New Development of a Three-dimensional Constrained Variational Algorithm to Derive ARM Model Forcing Data for Multiple Columns

 
Poster PDF

Authors

Minghua Zhang — Stony Brook University
Shuaiqi Tang — Pacific Northwest National Laboratory
Shaocheng Xie — Lawrence Livermore National Laboratory

Category

ARM next generation – Megasite and LES activities

Description

Large-scale forcing data (including atmospheric vertical velocities and advective tendencies) are required for process models of clouds and precipitation when their results need to be compared with observations. We present the new development of a three-dimensional ARM constrained variational analysis algorithm of large-scale forcing data along with other improvements at 0.5ox0.5o degree resolution by extending the original ARM constrained variational method for a single-column. The derived data satisfy—simultaneously for all horizontal grids—the vertical integrated conservations of mass, water vapor and energy as measured by surface and TOA measurements from ARM and other sources. These data can be used to force models at different spatial resolutions. They provide many more samples of atmospheric columns to test models. They also provide three-dimensional fields of diabatic heating (Q1) and moisture sink (Q2) that are coupled with atmospheric dynamics. Results are presented for the ARM March 2000 Cloud IOP at the SGP along with evaluations and comparison with diagnostics in six reanalysis products (ERA-Interim, NCEP CFSR, MERRA, JRA55, NARR, and RUC).