the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Sep 2023
tobac v1.5: Introducing Fast 3D Tracking, Splits and Mergers, and Other Enhancements for Identifying and Analysing Meteorological Phenomena
Abstract. There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous two decades that attempt to address this need, but most have limitations on the type of data they can be used with; computational and/or memory expenses that make them unwieldy with larger datasets; or require some form of data reduction prior to use that limits the tool’s utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-1722', Anonymous Referee #1, 05 Oct 2023
The manuscript is well-written and well-structured. It cites a good amount of related studies and provides a thorough explanation of what has been done in the former version, what are the potential concerns, and what are the improvements in the latest version of tobac. I don’t have major concerns about this manuscript just some quick questions and comments:
• How does the “watersheding” decide on the time when the boundary of two features meets?
• Will segmentation be twisted due to the vertical structure?
• In the detection of weather phenomena like atmospheric rivers, there are thresholds in geometric shape, size, and direction, how do these thresholds be applied in tobac? In your example, magnitude thresholds are used. But for atmospheric rivers, the magnitude threshold is the very first step. Could you explain more about the process of detecting atmospheric rivers?Citation: https://doi.org/10.5194/egusphere-2023-1722-RC1 -
AC1: 'Reply on RC1', G. Alexander Sokolowsky, 27 Feb 2024
Thank you very much for your helpful comments. They were very focused and allowed us to elaborate on details in the manuscript and of the tobac package itself in greater depth, and we appreciated the opportunity to improve the manuscript.
- How does the “watersheding” decide on the time when the boundary of two features meets?
Watershedding is performed on a data field independently on each timestep, and as such does not incorporate the time dimension directly. The watershedding algorithm acts by “flowing” outwards from a marker set at the feature location, with the expansion rate governed by the magnitude by which a particular grid point exceeds the segmentation threshold prescribed. Thus, where the boundaries of two or more features will meet depends both on the size of the region being segmented and the magnitudes of the data being segmented.
- Will segmentation be twisted due to the vertical structure?
Yes, tobac can accurately segment vertically tilted or irregular structures in 3D spatial data like a growing convective cloud in a highly sheared environment.
- In the detection of weather phenomena like atmospheric rivers, there are thresholds in geometric shape, size, and direction, how do these thresholds be applied in tobac? In your example, magnitude thresholds are used. But for atmospheric rivers, the magnitude threshold is the very first step. Could you explain more about the process of detecting atmospheric rivers?
As the reviewer points out, detection of atmospheric rivers (hereafter, ARs) typically requires more than a simple magnitude-based threshold detection. tobac is intended to be a flexible tracker, and as such isn’t specifically designed around any particular use case. However, tobac does provide a variety of options to allow users to tailor the feature detection to their particular phenomena of interest.
Size thresholds can be imposed for feature detection by prescribing the minimum number of contiguous grid cells that must exceed the magnitude threshold. A different value of this parameter can be set for each feature detection threshold, allowing for more flexible detection of different-sized features at different magnitudes.
Additional parameter settings and processes needed to detect ARs would likely depend on the particular case of use. One possibility is to follow a methodology similar to that used by Guan and Waliser (2019). ERA5 (they used ERA-interim) specific humidity and wind component fields can be used to calculate zonal and meridional components of Integrated Vapor Transport (IVT, see Equations 1a and 1b in Guan and Waliser, 2019). From these components, an IVT magnitude field and IVT direction field can be determined.
Guan and Waliser use a progressive application of percentile thresholds and geometry thresholds on IVT to identify coherent ARs. Percentile thresholds can straightforwardly be calculated from the overall ERA5 IVT field and used within the feature detection step of tobac. Imposing the geometry thresholding after the magnitude thresholding is more complex due to the cyclical nature of wind direction, but one approach would be to perform watershed segmentation on the IVT magnitude field based on the detected features. This segmentation field can then be used to analyze the IVT zonal and meridional components, as well as the IVT direction field within the segmentation mask. Guan and Waliser prescribed a poleward (meridional) IVT component >50 kg/m/s; more than half of IVT directions in the prospective area being within 45 degrees of the mean IVT; length > 2,000 km; and a length/width ratio > 2. These former two criteria can be assessed by applying the segmentation mask to the poleward IVT field and IVT direction field; and the latter two can be assessed by just examining the dimensions of the segmentation mask itself.
If all the above criteria are met, we have detected the presence of an AR feature that can then be spatiotemporally tracked using tobac. Otherwise, we would either discard this potential AR feature or follow the methology of Guan and Waliser to impose higher thresholds.
Citation: https://doi.org/10.5194/egusphere-2023-1722-AC1
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AC1: 'Reply on RC1', G. Alexander Sokolowsky, 27 Feb 2024
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CC1: 'Comment on egusphere-2023-1722', Bin Guan, 18 Oct 2023
The manuscript is a pleasant read and I congratulate the authors on the substantial improvements made to a promising, versatile tool for meteorological feature tracking. I just have a few minor comments as a community member.
- The manuscript did not mention whether the detection/identification of features (that is, the step before segmentation and tracking) can be based on multiple input fields (not just multiple thresholds for the same field) or can only take one input field in that step. For reference, in the case of atmospheric rivers (ARs), there are algorithms that rely on two or more of the following fields to detect the features: integrated water vapor (IWV), IWV transport, wind, precipitation, etc.
- Some of the improvements introduced in the manuscript have also been introduced in AR tracking algorithms, such as handling of separations and mergers, periodic boundary conditions, and grid agnosticity (Guan & Waliser, 2015, 2019).
- Given the number of studies dedicated to AR detection in recent years, including those contributing to the Atmospheric River Tracking Method Intercomparison Project (ARTMIP; Shields et al., 2018), some general discussion of AR detection might be helpful to provide more context and motivation for the development of tobac.
References:
https://doi.org/10.1002/2015JD024257
https://doi.org/10.1029/2019JD031205
https://doi.org/10.5194/gmd-11-2455-2018
Citation: https://doi.org/10.5194/egusphere-2023-1722-CC1 -
AC4: 'Reply on CC1', G. Alexander Sokolowsky, 28 Feb 2024
- The manuscript is a pleasant read and I congratulate the authors on the substantial improvements made to a promising, versatile tool for meteorological feature tracking. I just have a few minor comments as a community member.
Thank you for your kind words and thoughtful comments. They have improved the manuscript, particularly regarding our consideration of Atmospheric Rivers and discussion in the introduction.
- The manuscript did not mention whether the detection/identification of features (that is, the step before segmentation and tracking) can be based on multiple input fields (not just multiple thresholds for the same field) or can only take one input field in that step. For reference, in the case of atmospheric rivers (ARs), there are algorithms that rely on two or more of the following fields to detect the features: integrated water vapor (IWV), IWV transport, wind, precipitation, etc.
Thank you for your thoughtful comment. We agree with the reviewer that this would be extremely useful. Unfortunately, at this point in time, this version of tobac cannot be used on multiple input fields without multiple feature detection steps. However, implementing multivariate tracking while preserving our ability to remain variable-agnostic is already a key feature of our upcoming development plans for tobac, and initial results in the implementation of these capabilities are promising.
- Some of the improvements introduced in the manuscript have also been introduced in AR tracking algorithms, such as handling of separations and mergers, periodic boundary conditions, and grid agnosticity (Guan & Waliser, 2015, 2019).
Thank you for mentioning these publications. We were remiss in not citing these papers, and now include a description of their findings as well as the appropriate citations as shown below:
“Guan and Waliser (2015, 2019) have developed a tool called Tracking Atmospheric Rivers Globally as Elongated Targets (TARGET), which is designed for the detection and tracking of atmospheric rivers (ARs). TARGET includes techniques such as split and merger processing; periodic boundary condition treatments; and grid agnosticity; but can only be applied as presently designed to ARs.” (105-108)
- Given the number of studies dedicated to AR detection in recent years, including those contributing to the Atmospheric River Tracking Method Intercomparison Project (ARTMIP; Shields et al., 2018), some general discussion of AR detection might be helpful to provide more context and motivation for the development of tobac.
While we agree that AR detection and tracking are very important, we were reluctant to spend too much time expanding upon this particular use case due to the multitude of other atmospheric phenomena that can be detected with tobac, and the variety of improvements that needed to be discussed in the paper. Nonetheless, we have added a mention of ARTMIP and short discussion of the innovative techniques in TARGET:
“In recent years, there has also been a greater research focus on Atmospheric Rivers (ARs), including many existing within the Atmospheric River Tracking Method Intercomparison Project (ARTMIP; Shields et al. 2018). Guan and Waliser (2015, 2019) have developed a tool called Tracking Atmospheric Rivers Globally as Elongated Targets (TARGET), which is designed for the detection and tracking of atmospheric rivers (ARs). TARGET includes techniques such as split and merger processing; periodic boundary condition treatments; and grid agnosticity; but can only be applied as presently designed to ARs.” (103-108)
Citation: https://doi.org/10.5194/egusphere-2023-1722-AC4
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RC2: 'Comment on egusphere-2023-1722', Anonymous Referee #2, 23 Nov 2023
“tobac v1.5: Introducing Fast 3D Tracking, Splits and Mergers, and Other Enhancements for Identifying and Analysing Meteorological Phenomena”
The manuscript is mostly well written and gives important information on the update of tobac 1.5 compared to tobac 1.2. The new features are intriguing and attractive to end users. However, my main concern is the stability of tobac 1.5, especially in 3D segmentation and tracking. In addition, the author needs to clarify how much effort the end user will need to make in order to use tobac 1.5. It seems there’s quite a lot of thresholding test awaits for the users in order to use tobac 1.5, and we are not sure if the thresholds are good enough throughout the evolution of atmospheric processes. The overall paper is an useful contribution to the scientific community and requires major revision before publication.
- Introduction section, the authors keep referring to tracking object as “cloud”, as Tobac is largely applied to radar cell tracking, please make sure to clarify radar does not see cloud (most X-, C-, S-bands radars) but only observe rain droplets and hydrometeors that are much larger than cloud droplets.
- Line 70, regarding the introduction of TITAN, the major disadvantage is its centroid method based on reflectivity and even the modern version of TITAN allowing multiple thresholds perform not well for tracking storm cells from initiation to the very end.
- Line 95, the WDSS II system used SCIT as its default tracking toolbox and WDSS II can be open-source base on request. The main flaw of SCIT is similar to TITAN as they are both centroid based methods.
- Line 110-115, please elaborate on the reasoning behind this Tobac upgrade here, is it the tobac v1.2 cannot handle high resolution? Why the new missions like AOS and INCUS will require this upgrade? I see you listed a lot of new features in the upgraded tobac, please elaborate how these new features can contribute to cell tracking.
- Figure 1, is tobac v1.2 and tobac 1.5 both centroid based methods? If so, does that means the anvil part of the cell is missing as shown in figure 1? In addition, what kind of QC has been applied to the reflectivity dataset? SNR? Attenuation correction? And maybe self-consistency calibration?
- Line 144-157, it seems tobac is, in general, a centroid based method as the latest TITAN. So any feature selection such the size of object, the reflectivity thresholds, and more do require “ a great deal of human input and attention” as mentioned in the introduction when the authors are reviewing other tracking methods.
- Line 163-174, using watershed-based method for segmentation can be quite useful here, but the over or under segmentation happens often. Please share multiple (at least 5-time steps) before and after segmentation figures for the case shown in Figure 1. I’m curious to see how tobac segmentation performs in terms of stability here.
- Line 203-220, base on the example and text here, tobac is a user defined centroid based method. It is counter productive to use hard thresholds while using watershed-based method. This is just an opinion/comment and does not require authors to reply.
- Figure 3, tobac 1.5 has a nice touch with multi-layer clouds. It is clear this can be used in model outputs. Any idea what kind of observational data we can use to test the performance of tobac 1.5 on multi-layer clouds? I’m guessing not polar coordinate satellites as the temporal resolution is low, but geostationary satellites such as GOES-series are all passive sensors and cannot provide 3D structure features for tracking. It is also hard for most radars as shorter wavelengths suffer from attenuation but longer wavelengths are not sensitive enough to observe cirrus clouds.
- Figure 5, how sensitive is the 3D segmentation with boxing method is to user picked size? Please include seed size from 2,4,6, and 8 in the reply. I’m curious how stable this boxing method is here. Do the authors believe that changing the size of the box will greatly impact the quality of tracking here? Especially during different stages of cell involvement.
- Figure 6, please add contours of the cell segmentation to all panels. In addition, the authors mention the user need to pick Z threshold here, demonstrated in 30 dBZ. What will happen if the user wishes to include all the stratiform portion of the clouds and set Z limit to – 10 dBZ? Will tobac still be able to segment? Please also include the 3D view of panel e-h, I’m curious how the regeneration of cells as it propagates will impact 3D segmentation here, or if there is any?
- Line 301, “Hu et al.” typo, missing the year.
- Figure 9. Please add 4 time steps of 2D segmentation and tracking (with splits and mergers labelled) to this case. Also, what will happen if one uses 0 dBZ here as threshold.
- Sec 4.1. What is the 3D tracking efficiency here using tobac 1.5? Let’s say using MRMS gridded 3D Z field for 1 day using Z threshold of 15 dBZ.
Citation: https://doi.org/10.5194/egusphere-2023-1722-RC2 - AC2: 'Reply on RC2', G. Alexander Sokolowsky, 28 Feb 2024
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RC3: 'Comment on egusphere-2023-1722', Anonymous Referee #3, 25 Nov 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1722/egusphere-2023-1722-RC3-supplement.pdf
- AC3: 'Reply on RC3', G. Alexander Sokolowsky, 28 Feb 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1722', Anonymous Referee #1, 05 Oct 2023
The manuscript is well-written and well-structured. It cites a good amount of related studies and provides a thorough explanation of what has been done in the former version, what are the potential concerns, and what are the improvements in the latest version of tobac. I don’t have major concerns about this manuscript just some quick questions and comments:
• How does the “watersheding” decide on the time when the boundary of two features meets?
• Will segmentation be twisted due to the vertical structure?
• In the detection of weather phenomena like atmospheric rivers, there are thresholds in geometric shape, size, and direction, how do these thresholds be applied in tobac? In your example, magnitude thresholds are used. But for atmospheric rivers, the magnitude threshold is the very first step. Could you explain more about the process of detecting atmospheric rivers?Citation: https://doi.org/10.5194/egusphere-2023-1722-RC1 -
AC1: 'Reply on RC1', G. Alexander Sokolowsky, 27 Feb 2024
Thank you very much for your helpful comments. They were very focused and allowed us to elaborate on details in the manuscript and of the tobac package itself in greater depth, and we appreciated the opportunity to improve the manuscript.
- How does the “watersheding” decide on the time when the boundary of two features meets?
Watershedding is performed on a data field independently on each timestep, and as such does not incorporate the time dimension directly. The watershedding algorithm acts by “flowing” outwards from a marker set at the feature location, with the expansion rate governed by the magnitude by which a particular grid point exceeds the segmentation threshold prescribed. Thus, where the boundaries of two or more features will meet depends both on the size of the region being segmented and the magnitudes of the data being segmented.
- Will segmentation be twisted due to the vertical structure?
Yes, tobac can accurately segment vertically tilted or irregular structures in 3D spatial data like a growing convective cloud in a highly sheared environment.
- In the detection of weather phenomena like atmospheric rivers, there are thresholds in geometric shape, size, and direction, how do these thresholds be applied in tobac? In your example, magnitude thresholds are used. But for atmospheric rivers, the magnitude threshold is the very first step. Could you explain more about the process of detecting atmospheric rivers?
As the reviewer points out, detection of atmospheric rivers (hereafter, ARs) typically requires more than a simple magnitude-based threshold detection. tobac is intended to be a flexible tracker, and as such isn’t specifically designed around any particular use case. However, tobac does provide a variety of options to allow users to tailor the feature detection to their particular phenomena of interest.
Size thresholds can be imposed for feature detection by prescribing the minimum number of contiguous grid cells that must exceed the magnitude threshold. A different value of this parameter can be set for each feature detection threshold, allowing for more flexible detection of different-sized features at different magnitudes.
Additional parameter settings and processes needed to detect ARs would likely depend on the particular case of use. One possibility is to follow a methodology similar to that used by Guan and Waliser (2019). ERA5 (they used ERA-interim) specific humidity and wind component fields can be used to calculate zonal and meridional components of Integrated Vapor Transport (IVT, see Equations 1a and 1b in Guan and Waliser, 2019). From these components, an IVT magnitude field and IVT direction field can be determined.
Guan and Waliser use a progressive application of percentile thresholds and geometry thresholds on IVT to identify coherent ARs. Percentile thresholds can straightforwardly be calculated from the overall ERA5 IVT field and used within the feature detection step of tobac. Imposing the geometry thresholding after the magnitude thresholding is more complex due to the cyclical nature of wind direction, but one approach would be to perform watershed segmentation on the IVT magnitude field based on the detected features. This segmentation field can then be used to analyze the IVT zonal and meridional components, as well as the IVT direction field within the segmentation mask. Guan and Waliser prescribed a poleward (meridional) IVT component >50 kg/m/s; more than half of IVT directions in the prospective area being within 45 degrees of the mean IVT; length > 2,000 km; and a length/width ratio > 2. These former two criteria can be assessed by applying the segmentation mask to the poleward IVT field and IVT direction field; and the latter two can be assessed by just examining the dimensions of the segmentation mask itself.
If all the above criteria are met, we have detected the presence of an AR feature that can then be spatiotemporally tracked using tobac. Otherwise, we would either discard this potential AR feature or follow the methology of Guan and Waliser to impose higher thresholds.
Citation: https://doi.org/10.5194/egusphere-2023-1722-AC1
-
AC1: 'Reply on RC1', G. Alexander Sokolowsky, 27 Feb 2024
-
CC1: 'Comment on egusphere-2023-1722', Bin Guan, 18 Oct 2023
The manuscript is a pleasant read and I congratulate the authors on the substantial improvements made to a promising, versatile tool for meteorological feature tracking. I just have a few minor comments as a community member.
- The manuscript did not mention whether the detection/identification of features (that is, the step before segmentation and tracking) can be based on multiple input fields (not just multiple thresholds for the same field) or can only take one input field in that step. For reference, in the case of atmospheric rivers (ARs), there are algorithms that rely on two or more of the following fields to detect the features: integrated water vapor (IWV), IWV transport, wind, precipitation, etc.
- Some of the improvements introduced in the manuscript have also been introduced in AR tracking algorithms, such as handling of separations and mergers, periodic boundary conditions, and grid agnosticity (Guan & Waliser, 2015, 2019).
- Given the number of studies dedicated to AR detection in recent years, including those contributing to the Atmospheric River Tracking Method Intercomparison Project (ARTMIP; Shields et al., 2018), some general discussion of AR detection might be helpful to provide more context and motivation for the development of tobac.
References:
https://doi.org/10.1002/2015JD024257
https://doi.org/10.1029/2019JD031205
https://doi.org/10.5194/gmd-11-2455-2018
Citation: https://doi.org/10.5194/egusphere-2023-1722-CC1 -
AC4: 'Reply on CC1', G. Alexander Sokolowsky, 28 Feb 2024
- The manuscript is a pleasant read and I congratulate the authors on the substantial improvements made to a promising, versatile tool for meteorological feature tracking. I just have a few minor comments as a community member.
Thank you for your kind words and thoughtful comments. They have improved the manuscript, particularly regarding our consideration of Atmospheric Rivers and discussion in the introduction.
- The manuscript did not mention whether the detection/identification of features (that is, the step before segmentation and tracking) can be based on multiple input fields (not just multiple thresholds for the same field) or can only take one input field in that step. For reference, in the case of atmospheric rivers (ARs), there are algorithms that rely on two or more of the following fields to detect the features: integrated water vapor (IWV), IWV transport, wind, precipitation, etc.
Thank you for your thoughtful comment. We agree with the reviewer that this would be extremely useful. Unfortunately, at this point in time, this version of tobac cannot be used on multiple input fields without multiple feature detection steps. However, implementing multivariate tracking while preserving our ability to remain variable-agnostic is already a key feature of our upcoming development plans for tobac, and initial results in the implementation of these capabilities are promising.
- Some of the improvements introduced in the manuscript have also been introduced in AR tracking algorithms, such as handling of separations and mergers, periodic boundary conditions, and grid agnosticity (Guan & Waliser, 2015, 2019).
Thank you for mentioning these publications. We were remiss in not citing these papers, and now include a description of their findings as well as the appropriate citations as shown below:
“Guan and Waliser (2015, 2019) have developed a tool called Tracking Atmospheric Rivers Globally as Elongated Targets (TARGET), which is designed for the detection and tracking of atmospheric rivers (ARs). TARGET includes techniques such as split and merger processing; periodic boundary condition treatments; and grid agnosticity; but can only be applied as presently designed to ARs.” (105-108)
- Given the number of studies dedicated to AR detection in recent years, including those contributing to the Atmospheric River Tracking Method Intercomparison Project (ARTMIP; Shields et al., 2018), some general discussion of AR detection might be helpful to provide more context and motivation for the development of tobac.
While we agree that AR detection and tracking are very important, we were reluctant to spend too much time expanding upon this particular use case due to the multitude of other atmospheric phenomena that can be detected with tobac, and the variety of improvements that needed to be discussed in the paper. Nonetheless, we have added a mention of ARTMIP and short discussion of the innovative techniques in TARGET:
“In recent years, there has also been a greater research focus on Atmospheric Rivers (ARs), including many existing within the Atmospheric River Tracking Method Intercomparison Project (ARTMIP; Shields et al. 2018). Guan and Waliser (2015, 2019) have developed a tool called Tracking Atmospheric Rivers Globally as Elongated Targets (TARGET), which is designed for the detection and tracking of atmospheric rivers (ARs). TARGET includes techniques such as split and merger processing; periodic boundary condition treatments; and grid agnosticity; but can only be applied as presently designed to ARs.” (103-108)
Citation: https://doi.org/10.5194/egusphere-2023-1722-AC4
-
RC2: 'Comment on egusphere-2023-1722', Anonymous Referee #2, 23 Nov 2023
“tobac v1.5: Introducing Fast 3D Tracking, Splits and Mergers, and Other Enhancements for Identifying and Analysing Meteorological Phenomena”
The manuscript is mostly well written and gives important information on the update of tobac 1.5 compared to tobac 1.2. The new features are intriguing and attractive to end users. However, my main concern is the stability of tobac 1.5, especially in 3D segmentation and tracking. In addition, the author needs to clarify how much effort the end user will need to make in order to use tobac 1.5. It seems there’s quite a lot of thresholding test awaits for the users in order to use tobac 1.5, and we are not sure if the thresholds are good enough throughout the evolution of atmospheric processes. The overall paper is an useful contribution to the scientific community and requires major revision before publication.
- Introduction section, the authors keep referring to tracking object as “cloud”, as Tobac is largely applied to radar cell tracking, please make sure to clarify radar does not see cloud (most X-, C-, S-bands radars) but only observe rain droplets and hydrometeors that are much larger than cloud droplets.
- Line 70, regarding the introduction of TITAN, the major disadvantage is its centroid method based on reflectivity and even the modern version of TITAN allowing multiple thresholds perform not well for tracking storm cells from initiation to the very end.
- Line 95, the WDSS II system used SCIT as its default tracking toolbox and WDSS II can be open-source base on request. The main flaw of SCIT is similar to TITAN as they are both centroid based methods.
- Line 110-115, please elaborate on the reasoning behind this Tobac upgrade here, is it the tobac v1.2 cannot handle high resolution? Why the new missions like AOS and INCUS will require this upgrade? I see you listed a lot of new features in the upgraded tobac, please elaborate how these new features can contribute to cell tracking.
- Figure 1, is tobac v1.2 and tobac 1.5 both centroid based methods? If so, does that means the anvil part of the cell is missing as shown in figure 1? In addition, what kind of QC has been applied to the reflectivity dataset? SNR? Attenuation correction? And maybe self-consistency calibration?
- Line 144-157, it seems tobac is, in general, a centroid based method as the latest TITAN. So any feature selection such the size of object, the reflectivity thresholds, and more do require “ a great deal of human input and attention” as mentioned in the introduction when the authors are reviewing other tracking methods.
- Line 163-174, using watershed-based method for segmentation can be quite useful here, but the over or under segmentation happens often. Please share multiple (at least 5-time steps) before and after segmentation figures for the case shown in Figure 1. I’m curious to see how tobac segmentation performs in terms of stability here.
- Line 203-220, base on the example and text here, tobac is a user defined centroid based method. It is counter productive to use hard thresholds while using watershed-based method. This is just an opinion/comment and does not require authors to reply.
- Figure 3, tobac 1.5 has a nice touch with multi-layer clouds. It is clear this can be used in model outputs. Any idea what kind of observational data we can use to test the performance of tobac 1.5 on multi-layer clouds? I’m guessing not polar coordinate satellites as the temporal resolution is low, but geostationary satellites such as GOES-series are all passive sensors and cannot provide 3D structure features for tracking. It is also hard for most radars as shorter wavelengths suffer from attenuation but longer wavelengths are not sensitive enough to observe cirrus clouds.
- Figure 5, how sensitive is the 3D segmentation with boxing method is to user picked size? Please include seed size from 2,4,6, and 8 in the reply. I’m curious how stable this boxing method is here. Do the authors believe that changing the size of the box will greatly impact the quality of tracking here? Especially during different stages of cell involvement.
- Figure 6, please add contours of the cell segmentation to all panels. In addition, the authors mention the user need to pick Z threshold here, demonstrated in 30 dBZ. What will happen if the user wishes to include all the stratiform portion of the clouds and set Z limit to – 10 dBZ? Will tobac still be able to segment? Please also include the 3D view of panel e-h, I’m curious how the regeneration of cells as it propagates will impact 3D segmentation here, or if there is any?
- Line 301, “Hu et al.” typo, missing the year.
- Figure 9. Please add 4 time steps of 2D segmentation and tracking (with splits and mergers labelled) to this case. Also, what will happen if one uses 0 dBZ here as threshold.
- Sec 4.1. What is the 3D tracking efficiency here using tobac 1.5? Let’s say using MRMS gridded 3D Z field for 1 day using Z threshold of 15 dBZ.
Citation: https://doi.org/10.5194/egusphere-2023-1722-RC2 - AC2: 'Reply on RC2', G. Alexander Sokolowsky, 28 Feb 2024
-
RC3: 'Comment on egusphere-2023-1722', Anonymous Referee #3, 25 Nov 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1722/egusphere-2023-1722-RC3-supplement.pdf
- AC3: 'Reply on RC3', G. Alexander Sokolowsky, 28 Feb 2024
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Cited
G. Alexander Sokolowsky
Sean W. Freeman
William K. Jones
Julia Kukulies
Fabian Senf
Peter J. Marinescu
Max Heikenfeld
Kelcy N. Brunner
Eric C. Bruning
Scott M. Collis
Robert C. Jackson
Gabrielle R. Leung
Nils Pfeifer
Bhupendra A. Raut
Stephen M. Saleeby
Philip Stier
Susan C. van den Heever
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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