The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP - Part 2: Evaluation of aerosol optical thickness

  • Partha Sarathi Bhattacharjee
  • , Jun Wang
  • , Cheng Hsuan Lu
  • , Vijay Tallapragada

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

An accurate representation of aerosols in global numerical weather prediction (NWP) models is important to predict major air pollution events and to also understand aerosol effects on short-term weather forecasts. Recently the global aerosol forecast model at NOAA, the NOAA Environmental Modeling System (NEMS) GFS Aerosol Component (NGAC), was upgraded from its dust-only version 1 to include five species of aerosols (black carbon, organic carbon, sulfate, sea salt and dust). This latest upgrade, now called NGACv2, is an in-line aerosol forecast system providing three-dimensional aerosol mixing ratios along with aerosol optical properties, including aerosol optical thickness (AOT), every 3ĝ€h up to 5 days at global 1°ĝ€† × ĝ€†1° resolution. In this paper, we evaluated nearly 1.5 years of model AOT at 550ĝ€nm with available satellite retrievals, multi-model ensembles and surface observations over different aerosol regimes. Evaluation results show that NGACv2 has high correlations and low root mean square errors associated with African dust and also accurately represented the seasonal shift in aerosol plumes from Africa. Also, the model represented southern African and Canadian forest fires, dust from Asia, and AOT within the US with some degree of success. We have identified model underestimation for some of the aerosol regimes (particularly over Asia) and will investigate this further to improve the model forecast. The addition of a data assimilation capability to NGAC in the near future is expected to provide a positive impact in aerosol forecast by the model.

Original languageEnglish
Pages (from-to)2333-2351
Number of pages19
JournalGeoscientific Model Development
Volume11
Issue number6
DOIs
StatePublished - Jun 19 2018

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