the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Aug 2021
Atmospheric stratification over Namibia and the southeast Atlantic Ocean
Abstract. We currently have a limited understanding of the spatial and temporal variability in vertically stratified atmospheric layers over Namibia and the southeast Atlantic (SEA) Ocean. Stratified layers are relevant to the transport and dilution of local and long-range transported atmospheric constituents. This study used eleven years of global positioning system radio occultation (GPS-RO) signal refractivity data (2007–2017) over Namibia and the adjacent ocean surfaces, and three years of radiosonde data from Walvis Bay, Namibia, to study the character and variability in stratified layers. From the GPS-RO data and up to a height of 10 km, we studied the spatial and temporal variability in the point of minimum gradient in refractivity, and the temperature inversion height, depth and strength. We also present the temporal variability of temperature inversions and the boundary layer height (BLH) from radiosondes. The BLH was estimated by the parcel method, the top of a surface-based inversion, the top of a stable layer identified by the bulk Richardson number (RN), and the point of minimum gradient in the refractivity (for comparison with GPS-RO data). A comparison between co-located GPS-RO to radiosonde temperature profiles found good agreement between the two, and an average underestimation of GPS-RO to radiosonde temperatures of −0.45 ± 1.25 °C, with smaller differences further from the surface and with decreasing atmospheric moisture content. The minimum gradient (MG) of refractivity, calculated from these two datasets were generally in good agreement (230 ± 180 m), with an exeption of a few cases when differences exceeded 1000 m. The surface of MG across the region of interest was largely affected by macroscale circulation and changes in atmospheric moisture and cloud, and was not consistent with BLH(RN). We found correlations in the character of low-level inversions with macroscale circulation, radiation interactions with the surface, cloud cover over the ocean and the seasonal maximum in biomass burning over southern Africa. Radiative cooling on diurnal scales also affected elevated inversions between 2.5 and 10 km, with more co-occurring inversions observed at night and in the morning. Elevated inversions formed most frequently over the subcontinent and under subsidence by high-pressure systems in the colder months. Despite this macroscale influence peaking in the winter, the springtime inversions, like those at low levels, were strongest.
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RC1: 'Comment on acp-2021-668', Anonymous Referee #1, 11 Sep 2021
This paper presents an analysis of atmospheric profiles measured over the Southeast Atlantic Ocean derived from GPS-RO satellite retrievals and radiosonde profiles, and ends with a discussion of the broader atmospheric context over the study region. However, the paper is confusingly structured and unclear in many parts. Specifically, 1) the manuscript’s structure jumps back and forth between the different components (satellite, radiosonde, different BLH definitions) in a way that makes it very difficult to follow the methodology; 2) it is not clear what is the connection between the satellite and the radiosonde portions of the analysis, or indeed between the different BLH definitions just using the radiosonde data (difficult to follow the analysis); and ultimately 3) the intended scientific focus of this work is thus quite difficult to determine. I hope that the authors will address the below comments before the paper is considered for final publication.
1) The paper begins with a discussion of the COSMIC satellite data and its processing algorithm. For those not familiar with the COSMIC satellite data, Section 3 is rather difficult to follow regarding which processing (Abel inversion algorithm, “atmPrf” dataset, ECMWF “1-D var” moisture correction?) is provided or what is additional processing/analysis done by the authors e.g. following the Shyam reference?
Sections 3 and 4 seem disjointed, as, for example, Section 3.1 talks about the data processing for COSMIC and Section 4.1 also talks about COSMIC data processing and definitions, and Sections 3.2 and 4.2 both discuss radiosondes. As I said above, it’s not really clear in Section 3.1 which processing was done by the authors in the present work, and which was in an external dataset (as the references seem incomplete). Would it be better to combine the two COSMIC GPS-RO sections, and the two radiosonde processing sections into one Data/Methods section, followed by analysis of the results?
- Also, the paragraph starting on Line 134 seems to belong more in the background/introduction sections.
- Section 5.1.1: The conclusions section states that the GPS-RO method underestimated temperatures in the temperature profiles, but that doesn’t seem to be strongly supported by Section 5.1.1 (an absolute error of -0.3+/- 1.3C seems fairly evenly distributed between positive vs negative differences) or Figure 2, which shows pretty good agreement between the two methods at least as it is presented there (see below “other comment” about Fig 2 as well). I’d recommend finding a different visualization if the point of this figure is to say that one method systematically underestimates temperature.
- The authors mention it multiple times, but it’s not clear how the issue of superrefractivity might be affecting the analysis. Line 100: “even with the applied corrections, no reliable information about atmospheric structure can be collected below where the signal is super-refracted”: where is this point, typically, and how frequently do these conditions occur in the region of interest? Then Line 280 states the inversion heights were on average 190+/-480m lower than MG heights, “beyond these layers, the profile will likely be superrefracted” but doesn’t 190+/- 480m indicate a sizable fraction of cases where the MG of refractivity is lower than the inversion height? Are these cases included in Table 1?
- Then in Section 4 the authors mention three or four separate methods to calculate BLH only from the radiosondes, but the refractivity definition compared with the other three is never really explored. It’s mentioned briefly on Line 494 that it isn’t consistent with the RN definition, but by then we’re already in the conclusions. To my mind this needs to be addressed far before that because the analysis of Section 5 uses both refractivity definitions and inversion definitions, so those need to be reconciled. What are the considerations of each calculation? What are we supposed to take away from these different definitions beyond “the BLH can vary rather widely based on what definition of BLH you use” (this issue of definition was also discussed somewhat in this same special issue by Ryoo et al, https://doi.org/10.5194/acp-2021-274).
2) The value of the refractivity definition is understandable in that it allows a direct comparison to the satellite-based retrieval (see above), but Figure 3 shows it doesn’t do a particularly good job in that respect, and Figure 4 shows that the other three definitions aren’t consistent with one another either. So what’s the use of any of this? And the somewhat arbitrary throwing out of 6 points in Fig 3 doesn’t lend any confidence to the time series of these parameters in Fig 6 either.
- I’d first suggest a clear delineation of each BLH calculation description, either as a bulleted list or maybe even a table. Also, come up with clear names/abbreviations for each of the BLH definitions (e.g., one of them is described as “the point where the virtual potential temperature (VPT) aloft is the same as at the surface,” multiple times, when you could just call it BLH_VPT or something similar after Section 4). It’s difficult to keep all of those straight. And Line 141 says “the point of MG of refractivity (hereafter MG height)” but later in the text uses “MG height” and “the height of the MG of refractivity” (Fig 7) and “the height of MG N-refractivity profiles” (Line 269) etc… are these all the same thing? And “low-level inversions” and “surface-based inversions” are the same? It’s quite hard to follow.
- Section 5.1.2: I see the value in comparing the refractivity BLH calculations for GPS-RO and radiosondes, but Figure 3 doesn’t seem to support that these are comparable. In this section the authors eliminate several potential explanations for the poor agreement, but then exclude the worst-comparing points based on nothing other than they are the worst-comparing points. What’s to say that the majority of points in Fig 6 don’t show that same discrepancy, then? How can these really be compared?
- Then throughout Section 5.2 and 5.3, the analysis jumps back and forth between GPS-RO and radiosonde analysis, under the headings of “Spatial and temporal variability” although really only the GPS-RO data can give spatial variability here, right? Given the results of the earlier sections, it seems to me the takeaway is that they aren’t really interchangeable, although the structure of Tables 1/2 and Tables 3/4 make it rather difficult to compare the results from the two methods.
- Confusingly, Sections 5.2.3 and 5.3.1 are both titled “temperature inversions” but refer to either low-level or mid-level temperature inversions. It’s not clear how spatial plots of low-level temperature inversions are derived from only the GPS-RO data, given the superrefractivity questions above and the clear altitudinal limitations of these data as shown in Fig 2, especially relative to the radiosonde-based inversion height in Fig 4.
- I’d also move Fig 6 up to significantly earlier in the paper, e.g. just after Fig 3, as they are showing similar things and the context for how GPS-RO and radiosondes compare with one another is a necessary prerequisite before talking about the spatial and temporal patterns in their results.
3) Finally, it’s difficult to see how these observations (which I think are worth describing if the above issues can be addressed) fit into the broader meteorological picture, which I think is what the authors are trying to do in Section 6. These connections are tenuous at best. Most of the spatial maps presented (Figs 7-16) primarily show seasonally-averaged values and then standard deviations (or try to; the stdev figures are extremely hard to interpret, see additional comments below), so it’s not clear to me how this relates to transient meteorological events e.g. as discussed in Sections 6.1 and 6.2. Section 6.3 discusses cloud fraction but the spatial analysis will rely on the GPS-RO profiles which are less reliable in cloudy conditions, is that right? How is this addressed? The authors mention MERRA-2 (and also mention MODIS in the “data availability” section but apparently nowhere else in the paper?), I can’t help but think that a comparison of the profiles here with a large-scale reanalysis or model that gives atmospheric motion (MERRA-2 or perhaps ERA5 which performs better in the region; see Ryoo et al., 2021 https://doi.org/10.5194/acp-2021-274 or Pistone et al., 2021 https://doi.org/10.5194/acp-21-9643-2021) is necessary if the goal of this work is to contextualize these boundary layer height variations within the larger context of the regional atmospheric circulation.
Perhaps some of the above comments can be easily addressed simply by restructuring the manuscript, but I spent more time than I’d like to admit right now trying to understand it and these are the major questions I still had. I’d suggest the authors decide what they hope to convey with this manuscript as they revise their work, and I have hope this will lead to a greatly improved paper.
Other comments:
- It’s not very clear to me what you’re trying to convey with Figure 2. If the focus is on the BLH difference between the two datasets, then why show the full altitude scale up to 10.5km? It’s very difficult to see what the differences are between ~2-5km. On the other hand, if the point is to show that the lapse rate is generally in agreement, I don’t think you need 36 panels to do that (also, I’d suggest making the lower line thicker, it’s really difficult to see the underlying blue line there). These aren’t every single coincident profile, correct? How many are in the circle shown in Figure 1? Lines 112-113 indicated 4007 within the coastal region, were only 32 comparable? Also, why so many more valid retrievals the ocean? Based on just the surface area shown in in Fig 1, I’d expect maybe 2-3 times as many profiles over the ocean versus the continent… is there a further difference in what makes valid retrievals for land vs ocean beyond just the atmospheric moisture? I’d mention that.
- How many retrievals are going into Fig 4? Are there systematic differences in May vs June re: the 10am vs 9am launch time? Or could the sharp increase in the RN BLH range between those two be due to diurnal BLH development, or fewer radiosondes being launched in May 2015 vs April or June?
- Figure 5: I’d recommend a different color scheme especially for panel A; wind direction of 0 = 360 degrees, so having one be red and the other being blue is difficult to interpret e.g. northerly from easterly (also I’d recommend adding to the caption 0=east for ease of reading, assuming that’s the convention being used here).
- The 2x8 figures are overall very difficult to interpret; it’s not at all clear what is the main message of each of these very similar figures. Beyond that, the color scale on Figs 7b, 8b, 9b, 11b, 14b, and 16b makes it extremely difficult to interpret, beyond “they’re all small”. If that’s the message, you can lose all these panels altogether. If it's not, then a different scale should be used to show the variations between different panels. And are the black contours the same parameters? At what interval are those lines? And how much data is included in these figures (how many overpasses; is this also limited to mid-morning or is this all times of day; are the retrievals regularly distributed in time and space or are there particularly retrieval-rich times or overpasses which could bias the results preferentially towards a certain time or condition)?
- Relatedly, it’s not clear what the authors are intending to convey with the standard deviations throughout the paper, especially when the ranges are much larger than the mean values themselves. For example, how can you have an inversion depth of 200m +/- 300m or a temperature inversion strength of 0.55 +/- 0.56 C/depth [depth = 60 +/- 40 meters?]? Isn’t that saying a nonnegligible fraction of the data would have zero-to-imaginary temperature inversions? I think another metric of variability might be more instructive, either in terms of percentiles or just showing the frequency distributions of select parameters.
- Also, Figures 6-11 (except 10) show heights above mean sea level in a region where ground level is ~1-2km (e.g. Fig 1)? I’m not sure amsl is the most instructive height metric here. If “mean MG heights were consistently higher over land” (Line 265), were they relatively higher compared to magl?
- Figure 8: Why show the seasonal variability of the refractivity BLH in Fig 6, but show the diurnal variability this way? For all the discussion about diurnal variability (e.g. also Section 6.3), I think this would be better served as a time series. As with other figures, I’d like to know how many retrievals go into each of these.
- Section 6.4: if it is decided to keep in the larger discussion, is there any indication regarding whether the presence of aerosols would affect the validity of the GPS-RO profiles, as humidity/clouds do?
- Is “subcontinent” (Line 38) a common term to refer to this part of Africa? It seems to be more just the southern continent-proper.
- Lines 120-125: this is a bit confusing. Was the primary set of radiosondes always at 10am local, with an additional set between 10 and 11? Also, why say you’re converting time to UTC, and then describe the dataset in local time?
- Line 199: isn’t very low vapor pressure = very dry conditions, not a moist atmosphere?
Citation: https://doi.org/10.5194/acp-2021-668-RC1 -
AC1: 'Reply on RC1', Danitza Klopper, 15 Nov 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-668/acp-2021-668-AC1-supplement.pdf
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RC2: 'Comment on acp-2021-668', Anonymous Referee #2, 14 Sep 2021
Review for “Atmospheric stratification over Namibia and the southeast Atlantic Ocean” by Klopper et al.
This paper used retrievals from global positioning system radio occultation (GPS-RO) and radiosonde profiles to describe the characteristics of atmospheric stratification over Namibia and in the nearby regions.
While the research topic may be relevant to the broader understanding and characterization of the atmospheric boundary layer over the southeast Atlantic and southern Africa region, the overall presentation of the analysis in this paper is poor. For the most part, I also find the paper difficult to read! As such, I believe the authors may need to substantially rewrite the paper to make it easier for readers to understand.
The difficulty of understanding this paper occurs at several levels, and because of that, I find myself reading each sentence and paragraph more than I would like to admit. For example, a lot of the paragraphs are not well connected structurally, which makes it difficult to understand either the point of the paragraph or its relevance in the context of the section it belongs to. In addition, it is difficult to see the big-picture relevance of this analysis, whether in the introduction or anywhere else in the paper. The authors must ask (and answer the question): Why should the reader care about this work?
Another point to consider is that the results section presents values that sometimes have larger uncertainties than the average values themselves. Such large relative uncertainties render the results useless for practical purposes. For example, the authors find an error of -0.30 ± 1.30°C for temperature from GPS-RO when compared to the radiosonde below 7 km amsl. The same is true for cases above 7km or for the scatter plot in Fig. 3 (790 ± 990 m).
Almost all the figures lack detailed information that can let the reader better understand exactly what they are meant to convey. In addition, the authors showed seasonally averaged figures of standard deviations. Some of these figures are not discussed in the text.
Finally, the authors used several different methods to calculate the inversion. However, while discussing them in the results section, the authors sometimes did not clearly specify which of the three methods they referred to.
Other Comments:
- Line 8-9: Why? Why is there “a limited understanding of the spatial and temporal variability in vertically stratified atmospheric layers over Namibia and the southeast Atlantic”? Please be more specific.
- Line 18-21: This sentence says that the two profiles have a “good agreement” and then says that one profile underestimates the other. It is either one or the other. I suggest the authors remove the “good agreement part”, and rewrite the entire sentence for better clarity.
- Line 14: minimum gradient or minimum vertical gradient?
- Line 24: What does it mean to “found correlations in the character”? That statement needs to be clarified.
- Line 33-57: It is difficult for me to understand the point of this introduction. I will suggest that the authors rewrite it, paying close attention to telling the readers exactly why they should care about this study.
- Line 78: What a priori information? This place needs appropriate references.
- Line 83: “several times a day"? What time? You could provide a temporal interval.
- Line 90: “The Abel inversion algorithm was applied….” By who? Additionally, the whole sentence should be rewritten for clarity.
- Line 81 - 86: These lines mentioned "data" several times, without clearly specifying what data. Is this what the instrument measure? What exactly is it? What are the “raw data” separate from the “atmPrf “ dataset that the authors mentioned?
- Line 135-136: “… were on average 100 m higher in the autumn and 100 m lower in the spring” than what?
- Section 4: There are several places where the words like "define” or “definition” were used to signify the calculation of the boundary layer height. For example, in line 149, the authors stated that “….was performed based on four different definitions of BLH….”. I supposed the authors meant the four different methods used to calculate BLH. Would you please rewrite this section to reflect the right language that can better improve the clarity?
- Line 187 & Fig. 2: The text mentioned that the temperature profiles from GPS-RO are taken for the same day as the radiosonde. Is this an average over the entire day or the one with measurement time closest to the soundings? Please clarify.
- Line 200: Delete “where”.
- Line 221: Change “high cloud fractions” to “high fractions”
- Line 220-224: The authors should examine (and possibly include in the supplementary document) the cloud distribution/variability for the periods that are compared.
- Line 224-227: Given the large discrepancies and the fact that the authors have no explanation for the 6 data points, I don’t believe they can make this conclusion based on the exclusion of those “bad” 6 points
- 4: The authors should include a figure of the climatology that uses all the years in the supplementary document. That would allow readers to place the result of 2015 within a climatological context with all the uncertainties. Is this what is shown in Table S-1? If so, that is not clear in the main text and caption!
- Line 238: “equivalent” or similar? Also, the BLH values are not similar, the BLH for VPT is about twice that of surface-based inversion. However, the monthly variation or monthly consistency is similar. The authors should rephrase this sentence.
- Line 252-254: Comparing all data and not a subset of the data sounds like a more "sensible comparison" to me.
- Line 254: Do you mean the monthly variability? Fig. 6 shows monthly variability, not inter-annual variability. I also notice this in another part of the text. Please change all of them accordingly.
- Line 254-255: how different would this estimate/assessment be if all data were included?
- Line 266: Given that you talked about the difference between land and ocean, I wonder if this statement refers to zonal gradient and not "meridional” gradient.
- All Figures and Tables (main text and supplementary): Please all figure captions should be full and complete -- Meaning that it should include all the information used to make the plot (time range, date name, and so on), regardless of whether that information has been stated in the text or not. Also, all acronyms/abbreviations should be defined, irrespective of whether it has been used elsewhere or not.
- Line 491-493: This attribution was stated as speculation within the text. To make this type of conclusion requires more than speculation. I will suggest the authors better clarify their statement or remove it altogether.
- Line 505-508: Again, I don’t see where this analysis was laid out in the result section of this paper. If the authors are making speculation, they either have to back his by previous studies that have made this conclusion or clearly state that they are speculating.
Citation: https://doi.org/10.5194/acp-2021-668-RC2 -
AC2: 'Reply on RC2', Danitza Klopper, 15 Nov 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-668/acp-2021-668-AC2-supplement.pdf
Status: closed
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RC1: 'Comment on acp-2021-668', Anonymous Referee #1, 11 Sep 2021
This paper presents an analysis of atmospheric profiles measured over the Southeast Atlantic Ocean derived from GPS-RO satellite retrievals and radiosonde profiles, and ends with a discussion of the broader atmospheric context over the study region. However, the paper is confusingly structured and unclear in many parts. Specifically, 1) the manuscript’s structure jumps back and forth between the different components (satellite, radiosonde, different BLH definitions) in a way that makes it very difficult to follow the methodology; 2) it is not clear what is the connection between the satellite and the radiosonde portions of the analysis, or indeed between the different BLH definitions just using the radiosonde data (difficult to follow the analysis); and ultimately 3) the intended scientific focus of this work is thus quite difficult to determine. I hope that the authors will address the below comments before the paper is considered for final publication.
1) The paper begins with a discussion of the COSMIC satellite data and its processing algorithm. For those not familiar with the COSMIC satellite data, Section 3 is rather difficult to follow regarding which processing (Abel inversion algorithm, “atmPrf” dataset, ECMWF “1-D var” moisture correction?) is provided or what is additional processing/analysis done by the authors e.g. following the Shyam reference?
Sections 3 and 4 seem disjointed, as, for example, Section 3.1 talks about the data processing for COSMIC and Section 4.1 also talks about COSMIC data processing and definitions, and Sections 3.2 and 4.2 both discuss radiosondes. As I said above, it’s not really clear in Section 3.1 which processing was done by the authors in the present work, and which was in an external dataset (as the references seem incomplete). Would it be better to combine the two COSMIC GPS-RO sections, and the two radiosonde processing sections into one Data/Methods section, followed by analysis of the results?
- Also, the paragraph starting on Line 134 seems to belong more in the background/introduction sections.
- Section 5.1.1: The conclusions section states that the GPS-RO method underestimated temperatures in the temperature profiles, but that doesn’t seem to be strongly supported by Section 5.1.1 (an absolute error of -0.3+/- 1.3C seems fairly evenly distributed between positive vs negative differences) or Figure 2, which shows pretty good agreement between the two methods at least as it is presented there (see below “other comment” about Fig 2 as well). I’d recommend finding a different visualization if the point of this figure is to say that one method systematically underestimates temperature.
- The authors mention it multiple times, but it’s not clear how the issue of superrefractivity might be affecting the analysis. Line 100: “even with the applied corrections, no reliable information about atmospheric structure can be collected below where the signal is super-refracted”: where is this point, typically, and how frequently do these conditions occur in the region of interest? Then Line 280 states the inversion heights were on average 190+/-480m lower than MG heights, “beyond these layers, the profile will likely be superrefracted” but doesn’t 190+/- 480m indicate a sizable fraction of cases where the MG of refractivity is lower than the inversion height? Are these cases included in Table 1?
- Then in Section 4 the authors mention three or four separate methods to calculate BLH only from the radiosondes, but the refractivity definition compared with the other three is never really explored. It’s mentioned briefly on Line 494 that it isn’t consistent with the RN definition, but by then we’re already in the conclusions. To my mind this needs to be addressed far before that because the analysis of Section 5 uses both refractivity definitions and inversion definitions, so those need to be reconciled. What are the considerations of each calculation? What are we supposed to take away from these different definitions beyond “the BLH can vary rather widely based on what definition of BLH you use” (this issue of definition was also discussed somewhat in this same special issue by Ryoo et al, https://doi.org/10.5194/acp-2021-274).
2) The value of the refractivity definition is understandable in that it allows a direct comparison to the satellite-based retrieval (see above), but Figure 3 shows it doesn’t do a particularly good job in that respect, and Figure 4 shows that the other three definitions aren’t consistent with one another either. So what’s the use of any of this? And the somewhat arbitrary throwing out of 6 points in Fig 3 doesn’t lend any confidence to the time series of these parameters in Fig 6 either.
- I’d first suggest a clear delineation of each BLH calculation description, either as a bulleted list or maybe even a table. Also, come up with clear names/abbreviations for each of the BLH definitions (e.g., one of them is described as “the point where the virtual potential temperature (VPT) aloft is the same as at the surface,” multiple times, when you could just call it BLH_VPT or something similar after Section 4). It’s difficult to keep all of those straight. And Line 141 says “the point of MG of refractivity (hereafter MG height)” but later in the text uses “MG height” and “the height of the MG of refractivity” (Fig 7) and “the height of MG N-refractivity profiles” (Line 269) etc… are these all the same thing? And “low-level inversions” and “surface-based inversions” are the same? It’s quite hard to follow.
- Section 5.1.2: I see the value in comparing the refractivity BLH calculations for GPS-RO and radiosondes, but Figure 3 doesn’t seem to support that these are comparable. In this section the authors eliminate several potential explanations for the poor agreement, but then exclude the worst-comparing points based on nothing other than they are the worst-comparing points. What’s to say that the majority of points in Fig 6 don’t show that same discrepancy, then? How can these really be compared?
- Then throughout Section 5.2 and 5.3, the analysis jumps back and forth between GPS-RO and radiosonde analysis, under the headings of “Spatial and temporal variability” although really only the GPS-RO data can give spatial variability here, right? Given the results of the earlier sections, it seems to me the takeaway is that they aren’t really interchangeable, although the structure of Tables 1/2 and Tables 3/4 make it rather difficult to compare the results from the two methods.
- Confusingly, Sections 5.2.3 and 5.3.1 are both titled “temperature inversions” but refer to either low-level or mid-level temperature inversions. It’s not clear how spatial plots of low-level temperature inversions are derived from only the GPS-RO data, given the superrefractivity questions above and the clear altitudinal limitations of these data as shown in Fig 2, especially relative to the radiosonde-based inversion height in Fig 4.
- I’d also move Fig 6 up to significantly earlier in the paper, e.g. just after Fig 3, as they are showing similar things and the context for how GPS-RO and radiosondes compare with one another is a necessary prerequisite before talking about the spatial and temporal patterns in their results.
3) Finally, it’s difficult to see how these observations (which I think are worth describing if the above issues can be addressed) fit into the broader meteorological picture, which I think is what the authors are trying to do in Section 6. These connections are tenuous at best. Most of the spatial maps presented (Figs 7-16) primarily show seasonally-averaged values and then standard deviations (or try to; the stdev figures are extremely hard to interpret, see additional comments below), so it’s not clear to me how this relates to transient meteorological events e.g. as discussed in Sections 6.1 and 6.2. Section 6.3 discusses cloud fraction but the spatial analysis will rely on the GPS-RO profiles which are less reliable in cloudy conditions, is that right? How is this addressed? The authors mention MERRA-2 (and also mention MODIS in the “data availability” section but apparently nowhere else in the paper?), I can’t help but think that a comparison of the profiles here with a large-scale reanalysis or model that gives atmospheric motion (MERRA-2 or perhaps ERA5 which performs better in the region; see Ryoo et al., 2021 https://doi.org/10.5194/acp-2021-274 or Pistone et al., 2021 https://doi.org/10.5194/acp-21-9643-2021) is necessary if the goal of this work is to contextualize these boundary layer height variations within the larger context of the regional atmospheric circulation.
Perhaps some of the above comments can be easily addressed simply by restructuring the manuscript, but I spent more time than I’d like to admit right now trying to understand it and these are the major questions I still had. I’d suggest the authors decide what they hope to convey with this manuscript as they revise their work, and I have hope this will lead to a greatly improved paper.
Other comments:
- It’s not very clear to me what you’re trying to convey with Figure 2. If the focus is on the BLH difference between the two datasets, then why show the full altitude scale up to 10.5km? It’s very difficult to see what the differences are between ~2-5km. On the other hand, if the point is to show that the lapse rate is generally in agreement, I don’t think you need 36 panels to do that (also, I’d suggest making the lower line thicker, it’s really difficult to see the underlying blue line there). These aren’t every single coincident profile, correct? How many are in the circle shown in Figure 1? Lines 112-113 indicated 4007 within the coastal region, were only 32 comparable? Also, why so many more valid retrievals the ocean? Based on just the surface area shown in in Fig 1, I’d expect maybe 2-3 times as many profiles over the ocean versus the continent… is there a further difference in what makes valid retrievals for land vs ocean beyond just the atmospheric moisture? I’d mention that.
- How many retrievals are going into Fig 4? Are there systematic differences in May vs June re: the 10am vs 9am launch time? Or could the sharp increase in the RN BLH range between those two be due to diurnal BLH development, or fewer radiosondes being launched in May 2015 vs April or June?
- Figure 5: I’d recommend a different color scheme especially for panel A; wind direction of 0 = 360 degrees, so having one be red and the other being blue is difficult to interpret e.g. northerly from easterly (also I’d recommend adding to the caption 0=east for ease of reading, assuming that’s the convention being used here).
- The 2x8 figures are overall very difficult to interpret; it’s not at all clear what is the main message of each of these very similar figures. Beyond that, the color scale on Figs 7b, 8b, 9b, 11b, 14b, and 16b makes it extremely difficult to interpret, beyond “they’re all small”. If that’s the message, you can lose all these panels altogether. If it's not, then a different scale should be used to show the variations between different panels. And are the black contours the same parameters? At what interval are those lines? And how much data is included in these figures (how many overpasses; is this also limited to mid-morning or is this all times of day; are the retrievals regularly distributed in time and space or are there particularly retrieval-rich times or overpasses which could bias the results preferentially towards a certain time or condition)?
- Relatedly, it’s not clear what the authors are intending to convey with the standard deviations throughout the paper, especially when the ranges are much larger than the mean values themselves. For example, how can you have an inversion depth of 200m +/- 300m or a temperature inversion strength of 0.55 +/- 0.56 C/depth [depth = 60 +/- 40 meters?]? Isn’t that saying a nonnegligible fraction of the data would have zero-to-imaginary temperature inversions? I think another metric of variability might be more instructive, either in terms of percentiles or just showing the frequency distributions of select parameters.
- Also, Figures 6-11 (except 10) show heights above mean sea level in a region where ground level is ~1-2km (e.g. Fig 1)? I’m not sure amsl is the most instructive height metric here. If “mean MG heights were consistently higher over land” (Line 265), were they relatively higher compared to magl?
- Figure 8: Why show the seasonal variability of the refractivity BLH in Fig 6, but show the diurnal variability this way? For all the discussion about diurnal variability (e.g. also Section 6.3), I think this would be better served as a time series. As with other figures, I’d like to know how many retrievals go into each of these.
- Section 6.4: if it is decided to keep in the larger discussion, is there any indication regarding whether the presence of aerosols would affect the validity of the GPS-RO profiles, as humidity/clouds do?
- Is “subcontinent” (Line 38) a common term to refer to this part of Africa? It seems to be more just the southern continent-proper.
- Lines 120-125: this is a bit confusing. Was the primary set of radiosondes always at 10am local, with an additional set between 10 and 11? Also, why say you’re converting time to UTC, and then describe the dataset in local time?
- Line 199: isn’t very low vapor pressure = very dry conditions, not a moist atmosphere?
Citation: https://doi.org/10.5194/acp-2021-668-RC1 -
AC1: 'Reply on RC1', Danitza Klopper, 15 Nov 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-668/acp-2021-668-AC1-supplement.pdf
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RC2: 'Comment on acp-2021-668', Anonymous Referee #2, 14 Sep 2021
Review for “Atmospheric stratification over Namibia and the southeast Atlantic Ocean” by Klopper et al.
This paper used retrievals from global positioning system radio occultation (GPS-RO) and radiosonde profiles to describe the characteristics of atmospheric stratification over Namibia and in the nearby regions.
While the research topic may be relevant to the broader understanding and characterization of the atmospheric boundary layer over the southeast Atlantic and southern Africa region, the overall presentation of the analysis in this paper is poor. For the most part, I also find the paper difficult to read! As such, I believe the authors may need to substantially rewrite the paper to make it easier for readers to understand.
The difficulty of understanding this paper occurs at several levels, and because of that, I find myself reading each sentence and paragraph more than I would like to admit. For example, a lot of the paragraphs are not well connected structurally, which makes it difficult to understand either the point of the paragraph or its relevance in the context of the section it belongs to. In addition, it is difficult to see the big-picture relevance of this analysis, whether in the introduction or anywhere else in the paper. The authors must ask (and answer the question): Why should the reader care about this work?
Another point to consider is that the results section presents values that sometimes have larger uncertainties than the average values themselves. Such large relative uncertainties render the results useless for practical purposes. For example, the authors find an error of -0.30 ± 1.30°C for temperature from GPS-RO when compared to the radiosonde below 7 km amsl. The same is true for cases above 7km or for the scatter plot in Fig. 3 (790 ± 990 m).
Almost all the figures lack detailed information that can let the reader better understand exactly what they are meant to convey. In addition, the authors showed seasonally averaged figures of standard deviations. Some of these figures are not discussed in the text.
Finally, the authors used several different methods to calculate the inversion. However, while discussing them in the results section, the authors sometimes did not clearly specify which of the three methods they referred to.
Other Comments:
- Line 8-9: Why? Why is there “a limited understanding of the spatial and temporal variability in vertically stratified atmospheric layers over Namibia and the southeast Atlantic”? Please be more specific.
- Line 18-21: This sentence says that the two profiles have a “good agreement” and then says that one profile underestimates the other. It is either one or the other. I suggest the authors remove the “good agreement part”, and rewrite the entire sentence for better clarity.
- Line 14: minimum gradient or minimum vertical gradient?
- Line 24: What does it mean to “found correlations in the character”? That statement needs to be clarified.
- Line 33-57: It is difficult for me to understand the point of this introduction. I will suggest that the authors rewrite it, paying close attention to telling the readers exactly why they should care about this study.
- Line 78: What a priori information? This place needs appropriate references.
- Line 83: “several times a day"? What time? You could provide a temporal interval.
- Line 90: “The Abel inversion algorithm was applied….” By who? Additionally, the whole sentence should be rewritten for clarity.
- Line 81 - 86: These lines mentioned "data" several times, without clearly specifying what data. Is this what the instrument measure? What exactly is it? What are the “raw data” separate from the “atmPrf “ dataset that the authors mentioned?
- Line 135-136: “… were on average 100 m higher in the autumn and 100 m lower in the spring” than what?
- Section 4: There are several places where the words like "define” or “definition” were used to signify the calculation of the boundary layer height. For example, in line 149, the authors stated that “….was performed based on four different definitions of BLH….”. I supposed the authors meant the four different methods used to calculate BLH. Would you please rewrite this section to reflect the right language that can better improve the clarity?
- Line 187 & Fig. 2: The text mentioned that the temperature profiles from GPS-RO are taken for the same day as the radiosonde. Is this an average over the entire day or the one with measurement time closest to the soundings? Please clarify.
- Line 200: Delete “where”.
- Line 221: Change “high cloud fractions” to “high fractions”
- Line 220-224: The authors should examine (and possibly include in the supplementary document) the cloud distribution/variability for the periods that are compared.
- Line 224-227: Given the large discrepancies and the fact that the authors have no explanation for the 6 data points, I don’t believe they can make this conclusion based on the exclusion of those “bad” 6 points
- 4: The authors should include a figure of the climatology that uses all the years in the supplementary document. That would allow readers to place the result of 2015 within a climatological context with all the uncertainties. Is this what is shown in Table S-1? If so, that is not clear in the main text and caption!
- Line 238: “equivalent” or similar? Also, the BLH values are not similar, the BLH for VPT is about twice that of surface-based inversion. However, the monthly variation or monthly consistency is similar. The authors should rephrase this sentence.
- Line 252-254: Comparing all data and not a subset of the data sounds like a more "sensible comparison" to me.
- Line 254: Do you mean the monthly variability? Fig. 6 shows monthly variability, not inter-annual variability. I also notice this in another part of the text. Please change all of them accordingly.
- Line 254-255: how different would this estimate/assessment be if all data were included?
- Line 266: Given that you talked about the difference between land and ocean, I wonder if this statement refers to zonal gradient and not "meridional” gradient.
- All Figures and Tables (main text and supplementary): Please all figure captions should be full and complete -- Meaning that it should include all the information used to make the plot (time range, date name, and so on), regardless of whether that information has been stated in the text or not. Also, all acronyms/abbreviations should be defined, irrespective of whether it has been used elsewhere or not.
- Line 491-493: This attribution was stated as speculation within the text. To make this type of conclusion requires more than speculation. I will suggest the authors better clarify their statement or remove it altogether.
- Line 505-508: Again, I don’t see where this analysis was laid out in the result section of this paper. If the authors are making speculation, they either have to back his by previous studies that have made this conclusion or clearly state that they are speculating.
Citation: https://doi.org/10.5194/acp-2021-668-RC2 -
AC2: 'Reply on RC2', Danitza Klopper, 15 Nov 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-668/acp-2021-668-AC2-supplement.pdf
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