Breakout Summary Report
ARM/ASR User and PI Meeting
13 - 17 March 2017
13 March 2017
1:30 PM - 3:30 PM
60
Shaocheng Xie
Breakout Description
The goals of the session are to share with the modeling community information about the Atmospheric Radiation Measurement (ARM) Climate Research Facility data and tools that are (or are being) developed specifically for cloud modeling and global climate models (GCMs), coordinate relevant activities, and get feedback from the community.Main Discussion
The session includes a few invited short presentations that introduce some modeling-related data and tools that are being developed by ARM, Atmospheric System Research (ASR), and Climate Model Development and Validation science and data development teams. It started with a general overview by Jennifer Comstock on modeling-related data value-added products produced by the ARM infrastructure teams, including ARM best-estimate data products, large-scale forcing data, merged sounds, cloud type classification, and cloud properties data. Minghua Zhang then discussed his plan for developing high-resolution dynamical and thermodynamic fields over 3–4 km resolution through the operational Weather Research and Forecasting Gridpoint Statistical Interpolation data assimilation system with the variational constraints. Implementing observed surface constraints to conserve mass, energy, and moisture into the Weather Research and Forecasting Gridpoint Statistical Interpolation represents a significant improvement to current data assimilation systems and could make the analyzed data more suitable for process studies.
The LES ARM Symbiotic Simulation and Observation (LASSO) team made a significant effort to generate LES (large-eddy simulation) output fields and build a library of simulations with observations for studying shallow convection. At the breakout, Andy Vogelmann provided more details about the LASSO data bundle and model evaluation metrics that provide information to guide users of the LASSO data. The LASSO metrics can be applied to other large-eddy simulations. This session also discussed the plan for developing detailed cloud and rain properties and convective-scale winds from remote sensing or aircraft measurements for selected periods through the newly funded Climate Model Development and Validation projects (Andy Vogelmann, Scott Collis, and Scott Giangrande).
The modeling tools discussed in this session include the ARM metrics and diagnostics package and ARM cloud simulators for GCMs and cloud-resolving models (Chengzhu Zhang, Yuying Zhang, and Marilo Oue). These tools are used to facilitate use of ARM data in the modeling community and improve model-observation comparison. The plan is being discussed to extend the current ARM metrics package with process-oriented diagnostics and integrate the ARM diagnostics package into other community metrics packages such as those being developed in the Program for Climate Model Diagnosis and Intercomparison and ACME. Collaboration with the science community on developing process-oriented diagnostics is particularly needed. An ongoing effort is to collaborate with the UCLA group led by David Neelin on incorporating their diagnostics on convection onset into the ARM diagnostics. The ARM cloud radar simulator team is actively looking for collaboration with modeling centers around the world to apply the ARM cloud radar simulator to their cloud evaluations with ARM data. A lot of progress has been made in applying the Cloud-Resolving Model Radar Simulator to cloud-resolving models and various microphysical parameterizations. The Cloud-Resolving Model Radar Simulator has addressed some limitations of the ARM cloud radars in detecting cirrus clouds by developing and integrating an ARM micropulse lidar simulator and ceilometer simulator into the package.
Key Findings
This session highlighted the importance of communication between modeling and observation communities and coordination across projects. There was a strong demand for uncertainty information for ARM observational data products. Instrument simulators are useful tools for model evaluation. However, it is important to realize potential issues with the use of cloud simulators due to the uncertainty in assuming a subgrid distribution of precipitation and particle size distribution of hydrometeors in the simulators when they are applied to GCMs. Using simulators in cloud evaluation is a complement to the evaluation of model clouds with cloud retrievals. There is a need to continue to improve the quality of retrieved cloud products and quantify their uncertainties. The session also highlighted the challenge for ARM to implement efficient ways to deliver large volumes of high-resolution 3-D data products that are being developed from large-eddy simulation and high-resolution data assimilation systems (e.g., Weather Research and Forecasting Gridpoint Statistical Interpolation) to the user community.Needs
- Uncertainty in both ARM data and simulators needs to be better quantified and described.
- Strategy to deliver high-volume data products needs to be explored.