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Climatology or climate science is the scientific study of Earth's climate, typically defined as weather conditions averaged over a period of at least 30 years. This modern field of study is regarded as a branch of the atmospheric sciences and a subfield of physical geography, which is one of the Earth sciences. Climatology now includes aspects of oceanography and biogeochemistry.
For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.
The World Ocean Database Project, or WOD, is a project established by the Intergovernmental Oceanographic Commission (IOC). The project leader is Sydney Levitus who is director of the International Council for Science (ICSU) World Data Center (WDC) for Oceanography, Silver Spring. In recognition of the success of the IOC Global Oceanographic Data Archaeological and Rescue Project, a proposal was presented at the 16th Session of the Committee on International Oceanographic Data and Information Exchange (IODE), which was held in Lisbon, Portugal, in October–November 2000, to establish the World Ocean Database Project. This project is intended to stimulate international exchange of modern oceanographic data and encourage the development of regional oceanographic databases as well as the implementation of regional quality control procedures. This new Project was endorsed by the IODE at the conclusion of the Portugal meeting, and the IOC subsequently approved this project in June 2001.
Data assimilation is a mathematical discipline that seeks to optimally combine theory with observations. There may be a number of different goals sought – for example, to determine the optimal state estimate of a system, to determine initial conditions for a numerical forecast model, to interpolate sparse observation data using knowledge of the system being observed, to set numerical parameters based on training a model from observed data. Depending on the goal, different solution methods may be used. Data assimilation is distinguished from other forms of machine learning, image analysis, and statistical methods in that it utilizes a dynamical model of the system being analyzed.
A tropical cyclone forecast model is a computer program that uses meteorological data to forecast aspects of the future state of tropical cyclones. There are three types of models: statistical, dynamical, or combined statistical-dynamic. Dynamical models utilize powerful supercomputers with sophisticated mathematical modeling software and meteorological data to calculate future weather conditions. Statistical models forecast the evolution of a tropical cyclone in a simpler manner, by extrapolating from historical datasets, and thus can be run quickly on platforms such as personal computers. Statistical-dynamical models use aspects of both types of forecasting. Four primary types of forecasts exist for tropical cyclones: track, intensity, storm surge, and rainfall. Dynamical models were not developed until the 1970s and the 1980s, with earlier efforts focused on the storm surge problem.
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In fluid dynamics, wind wave modeling describes the effort to depict the sea state and predict the evolution of the energy of wind waves using numerical techniques. These simulations consider atmospheric wind forcing, nonlinear wave interactions, and frictional dissipation, and they output statistics describing wave heights, periods, and propagation directions for regional seas or global oceans. Such wave hindcasts and wave forecasts are extremely important for commercial interests on the high seas. For example, the shipping industry requires guidance for operational planning and tactical seakeeping purposes.
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The neutral density or empirical neutral density is a density variable used in oceanography, introduced in 1997 by David R. Jackett and Trevor McDougall. It is a function of the three state variables and the geographical location. It has the typical units of density (M/V). Isosurfaces of form “neutral density surfaces”, which are closely aligned with the "neutral tangent plane". It is widely believed, although this has yet to be rigorously proven, that the flow in the deep ocean is almost entirely aligned with the neutral tangent plane, and strong lateral mixing occurs along this plane vs weak mixing across this plane . These surfaces are widely used in water mass analyses. Neutral density is a density variable that depends on the particular state of the ocean, and hence is also a function of time, though this is often ignored. In practice, its construction from a given hydrographic dataset is achieved by means of a computational code, that contains the computational algorithm developed by Jackett and McDougall. Use of this code is currently restricted to the present day ocean.
The Simple Ocean Data Assimilation (SODA) analysis is an oceanic reanalysis data set consisting of gridded state variables for the global ocean, as well as several derived fields. SODA was developed in the 1990s as a collaborative project between the Department of Atmospheric and Oceanic Science at the University of Maryland and the Department of Oceanography at Texas A&M University with the goal of providing an improved estimate of ocean state from those based solely on observations or numerical simulations. Since its first release there have been several updates, the most recent of which extends from 1958-2008, as well as a “beta release” of a long-term reanalysis for 1871-2008.
DIVA allows the spatial interpolation/gridding of data (analysis) in an optimal way, comparable to optimal interpolation (OI), taking into account uncertainties on observations. In comparison to standard OI, used in Data assimilation, DIVA, when applied to ocean data, takes into account coastlines, sub-basins and advection because of its variational formulation on the real domain. Calculations are highly optimized and rely on a finite element resolution. Tools to generate the finite element mesh are provided as well as tools to optimize the parameters of the analysis. Quality control of data can be performed and error fields can be calculated. Also detrending of data is possible. Finally 3D and 4D extensions are included with emphasis on direct computations of climatologies from ODV spreadsheet files.
References
- 1 2 Carton, J.A., and A. Santorelli, 2008: Global upper ocean heat content as viewed in nine analyses, J. Clim., 21, 6015–6035.
- ↑ Hunt, B.R., Kostelich E.J., Szunyogh, I. Efficient Data Assimilation for Spatiotemporal Chaos: A Local Ensemble Transform Kalman Filter. arXiv:physics/0511236 v1 28 Nov 2005. Dated May 24, 2006.
