Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: Yi Jin x
  • Journal of Atmospheric and Oceanic Technology x
  • Refine by Access: Content accessible to me x
Clear All Modify Search
Jin Wu
and
Yi Wei

Abstract

An optical-electronic technique has been developed for simultaneous, remote measurements of the surface tension and wave attenuation over the water surface. The technique has been fully tested in a laboratory tank, and tried in the field. Sample results of measurements are presented to illustrate that the technique is an effective method for studying the surfactants on the air–sea interface.

Full access
Yu-Chiao Liang
,
Matthew R. Mazloff
,
Isabella Rosso
,
Shih-Wei Fang
, and
Jin-Yi Yu

Abstract

The ability to construct nitrate maps in the Southern Ocean (SO) from sparse observations is important for marine biogeochemistry research, as it offers a geographical estimate of biological productivity. The goal of this study is to infer the skill of constructed SO nitrate maps using varying data sampling strategies. The mapping method uses multivariate empirical orthogonal functions (MEOFs) constructed from nitrate, salinity, and potential temperature (N-S-T) fields from a biogeochemical general circulation model simulation Synthetic N-S-T datasets are created by sampling modeled N-S-T fields in specific regions, determined either by random selection or by selecting regions over a certain threshold of nitrate temporal variances. The first 500 MEOF modes, determined by their capability to reconstruct the original N-S-T fields, are projected onto these synthetic N-S-T data to construct time-varying nitrate maps. Normalized root-mean-square errors (NRMSEs) are calculated between the constructed nitrate maps and the original modeled fields for different sampling strategies. The sampling strategy according to nitrate variances is shown to yield maps with lower NRMSEs than mapping adopting random sampling. A k-means cluster method that considers the N-S-T combined variances to identify key regions to insert data is most effective in reducing the mapping errors. These findings are further quantified by a series of mapping error analyses that also address the significance of data sampling density. The results provide a sampling framework to prioritize the deployment of biogeochemical Argo floats for constructing nitrate maps.

Full access