Developing Continuous SCM/CRM Forcing Using NWP Products Constrained by ARM Observations

Abstract

This study examines the feasibility of using numerical weather prediction (NWP) model products to replace radiosondes to develop long-term continuous forcing, instead of just intensive operational periods (IOPs), for single-column models (SCMs) and cloud-resolving models (CRMs). This is motivated by the need for long-term continuous forcing for statistical studies of SCMs/CRMs results. Studies show that SCMs/CRMs results are sensitive to their detailed initial conditions. One way to reduce this sensitivity is through statistical studies of SCMs/CRMs results so that one can filter out those uninteresting random errors and focus on those physically important systematic errors. The long-term forcing can be obtained directly from NWP model analyses, but the forcing is largely affected by model physical parameterizations that are used in the data analysis procedure. To reduce the problem, we propose a combined NWP data analysis and the Atmospheric Radiation Measurement (ARM) Program objective variational analysis approach to derive the long-term continuous forcing. In the combined system, NWP data provide vertical distribution of atmospheric state variables and the variational analysis is used to derive the required large-scale forcing data constrained by the ARM surface and top of the atmosphere (TOA) measurements. We conducted a preliminary study using data from the National Oceanic and Atmospheric Administration (NOAA) mesoscale model Rapid Update Cycle (RUC) analysis during the ARM SCM Summer 97 and Spring 2000 IOPs for this study. Results are compared with those from the ARM operational objective variational analysis (Zhang and Lin 1997; Zhang et al. 2001a,b) and those from the European Center for Medium-range Weather Forecasts (ECMWF) analysis. Impacts of the derived forcing on Community Climate Model Version 3 (CCM3) SCM are also analyzed.