Abstract
The forecast sensitivity to observations (FSO) is embedded into a new optimization framework for improving the observation performance in atmospheric data assimilation. Key ingredients are introduced as follows: the innovation-weight parameterization of the analysis equation, the FSO-based evaluation of the forecast error gradient to parameters, a line search approach to optimization, and an efficient mechanism for step length specification. This methodology is tested in preliminary numerical experiments with the Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) and the U.S. Navy’s Global Environmental Model (NAVGEM) at a T425L60 resolution. The experimental setup relies on a verification state produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) to estimate the analysis and short-range forecast errors. Parameter tuning is implemented in a training stage valid for 1–14 April 2018 and aimed at improving the use of assimilated observations in reducing the initial-condition errors. Assessment is carried out for 15 April–31 May 2018 to investigate the performance of the weighted assimilation system in reducing the errors in analyses and 24-h model forecasts. In average, as compared with the control run and verified against the ECMWF analyses, the weighted approach provided 17.4% reduction in analysis errors and 3.1% reduction in 24-h forecast errors, measured in a dry total energy norm. Observation impacts are calculated to assess the use of various observation types in reducing the analysis and forecast errors. In particular, assimilation of satellite wind data is significantly improved through the innovation-weighting procedure.
Significance Statement
A new methodology is introduced to improve the information content of observations in numerical weather prediction (NWP). The computational procedure relies on observation sensitivity tools developed at all major NWP centers, and therefore, it appeals to a large audience for implementation and testing in practical applications. Our approach retains all available observations and provides a judicious optimization-based guidance to identify system deficiencies and improve the weighting assigned to various observing system components. The practical ability to implement this methodology is demonstrated in a computational environment that features all elements necessary for NWP applications. Preliminary results show that proper specification of the innovation weights can significantly improve the observation performance in reducing both the analysis errors and short-range forecast errors.
© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
