Evaluation of High-Level Clouds in Cloud Resolving Model Simulations with ARM and KWAJEX Observations

Abstract

The objective of this research is to evaluate the performance of microphysical parameterizations in cloud resolving model (CRM). The goal is to understand the reasons for differences between simulated cloud statistics and observations. I focus on simulations of two convective case studies. The first case is the Atmospheric Radiation Measurement (ARM) Program 1997 summer Intensive Observation Periods (IOP) over its Southern Great Plains (SGP) site (ARM9707 hereafter). The second case is the Kwajalein Experiment (KWAJEX) around the Kwajalein island in the tropical Pacific region. Simulated cloud occurrence and histograms by reflectivity and height are compared with radar and satellite observations obtained during these two cases. For ARM9707, double moment microphysics generates much more high–level cloud than observations and the simulated OLR is underestimated by both single and double moment microphysics. When running the CRM in a “forecast” mode, cloud occurrence and OLR come to a better agreement with observations, especially during relatively dry periods after precipitation events. Sensitivity tests with double moment microphysics show small changes to cloud occurrence compared to model ensemble spread and the bias with respect to observations. Experiments with water vapor nudging and the “forecast” runs demonstrate the periodic lateral boundary condition is the primary reason for the cloud occurrence overestimation in the month–long simulations. For KWAJEX, neither nudging on thermodynamics nor the “forecast” runs can show the impact of the periodic lateral boundary condition due to a much moister environment than at the ARM SGP site. I find that double moment microphyscis slightly underestimates high–level cloud cover and single moment microphysics generates less than half of the high–level cloud cover as is shown by ISCCP D1 cloud amount observations. These underestimations in high–level cloud cover may be related to the large scale forcing errors, especially in the upper troposphere.