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@@ -158,7 +158,7 @@ The material in this Jupyter book is presented over five sections. The first sec
 The book was created by and as part of M2LInES, an international collaboration supported by Schmidt Futures, to improve climate models with scientific machine learning. The original goal for these notebooks in this Jupyter book was for our team to work together and learn from each other; in particular, to get up to speed on the key scientific aspects of our collaboration (parameterizations, machine learning, data assimilation, uncertainty quantification) and to develop new ideas. This was done as a series of tutorials, each of which was led by a few team members and occurred with a frequency of roughly once every 2 weeks for about 6-7 months. This Jupyter book is a collection of the notebooks used during these tutorials, which have only slightly been edited for continuity and clarity. Ultimately, we are happy to share these resources with the scientific community to introduce our research ideas and foster the use of machine learning techniques for tackling climate science problems.
 
 # Statement of Need
-Parameterization of sub-grid processes is a major challenge in climate science. The exact details of this problem are often very context dependent (@christensen2022parametrization), ...
+Parameterization of sub-grid processes is a major challenge in climate modeling. The details of this problem may often be very context dependent (@christensen2022parametrization), but much can be learned by addressing the issue in a general and simpler sense. Also, a general approach allows non-domain experts, e.g. machine learning researchers, to engage and contribute more meaningfully. This JupyterBook aims to achieve this target with the help of a simple dynamical system model - Lorenz 96, such that the reader is introduced to the basic concepts with minimal superfluous complexity. It is possible to extend the concepts that are presented here to other dynamical systems, and even to more complex parameterization tasks (some examples can be found at https://m2lines.github.io/publications/), and we hope that researchers and learners aiming to do this find the concepts presented here as a useful stepping stone in this pursuit.
 
 As described above, these notebooks were originally created to introduce non-domain experts to ideas from the parameterization aspects of climate modeling and how machine learning could be used to potentially address these. Now they have been adapted to act as a pedagogical tool for self-learning, be used as a reference manual, or for teaching some modules in an introductory class on machine learning.
 The book is organized in sections that are relatively independent; with the exception that the first section provides a general overview to the parameterization problem in climate models.