adcme > ARM Diagnostics for Climate Model EvaluationData Source Type(s) > PI

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: 


This data set serves as part of the ARM data-oriented diagnostics package (ARMDIAG) for climate model simulations. The data were prepared for facilitating the use of ARM data sets in climate model evaluation. The data were collected from multiple ARM instrument data streams and VAP data streams. Please refer to table 1 in the technical reports. The data sets will be updated as needed (i.e., update of original data stream, new data available, adding more sites). Detailed information about this data set is available in the technical report available at


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Data Details

Developed By Chengzhu Zhang | Shaocheng Xie
Contact Chengzhu Zhang
Resource(s) Data Directory
Data format netcdf
Site NSA
Content time range 1 January 1999 - 31 December 2011
Attribute accuracy No formal attribute accuracy tests were conducted.
Positional accuracy No formal positional accuracy tests were conducted.
Data Consistency and Completeness Data set is considered complete for the information presented, as described in the technical report.Users are advised to read the rest of the metadata record and technical report carefully for additional details.
Access Restriction No access constraints are associated with this data.
Use Restriction No use constraints are associated with this data.
Citations Our GitHub repository

Zhang, C., S. Xie, C. Tao, S. Tang, T. Emmenegger, J. D. Neelin, K. A. Schiro, W. Lin, and Z. Shaheen. "The ARM Data-oriented Metrics and Diagnostics Package for Climate Models-A New Tool for Evaluating Climate Models with Field Data." Bulletin of the American Meteorological Society (2020).

Zhang, Chengzhu, Shaocheng Xie, Stephen A. Klein, Hsi???yen Ma, Shuaiqi Tang, Kwinten Van Weverberg, Cyril J. Morcrette, and Jon Petch. "CAUSES: Diagnosis of the summertime warm bias in CMIP5 climate models at the ARM Southern Great Plains site." Journal of Geophysical Research: Atmospheres 123, no. 6 (2018): 2968-2992.

Presentation at ARM/ASR meeting 2020: "ARM Data-Oriented Diagnostics to Evaluate the Climate Model Simulation"


Leung L, D Bader, M Taylor, and R McCoy. 2020. "An Introduction to the E3SM Special Collection: Goals, Science Drivers, Development, and Analysis." Journal of Advances in Modeling Earth Systems, 12(11), e2019MS001821, 10.1029/2019MS001821.

Zhang C, S Xie, and C Tao. 2020. ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation. Ed. by Robert Stafford, ARM Climate Research Facility. DOE/SC-ARM-TR-202.

Zhang C, S Xie, C Tao, S Tang, T Emmenegger, J Neelin, K Schiro, W Lin, and Z Shaheen. 2020. "The ARM Data-oriented Metrics and Diagnostics Package for Climate Models - A New Tool for Evaluating Climate Models with Field Data." Bulletin of the American Meteorological Society, 101(10), 10.1175/BAMS-D-19-0282.1.
Research Highlight


Zhang C, S Xie, S Klein, H Ma, S Tang, K Van Weverberg, C Morcrette, and J Petch. 2018. "CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site." Journal of Geophysical Research: Atmospheres, 123(6), doi:10.1002/2017JD027200.
Research Highlight

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