The Application of ARGO Data to the Global Ocean Data Assimilation Operational System of NCC - Acta Meteorologica Sinica Vol. 19 No. 3 (2005)

Sep 15, 2026

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This paper preliminarily examines the use of ARGO (Array for Real-time Geostrophic Oceanography) observations within the Global Ocean Data Assimilation System of the National Climate Center of China (NCC-GODAS). The study targets key sub-systems including data preprocessing, real-time wind stress calculation, variational analysis and interpolation, and an ocean dynamic model, with tailored adjustments to incorporate ARGO data. Numerical experiments using observations from 1981 to 2003, including ARGO data up to July 2003, show improved NCC-GODAS performance versus corresponding NCEP results in the Middle Pacific region.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

Introduction

Background: ocean role in climate and limited observations

ARGO program and its relevance

Principles and purpose of data assimilation

Algorithms of data assimilation and the choice of 3DV

Paper outline and structure

VOL. 19 NO. 3 ACTA METEOROLOGICA SINICA 2005 The Application of ARGO Data to the Global Ocean Data Assimilation Operational System of NCC * LIU Yimin 1 , ZHANG Renhe2 , YIN Yonghong2 , and NIU Tao2 1 National Climate Center of China, Beijing 100081 2 Chinese Academy of Meteorological Sciences, Beijing 100081 (Received November 2, 2003; revised September 16, 2004) ABSTRACT In this paper, we have preliminarily studied the application ofARGO (Array for Real-time Geostrophic Oceanography) data to the Global Ocean Data Assimilation System of National Climate Center of China (NCC-GODAS), which mainly contains 4 sub-systems such as data preprocessing, real-time wind stress calculating, variational analysis and interpolating, and ocean dynamic model. For the sake ofusing ARGO data, the relevant adjustment and improvement have been made at the corresponding aspects in the subsystems. Using the observation data from 1981 to 2003 including the ARGO data of2001 to July. 2003, we have performed a series of numerical experiments on this system. Comparing with the corresponding results of NCEP, It is illustrated that using ARGO data can improve the results of NCC-GODAS in the region of the Middle Pacific, for instance SST, SSTA (SST anomalies), Nino index, sea sub-surface temperature, etc. Furthermore, it is obtained that NCC-GODAS benefits from ARGO data in the other regions such as Atlantic Ocean, Indian Ocean, and extratropical Pacific Ocean much more than in the tropical Pacific. Key words: ARGO (Array for Real-time Geostrophic Oceanography) data, ocean data assimilation, dynamical ocean model, 3-dimensional variation, SST (sea suface temperature), Nino index 1. Introduction It is well-known that the state ofocean plays very important role in the climate change. But there is a paucity of the ocean observation data. The data distribution in the space, time and different components is very inhomogeneous, even in some areas, there are no any observation data. Hence, it brings some diffculties to the scientists to study many problems relevant to ocean. This situation has been being changed since ARGO (Array for Real-time Geostrophic Oceanography) plan was implemented. With ARGO plan’s further implementation and accumulation, it will play more and more important role in the relevant research fields. Though the time span of ARGO is short and the quantity of the data is small in some areas up to now, it is no doubt that it will be significant to study how to use these precious data in the operational system (Xu, 2002; Zhang et al., 2004). Data assimilation is very powerful in data anal- yses. When we analyze and diagnose some climate problems, the multi-year averaged field can be used, if its variability is weak. However, for the strong variability, the real signal may be missed by this method. Data assimilation is based on data statistic analyses and model dynamical evolution. Hence it can describe the variability to a certain extent. Meanwhile, there are unavoidable observation errors, we need data assimilation to correct them. Data assimilation can be used to supply reasonable initial fields to dynamical model. Because of model’s uncertainties and observation errors, they will result in the inaccuracy of performance of modeling. Data assimilation can estimate these errors and balance the errors of observation and model to some degree. Meanwhile, data assimilation can extrapolate information to some areas and some model components even without observation. In addition, the reasonable initial fields offered by data assimilation will make the different components of model in harmony. Therefore, a dynamical model system should have its own data *Supported by the National Natural Science Foundation of China under Grant No. 40231014. 356 ACTA METEOROLOGICA SINICA VOL.19 assimilation system. There are a variety of algorithms to approach the data assimilation problem (e.g., Daley, 1991; Bennett, 1992; Ide et al.,

FAQs

What is the main purpose of the study on ARGO data?

To evaluate how ARGO observations can be applied to the NCC-GODAS operational system and to identify necessary adjustments in its sub-systems.

Which NCC-GODAS sub-systems were modified to use ARGO data?

Data preprocessing, real-time wind stress calculation, variational analysis and interpolation, and the ocean dynamic model were adjusted to support ARGO data usage.

How do the numerical experiment results compare with NCEP?

Results indicate that using ARGO data improves NCC-GODAS outcomes in the Middle Pacific, including SST, SST anomalies, the Nino index, and sea sub-surface temperature.

Sep 15, 2026

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