GENERAL COMMENTS

This is a very interesting manuscript that deserves publication on the potential of using ZTDs derived from raw GNSS data obtained from private citizens smartphones. Supplemented with an example of ZTD data from a smartphone providing GNSS data in an idealized setting and compared to ZTD derived from a professional grade GNSS receiver.

I congratulate the authors of having written a nice, easy to read manuscript almost ready to publish.

  

Compared to previous tests of usage of other crowsourced smartphone data, such as for example pressure (see e.g. Hintz et al, 

https://doi.org/10.1002/met.1805) it is clear that for derivation of ZTD from raw GNSS smartphone data much longer un-interrupted timeseries are required for the mobile phone data to be useful. On top there is a benefit from obtaining the GNSS data, when the phone in an open environment. 

It would be interesting to include in the article information about:

1 The local time of day distribution of the full data volume versus the volume kept for analysis after filtering.

2 Were the volunteers providing the data given any information about how to use (e.g., how long in the proper mode) or place (indoor/outdoor/sky view) or not move their devices prior to the experiment? 

3 Many Android phones contain also a pressure sensor, data from that could be collected simultanously, the two types of data potentially 

improving usage when used together (just your thoughts on this).

SPECIFIC COMMENTS

The smartphone based curves in figure 11 appear to me surprisingly smooth. Is that due to constraints in the data processing of the raw GNSS data or subsequent smoothing of the ZTDs?

Tropospheric delay information is valuable to both spatial geodetic measuring and weather forecasting. Ground-based GNSS has advantages of high-accuracy, high temporal resolution and can be operated under all weather conditions. However, the number of traditional geodetic GNSS receivers is limited due to the cost. The use of crowdsourced smartphone GNSS data is promising to provide much denser tropospheric delay information due to the massive smartphone users. This paper carried out novel investigations on the ZTD estimation based on crowdsourced data collected from the CAMALIOT campaign. The paper is generally well organized and the experiments were well designed. But, in my opinion, there are still many work to do before you can say ‘this is the first successful demonstration of high-precision ZTD determination with crowdsourced smartphone GNSS data’. The main limitation of this study is that the selected data for the ZTD estimation are all from static users in an open-sky environment. Can these data be called real crowdsourced data? As we know, most smartphone users are indoor or kinematic. So the results from open-sky static users are just too ideal. The data from the 10 crowdsourced users in this study are similar to the data collected by repeating 10 times of experiments at ETH in sect 3.1.

Other minor comments include,

1. L56: why the accuracy of Benvenuto et al. (2021) is so bad with errors of several cm compared to several mm in other studies? Please make necessary comments here.
2. L132: the PCO corrections were not applied either, right?
3. L138: why the C/N0-dependent weighting tends to introduce artifacts in ZTD estimates in your results? Several studies have showed that the traditional elevation-dependent weighting strategy may not applicable for smartphone data. Why you still use this strategy?
4. L231: why did you attribute the 6 mm bias to the uncorrected PCV errors? Can it also be possible due to the worse data quality in smartphone observations? You didn’t apply PCV corrections for UBLX either, but the bias was much smaller.
5. Figure 10: you need to give the number of ZTD samples in each case.