Next Article in Journal
Identification of Seasonal Sub-Regions of the Drought in the North China Plain
Next Article in Special Issue
A Multivariate Balanced Initial Ensemble Generation Approach for an Atmospheric General Circulation Model
Previous Article in Journal
Water Loss Management in Small Municipalities: The Situation in Tyrol
Previous Article in Special Issue
Modelling Study of Transport Time Scales for a Hyper-Tidal Estuary
Article

Observation Strategies Based on Singular Value Decomposition for Ocean Analysis and Forecast

by 1,2 and 1,2,*
1
Institute of BioEconomy (IBE, C.N.R.), via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
2
LaMMA Consortium—Environmental Modelling and Monitoring Laboratory for Sustainable Development, via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
*
Author to whom correspondence should be addressed.
Water 2020, 12(12), 3445; https://doi.org/10.3390/w12123445
Received: 31 October 2020 / Revised: 2 December 2020 / Accepted: 3 December 2020 / Published: 8 December 2020
In this article, we discuss possible observing strategies for a simplified ocean model (Double Gyre (DG)), used as a preliminary tool to understand the observation needs for real analysis and forecasting systems. Observations are indeed fundamental to improve the quality of forecasts when data assimilation techniques are employed to obtain reliable analysis results. In addition, observation networks, particularly in situ observations, are expensive and require careful positioning of instruments. A possible strategy to locate observations is based on Singular Value Decomposition (SVD). SVD has many advantages when a variational assimilation method such as the 4D-Var is available, with its computation being dependent on the tangent linear and adjoint models. SVD is adopted as a method to identify areas where maximum error growth occurs and assimilating observations can give particular advantages. However, an SVD-based observation positioning strategy may not be optimal; thus, we introduce other criteria based on the correlation between points, as the information observed on neighboring locations can be redundant. These criteria are easily replicable in practical applications, as they require rather standard studies to obtain prior information. View Full-Text
Keywords: singular value decomposition; data assimilation; ocean models; observation strategies; ocean forecasting systems; ocean Double Gyre; 4D-Var; ROMS singular value decomposition; data assimilation; ocean models; observation strategies; ocean forecasting systems; ocean Double Gyre; 4D-Var; ROMS
Show Figures

Figure 1

MDPI and ACS Style

Fattorini, M.; Brandini, C. Observation Strategies Based on Singular Value Decomposition for Ocean Analysis and Forecast. Water 2020, 12, 3445. https://doi.org/10.3390/w12123445

AMA Style

Fattorini M, Brandini C. Observation Strategies Based on Singular Value Decomposition for Ocean Analysis and Forecast. Water. 2020; 12(12):3445. https://doi.org/10.3390/w12123445

Chicago/Turabian Style

Fattorini, Maria, and Carlo Brandini. 2020. "Observation Strategies Based on Singular Value Decomposition for Ocean Analysis and Forecast" Water 12, no. 12: 3445. https://doi.org/10.3390/w12123445

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop