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ARMBE

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armbe > ARM Best Estimate Data ProductsVAP Type(s) > Baseline • Evaluation

This data product, formerly known as the Climate Modeling Best Estimate (CMBE) data set, was created to encourage greater use of ARM data in climate studies and model development by the climate community. It assembles a best estimate of cloud, radiation, and atmospheric quantities, and surface/land properties that are both well observed by ARM and commonly used in model evaluation into one single data set. For over two decades, most of these quantities have been measured by ARM ground-based instruments, including cloud radars, micropulse lidars (MPLs), laser ceilometers, microwave radiometers (MWRs), and solar and infrared radiation stations (SIRSs). The ARMBE data product is specifically tailored to climate modelers for use in the evaluation of global climate models, with hourly temporal resolution comparable to a typical resolution used in climate model output. It also includes standard deviations within the averaged hour and quality control flags for the selected quantities to indicate the temporal variability and data quality.

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ARMBE provides detailed observations of clouds and their associated large-scale environment in different climate regimes and is widely applied in process studies to improve cloud parameterization in climate models. Additionally, the long-term ARMBE data set of cloud observations from ground-based radars and lidars, with hourly resolution and high vertical resolution, makes it a great fit to examine climate variability and change, as well as to statistically evaluate climate models. The ARMBE data product also serves as input to many ARM value-added products (VAPs), such as ARM Diagnostics for Climate Model Evaluation (ADCME) and the large-scale forcing data from constrained variational analysis (VARANAL).

The ARMBE data product includes ARMBEATM and ARMBECLDRAD. ARMBEATM contains basic atmospheric quantities, and ARMBECLDRAD provides cloud and radiation quantities.

ARMBE represents single-point measurements. Statistics are recommended for comparison with climate model output, which often represents a grid-box mean of 100 kilometers. In addition, although stringent quality control checks are applied, ARMBE still experiences most data quality issues in the VAPs that are used to produce ARMBE. These include Active Remote Sensing of CLouds (ARSCL), Data Quality Assessment for ARM Radiation Data (QCRAD), and Microwave Radiometer Retrievals (MWRRET).

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Data Details

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Resource(s) Data Directory
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Content time range 1 January 1994 - 14 June 2021

2022

Giangrande S, J Comstock, S Collis, J Shilling, K Gaustad, K Kehoe, S Xie, and D Zhang. 2022. Translator Plan: A Coordinated Vision for Fiscal Years 2023-2025. Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-22-003. 10.2172/1893730.

Emmenegger T, Y Kuo, S Xie, C Zhang, C Tao, and J Neelin. 2022. "Evaluating Tropical Precipitation Relations in CMIP6 Models with ARM data." Journal of Climate, 35(19), 10.1175/JCLI-D-21-0386.1.

Leung L, W Boos, J Catto, C A. DeMott, G Martin, J Neelin, T O’Brien, S Xie, Z Feng, N Klingaman, Y Kuo, R Lee, C Martinez-Villalobos, S Vishnu, M Priestley, C Tao, and Y Zhou. 2022. "Exploratory Precipitation Metrics: Spatiotemporal Characteristics, Process-Oriented, and Phenomena-Based." Journal of Climate, 35(12), 10.1175/JCLI-D-21-0590.1.

2021

Dai N, R Kramer, B Soden, and T L’Ecuyer. 2021. "Evaluation of CloudSat Radiative Kernels Using ARM and CERES Observations and ERA5 Reanalysis." Journal of Geophysical Research: Atmospheres, 126(23), e2021JD034510, 10.1029/2020JD034510.

Lawston-Parker P, J Santanello, and S Kumar. 2021. "Understanding the Impacts of Land Surface and PBL Observations on the Terrestrial and Atmospheric Legs of Land–Atmosphere Coupling." Journal of Hydrometeorology, 22(9), 10.1175/JHM-D-20-0263.1.


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Shaocheng Xie
Translator
Lawrence Livermore National Laboratory

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