A review of data assimilation techniques: Applications in engineering and agriculture

The technique of data assimilation has been comprehended for more than half a century, though there have been continuous additions in the methods and the fields of its applications. The primary objective of data assimilation is to optimally blend model with observations, so as to get the best possible output. This way, it caters to improving the forecast capability of any model. The maiden field was numerical weather prediction (in 1960s) and later on, the techniques were modified and utilized in other disciplines viz. geosciences, geomechanics, hydrology and even in agriculture. The goal in each of the fields would be different, however, this technique serves the common purpose of improving the performance. For instance, in meteorology/oceanography, better forecasts are obtained; in agriculture, the crop yield is better estimated. Major approaches to data assimilation include sequential and nonsequential data assimilation. Of the number of techniques available in each category, a technique may be chosen based upon the ultimate goal of the problem. The paper will open a landscape of the available techniques for data assimilation, along with their applications in various engineering fields, meteorology, oceanography and agriculture and discuss the limitations as well. The current study would serve as a beacon to a researcher, guiding which method to be used and the available resources in terms of software and data.Copyright (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference Additive Manufacturing and Advanced Materials-AM2 2021.