Generation of analysis and consistent error fields using the Data Interpolating Variational Analysis (Diva)

By C. Troupin, A. Barth, D. Sirjacobs, M. Ouberdous, J. Brankart, P. Brasseur, Michel Rixen, Aida Alvera-Azcárate, M. Belounis, A. Capet and 3 more. 2012

218 citations recorded by the source corpus.

Subfields: Atmospheric Science, Geophysics, Oceanography

View Paper: https://www.semanticscholar.org/paper/08ee87d50fb76d6b993b97b66c5f6c9fe5865b07

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Aida Alvera-Azcárate
2. C. Troupin
3. Jean-Marie Beckers
4. A. Barth
5. L. Vandenbulcke
6. Adrian Constantin

In Oceanography

1. Jean-Marie Beckers
2. Adrian Constantin

Citations in the corpus

• Marine Renewable Energy in the Mediterranean Sea: Status and Perspectives (2017, methodology): “The temperature data are obtained from the aggregated products of the Pan-European infrastructure for ocean and marine data management (SeaDataNet) [122], and analyzed by HCMR using the Data-Interpolating Variational Analysis (DIVA) software tool [123].”
• divand-1.0: n -dimensional variational data analysis for ocean observations (2013, methodology): “In the smoothing norm splines approach (Wahba and Wendelberger, 1980; Wahba, 1990; McIntosh, 1990; Brasseur and Haus, 1991; Brasseur et al., 1996; Troupin et al., 2012) a norm is defined using squares of derivatives up to a certain degree.”