Data-Interpolating Variational Analysis (DIVA) Method for the Generation of Argo Data Gridded Products

Jha, Ravi Kumar and Udaya Bhaskar, TVS (2020) Data-Interpolating Variational Analysis (DIVA) Method for the Generation of Argo Data Gridded Products. Technical Report. INCOIS, Hyderabad.

PDF ESSO-INCOIS-TPG-TR-01-2020.pdf - Published Version Download (8MB)

Abstract

In oceanography, a typical problem consists of determining a parameter on a regular grid of positions while knowing a set of data in locations that are randomly placed both in space and time. This is typically called the gridding problem and is useful for many applications such as data analysis, graphical display, forcing or initialization of a model. In this study temperature and salinity profiles, data obtained from Argo profiling floats were used and gridded data were generated. Data-Interpolating Variational Analysis (DIVA) method was chosen for generating the gridded product. Extensive analysis was done for obtaining the optimal correlation length L and signal-to-noise ratio λ for generating value-added products from the raw temperature and salinity. Gridded data obtained with a different choice of L and λ were validated with a set of hidden data that was not used for gridding and also with the data obtained from OMNI subsurface data sets. With the optimal estimates of L and λ, Argo gridded data set from 2004 to present will be produced from which a host of other derived parameters are obtained and made available on INCOIS Live Access Server

Actions (login required)

View Item