45478 KB, uploaded 3 May 2018]


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Hacker, J.; Draper, C.; Madaus, L. Challenges and Opportunities for Data Assimilation in Mountainous Environments. Atmosphere 2018, 9, 127.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Atmosphere EISSN 2073-4433 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top