The GEOS Retrospective Data Assimilation System: The 6-hour lag case
Yanqiu Zhu, Ricardo Todling, Jing Guoy, Stephen E. Cohn, I. Michael Navon, Yan Yang
The fixed-lag Kalman smoother (FLKS) has been proposed as the framework to construct data assimilation procedures capable of producing high-quality climate research datasets. The FLKS-based systems, referred to as retrospective data assimilation systems, are basically an extension to three-dimensional filtering procedures with the added capability of incorporating observations not only in the past and present time of the estimate, but also at future times. A variety of simplifications are necessary to render FLKS-based retrospective assimilation proce- dures practical.
In this article, we present an FLKS-based retrospective data assimilation system implemen- tation for the Goddard Earth Observing System Data Assimilation System (GEOSDAS). The practicality of this implementation comes from the practicality of its underlying (filter) analysis system, i.e., the physical-space statistical analysis system (PSAS). The behavior of two schemes are studied here. The first retrospective analysis (RA) scheme is designed to simply update the regular PSAS analyses with observations available at times ahead of the regular analysis times. Although our GEOSDAS implementation is general, results are only presented for when observations 6-hours ahead of the analysis time are used to update the PSAS analyses and cal- culate the so-called lag-1 retrospective analyses. Consistency tests for the RA scheme show that the lag-1 retrospective analyses indeed have better 6-hour predictive skills than the predictions from the regular analyses. This motivates the introduction of the second scheme which, at each analysis time, uses the 6-hour retrospective analysis to replace the first-guess normally used in the PSAS analysis, and therefore allows the calculation of a revised (filter) PSAS analysis. Since in this scheme the lag-1 retrospective analyses in uence the filter results, this procedure is referred to as the retrospective-based iterated analysis (RIA) scheme. Results from the RIA scheme indicate its potential for improving the overall features of the assimilation.