The Topographically Informed Regression (TIER) Model
The Topographically InformEd Regression (TIER) model (Newman and Clark, 2019) was developed to distribute in situ observations of precipitation and temperature to a grid. TIER accounts for complex terrain by using terrain attributes in a knowledge based meteorological variable-elevation simple linear regression framework. The knowledge based framework allows for our understanding of complex atmospheric processes (e.g. orographic precipitation) to be encoded into a statistical model in an easy to understand manner. This is based primarily on Daly et al. (1994, 2000, 2002, 2007, 2008).
TIER is developed in a modular fashion with all key model parameters exposed to the user. This will hopefully allow the user community to easily explore the impacts of our methodological choices made to distribute sparse, irregularly spaced observations to a grid. A modular design also allows for the addition of new capabilities to TIER.
Additionally, uncertainty estimates and intermediate processing variables are included in the output for a more complete understanding of the algorithm and any algorithmic changes. It is demonstrated that the TIER algorithm is functioning as expected with a brief model evaluation. Finally, several variations in model parameters and changes in the distributed variables are described. The key message is that seemingly small changes in a model parameter result in sometimes large changes to the final distributed fields and their associated uncertainty estimates.
|1.0||GitHub Code Repository | TIER Example Data (example input, configuration files, and output, .tgz)||2019-May-23|
Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain, J. Appl. Meteor., 33, 140-158.
Daly, C., G. Taylor, W. Gibson, T. Parzybok, G. Johnson, and P. Pasteris, P, 2000: High-quality spatial climate data sets for the United States and beyond. Transactions of the ASAE, 43, 1957.3.
Daly, C., W. P. Gibson, G. H. Taylor, G. L. Johnson, and P. Pasteris, 2002: A knowledge-based approach to the statistical mapping of climate. Clim. Res. 22, 99–113, doi: 10.3354/cr022099.
Daly, C., J. W. Smith, J. I. Smith, and R. B. McKane, 2007: High-resolution spatial modeling of daily weather elements for a catchment in the Oregon Cascade Mountains, United States. J. Appl. Meteorol. Climatol., 46, 1565-1586.
Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. A. Pasteris, 2008: Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064, doi: 10.1002/joc.1688.
Newman, A. J., and M. P. Clark, 2019: TIER Version 1.0: An open-source Topographically InformEd Regression (TIER) model to estimate spatial meteorological fields. Submitted to Geosci. Model Dev.