Variational Data Assimilation for the Global Ocean - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 311

c

Temperature Prediction Error (C) 300 M Depth Temperature Prediction Error (C) 300 M Depth

<0 .2 .4 .6 .8 1 1.2> <0 .2 .4 .6 .8 1 1.2>

Temperature Prediction Error (C) 300 M Depth

<0 .2 .4 .6 .8 1

1.2>

Fig. 13.5 (continued)

with typical global data counts per day. All ocean observations are subject to data
quality control (QC) procedures prior to assimilation. The need for quality control
is fundamental to a data assimilation system. Accepting erroneous data can cause
an incorrect analysis, while rejecting extreme, but valid, data can miss important
events. The NCODA 3DVAR analysis was co-developed and is tightly coupled to
an ocean data QC system. Cummings ( 2011 ) provides an overview of the NCODA
ocean data quality control procedures.

13.4.1 Surface Observations

Tab le 13.1 indicates that there are many high volume sources of satellite and in situ
SST, SSH, and sea ice observations. It is not uncommon to assimilate 40 million

satellite SST retrievals, 2 million sea ice concentration retrievals, and 500;000