A Region‐Optional Targeted Observation Method and Its Application in the Sea Surface Temperature Prediction Associated With the Indian Ocean Dipole

Abstract

AbstractAlthough data assimilation‐based targeted observation approaches have been widely used, obtaining optimal observation sites over a specific region for a prediction target remains a challenge. Hence, this study developed a more practical region‐optional targeted observation method by introducing a projection vector, allowing for the prediction targeted region to be different from the observation one. By minimizing the analysis error variance in a targeted region, the method identified optimal sites through a sequential assimilation framework. This region‐optional method was applied in the targeted observation study of the sea surface temperature (SST) prediction associated with Indian Ocean Dipole (IOD). The first 10 optimal observation sites were identified, with seven sites in the west IO, and three in the east. The results were further validated by conducting observation system simulation experiments using an ensemble adjustment Kalman filter assimilation system in the Community Earth System Model (CESM). The assimilation of observations from the 10 optimal sites was capable of reducing root mean squared errors (RMSEs) by 38.2% when assessing the SST across the IOD key regions, significantly more than the reduction from 10 optimal sites identified via the conventional method, or that from 10 random sites. This improvement was primarily due to the error reduction in the eastern IO, where SST RMSEs were reduced by >50%. The proposed region‐optional targeted observation method can seek optimal sites in any region of interest and is not confined to the targeted region as in conventional algorithms, thus providing a more reasonable method for designing optimal observation networks.