Volume 146, Issue 728 p. 1403-1422
RESEARCH ARTICLE

Inferring atmospheric dynamics from aerosol observations in 4D-Var

Žiga Zaplotnik

Corresponding Author

Žiga Zaplotnik

Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia

Correspondence

Ž. Zaplotnik, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana 1000, Slovenia.

Email: [email protected]

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Nedjeljka Žagar

Nedjeljka Žagar

Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia

CEN, Meteorologisches Institut, Universität Hamburg, Hamburg, Germany

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Angela BenedettiNoureddine Semane

Noureddine Semane

ECMWF, Reading, UK

Ecole Hassania des Travaux Publics, Casablanca, Morocco

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First published: 10 January 2020
Citations: 2

Funding information: ESA PECS,4000106739/12/NL/KML

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.

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