did some of the comments

paper/coverletter.tex

@@ -7,6 +7,7 @@
 \newcommand{\ac}{a_{\textrm{col,p-p}}}
 \newcommand{\Tp}{T_{\textrm{p}}}
 \newcommand{\verena}[1]{\textcolor{blue}{#1}}
+\newcommand{\sophie}[1]{\textcolor{green}{#1}}
 \newcommand{\reply}[1]{\textbf{Reply:} #1 \newline
 }
 \newcommand{\commentA}[1]{{\bf Comment: }
@@ -91,11 +92,17 @@ The noise resulting from the measurement uncertainty inhibits the accuracy of an
 
 \reply{There is indeed a limit on the length of the plain language summary which was however stated during the submission process. We have shortened the plain language summary further which is now well below the limit of ...}
 
-Line 35: These properties
+\commentB{Line 35}{ These properties}
 
-Line 39: "not influenced by..." This isn't true - these ions are subject to effects such as expansion, etc. However, this comment presumably refers to their charge states, which should be made clear.
+\reply{Thank you for the correction!}
 
-Line 42: "darker" in what sense. Also, high speed streams were associated with coronal holes long before charge state observations were made, for example by field line mapping and comparing the magnetic field direction in coronal holes and the associated streams.
+\commentB{Line 39}{"not influenced by..." This isn't true - these ions are subject to effects such as expansion, etc. However, this comment presumably refers to their charge states, which should be made clear.}
+
+\reply{Thank you for the comment, we added the charge states in the sentence. }
+
+\commentB{Line 42}{"darker" in what sense. Also, high speed streams were associated with coronal holes long before charge state observations were made, for example by field line mapping and comparing the magnetic field direction in coronal holes and the associated streams.}
+
+\reply{}
 
 Line 43: "The corona..." The solar wind is the expansion of the corona, so maybe this comment could be incorporated into the first sentence of the summary.
 
@@ -105,13 +112,17 @@ Line 48: What bias?
 
 Lines 69-71: Maybe this sentence referring to charge states could be moved to/merged with line 83?
 
-Line 70: charge states of the ions observed
+\commentB{Line 70}{charge states of the ions observed}
+\reply{Thank you for the comment we adjusted that in the text. }
 
-Line 72: "the majority" - they are the majority ion species, but don't forget electrons.
+\commentB{Line 72}{"the majority" - they are the majority ion species, but don't forget electrons.}
+\reply{We added Besides electrons \sophie{die Loesung ist nicht super elegant.. ist zur Korrektur freigeben}}
 
-Lines 73-74: Suggest: and proton temperature, as well as the magnetic field strength... [The magnetic field isn't a proton property].
+\commentB{Lines 73-74}{Suggest: and proton temperature, as well as the magnetic field strength... [The magnetic field isn't a proton property]. }
+\reply{This is a great suggestion.}
 
-Lines 84-91: Suggest breaking up this long sentence with multiple parentheses.
+\commetB{Lines 84-91}{ Suggest breaking up this long sentence with multiple parentheses.}
+\reply{this was indeed a very long sentence. We shortend it }
 
 Line 91: "In the absence of ..." - Further context could be provided for this comment. I suspect this refers to the partial failure of the ACE/SWICS instrument in 2011. Also, if I recall, Xu and Borovsky didn't use charge states in their classification because of the lack of SWICS data at the time they made their study but so that they could use the more extended set of standard solar wind parameters for their analysis as charge states are only available for a limited period of near-earth solar wind observations.
 
@@ -143,9 +154,12 @@ Line 204: The study mentions multiple times that certain parameters are subject
 
 What are the averaging times of the data sets used? This is mentioned later, but it would be natural to include this information in the data description.
 
-Line 217: Brackets around references.
+\commentB{Line 217}{Brackets around references.}
+\reply{Thank you for the helpful comment}
+
+\commentB{Line 218} {Delete "with the".}
+\reply{Done}
 
-Line 218: Delete "with the".
 
 Equation 1: Define the parameters in the equation. It's strange to have units in the denominator. There's a gap in the divisor line.
 
@@ -155,7 +169,8 @@ Line 234: Are ICME sheath regions also removed? i.e., does the preceding 6 hour
 
 Line 239: "To estimate their impact on our results..." - the details of the analysis haven't yet been presented, and some related knowledge is needed to understand many comments in this paragraph. I suggest moving this detailed discussion to later.
 
-Line 244: Why is a 12 minute averaging period chosen for this study? Wouldn't the SW parameters on this time scale be significantly influenced by small scale structures/waves/turbulence that are unrelated to the large scale structures of interest in this paper? Would the choice of 12 minutes vs. say 1 hour averaging impact the results of this type of analysis?
+\commentB{Line 244:}{ Why is a 12 minute averaging period chosen for this study? Wouldn't the SW parameters on this time scale be significantly influenced by small scale structures/waves/turbulence that are unrelated to the large scale structures of interest in this paper? Would the choice of 12 minutes vs. say 1 hour averaging impact the results of this type of analysis?}
+\reply{We chose the 12 min averaging period because we wanted to keep the highest resolution of the data which is limited by the 12 min resolution of the SWICS instrument. \sophie{wollen wir das mit der auflösung von einer probieren ? ich finde das einen interessanten Einwand}}
 
 Line 264: The slow and dense streamer wind is unrelated to SIRs. However, it may be swept into SIRs.
 
@@ -167,27 +182,35 @@ Figure 2: It's not clear what the left column of colored stripes indicates. Are
 
 Line 270: This was the main message of Neugebauer et al. (2016), that the classification schemes they examined produced inconsistent results.
 
-Line 274: Is the number of clusters k decided "manually" at the start of each run or does it emerge naturally during the run? Explain how the clustering addresses the problem of SW classification. E.g., are the different clusters that result expected to represent different types of solar wind? (Also line 284.)
+\commentB{Line 274}{ Is the number of clusters k decided "manually" at the start of each run or does it emerge naturally during the run? Explain how the clustering addresses the problem of SW classification. E.g., are the different clusters that result expected to represent different types of solar wind? (Also line 284.)}
+\reply{kmeans needs to be initialized with the number of clusters k. It does not emerge naturally within the run. The clusters should represent the differnt types of solar wind \sophie{ToDo im text verdeutlichen } }
 
-Line 297: This explanation is unclear if you are unfamiliar with the code. e.g., what is a hyper parameter? Can it also be explained in plain language for the reader? Or maybe discuss the operational details in an appendix?
+\commentB{Line 297}{ This explanation is unclear if you are unfamiliar with the code. e.g., what is a hyper parameter? Can it also be explained in plain language for the reader? Or maybe discuss the operational details in an appendix?}
+\reply{\sophei{ich denke das es eine gute Idee ist,  die Hyperparameter in den Appendix zu schieben}}
 
-Line 320: Folkwes or Fowlkes? Both are used throughout the text and figure labels. It should be Fowlkes. Please check the text/figures carefully.
+\reply{Line 320}{ Folkwes or Fowlkes? Both are used throughout the text and figure labels. It should be Fowlkes. Please check the text/figures carefully.}
+\reply{We are sorry we missed this sometimes in fixed it in our manuscript accordingly \sophie{ToDo:}}
 
-Line 335: exceptions discussed in Section
+\commentB{Line 335}{exceptions discussed in Section}
+\reply{We corrected it accordingly}
 
 Line 346: Should this be (see e.g., Figure 6) up to seven input parameters?
 
-Line 352: What does the "internal choice of starting points" mean?
+\commentB{Line 352}{ What does the "internal choice of starting points" mean?}
+\reply{kmeans starts at a random starting point every time it runs. To take this starting point into account we decided to run kmeans 100 times per combination. \sophie{eventuell das noch deutlicher im Manuskript schreiben} }
 
 Line 354: "Different colors" where in Figure 2?
 
-Line 355: How and why is the data "shuffled"?
+\reply{Line 355}{How and why is the data "shuffled"?}
+\reply{The data is shuffled with sklearns shuffle function.  We need the shuffling because of the random starting points of kmeans. When the data set is shuffled the algorithm will choose a different starting point. So we can obtain the bias introduced by the starting point from the shuffling. \sophie{Sollen wir das noch klarer im Manuskript machen ? } }
 
 Line 356: Explain Figure 3.
 
-Line 386: Would choosing samples at different solar activity levels (e.g., solar maximum vs. solar minimum) also influence the clustering? (Maybe less so since ICMEs are removed?)
+\commentB{Line 386}{ Would choosing samples at different solar activity levels (e.g., solar maximum vs. solar minimum) also influence the clustering? (Maybe less so since ICMEs are removed?)}
+\reply{We highly suspect so. Since we already see different solar wind types being more dominant at different solar activity levels. Training only on the solar maximum or solar minimum will change the resulting clustering.  }
 
-Line 400: It was, more generally, the limited charge state observations compared to other parameters rather than just the problem with SWICS that caused Xu and Borovsky to use just the three parameters not including charge states.
+\commentB{Line 400}{ It was, more generally, the limited charge state observations compared to other parameters rather than just the problem with SWICS that caused Xu and Borovsky to use just the three parameters not including charge states.}
+\reply{This is a good point. This is one of the points we want to make. We want to encourage people to also consider measuring charge state composition to make it wider available and we can understand the solar wind better \sophie{formulierung hier ist noch nciht optimal}}
 
 Line 412: The other properties are also "source-dependent", but the relevant point is that the charge states are preserved during transport to 1 AU.
 
@@ -195,9 +218,10 @@ Figure 5: Why is there little evidence of a peak for cluster 3 in all of the lef
 
 Line 419: What are the two types here? Does this mean that, if the compressed solar wind in SIRs is identified as a separate type of solar wind, then the two types of fast and slow wind are insufficient.
 
-Line 423: Already mentioned in line 414.
+\commentB{Line 423}{ Already mentioned in line 414.}
 
-Line 432: median of each score is computed
+\commentB{Line 432}{median of each score is computed}
+\reply{Thank you for your comment. We adjusted that.}
 
 Line 433: The rest of this paragraph is unclear, e.g., where is an elbow "visible" (line 434)? "we identify the comprising elbow between 7 and 9 clusters (lines 436-7) " - Are these features the reader can find somewhere, perhaps in figure 1? What exactly is an "elbow" in this context?
 

paper/introduction_agu.tex

@@ -2,12 +2,12 @@
 The solar wind, a stream of charged particles continuously emitted
 from the Sun, carries tracers of its solar origin. The electron
 temperature in the solar corona at the solar source region of the
-solar wind determines the charge states observed in the solar wind
+solar wind determines the \markme{charge states of the ions observed} in the solar wind
 which then remain frozen-in during the expansion. Outside the solar
 atmosphere, the magnetic field lines is frozen-in and is
-  carried by the radially flowing solar wind.  Protons constitute
+  carried by the radially flowing solar wind. \markme{Besides electrons, protons} constitute
 the majority of the solar wind and their basic properties, the proton
-speed, proton density, proton temperature, and the magnetic field
+speed, proton density, proton temperature, \markme{as well as} the magnetic field
 strength, also depend on the respective properties of the solar source
 region. However, during the travel time of the solar wind, the
 properties of the solar wind are modulated by transport
@@ -23,15 +23,24 @@ rise to the categories of slow and fast solar wind
 \cite{neugebauer1966mariner,mccomas1998solar}. However, the charge
 state composition turned out to be a better tracer for the solar wind
 origin \cite{zurbuchen2002solar,
-    zhao2009global,zhao2010comparison}. Since the charge states in
+    zhao2009global,zhao2010comparison}. 
-the solar wind are not affected by transport effects, the charge state ratios for O$^{7+}$ to O$^{6+}$ and C$^{6+}$ to C$^{5+}$, $\nO$ and $\nC$, respectively (Based in the observed densities of the relevant O
+    \delete{Since the charge states in 
+    the solar wind are not affected by transport effects, the charge state ratios for O$^{7+}$ to O$^{6+}$ and C$^{6+}$ to C$^{5+}$, $\nO$ and $\nC$, respectively (Based in the observed densities of the relevant O
 and C charge states which we refer to as $\nOsix$, $\nOseven$,
 $\nCsix$, and $\nCfive$.) have been frequently
 used to differentiate between coronal hole wind (which originates in
 coronal regions with a comparatively low electron temperature) and
 slow solar wind (which matches the composition of regions with a
 higher electron temperature) \cite{zurbuchen2002solar,
-  zhao2009global,zhao2010comparison}.  In the absence of charge state
+  zhao2009global,zhao2010comparison}.}
+  \markme{The charge states in
+the solar wind are not affected by transport effects an thus the charge state ratios for O$^{7+}$ to O$^{6+}$ and C$^{6+}$ to C$^{5+}$, $\nO$ and $\nC$ are not affected either. (Based in the observed densities of the relevant O
+and C charge states which we refer to as $\nOsix$, $\nOseven$,
+$\nCsix$, and $\nCfive$.) The charge state ratios have been frequently
+used to differentiate between coronal hole wind and
+slow solar wind \cite{zurbuchen2002solar,
+  zhao2009global,zhao2010comparison}. }
+    In the absence of charge state
 composition measurements at L1 after 2011, \citeA{xu2014new} derived a
 source driven solar wind classification method that requires only
 proton properties (proton density, proton speed, proton temperature

paper/methods_agu.tex

@@ -36,8 +36,8 @@ in different parts of the corona they contain different information
 about the solar wind. The $\nO$ is chosen because
 it is a well known tracer to distinguish between coronal hole wind and
 other types of solar wind
-\citeA{zurbuchen2002solar,zhao2009global,zhao2010comparison} and
+\cite{zurbuchen2002solar,zhao2009global,zhao2010comparison} and
-contains with the $\nOsix$ the most frequent solar wind ion (heavier than He). 
+contains \delete{with the} $\nOsix$ the most frequent solar wind ion (heavier than He). 
 
    Furthermore, we also consider the proton-proton collisional age ($\colage$, also called Coulomb number) which estimated the number of $90^{\degree}$-equivalent proton-proton collisions in the plasma as an additional parameter. As shown in \citeA{kasper2012evolution,heidrich2020proton}, the proton-proton collisional age (in the following also referred to as collisional age) is a suitable ordering parameter that summarizes the collisional transport history of the solar wind. We compute the collisional age in the same way as in \citeA{heidrich2020proton}:
    \begin{equation}
@@ -102,7 +102,7 @@ contains with the $\nOsix$ the most frequent solar wind ion (heavier than He).
 
    We quantify the comparison of two clusterings with similarity measures. We chose four similarity scores that are implemented in \texttt{scikit-learn}. All four of them take the label vectors, which assign each data point to one of the $k$ clusters, of two clusterings and calculate a value where one indicates equality of the two clusterings. The Folkwes-Mallow score \cite{fowlkes1983} takes the number of correctly labeled data points and weighs them with the number of falsely labeled data points. The Adjusted Rand Score \cite{hubert1985} divides the number of correctly labeled data points by the sample size but also takes accidentally correctly labeled data points into account. The Mutual Information Score \cite{strehl2002,yang2016} considers the shared information between the two clusterings by comparing the entropy (uncertainty of the label) of the two clusterings. The mutual information needs to be be normalized to make the scores obtained on different data sets comparable. Here, the normalized mutual information score and the adjusted mutual information score are employed. All four similarity measures are independent of the order in which labels are assigned to clusters (that is whether a specific cluster as referred to as ``Cluster 0'' or ``Cluster 1'', for example).
    
-   We note that the exact values of the similarities scores are difficult to interpret without additional information. For example, the score values do not indicate in which parts of the input space two clusterings differ or whether this difference is relevant for a particular research question. Also, (with a few exception in Sect.~\ref{sec:discussion}) we always compare to the same fixed reference clustering instead of comparing different sub-sets of input parameters directly to each other. Thus, the same similarity score (lower than one) for two different input parameter combinations does not indicate that the corresponding clusterings are the same, but only that their difference to the reference clustering is on the same scale. Where these differences occur might be different. Therefore, in the following section we focus on the comparison of similarity score instead of on the absolute score values. Therein, the relative change of the similarity to the reference clustering allows us to draw conclusions on the information content of each considered input parameter combination.
+   We note that the exact values of the similarities scores are difficult to interpret without additional information. For example, the score values do not indicate in which parts of the input space two clusterings differ or whether this difference is relevant for a particular research question. Also, (\delete{with a few exception in Sect.}\markme{exceptions discussed in Section}~\ref{sec:discussion}) we always compare to the same fixed reference clustering instead of comparing different sub-sets of input parameters directly to each other. Thus, the same similarity score (lower than one) for two different input parameter combinations does not indicate that the corresponding clusterings are the same, but only that their difference to the reference clustering is on the same scale. Where these differences occur might be different. Therefore, in the following section we focus on the comparison of similarity score instead of on the absolute score values. Therein, the relative change of the similarity to the reference clustering allows us to draw conclusions on the information content of each considered input parameter combination.
 
    \subsection{Experimental setup}\label{sec:setup}
    

paper/n_solar_wind_agu.tex

@@ -14,7 +14,7 @@ At the moment, there is no established ground truth for the number of solar wind
           
 The simple solar wind speed based classification does not cover the complexity of the solar wind at 1 AU. Since some of the properties of the solar wind change with the distance to the Sun, transport effects should be taken into account. Also the solar wind properties change with a different origin on the Sun. From our selection of solar wind parameters, the properties that are only source-dependent are the charge states of the heavier ions. All other properties considered here are influenced by transport effects. Transport effects do not only include expansion of the plasma but also SIRs, collisions, and wave-particle interactions. In SIRs the strength of the magnetic field increases due to compression of magnetic field lines and the proton density and proton temperature increase in boundary regions of both participating streams. The proton speed of the slow solar wind can increase in the SIR and decrease in the compressed fast solar wind. To identify the compression regions as separate solar wind types more than the two types need to be considered for solar wind classification. There is no consensus on the exact boundaries between the two (or three) solar wind types \cite{neugebauer2016comparison} and there is observational evidence for more sub-types of coronal hole and slow solar wind \cite{zhao2010comparison,d2015origin,sanchez2016very}. Other relevant transport effects are, for example, wave-particle interactions and collisions. While wave-particle interactions are relevant mainly on coronal hole wind, collisions are just frequent enough to impact the plasma properties at L1 for slow solar wind.
           
-In $k$-means applied to solar wind classification the pre-chosen number of clusters $k$ corresponds to the assumed number of solar wind types. Since the number of solar wind types is not known a priori, we employ here a data driven approach to choose $k$. To account for a balance between over- and underfitting the elbow method \cite{thorndike1953belongs} is chosen. The results are shown in Fig.~\ref{fig:elbow}. The quality scores (MICD, Calinski-Harabasz score and the Davies-Bouldin) are calculated for different numbers of clusters ($k=2, \dots, 13$) for each of the 100 trials. Subsequentially, the median is computed and the (very small) error bars again account for the uncertainty by the percentiles corresponding to a $1\sigma$ interval. Only for the mean inner cluster distance an elbow is visible. For clearer visualization, the difference between the MICD for the current number of clusters and the previous one is also shown (Fig.~\ref{fig:elbow} Panel 2) to account for the change in the score by adding another cluster. We identify the comprising ``elbow''  between 7 and 9 clusters. The two other scores are more sensitive to the convexity or non-convexity of the resulting clusters and favor convex clusters. Since in our application the resulting clusters are not expected to be convex it is not surprising that the qualitative behavior with $k$ is different for the Davies-Bouldin score and the Calinski-Harabasz score than for the MICD.
+In $k$-means applied to solar wind classification the pre-chosen number of clusters $k$ corresponds to the assumed number of solar wind types. Since the number of solar wind types is not known a priori, we employ here a data driven approach to choose $k$. To account for a balance between over- and underfitting the elbow method \cite{thorndike1953belongs} is chosen. The results are shown in Fig.~\ref{fig:elbow}. The quality scores (MICD, Calinski-Harabasz score and the Davies-Bouldin) are calculated for different numbers of clusters ($k=2, \dots, 13$) for each of the 100 trials. Subsequentially, the median \markme{of each score} is computed and the (very small) error bars again account for the uncertainty by the percentiles corresponding to a $1\sigma$ interval. Only for the mean inner cluster distance an elbow is visible. For clearer visualization, the difference between the MICD for the current number of clusters and the previous one is also shown (Fig.~\ref{fig:elbow} Panel 2) to account for the change in the score by adding another cluster. We identify the comprising ``elbow''  between 7 and 9 clusters. The two other scores are more sensitive to the convexity or non-convexity of the resulting clusters and favor convex clusters. Since in our application the resulting clusters are not expected to be convex it is not surprising that the qualitative behavior with $k$ is different for the Davies-Bouldin score and the Calinski-Harabasz score than for the MICD.
           
 Even though the computations are performed for all the different number of clusters from two to twelve, in the following we focus on two cases: three and seven clusters. Based on Fig.~\ref{fig:elbow}, we chose $k=3$ clusters as an interesting case since this corresponds to the maximum of the Calinski-Harabasz score and a minimum of the Davies-Bouldin score. This indicates that the resulting clusters are convex. In addition, $k=3$ allows a direct comparison to the \citeA{xu2014new} classification. We chose $k=7$ as a representative for the elbow in the MICD in Fig.~\ref{fig:elbow}. We also analyzed the results of all clusterings with $k=2,\dots,13$ to ensure that the qualitative results do not depend on the particular choice of $k$.