<p>I been accessing daily historical NOAA GSOD data through Google BigQuery. I am interested in long term time series of snow, precipitation, and to a lesser extent temperature for areas around the Volga Basin.</p>

<p>Initially, I have looked at weather station 274530, which is near Gorodets in Russia, and have noticed that there are oddities in the data. The snow depth changes substantially over a short period of time. This doesn't seem to be a system recognized 'error' or 'lack of reading' code, which according to the documentation and data is usually a 999.</p>

<p><a href="https://i.sstatic.net/8H5nk.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/8H5nk.png" alt="Some of the oddities in the snow depth data"></a></p>

<p>(Excuse my red squiggles).</p>

<p>I am not sure how to deal with this data, it isn't "missing". I do not have a good understanding of the underlying physical system but I suspect that snow level doesn't change suddenly like the above graph, only to revert back to a similar level the next day.</p>

<p>How should I interpret this data, and what actions should I take to 'clean' it (if any)? Ultimately I would like to have confidence in this data so that I can use it for modelling purposes across hundreds of weather stations.</p>

CC BY-SA 3.0
2017-11-15T11:02:36.587Z
Not sure if this is the reason: In the original data set, bad values are flagged by 999. However, you access the data not directly but via another service (Google BigQuery). Therefore, you cannot be sure, if special values are properly considered. Particularly, this might be a problem if you calculate (monthly, annual, ...) mean values.
daniel.heydebreck
CC BY-SA 3.0
2017-11-16T13:17:12.723Z
Hi guys, thanks for looking at this. I understand that the snow depth data uses the code 999.9 when there is nothing reported and I have accounted for it. These lower than expected values are actual reported values; I suppose I was wondering whether readings are unreliable and people have methods of modelling around it.
Hamish
<meteorology><hydrology><snow>
Missing Data in GSOD Data
Hamish
# Missing Data in GSOD Data

(Asked by: username_0 on 2017-11-15)

I been accessing daily historical NOAA GSOD data through Google BigQuery. I am interested in long term time series of snow, precipitation, and to a lesser extent temperature for areas around the Volga Basin.

Initially, I have looked at weather station 274530, which is near Gorodets in Russia, and have noticed that there are oddities in the data. The snow depth changes substantially over a short period of time. This doesn't seem to be a system recognized 'error' or 'lack of reading' code, which according to the documentation and data is usually a 999.

[![Some of the oddities in the snow depth data](https://i.sstatic.net/8H5nk.png)](https://i.sstatic.net/8H5nk.png)

(Excuse my red squiggles).

I am not sure how to deal with this data, it isn't "missing". I do not have a good understanding of the underlying physical system but I suspect that snow level doesn't change suddenly like the above graph, only to revert back to a similar level the next day.

How should I interpret this data, and what actions should I take to 'clean' it (if any)? Ultimately I would like to have confidence in this data so that I can use it for modelling purposes across hundreds of weather stations.

**Tags:** meteorology, hydrology, snow

> **Comments:**
> **username_1**: Not sure if this is the reason: In the original data set, bad values are flagged by 999. However, you access the data not directly but via another service (Google BigQuery). Therefore, you cannot be sure, if special values are properly considered. Particularly, this might be a problem if you calculate (monthly, annual, ...) mean values.  
> **username_0**: Hi guys, thanks for looking at this. I understand that the snow depth data uses the code 999.9 when there is nothing reported and I have accounted for it. These lower than expected values are actual reported values; I suppose I was wondering whether readings are unreliable and people have methods of modelling around it.  

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## 0 answers

