Open AccessAssimilation of Satellite Cloud Data into the GMAO Finite-Volume Data Assimilation System Using a Parameter Estimation Method. Part I: Motivation and Algorithm Description
Author(s) -
P. M. Norris,
Arlindo da Silva
Publication year - 2007
Publication title -
journal of the atmospheric sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.853
H-Index - 173
eISSN - 1520-0469
pISSN - 0022-4928
DOI - 10.1175/2006jas2046.1
Subject(s) - data assimilation , cloud computing , cloud cover , cloud fraction , meteorology , environmental science , satellite , computer science , radiative forcing , scale (ratio) , forcing (mathematics) , remote sensing , algorithm , climatology , geology , physics , aerosol , quantum mechanics , astronomy , operating system
General circulation models are unable to resolve subgrid-scale moisture variability and associated cloudiness and so must parameterize grid-scale cloud properties. This typically involves various empirical assumptions and a failure to capture the full range (synoptic, geographic, diurnal) of the subgrid-scale variability. A variational parameter estimation technique is employed to adjust empirical model cloud parameters in both space and time, in order to better represent assimilated International Satellite Cloud Climatology Project (ISCCP) cloud fraction and optical depth and Special Sensor Microwave Imager (SSM/I) liquid water path. The value of these adjustments is verified by much improved cloud radiative forcing and persistent improvement in cloud fraction forecasts.