Inferring atmospheric dynamics from aerosol observations in 4D‐Var. (18th February 2020)

• Record Type: Journal Article
• Authors: Zaplotnik, Žiga; Žagar, Nedjeljka; Benedetti, Angela; Semane, Noureddine
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 146:Number 728(2020)
• Page Start: 1403
• Page End: 1422
• Publication Date: 2020-02-18
• Subjects: aerosols -- background‐error model -- data assimilation -- humidity -- tracing -- wind extraction -- 4D‐Var; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3743
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract: This article explores the potential of aerosol observations to provide wind information in four‐dimensional variational data assimilation (4D‐Var). It is shown that the relative horizontal gradients, crucial for wind extraction from tracers, are on average greater for the aerosol mixing ratio than for the specific humidity, observations of which are known to provide significant information on the wind field. The potential of aerosols to infer atmospheric dynamics is investigated in the Tropics, where the wind information is most critical. An intermediate‐complexity incremental 4D‐Var system, the Moist Atmosphere Dynamics Data Assimilation Model (MADDAM), has been developed, with a forecast model that simulates most dominant processes involving moisture, aerosols, and dynamics: nonlinear advection, condensation, and wet deposition. The results of 4D‐Var experiments reveal a detrimental impact of saturation‐related nonlinearities and aerosol wet deposition on the extraction of wind from aerosol data. Fraternal‐twin experiments show about 30% smaller impact of aerosol data on the wind analysis compared with humidity data, mainly due to the greater aerosol observation error and suboptimal background‐error covariance model. However, the assimilation of aerosol data together with temperature and humidity observations shows significant added value for wind analyses. Abstract : In operational numerical weather prediction, humidity data already provide significant wind information to the initial conditions through 4D‐Var adjustment. Given the increasing amount of aerosol vertical profiles and relatively greater horizontal gradients of the aerosol mixing ratio compared with specific humidity fields, we ask whether aerosols have similar potential to infer winds as humidity data.