Multiscale Data Assimilation Forcing for LASSO
Poster PDF
Authors
Zhijin Li — University of California
Xiaoping Cheng — National University of Defense Technology
William I. Gustafson — Pacific Northwest National Laboratory
Heng Xiao — Pacific Northwest National Laboratory
Andrew M. Vogelmann — Brookhaven National Laboratory
Satoshi Endo — Brookhaven National Laboratory
Tami Fairless — Pacific Northwest National Laboratory
Category
ARM next generation – Megasite and LES activities
Description
Large-eddy simulation (LES) is a powerful tool for many applications and computational capacity now is sufficient for many users to easily apply LES for their research. The LES ARM Symbiotic Simulation and Observation (LASSO) project aims to provide routine LES of shallow convection cases over the Southern Great Plains megafacility. A successful LES simulation requires careful initialization and accurate large-scale forcing that represents the impact of lateral boundaries on the LES. However, the limited LES domain (~20 km) poses challenges when deriving the large-scale forcing. In support of LASSO, we have implemented a multiscale data assimilation (MSDA) system for producing data needed for LES initializations and the derivation of large-scale forcings. The MSDA has been implemented in the regional Weather Research and Forecasting (WRF) model at a cloud-resolving resolution (~1 km). It is designed to leverage existing analysis and reanalysis products (e.g., the NCEP North American Regional Reanalysis (NARR), the Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and the High-Resolution Rapid Refresh (HRRR). The MSDA focuses on taking advantage of ARM observations and high-resolution information from satellite radiances to constrain the spectra of spatial scales down to a few km. A variety of cases have shown that an ensemble of large-scale forcings derived from MSDA can generate the desired ensemble spread of LES simulations, with some of them accurately simulating observed shallow convection.
