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Advances in Meteorology
Volume 2016 (2016), Article ID 1905076, 20 pages
http://dx.doi.org/10.1155/2016/1905076
Research Article

Retrieval of Land Surface Model State Variables through Assimilating Screen Level Humidity and Temperature Measurements

1Department of Physics and Astronomy, Curtin University, Perth, WA 6845, Australia
2School of Meteorology, University of Oklahoma, Norman, OK 73072, USA

Received 9 November 2015; Accepted 28 April 2016

Academic Editor: Francois Counillon

Copyright © 2016 Diandong Ren and Ming Xue. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study demonstrates successful variational retrieval of land surface states by assimilating screen level atmospheric measurements of specific humidity and air temperature. To this end, the land surface scheme is first validated against the Oklahoma Atmospheric Surface Layer Instrumentation System measurements with necessary refinements to the forward model implemented. The retrieval scheme involves a 1D land surface-atmosphere model, the corresponding adjoint codes, and a cost function that measures residuals between observed and modeled screen level atmospheric temperature and specific humidity. The retrieval scheme is robust when subjected to observational errors with magnitudes comparable to instrument accuracy and for initial guess errors larger than typical model forecast errors. Using varying assimilation window lengths centered on different periods of a day, the sampling strategy is assessed. The daytime observations are more informative compared to nocturnal observations. An assimilation window as narrow as four hours, if centered on local noon, contains comparable information to an expanded window covering the whole day. There exists an optimal assimilation window length resulting from the contest between degrading forecast accuracy and increasing information content. For an assimilation window less than two days, the “optimal” assimilation window length is inversely proportional to the data ingesting frequency.