An ARM data-oriented diagnostics package to evaluate the climate model simulation of clouds, precipitation, and radiation
Authors
Chengzhu Zhang — Lawrence Livermore National Laboratory
Shaocheng Xie — Lawrence Livermore National Laboratory
Stephen Klein — Lawrence Livermore National Laboratory
Hsi-Yen Ma — Lawrence Livermore National Laboratory
Yuying Zhang — Lawrence Livermore National Laboratory
Category
ARM infrastructure
Description
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, a python-based diagnostics package is developed at LLNL infrastructure team. This diagnostics package computes climatological means of targeted climate model simulation (i.e., ACME) and generates tables and plots for comparing the model simulation with ARM observational data.
Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include vertical profiles of clouds, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes and radiative fluxes. Process-oriented diagnostics focusing on individual cloud and precipitation-related phenomena (i.e., precipitation diurnal cycle) are developed for the evaluation and development of specific model physical parameterizations.
The basic directory structure of the diagnostics package and initial application of the package to ACME simulation and the CAUSES (Clouds Above the United States and Errors at the Surface) project will be demonstrated at the meeting.
