Evaluating Mesoscale Model Predictions of Clouds and Radiation with SGP ARM Data over a Seasonal Timescale

Abstract

This study evaluates the predictions of radiative and cloud-related processes of the fifth-generation Pennsylvania State University?National Center for Atmospheric Research (PSU?NCAR) Mesoscale Model (MM5). It is based on extensive comparison of three-dimensional forecast runs with local data from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site collected at the Central Facility in Lamont, Oklahoma, over a seasonal timescale. Time series are built from simulations performed every day from 15 April to 23 June 1998 with a 10-km horizontal resolution. For the one single column centered on this site, a reasonable agreement is found between observed and simulated precipitation and surface fields time series. Indeed, the model is able to reproduce the timing and vertical extent of most major cloudy events, as revealed by radiative flux measurements, radar, and lidar data. The model encounters more difficulty with the prediction of cirrus and shallow clouds whereas deeper and long-lasting systems are much better captured. Day-to-day fluctuations of surface radiative fluxes, mostly explained by cloud cover changes, are similar in simulations and observations. Nevertheless, systematic differences have been identified. The downward longwave flux is overestimated under moist clear sky conditions. It is shown that the bias disappears with more sophisticated parameterizations such as Rapid Radiative Transfer Model (RRTM) and Community Climate Model, version 2 (CCM2) radiation schemes. The radiative impact of aerosols, not taken into account by the model, explains some of the discrepancies found under clear sky conditions. The differences, small compared to the short timescale variability, can reach up to 30 W m?2 on a 24-h timescale. Overall, these results contribute to strengthen confidence in the realism of mesoscale forecast simulations. They also point out model weaknesses that may affect regional climate simulations: representation of low clouds, cirrus, and aerosols. Yet, the results suggest that these finescale simulations are appropriate for investigating parameterizations of cloud microphysics and radiative properties, as cloud timing and vertical extension are both reasonably captured.