Spatial modeling of winter temperature and precipitation in Arizona and New Mexico, USA

David P. Brown, Andrew Comrie

Research output: Contribution to journalArticle

64 Citations (Scopus)

Abstract

The development of a statistical modeling technique suitable for producing mean and interannual gridded climate datasets for a topographically varying domain is undertaken. Stepwise regression models at 1 x 1 km resolution are generated to estimate mean winter temperature and precipitation for the Southwest United States for the years 1961-1990. Topographic predictor variables are used to explain spatial variance in the datasets. Kriging and inverse distance weighting interpolation algorithms are utilized to account for model residuals. The final regression models show a high degree of explained variance for temperature (R2 = 0.98, mean bias error [MBE] = -0.15°C, root-mean-squared error [RMSE] = 0.74°C) and a moderate degree of explained variance for precipitation (R2 = 0.63, MBE = -1.4 mm, RMSE = 27.0 mm). Several smaller-scale precipitation regression models are developed for comparison to the domain-wide model, but do not show marked accuracy improvements. Observed values of winter temperature and precipitation from the years 1961-1999 are compared to the 30 yr modeled means, and the differences are interpolated using kriging (temperature) and inverse distance weighting (precipitation). The result is a 39 yr time series of maps and datasets of winter temperature and precipitation at 1 x 1 km resolution for the Southwest United States.

Original languageEnglish (US)
Pages (from-to)115-128
Number of pages14
JournalClimate Research
Volume22
Issue number2
StatePublished - Sep 6 2002

Fingerprint

winter
modeling
kriging
temperature
Temperature
interpolation
Time series
Interpolation
time series
climate
comparison

Keywords

  • GIS
  • Interpolation
  • Precipitation
  • Regression
  • Southwest US
  • Temperature

ASJC Scopus subject areas

  • Atmospheric Science
  • Environmental Science(all)
  • Environmental Chemistry

Cite this

Spatial modeling of winter temperature and precipitation in Arizona and New Mexico, USA. / Brown, David P.; Comrie, Andrew.

In: Climate Research, Vol. 22, No. 2, 06.09.2002, p. 115-128.

Research output: Contribution to journalArticle

@article{c8a922a1a8c54b118cf49a1833d851cf,
title = "Spatial modeling of winter temperature and precipitation in Arizona and New Mexico, USA",
abstract = "The development of a statistical modeling technique suitable for producing mean and interannual gridded climate datasets for a topographically varying domain is undertaken. Stepwise regression models at 1 x 1 km resolution are generated to estimate mean winter temperature and precipitation for the Southwest United States for the years 1961-1990. Topographic predictor variables are used to explain spatial variance in the datasets. Kriging and inverse distance weighting interpolation algorithms are utilized to account for model residuals. The final regression models show a high degree of explained variance for temperature (R2 = 0.98, mean bias error [MBE] = -0.15°C, root-mean-squared error [RMSE] = 0.74°C) and a moderate degree of explained variance for precipitation (R2 = 0.63, MBE = -1.4 mm, RMSE = 27.0 mm). Several smaller-scale precipitation regression models are developed for comparison to the domain-wide model, but do not show marked accuracy improvements. Observed values of winter temperature and precipitation from the years 1961-1999 are compared to the 30 yr modeled means, and the differences are interpolated using kriging (temperature) and inverse distance weighting (precipitation). The result is a 39 yr time series of maps and datasets of winter temperature and precipitation at 1 x 1 km resolution for the Southwest United States.",
keywords = "GIS, Interpolation, Precipitation, Regression, Southwest US, Temperature",
author = "Brown, {David P.} and Andrew Comrie",
year = "2002",
month = "9",
day = "6",
language = "English (US)",
volume = "22",
pages = "115--128",
journal = "Climate Research",
issn = "0936-577X",
publisher = "Inter-Research",
number = "2",

}

TY - JOUR

T1 - Spatial modeling of winter temperature and precipitation in Arizona and New Mexico, USA

AU - Brown, David P.

AU - Comrie, Andrew

PY - 2002/9/6

Y1 - 2002/9/6

N2 - The development of a statistical modeling technique suitable for producing mean and interannual gridded climate datasets for a topographically varying domain is undertaken. Stepwise regression models at 1 x 1 km resolution are generated to estimate mean winter temperature and precipitation for the Southwest United States for the years 1961-1990. Topographic predictor variables are used to explain spatial variance in the datasets. Kriging and inverse distance weighting interpolation algorithms are utilized to account for model residuals. The final regression models show a high degree of explained variance for temperature (R2 = 0.98, mean bias error [MBE] = -0.15°C, root-mean-squared error [RMSE] = 0.74°C) and a moderate degree of explained variance for precipitation (R2 = 0.63, MBE = -1.4 mm, RMSE = 27.0 mm). Several smaller-scale precipitation regression models are developed for comparison to the domain-wide model, but do not show marked accuracy improvements. Observed values of winter temperature and precipitation from the years 1961-1999 are compared to the 30 yr modeled means, and the differences are interpolated using kriging (temperature) and inverse distance weighting (precipitation). The result is a 39 yr time series of maps and datasets of winter temperature and precipitation at 1 x 1 km resolution for the Southwest United States.

AB - The development of a statistical modeling technique suitable for producing mean and interannual gridded climate datasets for a topographically varying domain is undertaken. Stepwise regression models at 1 x 1 km resolution are generated to estimate mean winter temperature and precipitation for the Southwest United States for the years 1961-1990. Topographic predictor variables are used to explain spatial variance in the datasets. Kriging and inverse distance weighting interpolation algorithms are utilized to account for model residuals. The final regression models show a high degree of explained variance for temperature (R2 = 0.98, mean bias error [MBE] = -0.15°C, root-mean-squared error [RMSE] = 0.74°C) and a moderate degree of explained variance for precipitation (R2 = 0.63, MBE = -1.4 mm, RMSE = 27.0 mm). Several smaller-scale precipitation regression models are developed for comparison to the domain-wide model, but do not show marked accuracy improvements. Observed values of winter temperature and precipitation from the years 1961-1999 are compared to the 30 yr modeled means, and the differences are interpolated using kriging (temperature) and inverse distance weighting (precipitation). The result is a 39 yr time series of maps and datasets of winter temperature and precipitation at 1 x 1 km resolution for the Southwest United States.

KW - GIS

KW - Interpolation

KW - Precipitation

KW - Regression

KW - Southwest US

KW - Temperature

UR - http://www.scopus.com/inward/record.url?scp=0037032069&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037032069&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0037032069

VL - 22

SP - 115

EP - 128

JO - Climate Research

JF - Climate Research

SN - 0936-577X

IS - 2

ER -