Data quality control and calibration for mini-radiosonde system &ldquo;Storm Tracker&rdquo; in Taiwan

Kuo, Hung-Chi; Yo, Ting-Shuo; Yu, Hungjui; Su, Shih-Hao; Liu, Ching-Hwang; Lin, Po-Hsiung

doi:https://doi.org/10.5194/egusphere-2024-661

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https://doi.org/10.5194/egusphere-2024-661

Preprints

08 Apr 2024

| 08 Apr 2024

Data quality control and calibration for mini-radiosonde system “Storm Tracker” in Taiwan

Hung-Chi Kuo, Ting-Shuo Yo, Hungjui Yu, Shih-Hao Su, Ching-Hwang Liu, and Po-Hsiung Lin

Abstract. This study introduced and evaluated the calibration schemes of a newly developed upper-air radiosonde instrument, “Storm Tracker” (ST), with data collected in field observations during 2016–2022. The ST is a radiosonde instrument developed and tested in 2016 (Hwang et al., 2020). In a series of field campaigns in the Taiwan area, more than one thousand co-launches of ST and Vaisala RS41-SGP (VS) are conducted. Using the VS measurements as the reference, we developed data correction methods and examined the characteristics of the ST sounding. The corrected ST soundings have 1-K temperature and 7 % relative humidity root mean square difference to the VS soundings. These error differences can be reduced to 0.66-K and 4.61 % below the 700-hPa height. The GPS estimated ST wind error difference is about 0.05 ms-1. The results suggested that the ST can perform similarly to the reference sounding and has reached the level required for environmental sampling and scientific research. The geostrophic adjustment dynamics indicate that the spatial temperature variation in the free atmosphere may not be large. However, the lower atmosphere may have large wind, temperature, and moisture variations. Due to the relatively low cost and accuracy after correction, ST can complement regular upper-air observations for high spatial and temporal resolution.

Received: 05 Mar 2024 – Discussion started: 08 Apr 2024

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Hung-Chi Kuo, Ting-Shuo Yo, Hungjui Yu, Shih-Hao Su, Ching-Hwang Liu, and Po-Hsiung Lin

Status: closed

RC1:
'Comment on egusphere-2024-661', Anonymous Referee #1, 29 Apr 2024

The manuscript "Data quality control and calibration for mini-radiosonde system Storm Tracker in Taiwan" describes

a twin sounding campaign of the Storm Tracker and the Vaisala RS41 radiosonde, where the latter is used as reference.

The results of the twinsoundings are used to derive a correction for the Storm Tracker temperature and humidity profiles, using a statistical method based on the cumulative distribution function (CDF).

I have considerable concerns with the way the twinsoundings were performed (payload configuration) as well as the applied analysis method, both of which I think affect the results and conclusions of this study.
One concern is with the method applied to analyse the data from the coincident twinsoudings. Since

these soundings are performed with two different radiosondes on the same balloon, this allows for a

direct comparison of the profile data. Using a statistical method like the cumulative distribution

function (CDF) seems to me like an unnecessary complication. To my understanding the CDF-based

method as employed by Ciesielski et al., is applied when comparing radiosonde data taken under

similar meteorological conditions albeit not coincidently on the same rig.

The advantage of coincident twinsounding data is that it allows to directly determine the bias +

associated uncertainty between both systems. Furthermore, the physical mechanism that is causing

the bias between both radiosondes is warming by solar radiation that is counteracted by convective

cooling by the air flowing over the sensor (ventilation). The efficiency of the convective cooling

is directly linked to the altitude-dependent ambient air pressure. The CDF method that is applied

in the manuscript does indeed derive corrections for pressure ranges, but the purpose of further

analysing the differences in sense of ambient temperature is not clear to me.
For full comment/report see supplement.

Citation: https://doi.org/10.5194/egusphere-2024-661-RC1
- AC1: 'Reply on RC1', Ting-Shuo Yo, 10 Jun 2024
  
  We really appreciate the comments on the manuscript. We put the major response here, and the detailed response can be found in the attachment.
  We acknowledge that the design of our co-launch experiment differs from some previous field campaigns. In this study, attaching the Storm Tracker (ST) to the Vaisala radiosonde (VS) allowed for direct inter-comparison at native temporal resolution (every second). Additionally, the Cumulative Distribution Function (CDF) is a well-established non-parametric statistical model for regression and classification within the community. The concept of correction processes for temperature (based on different pressure levels) and relative humidity (based on different temperature levels) between different instruments is similar to methods used in other field campaigns (e.g., Ciesielski et al. 2014). Furthermore, in this study, we compared CDF with a parametric alternative, Generalized Linear Models (GLM), for data correction purposes. Our results indicate that both models perform well for this task.
  In over 1,000 co-launches, we consistently bound ST and VS in the same configuration, as we believe this is essential for a controlled experiment. While it is challenging to definitively prove that the observed biases are unrelated to the binding method, we have conducted thorough checks, including in-lab measurements of both sensors and several co-launches with the sensors in separate balloons (although this data was not used for correction). We found that the bias patterns observed were consistent with those in the co-launch dataset.
  Regarding the larger temperature biases near ground level, these issues are primarily due to the sun directly heating the ST while waiting to launch. In most cases, we prevent this, and the correction table excludes such instances as outliers.
  We must also acknowledge that we cannot entirely rule out the possibility that the payload configuration (especially the casing) could contaminate the T and RH measurements of ST. The overall idea behind the ST hardware design is to leverage easily accessible commercial sensors and serve as a supplement to regular sounding observations, specifically in the lower boundary layer. While further hardware upgrades are underway, the ST cannot replace the more mature design instruments. Therefore, we suggest co-launches for future applications to ensure data accuracy.
  
  *Ciesielski, P. E., Yu, H., Johnson, R. H., Yoneyama, K., Katsumata, M., Long, C. N., ... & Van Hove, T. (2014). Quality-controlled upper-air sounding dataset for DYNAMO/CINDY/AMIE: Development and corrections. Journal of Atmospheric and Oceanic Technology, 31(4), 741-764.
  
  Citation: https://doi.org/10.5194/egusphere-2024-661-AC1
RC2:
'Comment on egusphere-2024-661', Anonymous Referee #2, 01 May 2024
This paper discusses the results of corrections of temperature and relative humidity (RH) measurements of a mini-radiosonde system “Storm Tracker” in the troposphere in Taiwan. The Storm Tracker (ST) is described in the paper by Hwang et al. (AMT, 2020, https://doi.org/10.5194/amt-13-5395-2020), which uses a low-cost plug-in temperature-humidity sensor (TE-Connectivity HTU21D, https://www.te.com/) which is not developed specifically for upper-air sounding. Using more than 1000 flights of ST together with Vaisala RS41-SGP radiosonde (VS), the authors developed correction tables/functions using VS measurements as the reference. Furthermore, the authors tested different correction methods and compared the results. The best results are obtained with the simplest method called the cumulative distribution function (CDF) method, rather than more sophisticated machine-learning-based methods, at least for temperature.

While making more than 1000 dual flights with VS to deduce correction tables/functions for the ST measurements is a very good effort and the results are worth being reported in AMT, I tend to feel that the current manuscript is “overselling” the ST. This is mainly because the temperature and RH sensors of the ST, as shown in Figures 2 and 3 of Hwang et al. (2020), are never optimal for balloon upper-air sounding, e.g. with possible large contaminations to the temperature measurements depending on solar radiative heating and thus cloud cover status as well as day-versus-night difference. I would also have concern about the production stability of the sensor, i.e. whether the characteristics and the quality are within the reasonable range of uncertainty in different production batches (e.g. in different years). This may mean that in the end, we may always need dual flights with a well characterized and reliable radiosonde like the VS. In addition, the radiowave used for ST is from 432 to 436.5 MHz (Hwang et al., 2020) which is for amateur radiolocation, not for meteorological aids (e.g. International Telecommunication Union (ITU), “Radio Regulations”, Volume 1, 2020, available from https://www.itu.int/pub/R-REG-RR-2020); thus, in some countries ST cannot be used as a meteorological radiosonde officially. This may mean that the ST cannot be “widely” used in the future. Note also that one-hourly sounding campaigns like the one shown in Section 5.2 are not impossible with modern radiosondes like the VS; thus it is not easy for me to imagine possible applications of the ST aiming at new scientific studies that are only possible with the ST.

Therefore, I think that the manuscript might be published in AMT after reconsidering the “overselling” parts (which will be specifically pointed out below).

Specific comments:

Line 16 (and line 369): I am afraid that “geostrophic adjustment dynamics” is never discussed in the manuscript.

Introduction and Figure 1: Please describe the main technological points of the ST in more detail, including the model and characteristics of the equipped temperature-RH sensor. Figure 1(b) should be much greater, and an enlarged photo for the sensor part may be added. Also, Figure 1(c) is not a good one, because the ST sensor boom (or “sensor box”) is not well shown.

Introduction, the review part of various radiosonde issues: The papers cited here tend to be too old. More recent papers for more recent radiosondes need to be cited. These include:
Vaisala RS41 radiosonde (but with GRUAN data processing): Sommer et al., GRUAN characterisation and data processing of the Vaisala RS41 radiosonde. GRUAN Technical Document 8 (GRUAN-TD-8), v1.0.0 (2023-06-28), https://www.gruan.org/documentation/gruan/td/gruan-td-8.

Modem M10 radiosonde: Dupont, J., M. Haeffelin, J. Badosa, G. Clain, C. Raux, and D. Vignelles, 2020: Characterization and Corrections of Relative Humidity Measurement from Meteomodem M10 Radiosondes at Midlatitude Stations. J. Atmos. Oceanic Technol., 37, 857–871, https://doi.org/10.1175/JTECH-D-18-0205.1.

Meisei RS-11G and iMS-100 radiosondes: Kizu et al., Technical characteristics and GRUAN data processing for the Meisei RS-11G and iMS-100 radiosondes, GRUAN-TD-5, v1.0 (2018-02-21), https://www.gruan.org/documentation/gruan/td/gruan-td-5.

These also include very useful information on the modern radiosonde sensor characteristics and on all necessary corrections to the radiosonde measurements.

Figure 2: Are the ST RH measurements really dry biased in comparison with the VS measurements below the 500 hPa level? They look wet biased in this case.

Lines 98-105: Testing mathematically more sophisticated machine-learning-based methods is good and interesting, but if the sensor characteristics are not optimal for upper air sounding (e.g. ~5 deg.C temperature error as shown in Figure 2 is just too large!), I tend to think that we should improve the sensor itself before reconsidering correction methods.

Table 1: Please also add the information on e.g. climate zone, season, etc., i.e. the information listed at Lines 92-93, to the place e.g. right after Location.

Figure 3: I confused with the three horizontal lines within the gray box. I thought that the data processing of the ST and the VS is independent before “L2_ST-VS”. In other words, I thought that the “Paired Entries” are used to establish correction tables/functions with various different methods. Or, in other words, I thought that for the VS, the authors simply use the manufacturer-processed data set, while I have impression from the current figure that the authors make their own (and perhaps common) corrections from L1_VS to L2_VS. The term Level 2 may be confusing if it is used for the ST at this stage, because the ST data will be further corrected by using the VS data as the reference if my understanding is correct.

Section 3.1 (and Figure 4 and its caption): Please add the explanation how to obtain delta T (and its uncertainty) from the CDF.

Line228: three-dimensional?

Lines 241-244: Please also add “(GLM1)”, “(GLM2)”, and “(GLM3)” here, not later.

Lines 255-258: Solar elevation angle may be a better variable?

Figure 7: Results from which method, CDF, GLM*?

Lines 283-285: I have a difficulty on this discussion. I am not an expert of metrology (i.e. science of measurement), but I think that that the “average errors” here are comparable to the uncertainties of the VS measurements does not mean that the uncertainties of the ST measurements (after the corrections) are comparable to the uncertainties of the VS measurements. Can we say roughly how large are the uncertainties of the corrected ST measurements in this case? Is it ~1.414 times?

Figures 10 and 11: The authors should also show delta T and delta RH profiles as well. In particular, delta T profiles are very important because we need tropospheric temperature measurements with uncertainties less than e.g. 0.5 K or even 0.1 K usually, and this degree of differences is hard to see with the current panels.

Line 330: A wavy pattern. Which pattern are the authors referring to? With this figure, I am afraid that we cannot judge that GLM may be better.

Section 5.2: One-hourly sounding campaign is possible with the VS as well.

Line 369: Geostrophic adjustment dynamics has never been discussed, I am afraid.

Lines 373-377: As discussed above, I personally think that the sensor characteristics (including the sensor covering and orientation) need to be improved and more optimized for upper-air sounding, before considering mathematically more sophisticated correction methods.

Lines 378-386: As discussed in the beginning, I tend to think that the authors are overselling the ST here.
Citation: https://doi.org/10.5194/egusphere-2024-661-RC2
- AC2: 'Reply on RC2', Ting-Shuo Yo, 10 Jun 2024
  
  We really appreciate the comments on the manuscript. We have carefully reviewed the comments and adjusted our work accordingly. We put the general response here and the detailed response in the attachment.
  
      Regarding the issue of overselling, it is important to clarify that while Soundings from Triggers (ST) are indeed suitable for Planetary Boundary Layer (PBL) studies and areas with complex terrain, they should be viewed as a supplement to Vaisala Soundings (VS) rather than a replacement. Atmospheric conditions at higher altitudes may not always require high-frequency observations due to geostrophic adjustment, but ST can significantly enhance our understanding of PBL dynamics.
      In regions such as Southeast Asia, where PBL conditions can vary within short distances, the high spatial frequency observations provided by ST are particularly beneficial. It is important to note, however, that the correction results presented are specific to Taiwan. To ensure broader applicability, we suggest conducting co-launches during field campaigns. This approach would allow users to derive in-situ correction formulas using the proposed methods.
      Furthermore, we recommend utilizing ST between VS launches to optimize data collection and analysis. This combined approach will enhance the overall effectiveness of atmospheric observations and improve the accuracy of data interpretation. New statements are added in section 6 to address this.
      Finally, regarding radio regulations, the hardware design for STs allows for adjustments to different radio bands if needed. While a hardware update is beyond the scope of this manuscript, it is important to note that radio regulation should not pose an issue for field campaigns using STs.
  
  Citation: https://doi.org/10.5194/egusphere-2024-661-AC2

Status: closed

RC1:
'Comment on egusphere-2024-661', Anonymous Referee #1, 29 Apr 2024

The manuscript "Data quality control and calibration for mini-radiosonde system Storm Tracker in Taiwan" describes

a twin sounding campaign of the Storm Tracker and the Vaisala RS41 radiosonde, where the latter is used as reference.

The results of the twinsoundings are used to derive a correction for the Storm Tracker temperature and humidity profiles, using a statistical method based on the cumulative distribution function (CDF).

I have considerable concerns with the way the twinsoundings were performed (payload configuration) as well as the applied analysis method, both of which I think affect the results and conclusions of this study.
One concern is with the method applied to analyse the data from the coincident twinsoudings. Since

these soundings are performed with two different radiosondes on the same balloon, this allows for a

direct comparison of the profile data. Using a statistical method like the cumulative distribution

function (CDF) seems to me like an unnecessary complication. To my understanding the CDF-based

method as employed by Ciesielski et al., is applied when comparing radiosonde data taken under

similar meteorological conditions albeit not coincidently on the same rig.

The advantage of coincident twinsounding data is that it allows to directly determine the bias +

associated uncertainty between both systems. Furthermore, the physical mechanism that is causing

the bias between both radiosondes is warming by solar radiation that is counteracted by convective

cooling by the air flowing over the sensor (ventilation). The efficiency of the convective cooling

is directly linked to the altitude-dependent ambient air pressure. The CDF method that is applied

in the manuscript does indeed derive corrections for pressure ranges, but the purpose of further

analysing the differences in sense of ambient temperature is not clear to me.
For full comment/report see supplement.

Citation: https://doi.org/10.5194/egusphere-2024-661-RC1
- AC1: 'Reply on RC1', Ting-Shuo Yo, 10 Jun 2024
  
  We really appreciate the comments on the manuscript. We put the major response here, and the detailed response can be found in the attachment.
  We acknowledge that the design of our co-launch experiment differs from some previous field campaigns. In this study, attaching the Storm Tracker (ST) to the Vaisala radiosonde (VS) allowed for direct inter-comparison at native temporal resolution (every second). Additionally, the Cumulative Distribution Function (CDF) is a well-established non-parametric statistical model for regression and classification within the community. The concept of correction processes for temperature (based on different pressure levels) and relative humidity (based on different temperature levels) between different instruments is similar to methods used in other field campaigns (e.g., Ciesielski et al. 2014). Furthermore, in this study, we compared CDF with a parametric alternative, Generalized Linear Models (GLM), for data correction purposes. Our results indicate that both models perform well for this task.
  In over 1,000 co-launches, we consistently bound ST and VS in the same configuration, as we believe this is essential for a controlled experiment. While it is challenging to definitively prove that the observed biases are unrelated to the binding method, we have conducted thorough checks, including in-lab measurements of both sensors and several co-launches with the sensors in separate balloons (although this data was not used for correction). We found that the bias patterns observed were consistent with those in the co-launch dataset.
  Regarding the larger temperature biases near ground level, these issues are primarily due to the sun directly heating the ST while waiting to launch. In most cases, we prevent this, and the correction table excludes such instances as outliers.
  We must also acknowledge that we cannot entirely rule out the possibility that the payload configuration (especially the casing) could contaminate the T and RH measurements of ST. The overall idea behind the ST hardware design is to leverage easily accessible commercial sensors and serve as a supplement to regular sounding observations, specifically in the lower boundary layer. While further hardware upgrades are underway, the ST cannot replace the more mature design instruments. Therefore, we suggest co-launches for future applications to ensure data accuracy.
  
  *Ciesielski, P. E., Yu, H., Johnson, R. H., Yoneyama, K., Katsumata, M., Long, C. N., ... & Van Hove, T. (2014). Quality-controlled upper-air sounding dataset for DYNAMO/CINDY/AMIE: Development and corrections. Journal of Atmospheric and Oceanic Technology, 31(4), 741-764.
  
  Citation: https://doi.org/10.5194/egusphere-2024-661-AC1
RC2:
'Comment on egusphere-2024-661', Anonymous Referee #2, 01 May 2024
This paper discusses the results of corrections of temperature and relative humidity (RH) measurements of a mini-radiosonde system “Storm Tracker” in the troposphere in Taiwan. The Storm Tracker (ST) is described in the paper by Hwang et al. (AMT, 2020, https://doi.org/10.5194/amt-13-5395-2020), which uses a low-cost plug-in temperature-humidity sensor (TE-Connectivity HTU21D, https://www.te.com/) which is not developed specifically for upper-air sounding. Using more than 1000 flights of ST together with Vaisala RS41-SGP radiosonde (VS), the authors developed correction tables/functions using VS measurements as the reference. Furthermore, the authors tested different correction methods and compared the results. The best results are obtained with the simplest method called the cumulative distribution function (CDF) method, rather than more sophisticated machine-learning-based methods, at least for temperature.

While making more than 1000 dual flights with VS to deduce correction tables/functions for the ST measurements is a very good effort and the results are worth being reported in AMT, I tend to feel that the current manuscript is “overselling” the ST. This is mainly because the temperature and RH sensors of the ST, as shown in Figures 2 and 3 of Hwang et al. (2020), are never optimal for balloon upper-air sounding, e.g. with possible large contaminations to the temperature measurements depending on solar radiative heating and thus cloud cover status as well as day-versus-night difference. I would also have concern about the production stability of the sensor, i.e. whether the characteristics and the quality are within the reasonable range of uncertainty in different production batches (e.g. in different years). This may mean that in the end, we may always need dual flights with a well characterized and reliable radiosonde like the VS. In addition, the radiowave used for ST is from 432 to 436.5 MHz (Hwang et al., 2020) which is for amateur radiolocation, not for meteorological aids (e.g. International Telecommunication Union (ITU), “Radio Regulations”, Volume 1, 2020, available from https://www.itu.int/pub/R-REG-RR-2020); thus, in some countries ST cannot be used as a meteorological radiosonde officially. This may mean that the ST cannot be “widely” used in the future. Note also that one-hourly sounding campaigns like the one shown in Section 5.2 are not impossible with modern radiosondes like the VS; thus it is not easy for me to imagine possible applications of the ST aiming at new scientific studies that are only possible with the ST.

Therefore, I think that the manuscript might be published in AMT after reconsidering the “overselling” parts (which will be specifically pointed out below).

Specific comments:

Line 16 (and line 369): I am afraid that “geostrophic adjustment dynamics” is never discussed in the manuscript.

Introduction and Figure 1: Please describe the main technological points of the ST in more detail, including the model and characteristics of the equipped temperature-RH sensor. Figure 1(b) should be much greater, and an enlarged photo for the sensor part may be added. Also, Figure 1(c) is not a good one, because the ST sensor boom (or “sensor box”) is not well shown.

Introduction, the review part of various radiosonde issues: The papers cited here tend to be too old. More recent papers for more recent radiosondes need to be cited. These include:
Vaisala RS41 radiosonde (but with GRUAN data processing): Sommer et al., GRUAN characterisation and data processing of the Vaisala RS41 radiosonde. GRUAN Technical Document 8 (GRUAN-TD-8), v1.0.0 (2023-06-28), https://www.gruan.org/documentation/gruan/td/gruan-td-8.

Modem M10 radiosonde: Dupont, J., M. Haeffelin, J. Badosa, G. Clain, C. Raux, and D. Vignelles, 2020: Characterization and Corrections of Relative Humidity Measurement from Meteomodem M10 Radiosondes at Midlatitude Stations. J. Atmos. Oceanic Technol., 37, 857–871, https://doi.org/10.1175/JTECH-D-18-0205.1.

Meisei RS-11G and iMS-100 radiosondes: Kizu et al., Technical characteristics and GRUAN data processing for the Meisei RS-11G and iMS-100 radiosondes, GRUAN-TD-5, v1.0 (2018-02-21), https://www.gruan.org/documentation/gruan/td/gruan-td-5.

These also include very useful information on the modern radiosonde sensor characteristics and on all necessary corrections to the radiosonde measurements.

Figure 2: Are the ST RH measurements really dry biased in comparison with the VS measurements below the 500 hPa level? They look wet biased in this case.

Lines 98-105: Testing mathematically more sophisticated machine-learning-based methods is good and interesting, but if the sensor characteristics are not optimal for upper air sounding (e.g. ~5 deg.C temperature error as shown in Figure 2 is just too large!), I tend to think that we should improve the sensor itself before reconsidering correction methods.

Table 1: Please also add the information on e.g. climate zone, season, etc., i.e. the information listed at Lines 92-93, to the place e.g. right after Location.

Figure 3: I confused with the three horizontal lines within the gray box. I thought that the data processing of the ST and the VS is independent before “L2_ST-VS”. In other words, I thought that the “Paired Entries” are used to establish correction tables/functions with various different methods. Or, in other words, I thought that for the VS, the authors simply use the manufacturer-processed data set, while I have impression from the current figure that the authors make their own (and perhaps common) corrections from L1_VS to L2_VS. The term Level 2 may be confusing if it is used for the ST at this stage, because the ST data will be further corrected by using the VS data as the reference if my understanding is correct.

Section 3.1 (and Figure 4 and its caption): Please add the explanation how to obtain delta T (and its uncertainty) from the CDF.

Line228: three-dimensional?

Lines 241-244: Please also add “(GLM1)”, “(GLM2)”, and “(GLM3)” here, not later.

Lines 255-258: Solar elevation angle may be a better variable?

Figure 7: Results from which method, CDF, GLM*?

Lines 283-285: I have a difficulty on this discussion. I am not an expert of metrology (i.e. science of measurement), but I think that that the “average errors” here are comparable to the uncertainties of the VS measurements does not mean that the uncertainties of the ST measurements (after the corrections) are comparable to the uncertainties of the VS measurements. Can we say roughly how large are the uncertainties of the corrected ST measurements in this case? Is it ~1.414 times?

Figures 10 and 11: The authors should also show delta T and delta RH profiles as well. In particular, delta T profiles are very important because we need tropospheric temperature measurements with uncertainties less than e.g. 0.5 K or even 0.1 K usually, and this degree of differences is hard to see with the current panels.

Line 330: A wavy pattern. Which pattern are the authors referring to? With this figure, I am afraid that we cannot judge that GLM may be better.

Section 5.2: One-hourly sounding campaign is possible with the VS as well.

Line 369: Geostrophic adjustment dynamics has never been discussed, I am afraid.

Lines 373-377: As discussed above, I personally think that the sensor characteristics (including the sensor covering and orientation) need to be improved and more optimized for upper-air sounding, before considering mathematically more sophisticated correction methods.

Lines 378-386: As discussed in the beginning, I tend to think that the authors are overselling the ST here.
Citation: https://doi.org/10.5194/egusphere-2024-661-RC2
- AC2: 'Reply on RC2', Ting-Shuo Yo, 10 Jun 2024
  
  We really appreciate the comments on the manuscript. We have carefully reviewed the comments and adjusted our work accordingly. We put the general response here and the detailed response in the attachment.
  
      Regarding the issue of overselling, it is important to clarify that while Soundings from Triggers (ST) are indeed suitable for Planetary Boundary Layer (PBL) studies and areas with complex terrain, they should be viewed as a supplement to Vaisala Soundings (VS) rather than a replacement. Atmospheric conditions at higher altitudes may not always require high-frequency observations due to geostrophic adjustment, but ST can significantly enhance our understanding of PBL dynamics.
      In regions such as Southeast Asia, where PBL conditions can vary within short distances, the high spatial frequency observations provided by ST are particularly beneficial. It is important to note, however, that the correction results presented are specific to Taiwan. To ensure broader applicability, we suggest conducting co-launches during field campaigns. This approach would allow users to derive in-situ correction formulas using the proposed methods.
      Furthermore, we recommend utilizing ST between VS launches to optimize data collection and analysis. This combined approach will enhance the overall effectiveness of atmospheric observations and improve the accuracy of data interpretation. New statements are added in section 6 to address this.
      Finally, regarding radio regulations, the hardware design for STs allows for adjustments to different radio bands if needed. While a hardware update is beyond the scope of this manuscript, it is important to note that radio regulation should not pose an issue for field campaigns using STs.
  
  Citation: https://doi.org/10.5194/egusphere-2024-661-AC2

Hung-Chi Kuo, Ting-Shuo Yo, Hungjui Yu, Shih-Hao Su, Ching-Hwang Liu, and Po-Hsiung Lin

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Short summary

The study introduces "Storm Tracker," a budget-friendly device for weather observation. It highlights the calibration and testing against more expensive devices, ensuring its accuracy for temperature and humidity. This innovation could improve weather forecasting by making detailed atmospheric data more accessible, especially in challenging regions. Through rigorous comparison and enhancement, "Storm Tracker" promises to improve disaster preparedness and understanding of local weather patterns.


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