Impact of Atmospheric and Aerosol Optical Depth Observations on Aerosol Initial Conditions in a strongly-coupled data assimilation system
Abstract
Abstract. Strongly coupled data assimilation frameworks provide a mechanism for including additional information about aerosols through the coupling between aerosol and atmospheric variables, effectively utilizing atmospheric observations to change the aerosol analysis. Here, we investigate the impact of these observations on aerosol using the Maximum Likelihood Ensemble Filter (MLEF) algorithm with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) which includes the Godard Chemistry Aerosol Radiation and Transport (GOCART) module. We apply this methodology to a dust storm event over the Arabian Peninsula and examine in detail the error covariance and in particular the impact of atmospheric observations on improving the aerosol initial conditions. The assimilated observations include conventional atmospheric observations and Aerosol Optical Depth (AOD) retrievals. Results indicate a positive impact of using strongly coupled data assimilation and atmospheric observations on the aerosol initial conditions, quantified using Degrees of Freedom for Signal.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 15, 2019
- Source ID
- 10.5194/acp-2019-2
Entities
People
- Anton Kliewer
- Jun Wang
- Karina Apodaca
- Milija Županski
- Qijing Bian
- Samuel A. Atwood
- Steven D. Miller
- Ting‐Chi Wu
- Yi Wang
Organizations
- Office of Naval Research Global