{"id":67,"date":"2018-04-24T13:51:36","date_gmt":"2018-04-24T12:51:36","guid":{"rendered":"https:\/\/research.reading.ac.uk\/met-darc\/?page_id=67"},"modified":"2022-11-07T16:33:56","modified_gmt":"2022-11-07T16:33:56","slug":"what-is-data-assimilation","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/met-darc\/aboutus\/what-is-data-assimilation\/","title":{"rendered":"What is data assimilation?"},"content":{"rendered":"

“Data assimilation is often thought of as a way of keeping a model ‘on the tracks’ by constantly correcting it with fresh observations.”<\/span><\/h3>\n

What is data assimilation?<\/h2>\n

Data assimilation<\/em> is the science of combining different sources of information to estimate possible states of a system as it evolves in time.\u00a0 Often only one state is estimated at a time, e.g. the most likely state, or the mean state. We call this state an analysis<\/em>.\u00a0 More generally data assimilation can determine an evolving probability density function, which specifies the range of possible states<\/em> and the probabilities that they represent reality.<\/p>\n

The information used comes from observations and a numerical model.\u00a0 Data assimilation can be applied to any classical system, but our focus is on geophysical systems like the Earth’s atmosphere, ocean, and land surface.\u00a0 Data assimilation has many names, depending on the field of application (e.g. state estimation<\/em>, history matching<\/em>, filtering<\/em>, smoothing<\/em>), and it is often combined with so-called inverse methods<\/em> to extract maximum information from observations.<\/p>\n

Why not simply use observations to estimate a system’s state?<\/h2>\n

In most situations, observations are sparse and are not made on a grid.\u00a0 All observations are imperfect versions of reality, and so will be in error.\u00a0 Furthermore some observations provide only indirect information, e.g. they may be made at different times to the required analysis and may measure different variables (e.g. photon counts instead of geophysical variables).<\/p>\n

Why not simply use models?<\/h2>\n

A free running model (that is without the influence from new observations) will drift from reality.\u00a0 This is due to modelling approximations, unknown processes acting, and uncertain initial conditions.\u00a0 These errors may not be a problem for some applications (e.g. in many climate studies if the model has a good average state), but they are a source of inaccuracy in applications like weather forecasting.\u00a0 For such problems, models on their own are unable to make realistic state estimates indefinitely through prediction, although they are still very useful.<\/p>\n

Data Assimilation<\/h2>\n

Data assimilation combines observations and models in a way that accounts for the uncertainties in each, while simultaneously respecting certain constraints.\u00a0 These include the laws of motion of the system through the model equations, and how the measurements physically relate to the system’s variables.\u00a0 In weather forecasting, recent weather observations are combined with today’s model forecast to obtain a complete picture of the atmosphere now in order to start a new forecast for the days ahead.\u00a0 Data assimilation is often thought of as a way of keeping a model ‘on the tracks’ by constantly correcting it with fresh observations.<\/p>\n

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