New Tool Facilitates Evaluation of Models with ARM Data

Directly comparing modeled output to observations is often difficult; a new tool converts modeled cloud properties into the variables (radar reflectivity) actually observed by ARM radars to facilitate evaluation of Earth system models with observations.

The Science

Significant differences exist between how Earth system models represent clouds and what instruments directly observe, both in terms of spatial scale and the cloud properties.  For instance, models output variables such as cloud particle size, which are not directly observed while observations may be impacted by limitations in instrument sensitivity. To address these issues and conduct “apples-to-apples” comparisons between models and observations, this study developed an instrument simulator that converts model variables to the parameter that ground-based cloud radars operated by the Atmospheric Radiation Measurement (ARM) Facility more directly observe (radar reflectivity).

The Impact

The ARM radar simulator was developed within the framework of the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) to make it broadly applicable to the international modeling community. This work extends the capability of COSP, which to date has primarily focused on satellite data, to include the simulation of cloud radar reflectivities from ground-based radars that ARM operates at its research sites.  The ARM simulator will facilitate the comparison of modeled clouds with detailed ARM cloud observations, allowing evaluation of models with ARM observations to be done more routinely and providing insight into model performance.


In this study, the ARM radar simulator was applied to the DOE Energy, Exascale, and Earth System Model (E3SM) atmosphere model version 0 to evaluate its simulated clouds. One unique feature of ARM cloud radar observations is that the high temporal resolution allows examination of detailed cloud vertical structures over the diurnal cycle. The study found that, for non-precipitating clouds with radar reflectivities less than -20 dBZ, the model fails to capture the occurrence of shallow cumulus clouds that grow atop the daytime boundary layer. For precipitating hydrometeors, estimated by the occurrence of reflectivities larger than -20 dBZ, the model significantly overestimates clouds at all levels. Also, modeled frequency of precipitating clouds peaked in the afternoon around 4 PM LST, in contrast to the observed clouds, which peaked in frequency near local midnight.  Compared to previous evaluation of cloud property biases in models, the ARM simulator provides more detailed cloud information, which should help model developers better identify causes and solutions of these biases. Future work includes further improvements of data quality via uncertainty quantification and better calibration of ARM cloud radar data.  The team is also considering adding diurnal cycle metrics to the simulator for ease of use by the community.

Principal Investigator(s)

Yuying Zhang
Lawrence Livermore National Laboratory

Shaocheng Xie
Lawrence Livermore National Laboratory

Related Links


This research is supported by the US DOE ARM, Atmospheric System Research, Regional and Global Climate Modeling, and Earth System Modeling programs.


Zhang, Y., S. Xie, S. Klein, R. Marchand, P. Kollias, E. Clothiaux, W. Lin, K. Johnson, D. Swales, A. Bodas-Salcedo, S. Tang, J. Haynes, S. Collis, M. Jensen, N. Bharadwaj, J. Hardin, and B. Isom. 2017. “The ARM Cloud Radar Simulator for Global Climate Models: A New Tool for Bridging Field Data and Climate Models,” Bull. Amer. Meteor. Soc. doi: 10.1175/BAMS-D-16-0258.1.