Update on LLNL ARM Value-Added Products and Tools for Cloud Modeling Studies
Authors
Shaocheng Xie — Lawrence Livermore National Laboratory
Shuaiqi Tang — Pacific Northwest National Laboratory
Qi Tang — Lawrence Livermore National Laboratory
Chengzhu Zhang — Lawrence Livermore National Laboratory
Yuying Zhang — Lawrence Livermore National Laboratory
Xiao Chen — Lawrence Livermore National Laboratory
Cheng Tao — Lawrence Livermore National Laboratory
Category
ARM infrastructure
Description
The goal of the Lawrence Livermore National Laboratory (LLNL) ARM Infrastructure Project is to provide necessary ARM Value Added Products (VAPs) and develop useful tools for cloud modeling studies. This poster provides an update on recent ARM infrastructure and research activities conducted by the LLNL ARM project. Specifically, we will report the progress on 1) ARM Best Estimate (ARMBE) Data developments; 2) exploring forcing strategies for capturing nocturnal propagating mesoscale convective systems at different locations over the Great Plains and for complex surface terrains; 3) developing new land-atmosphere coupling strength diagnostics using ARM surface/land data at SGP; 4) developing ARM metrics and diagnostics including an ARM radar simulator for cloud evaluation with ARM detailed cloud observations; 5) developing a machine learning framework for automatic ARM microwave radiometers (MWR) data contamination detection. Examples of using these data and tools in cloud/climate modeling studies will be given.
(This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-767647)
