| Literature DB >> 35664957 |
Patrick C Campbell1,2, Youhua Tang1,2, Pius Lee1, Barry Baker1, Daniel Tong1,2, Rick Saylor1, Ariel Stein1, Jianping Huang3,4, Ho-Chun Huang3,4, Edward Strobach3,4, Jeff McQueen3, Li Pan3,4, Ivanka Stajner3, Jamese Sims5, Jose Tirado-Delgado5,6, Youngsun Jung5, Fanglin Yang3, Tanya L Spero7, Robert C Gilliam7.
Abstract
A new dynamical core, known as the Finite-Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA's Global Forecast System (GFS) and in limited-area models for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecasting Capability (NAQFC) that will continue to protect human and ecosystem health in the US. Here we describe the development of the FV3GFSv16 coupling with a "state-of-the-science" CMAQ model version 5.3.1. The GFS-CMAQ coupling is made possible by the seminal version of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC), which became a major piece of the next operational NAQFC system (i.e., NACC-CMAQ) on 20 July 2021. NACC-CMAQ has a number of scientific advancements that include satellite-based data acquisition technology to improve land cover and soil characteristics and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ model has significantly different meteorological and chemical predictions compared to the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM2.5 predictions and diurnal patterns, both of which are extended to a 72 h (3 d) forecast with this system.Entities:
Year: 2022 PMID: 35664957 PMCID: PMC9157742 DOI: 10.5194/gmd-15-3281-2022
Source DB: PubMed Journal: Geosci Model Dev ISSN: 1991-959X Impact factor: 6.892