Development of a four‐dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations
By N. Sugiura, T. Awaji, S. Masuda, T. Mochizuki, T. Toyoda, T. Miyama, H. Igarashi, Y. Ishikawa. 2008
139 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change, Oceanography
View Paper: https://www.semanticscholar.org/paper/c5dd1e12374bfdf5945410bf7561537e34911b84
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
In Global and Planetary Change
- A. Köhl
- Shaoqing Zhang
- T. Delworth
- A. Rosati
- You‐Soon Chang
- Kristian Mogensen
- Dick Dee
- Peter A. E. M. Janssen
In Oceanography
- Kristian Mogensen
- M. Balmaseda
- A. Köhl
- Dick Dee
- Peter A. E. M. Janssen
- Zhengyu Liu
- Yun Liu
- A. Rosati
Citations in the corpus
- Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study* (2015, methodology): “The WCDA has been adopted in previous researches (Zhang et al. 2007; Sugiura et al. 2008; Saha et al. 2010), however, the study of the SCDA has remained in the exploration stage.”
- Collaboration of the Weather and Climate Communities to Advance Subseasonal-to-Seasonal Prediction (2010, unknown): “Recent attempts to build such a composite system use a two-way interaction model for the forecast step; however, they apply assimilation to each component of the Earth system separately (Stammer et al. 2002; Galanti et al. 2003; Sugiura et al. 2008).”
- An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation (2013, background): “1b) is much smaller than ones during the twentieth century, even though it is still consistent with the observational estimate of 0.73 ± 0.2 PW at 8 N (Talley 2003).”
- A coupled data assimilation system for climate reanalysis (2016, methodology): “The 4D-Var method assimilates ocean and atmospheric 10 day mean values and produces a sequence of 10 day mean states that can be used to initialize seasonal predictions (Sugiura et al., 2008).”
- Detailed source term estimation of the atmospheric release for the Fukushima Daiichi Nuclear Power Station accident by coupling simulations of an atmospheric dispersion model with an improved deposition scheme and oceanic dispersion model (2014, methodology): “SEA-GEARN-FDM uses the 10 day mean ocean current fields from the ocean–atmosphere global model K7 (Sugiura et al., 2008).”
- Source term estimation of atmospheric release due to the Fukushima Dai-ichi Nuclear Power Plant accident by atmospheric and oceanic dispersion simulations (2013, methodology): “SEA-GEARN used the 10-day mean ocean current, simulated by the coupled ocean–atmosphere global model K7 [17], as an input variable.”
- Evaluation of the GECCO2 ocean synthesis: transports of volume, heat and freshwater in the Atlantic (2015, methodology): “For the following iterations that covered the complete period 1948–2011, the strategy was changed again and the period was partitioned into five-year segments that include one year overlap at the end of each part, similar to the strategy of Sugiura et al. (2008).”