Assessment of the SCM/CRM Forcing Data Derived from North American Regional Reanalysis

Shaocheng Xie Lawrence Livermore National Laboratory
Yunyan Zhang Lawrence Livermore National Laboratory
Stephen Klein Lawrence Livermore National Laboratory
Aaron Kennedy University of North Dakota
Xiquan Dong University of North Dakota
Minghua Zhang Stony Brook University

Category: Modeling

Working Group: Cloud Life Cycle

One concern about using the large-scale forcing data (i.e., vertical velocity and advective tendencies) obtained through data assimilation to drive single-column models (SCMs) and cloud-resolving models (CRMs) is that the forcing data themselves are affected by deficiencies of the model physical parameterizations used in generating the data. As shown in earlier studies, errors in such forcing are particularly large over periods where surface precipitation is not well simulated by the forecast model used in data assimilation. The quality of analysis data has been improved in recent years with improvements in data assimilation techniques and forecast models, as well as more observations (including precipitation) being assimilated. This has motivated us to revisit the issue on the suitability of the analysis forcing for SCM/CRM studies by examining the quality of the latest analysis data. In this study, we assessed the quality of the large-scale forcing diagnosed from the North American Regional Reanalysis (NARR), which has successfully assimilated precipitation into its data assimilation system. The assessment was performed by comparing the NARR forcing with the ARM observed forcing and the ARM 1999–2001 three-year continuous forcing, which were respectively derived from ARM sounding measurements and the NOAA’s Rapid Update Cycle (RUC) analyses constrained with surface and top-of-the-atmosphere (TOA) observations through a constrained variational analysis method. We compared the forcing fields and relevant surface and TOA fluxes between ARM and NARR over both wet and dry periods. We also compared the correlation between the ARM-observed cloud fraction and the vertical velocity field obtained from these forcing data sets. Since cloud fraction is not a constraint used in both the variational analysis and the NARR, this correlation provides an independent check on the quality of these forcing data sets. Detailed results from this study will be discussed in the meeting.

This poster will be displayed at ASR Science Team Meeting.