Cycling: Garmin altimeter compared to elevation databases

7 September 2013

During a very rainy ride in Scotland, my Garmin altimeter appeared to be off: on some of the steepest climbs it failed to register any gradient. Afterwards, I tried the «elevation correction» feature on the Garmin website, which generously added over 750m to the total ascent the device had measured. This was certainly more satisfying, but it left me wondering. Can the weather affect the Garmin altimeter? And how accurate is the recalculated ascent?

Garmin’s recalculation service works basically by looking up the gps locations of your ride in an elevation database. Strava offers a similar service. Below, I analyse the Garmin and Strava recalculations for a number of rides. Note that this is only an exploratory analysis and that no firm conclusions can be drawn on the basis of this rather small set of observations. That said, here are some preliminary conclusions:

In the graphs below, the colour of the dots represents the region of the ride. Red dots represent the Ronde Hoep, a flat ride to the south of Amsterdam. Blue ones represent the Kopje van Bloemendaal (north, south), the closest thing to a climb near Amsterdam (it’s not high but quite steep). Green dots represent the central area of the country and include the Utrechtse Heuvelrug, Veluwezoom, Rijk van Nijmegen and Kreis Kleve (the latter in Germany).


By default, the graph above shows how much the Garmin recalculation differs from the ascent measured by the device (graphs may not show in older versions of Internet Explorer). The closer a dot is to the dashed line, the the closer the recalculated ascent is to the original measurement.

For rides shown on the left part of the graph, where the device measured less than 500m ascent, Garmin’s recalculation often adds about 50 to 100% or more. With higher ascents, the recalculated ascent is closer to the original measurement, although it still tends to add about 30 to 50%. The highest dot to the far right of the graph is the rainy ride in Scotland; here Garmin’s recalculation added over 35%.

With the selector above the graph, you can select the Strava recalculation. You’ll notice the scale on the y axis changes (and the dashed line moves up). Also, a few red dots enter the graph. These are rides along the Ronde Hoep, which is a flat ride. For these rides, Garmin’s recalculation added up to 750% to the ascent measured by the device; therefore these dots were initially outside the graph area.

The Strava recalculations are similar to the Garmin ones in that the correction is larger for relatively flat rides. Unlike Garmin, Strava lowers the ascent in these cases, often by 15 to 50%. For rides where the device measured a total ascent of over 500m, the Strava recalculation tends to be pretty close to the original measurement.

Weather changes

It has been suggested that changes in the weather may affect elevation measurements. This makes sense, since the Garmin altimeter is in fact a barometer. Wikipedia says that pressure decreases by about 1.2 kPa for every 100 metres in ascent. In other words, if net atmospheric pressure would rise by 6 mBar, this would cause the device to underestimate total ascent by about 50 metres, so the theoretical effect wouldn’t seem to be huge.

The graph above shows how much recalculations differed from the original measurement, with change in pressure on the x axis. Note that the effect of recalculations is here in metres, not percent. I tried different combinations of pressure measures and recalculations and in only one case - the Garmin recalculation shown above - the correlation was statistically significant (and the regression line much steeper than the Wikipedia data would suggest), so this is not exactly firm evidence for an effect of weather change on elevation measurement.

Heavy rain

It has been suggested that heavy rain may block the sensor hole and thus affect elevation measurement. This may sound a bit weird, but I have seen the device stop registering any ascent during very heavy rain. Among the rides considered here, there are two that saw really heavy rainfall (the Scottish ride and a ride in Utrechtse Heuvelrug on 27 July). These do show some of the largest corrections, especially in the Strava recalculation. So it does seem plausible that rain does in fact affect elevation measurement.

In the spirit of true pseudoscientific enquiry, I tried to replicate the effect of heavy rain by squirting water from my bidon onto the device during a ride in Utrechtse Heuvelrug. This didn’t yield straightforward results. At first, the device registered implausibly steep gradients and it turned out it had interpreted the hump between Maarn and Doorn as 115m high, more than twice its real height. About halfway, unpredicted rain started to fall, mocking my experiment. Strava recalculation didn’t change much to the total ascent but it did correct the height of the bit between Maarn and Doorn, so it must have added some 50+ metres elsewhere. Be it as it may, the «experiment» does seem to confirm that water can do things to the altimeter.

UPDATE 8 November 2019 - I just learned that scientific research has confirmed that «the measures of elevation gain recorded by the same devices in wet weather conditions are considerably worsened» (I also learned that there is a journal called Journal of Science and Cycling, which is cool). Paolo Menaspá, Eric Haakonssen, Avish Sharma and Brad Clark (2016), Accuracy in measurement of elevation gain in road cycling. Journal of Science and Cycling, 5(1), 10-12.


I took total ascent data measured by my Garmin Edge 800 and obtained a recalculation from the Garmin Connect and Strava websites. Subsequently, I looked up weather data from Weather Underground (as an armchair activist I do appreciate their slightly subversive name). Weather Underground offers historical weather data by location, with numerous observations per day. I wrote a Python script that looks up the data for the day and location of the ride and then selects the observations that roughly overlap with the duration of the ride. There turned out to be two limitations to the data. First, it appears that only data at the national level are available (the Scottish ride yielded data for London and all Dutch ones data for Amsterdam). Second, for the day / location combinations I tried there was no time-specific data for precipitation available, only for the entire day.

Because of these limitations, I also took an alternative approach, looking up data from the Royal Netherlands Meteorological Institute KNMI. This did yield more fine-grained data, although obviously limited to the Netherlands. In the end it turned out that it didn’t make much difference for the analysis whether KNMI or Weather Underground data is used. Code from the scripts I used for looking up weather data is here.

I tested quite a few correlations so a couple of ‘false positives’ may be expected. I didn’t statistically correct for this. Instead, I took a rather pragmatic approach: I’m cautious when there’s simply a significant correlation between two phenomena but I’m more confident when there’s a pattern to the correlations (e.g., Garmin and Strava recalculations are correlated in a similar way to another variable).

7 September 2013 | Categories: cycling, d3js, data, garmin, python, strava