U.S. Department of EnergyOffice of Scientific and Technical Information
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:
-
- ORNL
- Publication Date:
- DOE Contract Number:
- AC05-00OR22725
- Research Org.:
- Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US); ARM Data Center, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Collaborations:
- PNNL, BNL, ANL, ORNL
- Subject:
- 54 ENVIRONMENTAL SCIENCES; ARM; Cloud fraction; DOE.; Liquid water path; Longwave broadband downwelling irradiance; Longwave broadband upwelling irradiance; Precipitable water; Precipitation; Sensible heat flux; Shortwave broadband total downwelling irradiance; Shortwave broadband total upwelling irradiance; latent heat flux; surface air temperature; surface relative humidity
- 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., 1998.
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. 1998.
"ARM Diagnostics for Climate Model Evaluation". United States. doi:https://doi.org/10.5439/1646838. https://www.osti.gov/servlets/purl/1646838. Pub date:Thu Dec 31 23:00:00 EST 1998
@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 = {Thu Dec 31 23:00:00 EST 1998},
month = {Thu Dec 31 23:00:00 EST 1998}
}