A decadal climatology of atmospheric state at SGP

Description

Parameterizations in models attempt to statistically relate large-scale atmospheric variables such as temperature, humidity, and winds with variables that change on scales too small to resolve, such as cloud properties. We seek to study the observed relationships between such large- and small-scale variables through the creation of a set of atmospheric states and associated distributions of small-scale variables.

We use an iterative clustering technique developed by Marchand et al. (2006, 2009) to define a set of atmospheric states for a region surrounding the ARM Southern Great Plains (SGP) site. Atmospheric state in this context can be thought of as a frequently occurring regional weather pattern. We use 13 years of dynamic and thermodynamic variables from the ERA-Interim reanalysis as the input to a clustering algorithm to define the states and cloud occurrence data from the vertically pointing millimeter wavelength cloud radar at the SGP site to validate the statistical significance of each state. Once the states are defined, we classify the state of the atmosphere every six hours for the duration of the study, creating a time series of atmospheric state. This time series can be used as a basis for compositing cotemporaneous observations of interest and creating distributions of small-scale variables associated with each of the states. Distributions of both ground-based observations from the SGP site such as cloud occurrence, precipitation, liquid water path, and radiative fluxes as well as satellite-derived equivalents can be created in this fashion. Further, the long time series of state allows us to investigate the interannual variability of the occurrence of states, their duration, diurnal cycles of states, and the probability of any one state transitioning to another state. We present here a selection of the states, distributions, and cycles we find most interesting.