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Title: Model performance in spatiotemporal patterns of precipitation: New methods for identifying value added by a regional climate model: Value added by a regional climate model

Abstract

Regional climate models (RCMs) are a standard tool for downscaling climate forecasts to finer spatial scales. The evaluation of RCMs against observational data is an important step in building confidence in the use of RCMs for future prediction. In addition to model performance in climatological means and marginal distributions, a model’s ability to capture spatio-temporal relationships is important. This study develops two approaches: (1) spatial correlation/variogram for a range of spatial lags, with total monthly precipitation and non-seasonal precipitation components used to assess the spatial variations of precipitation; and (2) spatio-temporal correlation for a wide range of distances, directions, and time lags, with daily precipitation occurrence used to detect the dynamic features of precipitation. These measures of spatial and spatio-temporal dependence are applied to a high-resolution RCM run and to the National Center for Environmental Prediction (NCEP)-U.S. Department of Energy (DOE) AMIP II reanalysis data (NCEP-R2), which provides initial and lateral boundary conditions for the RCM. The RCM performs better than NCEP-R2 in capturing both the spatial variations of total and non-seasonal precipitation components and the spatio-temporal correlations of daily precipitation occurrences, which are related to dynamic behaviors of precipitating systems. The improvements are apparent not just at resolutions finermore » than that of NCEP-R2, but also when the RCM and observational data are aggregated to the resolution of NCEP-R2.« less

Authors:
 [1];  [2];  [2]; ORCiD logo [1]
  1. Environmental Science Division, Argonne National Laboratory, Argonne Illinois USA
  2. Department of Statistics, University of Chicago, Chicago Illinois USA
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOD
OSTI Identifier:
1396259
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Geophysical Research: Atmospheres; Journal Volume: 120; Journal Issue: 4
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Wang, Jiali, Swati, F. N. U., Stein, Michael L., and Kotamarthi, V. Rao. Model performance in spatiotemporal patterns of precipitation: New methods for identifying value added by a regional climate model: Value added by a regional climate model. United States: N. p., 2015. Web. doi:10.1002/2014JD022434.
Wang, Jiali, Swati, F. N. U., Stein, Michael L., & Kotamarthi, V. Rao. Model performance in spatiotemporal patterns of precipitation: New methods for identifying value added by a regional climate model: Value added by a regional climate model. United States. doi:10.1002/2014JD022434.
Wang, Jiali, Swati, F. N. U., Stein, Michael L., and Kotamarthi, V. Rao. Wed . "Model performance in spatiotemporal patterns of precipitation: New methods for identifying value added by a regional climate model: Value added by a regional climate model". United States. doi:10.1002/2014JD022434.
@article{osti_1396259,
title = {Model performance in spatiotemporal patterns of precipitation: New methods for identifying value added by a regional climate model: Value added by a regional climate model},
author = {Wang, Jiali and Swati, F. N. U. and Stein, Michael L. and Kotamarthi, V. Rao},
abstractNote = {Regional climate models (RCMs) are a standard tool for downscaling climate forecasts to finer spatial scales. The evaluation of RCMs against observational data is an important step in building confidence in the use of RCMs for future prediction. In addition to model performance in climatological means and marginal distributions, a model’s ability to capture spatio-temporal relationships is important. This study develops two approaches: (1) spatial correlation/variogram for a range of spatial lags, with total monthly precipitation and non-seasonal precipitation components used to assess the spatial variations of precipitation; and (2) spatio-temporal correlation for a wide range of distances, directions, and time lags, with daily precipitation occurrence used to detect the dynamic features of precipitation. These measures of spatial and spatio-temporal dependence are applied to a high-resolution RCM run and to the National Center for Environmental Prediction (NCEP)-U.S. Department of Energy (DOE) AMIP II reanalysis data (NCEP-R2), which provides initial and lateral boundary conditions for the RCM. The RCM performs better than NCEP-R2 in capturing both the spatial variations of total and non-seasonal precipitation components and the spatio-temporal correlations of daily precipitation occurrences, which are related to dynamic behaviors of precipitating systems. The improvements are apparent not just at resolutions finer than that of NCEP-R2, but also when the RCM and observational data are aggregated to the resolution of NCEP-R2.},
doi = {10.1002/2014JD022434},
journal = {Journal of Geophysical Research: Atmospheres},
number = 4,
volume = 120,
place = {United States},
year = {Wed Feb 18 00:00:00 EST 2015},
month = {Wed Feb 18 00:00:00 EST 2015}
}