skip to main content
DOE Data Explorer title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: ARM Diagnostics for Climate Model Evaluation

Abstract

A Python-based diagnostics package is currently being developed by the ARM Infrastructure Team to facilitate the use of long-term high-frequency measurements from the ARM program in evaluating the regional climate simulation of clouds, radiation, and precipitation. This diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The CMIP model data sets are also included in the package to enable model inter-comparison. The ARM observational data constitute the core content of the diagnostics package. These data products include two types of data sets: 1. Observational data: we use long-term data sets available at SGP, NSA, and TWP  to build representative climatology. 2. CMIP5 climate model simulation data sets: these are auxiliary data sets for climate model evaluation. The Python-based diagnostics package is available at: https://github.com/ARM-DOE/arm-gcm-diagnostics 

Authors:
;
Publication Date:
DOE Contract Number:  
DE-AC05-00OR22725
Product Type:
Dataset
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Archive; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Subject:
54 Environmental Sciences
Keywords:
surface air temperature,Precipitation,Cloud fraction,surface relative humidity,Sensible heat flux,latent heat flux,Longwave broadband upwelling irradiance,Longwave broadband downwelling irradiance,Shortwave broadband total downwelling irradiance,Shortwave broadband total upwelling irradiance,Precipitable water,Liquid water path, ARM, DOE.
OSTI Identifier:
1646838
DOI:
https://doi.org/10.5439/1646838

Citation Formats

Zhang, Chengzhu, and Xie, Shaocheng. ARM Diagnostics for Climate Model Evaluation. United States: N. p., 1999. Web. doi:10.5439/1646838.
Zhang, Chengzhu, & Xie, Shaocheng. ARM Diagnostics for Climate Model Evaluation. United States. doi:https://doi.org/10.5439/1646838
Zhang, Chengzhu, and Xie, Shaocheng. 1999. "ARM Diagnostics for Climate Model Evaluation". United States. doi:https://doi.org/10.5439/1646838. https://www.osti.gov/servlets/purl/1646838. Pub date:Fri Jan 01 00:00:00 EST 1999
@article{osti_1646838,
title = {ARM Diagnostics for Climate Model Evaluation},
author = {Zhang, Chengzhu and Xie, Shaocheng},
abstractNote = {A Python-based diagnostics package is currently being developed by the ARM Infrastructure Team to facilitate the use of long-term high-frequency measurements from the ARM program in evaluating the regional climate simulation of clouds, radiation, and precipitation. This diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The CMIP model data sets are also included in the package to enable model inter-comparison. The ARM observational data constitute the core content of the diagnostics package. These data products include two types of data sets: 1. Observational data: we use long-term data sets available at SGP, NSA, and TWP  to build representative climatology. 2. CMIP5 climate model simulation data sets: these are auxiliary data sets for climate model evaluation. The Python-based diagnostics package is available at: https://github.com/ARM-DOE/arm-gcm-diagnostics },
doi = {10.5439/1646838},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1999},
month = {1}
}