Thanks a lot, I will try this!

I finally got it working. First read the data:

temp = AirTemperatureData[
   "Linkoping", {DateObject[{2015, 12, 27}, TimeObject[{12}]], 
    DateObject[{2016, 1, 6}, TimeObject[{12}]]}];

The trick then was to interpolate missing data using

tempI = TimeSeries[temp, 
MissingDataMethod -> {"Interpolation", InterpolationOrder -> 2}];

and finally remove units from the weather data

tempVal = QuantityMagnitude[tempI["Values"]];

Then using NDSolveValue (using a simple ice growth model):

solution = 
  NDSolveValue[{he'[t] == 1/(rho L) (Tm - Ta[t])/(he[t]/k + 1/Hia[t]),
     he[0] == 0.0}, he, {t, 0, Last[tempTimes]}]; 

I had to fake the heat exchange parameter, recommended values are 20-40 w/m2, and I used 20 with a sinusoidal variation over 24 h.

Thanks!

Sören

When you say the data is a timeseries, do you mean that it is a TimeSeries expression?

https://reference.wolfram.com/language/ref/TimeSeries.html

You can sample a TimeSeries expression at any time value and it will interpolate between known values.

Otherwise, you can use Interpolation and interpolate the data yourself into a function.