Macro-physical Properties of Shallow Cumulus from Integrated ARM Observations: Development of a New Data Product for Model Evaluation
Poster PDF
Authors
Erin Allegra Riley — Lewis and Clark College
Jessica M Kleiss — Lewis and Clark College
Chuck N. Long (deceased) — NOAA- Earth System Research Laboratory
Laura Dian Riihimaki — CIRES | NOAA ESRL GML
Larry Berg — Pacific Northwest National Laboratory
Evgueni Kassianov — Pacific Northwest National Laboratory
Category
Warm low clouds, including aerosol interactions
Description
Information about cloud field inhomogeneity is required for better understanding differences between cloud statistics offered by model simulations and observations due to complications in comparing model grid-box results with data from vertically pointing instruments. Commonly, this valuable information about cloud inhomogeneity is obtained from high-resolution satellite images. Here, we introduce a simple approach for acquiring such information from high-resolution ground-based images. Our approach is based on Total Sky Imager (TSI) data with a wide field of view (FOV) and involves statistical analysis of spatial distribution of cloud cover over the full 160-degree FOV TSI image. We apply our approach to TSI data collected at the DOE ARM Southern Great Plains (SGP) site. These data are collected on days with single-layer shallow cumulus clouds and also involve those identified by the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) project and recent Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) campaign. There are two main objectives of our approach: (1) selection of days where spatial distribution of clouds over a given time period (e.g., 15-min) is statistically uniform, (2) comparison of macro-physical cloud properties, such as cloud cover and cloud aspect ratio (cloud thickness/cloud chord length), obtained from wide-FOV (TSI data) and narrow-FOV (ARSCL data) observations for the selected days. We iscuss the comparison results and their relevance for the expected model evaluations.
