Accession Number:

ADA610873

Title:

Time Series of SST Anomalies Off Western Africa

Descriptive Note:

Journal article

Corporate Author:

NAVAL RESEARCH LAB STENNIS DETACHMENT STENNIS SPACE CENTER MS OCEANOGRAPHY DIV

Report Date:

2014-09-09

Pagination or Media Count:

8.0

Abstract:

An assimilative ocean forecast model encompassing the southern tip of Africa is examined to evaluate the impact of alternate satellite data streams and to demonstrate the use of such forecast systems to understand the processes and evolution of regional ocean environments. Assimilative ocean forecast systems are a product of three pillars of oceanographic research models, observations, and data assimilation. A regional model on a 3-km grid portrays evolving conditions around the southern tip of Africa in response to boundary, atmospheric, and riverine inputs. It is guided by satellite observations, comparing its performance when provided NOAA 1819 AVHRR andor Suomi-NPP VIIRS SST. Guided by assimilation of these observations, a model provides one avenue to understand the balances and processes controlling the African ocean environment the degree to which such simulations correspond to reality is assessed in part by comparisons with independent ocean observations. In situ and remote observations provide irregularly distributed glimpses of the true ocean state. As in situ observations are fairly sparse in the region around southern Africa, particularly in real time, relatively greater reliance is placed upon satellite SST and other types of remote observations. A system of data assimilation uses the varied observations to guide the ocean forecasts, transforming the realistic ocean simulations into forecasts of likely conditions in the real ocean with accompanying estimates of forecast uncertainty. Assimilative ocean forecast around South Africa are evaluated from January to April 2014, investigating the impact of alternative SST data streams and reserving in situ observations of SST as an independent reference for validating the forecast ocean state.

Subject Categories:

  • Physical and Dynamic Oceanography
  • Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE