A strange case of singular fit in lme4 glmer - simple random structure with variation among groups

Background

I am trying to test for differences in wind speed data among different groups. For the purpose of my question, I am looking only on side wind (wind direction that is 90 deg from the individual), and I only care about the strength of the wind. Thus, I use absolute values. The range of wind speeds is 0.0004-6.8 m/sec and because I use absolute values, Gamma distribution describes it much better than normal distribution.

My data contains 734 samples from 68 individuals, with each individual having between 1-30 repeats. However, even if I reduce my samples to only include individuals with at least 10 repeats (which leaves me with 26 individuals and a total of 466 samples), I still get the problematic error.

The model

The full model is Wind ~ a*b + c*d + (1|individual), but for the purpose of this question, the simple model of Wind ~ 1 + (1|individual) gives the same singularity error, so I do not think that the explanatory variables are the problem. The complete code line is glmer(Wind ~ 1 + (1|individual), data = X, family = Gamma(log))

The problem and the strange part

When running the model, I get the boundary (singular) fit: see ?isSingular error, although, as you can see, I use a very simple model and random structure. The strange part is that I can solve this by adding 0.1 to the Wind variable (i.e. glmer(Wind+0.1 ~ 1 + (1|Tag), data = X, family = Gamma(log)) does not give any error). I honestly do not remember why I added 0.1 the first time I did it, but I was surprised to see that it solved the error.

The question

Is this a problem with lme4? Am I missing something? Any ideas what might cause this and why does me adding 0.1 to the variable solve this problem?

Edit following questions

1. I am not sure what's the best way to add data, so here is a link to a csv file in Google drive

2. using glmmTMB does not produce any warnings with the basic formula glmmTMB(Wind ~ 1 + (1|Tag), data = X, family = Gamma(log)), but gives convergence problems warnings ('non-positive-definite Hessian matrix') when using the full model (i.e., Wind ~ a*b + c*d + (1|individual)), which are then solved if I scale the continuous variables