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Ensemble Transform Sensitivity Method For Adaptive Observations


The Ensemble Transform (ET) method has been shown to be useful in providing guidance for adaptive observation deployment. It predicts forecast error variance reduction for each possible deployment using its corresponding transformation matrix in an ensemble subspace. In this paper, a new ET-based sensitivity (ETS) method, which calculates the gradient of forecast error variance reduction in terms of analysis error variance reduction, is proposed to specify regions for possible adaptive observations. ETS is a first order approximation of the ET; it requires just one calculation of a transformation matrix, increasing computational efficiency (60%–80% reduction in computational cost). An explicit mathematical formulation of the ETS gradient is derived and described. Both the ET and ETS methods are applied to the Hurricane Irene (2011) case and a heavy rainfall case for comparison. The numerical results imply that the sensitive areas estimated by the ETS and ET are similar. However, ETS is much more efficient, particularly when the resolution is higher and the number of ensemble members is larger.

Article / Publication Data
Available Metadata
Accepted On
June 15, 2015
Early Online Release
November 27, 2015
Fiscal Year
Peer Reviewed
Publication Name
Advances In Atmospheric Sciences
Published On
January 01, 2016
Print Volume
Print Number
Page Range
Submitted On
January 23, 2015


Authors who have authored or contributed to this publication.