The authors significantly improved the Introduction and the Methods part, although some of the newly added text are not entirely clear (see below, detailed comments). My concern about the applicability of the proposed approach is not resolved. The authors significantly revised the relevant section 3.2, improving its readability, but preserved the previously presented example, which is too synthetic in terms of its simplicity and data density, failing therefore to make a convincing case (see below, the specific comments 1-2). Finally, my experience with the software was not entirely convincing either (specific comment 3). I still can not support publication of this work in GMD in its current form. I recommend the authors either to make a convincing case of the applicability of their approach with a non-synthetic data set with a more realistic data density, and displaying a realistic level of complexity. If this would be really beyond the scope of the intended work, authors may want to consider a more theoretically oriented journal specializing on statistical optimization.
1) Applicability of the approach in terms of complexity
The authors argue in P.17,L.3 that application of their approach to more complex systems is 'beyond the scope of this contribution'. I do not agree. For demonstrating that the approach is not only of theoretical interest, but intended to be of practical relevance to the target audience of GMD, an application to a realistically complex data set is needed.
2) Applicability of the approach in terms of data density
As an issue that seem to have emerging anew, the authors warn that their method require dense observations. Again, in reality, such dense observations (for the systems the authors claim their method is suitable for) almost never exist. So the applicability of the approach to a realistic observation set (e.g., with monthly or bi-weekly observations) is needed, with a thorough elaboration of the specific problems, and proposed solutions.
3) Software and its documentation
I downloaded and installed the code (NOMMA-1.0) and the prerequisite CPLEX software (IBM ILOG CPLEX Optimization Studio V12.7.0 for Linux), which went all fine by following the instructions in README files. The compiled 'regEx' binaries in both 'regression' and 'regressionCPX' folders seemed to run successfully, producing some .txt output. However, documentation of the software beyond installation seems to be entirely missing. In the absence of such documentation, my possibly incorrect understanding is that, the software is currently hardwired only to operate on a given data set, run a predefined set of tests with predefined parameters (such as the definition of number of extremes. If the code is provided for potential users, some documentation should describe how basic user options can be specified, or if the present code is provided only for exemplifying, an interpretation of examples should be provided.
P5, L7-8. I don't see the logical link of this sentence to the previous text suggested by 'Therefore'.
P5, L17: 'intuitively clear': not to me really.