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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-162
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-162
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 03 Jul 2019

Model description paper | 03 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

TIER Version 1.0: An open-source Topographically InformEd Regression (TIER) model to estimate spatial meteorological fields

Andrew J. Newman1 and Martyn P. Clark2 Andrew J. Newman and Martyn P. Clark
  • 1Research Applications Laboratory, National Center for Atmospheric Research, Boulder CO, 80307-3000, USA
  • 2University of Saskatchewan, Centre for Hydrology and the Global Institute for Water Security, Saskatoon, SK

Abstract. This paper introduces the Topographically InformEd Regression (TIER) model, which uses terrain attributes in a regression framework to distribute in situ observations of precipitation and temperature to a grid. The framework enables our understanding of complex atmospheric processes (e.g. orographic precipitation) to be encoded into a statistical model in an easy to understand manner. TIER is developed in a modular fashion with key model parameters exposed to the user. This enables the user community to easily explore the impacts of our methodological choices made to distribute sparse, irregularly spaced observations to a grid in a systematic fashion. The modular design allows incorporating new capabilities in TIER. Intermediate processing variables are also output to provide a more complete understanding of the algorithm and any algorithmic changes. The framework also provides uncertainty estimates. This paper presents a brief model evaluation and demonstrates that the TIER algorithm is functioning as expected. Several variations in model parameters and changes in the distributed variables are described. A key conclusion is that seemingly small changes in a model parameter result in large changes to the final distributed fields and their associated uncertainty estimates.

Andrew J. Newman and Martyn P. Clark
Interactive discussion
Status: open (until 28 Aug 2019)
Status: open (until 28 Aug 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Andrew J. Newman and Martyn P. Clark
Model code and software

NCAR/TIER: Topographically InformEd Regression Version 1.0 A. Newman https://doi.org/10.5281/zenodo.3234938

Andrew J. Newman and Martyn P. Clark
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Latest update: 20 Jul 2019
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Short summary
This paper introduces the Topographically InformEd Regression (TIER) model, which uses terrain attributes to turn observations of precipitation and temperature into spatial maps. TIER allows our understanding of complex atmospheric processes such as terrain enhanced precipitation to be modeled in a very simple way. TIER lets users change the model so they can experiment with different ways of making maps. A key conclusion is that small changes in TIER will change the final map.
This paper introduces the Topographically InformEd Regression (TIER) model, which uses terrain...
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